{
 "cells": [
  {
   "cellId": "96ac5ef8be584bf995a9d4011ccb10a3",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [
     {
      "type": "marks",
      "marks": {
       "bold": true
      },
      "toCodePoint": 26,
      "fromCodePoint": 0
     }
    ],
    "cell_id": "96ac5ef8be584bf995a9d4011ccb10a3",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Update on the Voice Model:",
   "block_group": "b410acfb91ff408b9a53708770626b23"
  },
  {
   "cellId": "0537021b96c04efa8a052bd4d6def00b",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "0537021b96c04efa8a052bd4d6def00b",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "For the regression task (predicting total UPDRS), I experimented with various models including multiple neural networks. The best validation R² I achieved was around 0.47, but the test R² dropped to ~0.39, suggesting some overfitting or data imbalance. I believe there's room for improvement in several areas:",
   "block_group": "798e83a68746489e8ce0048f9655492d"
  },
  {
   "cellId": "375894f91ff0433aa81e64040a19cf9d",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "375894f91ff0433aa81e64040a19cf9d",
    "deepnote_cell_type": "text-cell-bullet"
   },
   "source": "- Data processing, particularly stratified splitting and potential outlier handling",
   "block_group": "1bb5675ef34d4ebc82695607fadf8831"
  },
  {
   "cellId": "c5484e35def34ccc9f9140f1d01a882a",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "c5484e35def34ccc9f9140f1d01a882a",
    "deepnote_cell_type": "text-cell-bullet"
   },
   "source": "- Hyperparameter tuning, especially learning rate schedules and network depth",
   "block_group": "e15988122ff34692bf5ccef330fd7677"
  },
  {
   "cellId": "9317d1e787a345eaa09298dbfcc7fc48",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [
     {
      "type": "marks",
      "marks": {
       "bold": true
      },
      "toCodePoint": 33,
      "fromCodePoint": 23
     },
     {
      "type": "marks",
      "marks": {
       "bold": true
      },
      "toCodePoint": 65,
      "fromCodePoint": 47
     }
    ],
    "cell_id": "9317d1e787a345eaa09298dbfcc7fc48",
    "deepnote_cell_type": "text-cell-bullet"
   },
   "source": "- Advanced methods, like ensembling or leveraging pretrained models for feature extraction",
   "block_group": "88c63f34c05448aa9cf4d643e8ab8927"
  },
  {
   "cellId": "12d7d957a67941a5bb8b6114b59a29cc",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [
     {
      "url": "https://github.com/mrpintime/Parkinsons-Telemonitoring/tree/main",
      "type": "link",
      "ranges": [],
      "toCodePoint": 228,
      "fromCodePoint": 164
     }
    ],
    "cell_id": "12d7d957a67941a5bb8b6114b59a29cc",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "For the classification task, my best model reached 66% validation accuracy. There's likely headroom here too, I found a published model that achieved 86% accuracy. https://github.com/mrpintime/Parkinsons-Telemonitoring/tree/main",
   "block_group": "2607aa59d0d543f0a1581b6df3a93ffc"
  },
  {
   "cellId": "9c2699f2b913452fb56982570bc88cc0",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "9c2699f2b913452fb56982570bc88cc0",
    "deepnote_cell_type": "text-cell-bullet"
   },
   "source": "- Same process as above",
   "block_group": "df91adc841064439b2d03354bb1e2a92"
  },
  {
   "cellId": "1fa4d565b2b84b68bec5a466a396e831",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "1fa4d565b2b84b68bec5a466a396e831",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Further details and specs are documented throughout the notebook",
   "block_group": "62522b744b01453a88f6efc1ce0f4360"
  },
  {
   "cellId": "4c9098500a8f44509d9457d2f450aeed",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "4c9098500a8f44509d9457d2f450aeed",
    "deepnote_cell_type": "text-cell-h1"
   },
   "source": "# Voice Processing",
   "block_group": "a46fcd9e542b46a3a7092ce0ebfa2627"
  },
  {
   "cellId": "431274a646dc4b86bb9a29b676463031",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "431274a646dc4b86bb9a29b676463031",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Imports ",
   "block_group": "de7b7d07de4a48f098dd7b8a7dfc58dd"
  },
  {
   "cellId": "4f3bcff41e0f463cb2cb1c8ca352fa38",
   "cell_type": "code",
   "metadata": {
    "source_hash": "c24a59f1",
    "execution_start": 1754180705595,
    "execution_millis": 55672,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "4f3bcff41e0f463cb2cb1c8ca352fa38",
    "deepnote_cell_type": "code"
   },
   "source": "!pip install ucimlrepo\n!pip install xgboost\n\nimport math\n\nimport numpy as np\nimport pandas as pd\nimport scipy\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LeakyReLU\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nfrom tensorflow.keras.utils import to_categorical\n\nfrom sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor,RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, GridSearchCV, ParameterGrid\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\nfrom sklearn.svm import SVR\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.metrics import mean_squared_error, r2_score, classification_report, confusion_matrix\n\nfrom sklearn.preprocessing import StandardScaler, RobustScaler\nfrom ucimlrepo import fetch_ucirepo\nfrom xgboost import XGBRegressor\n\n\n\n\n",
   "block_group": "c9d5049f90294340a32246a0845f6384",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "text": "Requirement already satisfied: ucimlrepo in /root/venv/lib/python3.10/site-packages (0.0.7)\nRequirement already satisfied: certifi>=2020.12.5 in /root/venv/lib/python3.10/site-packages (from ucimlrepo) (2025.7.14)\nRequirement already satisfied: pandas>=1.0.0 in /root/venv/lib/python3.10/site-packages (from ucimlrepo) (2.1.4)\nRequirement already satisfied: python-dateutil>=2.8.2 in /root/venv/lib/python3.10/site-packages (from pandas>=1.0.0->ucimlrepo) (2.9.0.post0)\nRequirement already satisfied: pytz>=2020.1 in /root/venv/lib/python3.10/site-packages (from pandas>=1.0.0->ucimlrepo) (2025.2)\nRequirement already satisfied: tzdata>=2022.1 in /root/venv/lib/python3.10/site-packages (from pandas>=1.0.0->ucimlrepo) (2025.2)\nRequirement already satisfied: numpy<2,>=1.22.4 in /root/venv/lib/python3.10/site-packages (from pandas>=1.0.0->ucimlrepo) (1.25.2)\nRequirement already satisfied: six>=1.5 in /root/venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.17.0)\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\nCollecting xgboost\n  Downloading xgboost-3.0.3-py3-none-manylinux_2_28_x86_64.whl (253.8 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.8/253.8 MB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hCollecting nvidia-nccl-cu12\n  Downloading nvidia_nccl_cu12-2.27.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (322.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m322.5/322.5 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hRequirement already satisfied: scipy in /root/venv/lib/python3.10/site-packages (from xgboost) (1.9.3)\nRequirement already satisfied: numpy in /root/venv/lib/python3.10/site-packages (from xgboost) (1.25.2)\nInstalling collected packages: nvidia-nccl-cu12, xgboost\nSuccessfully installed nvidia-nccl-cu12-2.27.6 xgboost-3.0.3\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n2025-08-03 00:25:58.008054: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n2025-08-03 00:25:58.059756: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2025-08-03 00:25:58.060536: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-08-03 00:25:58.061727: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2025-08-03 00:25:58.069839: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n2025-08-03 00:25:58.070577: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2025-08-03 00:25:59.640362: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/b232e7fe-c495-4f53-9a0a-4161ad5a59e7",
   "content_dependencies": null
  },
  {
   "cellId": "d1c4f9cf13204abd8e7410d2d84150c6",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "d1c4f9cf13204abd8e7410d2d84150c6",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## xShortcut for all the processing - So after running this block you can go immediately to model development - Not for classifications tho (there's further processing that has to be done)",
   "block_group": "20ea9b39bf014df2ad7c4a8820653961"
  },
  {
   "cellId": "e05d2c493e194665a6bf168bce67b388",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [
     {
      "type": "marks",
      "marks": {
       "bold": true
      },
      "toCodePoint": 76,
      "fromCodePoint": 0
     }
    ],
    "cell_id": "e05d2c493e194665a6bf168bce67b388",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "\nFeel free to experiment with these since some of the choices were arbitrary",
   "block_group": "271d490c9980492e8f66939e819a73f4"
  },
  {
   "cellId": "c39a7c5219bf40b2b3362ff5e8bce6d8",
   "cell_type": "code",
   "metadata": {
    "source_hash": "b65db645",
    "execution_start": 1754180761315,
    "execution_millis": 1492,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "c39a7c5219bf40b2b3362ff5e8bce6d8",
    "deepnote_cell_type": "code"
   },
   "source": "seed = 42\nnp.random.seed(seed)          \ntf.random.set_seed(seed) \nparkinsons_voice = fetch_ucirepo(id=189)\nX = parkinsons_voice.data.features\ny = parkinsons_voice.data.targets\n\nfeatures_to_drop = [\n    'Jitter(%)', 'Jitter:RAP', 'Jitter:DDP',\n    'Shimmer(dB)', 'Shimmer:APQ5', 'Shimmer:DDA',\n    'Shimmer', 'NHR',   'age',\n    'test_time', 'subject#'\n] #to address multicollinearity and also to overcome overfitting based on age as id \n\nX_filtered = X.drop(columns=[col for col in features_to_drop if col in X.columns])\n\ny_target = y[\"total_UPDRS\"]\n#y_target = y[\"motor_UPDRS\"]\n\n#this section can definitely be improved to split the data using subject# so that testings willencounter patients that they've never seen\n#or any other type of good stratification\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X_filtered, y_target, test_size=0.5, random_state=seed\n)\n\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, random_state=seed\n)\n\n#Necessary for NN\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)",
   "block_group": "7a22c573263d436f98bc4719fbc9ce3b",
   "execution_count": 2,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "7e2e3b1683fe4a279201ce5cbadd5c48",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "7e2e3b1683fe4a279201ce5cbadd5c48",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "##  Downloading Data & Other Stuff",
   "block_group": "18fa5a3783f8475b89484b4e8d5fb08b"
  },
  {
   "cellId": "5ffe7258c0a5486fa027f24550b99026",
   "cell_type": "code",
   "metadata": {
    "source_hash": "640eb3fb",
    "execution_start": 1753983982726,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "5ffe7258c0a5486fa027f24550b99026",
    "deepnote_cell_type": "code"
   },
   "source": "seed = 42\nnp.random.seed(seed)          \ntf.random.set_seed(seed) ",
   "block_group": "a8e4c2cc4b0e4ca9b31b97b3be1ea899",
   "execution_count": 3,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "d1a70cbc87c44fc2b2a38afe305c885f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "13f7aa22",
    "execution_start": 1754055796741,
    "execution_millis": 2035,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "d1a70cbc87c44fc2b2a38afe305c885f",
    "deepnote_cell_type": "code"
   },
   "source": "parkinsons_voice = fetch_ucirepo(id=189)\nX = parkinsons_voice.data.features\ny = parkinsons_voice.data.targets",
   "block_group": "0cea16fd504140c5958f28d0dfa72cd5",
   "execution_count": 15,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "44a990a5041246399d461ed4ea982374",
   "cell_type": "code",
   "metadata": {
    "source_hash": "7fc240ac",
    "execution_start": 1753983983926,
    "execution_millis": 0,
    "is_output_hidden": false,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "deepnote_app_is_output_hidden": true,
    "cell_id": "44a990a5041246399d461ed4ea982374",
    "deepnote_cell_type": "code"
   },
   "source": "print(parkinsons_voice.metadata)\nprint(parkinsons_voice.variables)",
   "block_group": "7dbf144cadb24aa0a9cfbbc9695bda54",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "text": "{'uci_id': 189, 'name': 'Parkinsons Telemonitoring', 'repository_url': 'https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring', 'data_url': 'https://archive.ics.uci.edu/static/public/189/data.csv', 'abstract': \"Oxford Parkinson's Disease Telemonitoring Dataset\", 'area': 'Health and Medicine', 'tasks': ['Regression'], 'characteristics': ['Tabular'], 'num_instances': 5875, 'num_features': 19, 'feature_types': ['Integer', 'Real'], 'demographics': ['Age', 'Sex'], 'target_col': ['motor_UPDRS', 'total_UPDRS'], 'index_col': ['subject#'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2009, 'last_updated': 'Fri Nov 03 2023', 'dataset_doi': '10.24432/C5ZS3N', 'creators': ['Athanasios Tsanas', 'Max Little'], 'intro_paper': {'ID': 229, 'type': 'NATIVE', 'title': \"Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests\", 'authors': 'A. Tsanas, Max A. Little, P. McSharry, L. Ramig', 'venue': 'IEEE Transactions on Biomedical Engineering', 'year': 2010, 'journal': None, 'DOI': None, 'URL': 'https://www.semanticscholar.org/paper/1fdf33b6d8b1bdb38866ba824c1dcaecdfb6bdd6', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': '19932995', 'pmcid': None}, 'additional_info': {'summary': \"This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson's disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient's homes.\\r\\n\\r\\nColumns in the table contain subject number, subject age, subject gender, time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 16 biomedical voice measures. Each row corresponds to one of 5,875 voice recording from these individuals. The main aim of the data is to predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 voice measures.\\r\\n\\r\\nThe data is in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around 200 recordings per patient, the subject number of the patient is identified in the first column. For further information or to pass on comments, please contact Athanasios Tsanas (tsanasthanasis@gmail.com) or Max Little (littlem@physics.ox.ac.uk).\\r\\n\\r\\nFurther details are contained in the following reference -- if you use this dataset, please cite:\\r\\nAthanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009),\\r\\n'Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests',\\r\\nIEEE Transactions on Biomedical Engineering (to appear).\\r\\n\\r\\nFurther details about the biomedical voice measures can be found in:\\r\\nMax A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009), \\r\\n'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', \\r\\nIEEE Transactions on Biomedical Engineering, 56(4):1015-1022\\r\\n\", 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': \"subject# - Integer that uniquely identifies each subject\\r\\nage - Subject age\\r\\nsex - Subject gender '0' - male, '1' - female\\r\\ntest_time - Time since recruitment into the trial. The integer part is the number of days since recruitment. \\r\\nmotor_UPDRS - Clinician's motor UPDRS score, linearly interpolated\\r\\ntotal_UPDRS - Clinician's total UPDRS score, linearly interpolated\\r\\nJitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of variation in fundamental frequency\\r\\nShimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA - Several measures of variation in amplitude\\r\\nNHR,HNR - Two measures of ratio of noise to tonal components in the voice\\r\\nRPDE - A nonlinear dynamical complexity measure\\r\\nDFA - Signal fractal scaling exponent\\r\\nPPE - A nonlinear measure of fundamental frequency variation \\r\\n\", 'citation': None}}\n             name     role        type demographic  \\\n0        subject#       ID     Integer        None   \n1             age  Feature     Integer         Age   \n2       test_time  Feature  Continuous        None   \n3       Jitter(%)  Feature  Continuous        None   \n4     Jitter(Abs)  Feature  Continuous        None   \n5      Jitter:RAP  Feature  Continuous        None   \n6     Jitter:PPQ5  Feature  Continuous        None   \n7      Jitter:DDP  Feature  Continuous        None   \n8         Shimmer  Feature  Continuous        None   \n9     Shimmer(dB)  Feature  Continuous        None   \n10   Shimmer:APQ3  Feature  Continuous        None   \n11   Shimmer:APQ5  Feature  Continuous        None   \n12  Shimmer:APQ11  Feature  Continuous        None   \n13    Shimmer:DDA  Feature  Continuous        None   \n14            NHR  Feature  Continuous        None   \n15            HNR  Feature  Continuous        None   \n16           RPDE  Feature  Continuous        None   \n17            DFA  Feature  Continuous        None   \n18            PPE  Feature  Continuous        None   \n19    motor_UPDRS   Target  Continuous        None   \n20    total_UPDRS   Target  Continuous        None   \n21            sex  Feature      Binary         Sex   \n\n                                          description units missing_values  \n0       Integer that uniquely identifies each subject  None             no  \n1                                         Subject age  None             no  \n2   Time since recruitment into the trial. The int...  None             no  \n3   Several measures of variation in fundamental f...  None             no  \n4   Several measures of variation in fundamental f...  None             no  \n5   Several measures of variation in fundamental f...  None             no  \n6   Several measures of variation in fundamental f...  None             no  \n7   Several measures of variation in fundamental f...  None             no  \n8          Several measures of variation in amplitude  None             no  \n9          Several measures of variation in amplitude  None             no  \n10         Several measures of variation in amplitude  None             no  \n11         Several measures of variation in amplitude  None             no  \n12         Several measures of variation in amplitude  None             no  \n13         Several measures of variation in amplitude  None             no  \n14  Two measures of ratio of noise to tonal compon...  None             no  \n15  Two measures of ratio of noise to tonal compon...  None             no  \n16           A nonlinear dynamical complexity measure  None             no  \n17                    Signal fractal scaling exponent  None             no  \n18  A nonlinear measure of fundamental frequency v...  None             no  \n19  Clinician's motor UPDRS score, linearly interp...  None             no  \n20  Clinician's total UPDRS score, linearly interp...  None             no  \n21               Subject sex '0' - male, '1' - female  None             no  \n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/b4fefa84-a5a6-4649-b40f-9beac8e48f8e",
   "content_dependencies": null
  },
  {
   "cellId": "0da30253402c4b94b084dcc44d13df1b",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "0da30253402c4b94b084dcc44d13df1b",
    "deepnote_cell_type": "text-cell-h1"
   },
   "source": "# Data Processing",
   "block_group": "e6b2c8d7e81a4a1485fc70d363434b14"
  },
  {
   "cellId": "667f765119874e33a8438d919706b794",
   "cell_type": "code",
   "metadata": {
    "source_hash": "63f02538",
    "execution_start": 1754058272755,
    "execution_millis": 32,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "667f765119874e33a8438d919706b794",
    "deepnote_cell_type": "code"
   },
   "source": "df = parkinsons_voice.data.features.copy()\ndf['total_UPDRS'] = parkinsons_voice.data.targets['total_UPDRS']\nprint(df.shape)       \nprint(df.columns.tolist())\nprint(df.head(5))\n\nprint(df.info())\nprint(df.isna().sum())\n\nprint(df.describe().T)  ",
   "block_group": "2e1503013ce34c2ba31df7641e920f47",
   "execution_count": 57,
   "outputs": [
    {
     "name": "stdout",
     "text": "(5875, 20)\n['age', 'test_time', 'Jitter(%)', 'Jitter(Abs)', 'Jitter:RAP', 'Jitter:PPQ5', 'Jitter:DDP', 'Shimmer', 'Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5', 'Shimmer:APQ11', 'Shimmer:DDA', 'NHR', 'HNR', 'RPDE', 'DFA', 'PPE', 'sex', 'total_UPDRS']\n   age  test_time  Jitter(%)  Jitter(Abs)  Jitter:RAP  Jitter:PPQ5  \\\n0   72     5.6431    0.00662     0.000034     0.00401      0.00317   \n1   72    12.6660    0.00300     0.000017     0.00132      0.00150   \n2   72    19.6810    0.00481     0.000025     0.00205      0.00208   \n3   72    25.6470    0.00528     0.000027     0.00191      0.00264   \n4   72    33.6420    0.00335     0.000020     0.00093      0.00130   \n\n   Jitter:DDP  Shimmer  Shimmer(dB)  Shimmer:APQ3  Shimmer:APQ5  \\\n0     0.01204  0.02565        0.230       0.01438       0.01309   \n1     0.00395  0.02024        0.179       0.00994       0.01072   \n2     0.00616  0.01675        0.181       0.00734       0.00844   \n3     0.00573  0.02309        0.327       0.01106       0.01265   \n4     0.00278  0.01703        0.176       0.00679       0.00929   \n\n   Shimmer:APQ11  Shimmer:DDA       NHR     HNR     RPDE      DFA      PPE  \\\n0        0.01662      0.04314  0.014290  21.640  0.41888  0.54842  0.16006   \n1        0.01689      0.02982  0.011112  27.183  0.43493  0.56477  0.10810   \n2        0.01458      0.02202  0.020220  23.047  0.46222  0.54405  0.21014   \n3        0.01963      0.03317  0.027837  24.445  0.48730  0.57794  0.33277   \n4        0.01819      0.02036  0.011625  26.126  0.47188  0.56122  0.19361   \n\n   sex  total_UPDRS  \n0    0       34.398  \n1    0       34.894  \n2    0       35.389  \n3    0       35.810  \n4    0       36.375  \n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5875 entries, 0 to 5874\nData columns (total 20 columns):\n #   Column         Non-Null Count  Dtype  \n---  ------         --------------  -----  \n 0   age            5875 non-null   int64  \n 1   test_time      5875 non-null   float64\n 2   Jitter(%)      5875 non-null   float64\n 3   Jitter(Abs)    5875 non-null   float64\n 4   Jitter:RAP     5875 non-null   float64\n 5   Jitter:PPQ5    5875 non-null   float64\n 6   Jitter:DDP     5875 non-null   float64\n 7   Shimmer        5875 non-null   float64\n 8   Shimmer(dB)    5875 non-null   float64\n 9   Shimmer:APQ3   5875 non-null   float64\n 10  Shimmer:APQ5   5875 non-null   float64\n 11  Shimmer:APQ11  5875 non-null   float64\n 12  Shimmer:DDA    5875 non-null   float64\n 13  NHR            5875 non-null   float64\n 14  HNR            5875 non-null   float64\n 15  RPDE           5875 non-null   float64\n 16  DFA            5875 non-null   float64\n 17  PPE            5875 non-null   float64\n 18  sex            5875 non-null   int64  \n 19  total_UPDRS    5875 non-null   float64\ndtypes: float64(18), int64(2)\nmemory usage: 918.1 KB\nNone\nage              0\ntest_time        0\nJitter(%)        0\nJitter(Abs)      0\nJitter:RAP       0\nJitter:PPQ5      0\nJitter:DDP       0\nShimmer          0\nShimmer(dB)      0\nShimmer:APQ3     0\nShimmer:APQ5     0\nShimmer:APQ11    0\nShimmer:DDA      0\nNHR              0\nHNR              0\nRPDE             0\nDFA              0\nPPE              0\nsex              0\ntotal_UPDRS      0\ndtype: int64\n                count       mean        std        min        25%        50%  \\\nage            5875.0  64.804936   8.821524  36.000000  58.000000  65.000000   \ntest_time      5875.0  92.863722  53.445602  -4.262500  46.847500  91.523000   \nJitter(%)      5875.0   0.006154   0.005624   0.000830   0.003580   0.004900   \nJitter(Abs)    5875.0   0.000044   0.000036   0.000002   0.000022   0.000034   \nJitter:RAP     5875.0   0.002987   0.003124   0.000330   0.001580   0.002250   \nJitter:PPQ5    5875.0   0.003277   0.003732   0.000430   0.001820   0.002490   \nJitter:DDP     5875.0   0.008962   0.009371   0.000980   0.004730   0.006750   \nShimmer        5875.0   0.034035   0.025835   0.003060   0.019120   0.027510   \nShimmer(dB)    5875.0   0.310960   0.230254   0.026000   0.175000   0.253000   \nShimmer:APQ3   5875.0   0.017156   0.013237   0.001610   0.009280   0.013700   \nShimmer:APQ5   5875.0   0.020144   0.016664   0.001940   0.010790   0.015940   \nShimmer:APQ11  5875.0   0.027481   0.019986   0.002490   0.015665   0.022710   \nShimmer:DDA    5875.0   0.051467   0.039711   0.004840   0.027830   0.041110   \nNHR            5875.0   0.032120   0.059692   0.000286   0.010955   0.018448   \nHNR            5875.0  21.679495   4.291096   1.659000  19.406000  21.920000   \nRPDE           5875.0   0.541473   0.100986   0.151020   0.469785   0.542250   \nDFA            5875.0   0.653240   0.070902   0.514040   0.596180   0.643600   \nPPE            5875.0   0.219589   0.091498   0.021983   0.156340   0.205500   \nsex            5875.0   0.317787   0.465656   0.000000   0.000000   0.000000   \ntotal_UPDRS    5875.0  29.018942  10.700283   7.000000  21.371000  27.576000   \n\n                      75%         max  \nage             72.000000   85.000000  \ntest_time      138.445000  215.490000  \nJitter(%)        0.006800    0.099990  \nJitter(Abs)      0.000053    0.000446  \nJitter:RAP       0.003290    0.057540  \nJitter:PPQ5      0.003460    0.069560  \nJitter:DDP       0.009870    0.172630  \nShimmer          0.039750    0.268630  \nShimmer(dB)      0.365000    2.107000  \nShimmer:APQ3     0.020575    0.162670  \nShimmer:APQ5     0.023755    0.167020  \nShimmer:APQ11    0.032715    0.275460  \nShimmer:DDA      0.061735    0.488020  \nNHR              0.031463    0.748260  \nHNR             24.444000   37.875000  \nRPDE             0.614045    0.966080  \nDFA              0.711335    0.865600  \nPPE              0.264490    0.731730  \nsex              1.000000    1.000000  \ntotal_UPDRS     36.399000   54.992000  \n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/c7feb4b7-9efa-475d-9248-acfc06626d18",
   "content_dependencies": null
  },
  {
   "cellId": "e515f022b2584fb9af3d98b81d16e3be",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "e515f022b2584fb9af3d98b81d16e3be",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Correlation Heat Maps ",
   "block_group": "02499bc78fce48c9b918b1180f68e54c"
  },
  {
   "cellId": "359be7ac69f84254b4e9418fbcd4da6b",
   "cell_type": "code",
   "metadata": {
    "source_hash": "339e001",
    "execution_start": 1753983984057,
    "execution_millis": 2014,
    "deepnote_table_state": {
     "sortBy": [],
     "filters": [],
     "pageSize": 25,
     "pageIndex": 0,
     "columnOrder": [
      "name",
      "role",
      "type",
      "demographic",
      "description",
      "units",
      "missing_values"
     ],
     "hiddenColumnIds": [],
     "columnDisplayNames": [],
     "conditionalFilters": [],
     "cellFormattingRules": [],
     "wrappedTextColumnIds": []
    },
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_table_loading": false,
    "deepnote_to_be_reexecuted": true,
    "cell_id": "359be7ac69f84254b4e9418fbcd4da6b",
    "deepnote_cell_type": "code"
   },
   "source": "df = parkinsons_voice.data.original.copy()\ncorr_matrix = df.corr()\n\nplt.figure(figsize=(15, 12))\nsns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap=\"coolwarm\", square=True, linewidths=0.5)\nplt.title(\"Correlation Heatmap of Parkinson's Voice Dataset Features\")\nplt.tight_layout()\nplt.show()",
   "block_group": "379a531b4df84275a7132df929000691",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1200 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1338,
       "height": 1190
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/11a78dd4-24d9-4511-b11c-b6d5f5640280",
   "content_dependencies": null
  },
  {
   "cellId": "602a9808c1b0498b95e0056d0fc6d817",
   "cell_type": "code",
   "metadata": {
    "source_hash": "70bc626d",
    "execution_start": 1753983986137,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "602a9808c1b0498b95e0056d0fc6d817",
    "deepnote_cell_type": "code"
   },
   "source": "if 'subject#' in X.columns:\n    X = X.drop(columns=['subject#'])\nif 'test_time' in X.columns:\n    X = X.drop(columns=['test_time'])",
   "block_group": "9c2e8e8b38884b03bfe2f771d20327e5",
   "execution_count": 8,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "b199134be458447a9d19022f0236bab5",
   "cell_type": "code",
   "metadata": {
    "source_hash": "95bdb296",
    "execution_start": 1753983986205,
    "execution_millis": 965,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "b199134be458447a9d19022f0236bab5",
    "deepnote_cell_type": "code"
   },
   "source": "plt.figure(figsize=(14, 10))\nsns.heatmap(X.corr(), cmap='coolwarm',annot = True)\nplt.title(\"Correlation Heatmap of Voice Features\")\nplt.xticks(rotation=90)\nplt.show()",
   "block_group": "3564bf01458c4ef990c4f3eea87df988",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1155,
       "height": 935
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/d73cfaf3-6bd8-40a6-a05f-8b43defaedc6",
   "content_dependencies": null
  },
  {
   "cellId": "f03fbe58b8814a57945d081988a3f95f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "49b0fbb0",
    "execution_start": 1753983987226,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "f03fbe58b8814a57945d081988a3f95f",
    "deepnote_cell_type": "code"
   },
   "source": "'''sns.pairplot(X, corner=True, plot_kws={\"s\": 10})\nplt.suptitle(\"All Feature vs Feature Pairwise Plots\", y=1.02)\nplt.show()'''\n#Takes too long - No need to run",
   "block_group": "3361353e70c5427795b3136b823c9862",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 10,
     "data": {
      "text/plain": "'sns.pairplot(X, corner=True, plot_kws={\"s\": 10})\\nplt.suptitle(\"All Feature vs Feature Pairwise Plots\", y=1.02)\\nplt.show()'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/8de4b395-8535-4a9b-a46a-e93d220732ef",
   "content_dependencies": null
  },
  {
   "cellId": "ac0097b97f104bca96d14e3cdcbb3313",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "ac0097b97f104bca96d14e3cdcbb3313",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Dropping Columns with over 90% correlation",
   "block_group": "f9a4d75d5d434adea98878bc3505efd8"
  },
  {
   "cellId": "710c8e2186684dd38276922b680d9e28",
   "cell_type": "code",
   "metadata": {
    "source_hash": "88384477",
    "execution_start": 1753983987276,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "710c8e2186684dd38276922b680d9e28",
    "deepnote_cell_type": "code"
   },
   "source": "features_to_drop = [\n    'Jitter(%)', 'Jitter:RAP', 'Jitter:DDP',\n    'Shimmer(dB)', 'Shimmer:APQ5', 'Shimmer:DDA',\n    'Shimmer', 'NHR'  \n]\nX_filtered = X.drop(columns=[col for col in features_to_drop if col in X.columns])\n",
   "block_group": "f2e7e8f698104ec29668812096db6596",
   "execution_count": 11,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "4ff014e2661248c39247e394e61f5635",
   "cell_type": "code",
   "metadata": {
    "source_hash": "383f0b68",
    "execution_start": 1753983987336,
    "execution_millis": 456,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "4ff014e2661248c39247e394e61f5635",
    "deepnote_cell_type": "code"
   },
   "source": "plt.figure(figsize=(7, 5))\nsns.heatmap(X_filtered.corr(), cmap='coolwarm', annot = True)\nplt.title(\"Correlation Heatmap of Voice Features After filtering\")\nplt.xticks(rotation=90)\nplt.show()",
   "block_group": "97cc508c2d3f4f4da0faa581f9710459",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x500 with 2 Axes>",
      "image/png": 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2cHDAy8tL4987N0uWLNF6b/35559ERUXRtWtXjeMrFAqqV6+e4/2UlJREeHi4evyqtn/PwvD2+yqv5ZHRYnno0CESEhLyfd6NGzfSqlUr9fPRy8sLf39/Nm7cWOBryU9ZZxgyZEiBz/e+ijreLl26YGlpqf67bt26AOpnyIsXL7hx4wa9evXS6H2qX78+5cuXL+hlCUVEdJ0L2ZiZmQHpFbO8ePr0KXK5HE9PT410BwcHLCwsePr0qUa6m5tbjsd6+7UHDx4A6RXQnERHR2s8lLK6desW33//PceOHctWsctPhSpDxrVk7e7P4Ovry6FDh7INzC9ZsqRGvoxYIyMj1WWdE4VCQZMmTfIU24MHD7hz506OXb4ZkwIA/v77byZOnMjZs2ezfdhGR0e/s/sww9vXlrGfi4tLtvTIyEiNtH379jFt2jSuXr1KcnKyOv3ttf0g/cP8bd7e3iQkJBAWFoaDg4PW+J4+fYqXl1e2CWUZ3X9v35t5ZW1tTUBAADt37mTZsmUYGBiwadMmdHR06Ny5s8ax375X9PT0cHd3z/Xcjx49wsnJSePLzNsePHiAJElaywbyPsGoWrVqWicDZbz3Mr6ovC3rvRsREcHkyZPZsmWLxn0GBXuf5YW2Z0VeysPNzY1Ro0Yxb948Nm7cSN26dWnTpg09evR4531/584drly5Qq9evXj48KE6vUGDBixZsoSYmJh3vqe1yU9ZQ/pEnhIlSuT7PIWlqOPN7ZkJme+ttz9zMtKK6suNUDCioilkY2ZmhpOTEzdv3szXftoqCNpom2Ge02sZrZWzZ8/Gz89P6z45jaeMioqifv36mJmZMWXKFDw8PDAwMODy5ct89913ubaEFqasY/iykt6MKyssKpWK8uXLM2/ePK2vZ1T+Hj16ROPGjfHx8WHevHm4uLigp6fHgQMHmD9/fr7KJadr05ae9XpPnz5NmzZtqFevHkuXLsXR0RFdXV1Wr16t0Ur4MevRowf79u1j3759tGnThu3bt9OsWbMcK/qFTaVSIZPJ+OOPP7SWd27jjPN6fEgfi6etIp91xnLnzp05c+YM33zzDX5+fpiYmKBSqWjevHme7qecnh25tVZre1bktTzmzp1Lnz592L17N4cPH2bEiBHMnDmTc+fO5Voh2rBhAwAjR45k5MiR2V7fvn07ffv2zXH/nOSnrAH09fWLdTWOoo73Qz0zhQ9DVDQFrT755BN+/fVXzp49S82aNXPNW6pUKVQqFQ8ePFC3FAGEhoYSFRWV58WBtfHw8ADSK795bdnLcOLECV6/fs2OHTuoV6+eOv3JkyfZ8ua1kpxxLffu3cv22t27d7Gxsfmgy6xk5eHhwbVr12jcuHGu17N3716Sk5PZs2ePRsuBtu6uvJZLfm3fvh0DAwMOHTqEvr6+On316tVa82e0oGR1//59jIyMcq3YlSpViuvXr6NSqTQ+6DKGTbzPvdmmTRtMTU3ZtGkTurq6REZGqrvNsx773r17uLu7q9NTUlJ48uRJrvezh4cHhw4dIiIiIsdWTQ8PDyRJws3NDW9v7wJfR24xANjZ2eUaa2RkJEePHmXy5MlMmDBBna7t3yyn+ymjxerthdzz0+Kc3/IoX7485cuX5/vvv+fMmTPUrl2bZcuWMW3aNK35JUli06ZNNGzYkC+++CLb61OnTmXjxo25VjRzuv68lvWH9rHGm/HeytqqnEFbmlC8xBhNQatvv/0WY2NjBgwYQGhoaLbXHz16xE8//QRAy5YtAbL9sk5Gy9rbM4jzw9/fHw8PD+bMmUNcXFy213ObrZrxrTjrt+CUlBSWLl2aLa+xsXGeuvgcHR3x8/Nj7dq1Gh+KN2/e5PDhw+qyKA6dO3cmODiY5cuXZ3stMTGR+Ph4QHu5REdHa63kGRsbF8mvuCgUCmQymUaLVWBgoMbM+KzOnj2r0R327Nkzdu/eTbNmzXJs/YD0ezMkJIStW7eq09LS0li0aBEmJibZ1j7MD0NDQ9q3b8+BAwf4+eefMTY2pm3bturXmzRpgp6eHgsXLtQo65UrVxIdHZ3r+6Jjx45IksTkyZOzvZZxrA4dOqBQKJg8eXK2lh5Jknj9+nWBrw0gICAAMzMzZsyYQWpqarbXM9572u4nyP48ANRfwt6+p8zMzLCxseHUqVMa6dreqznJa3nExMSQlpam8Xr58uWRy+UaQzje9vfffxMYGEjfvn359NNPs21dunTh+PHjvHjxIsdj5HT9eS3rD+1jjdfJyYly5cqxbt06jc+FkydPcuPGjSI9t5B/okVT0MrDw4NNmzbRpUsXfH196dWrF+XKlSMlJYUzZ86ol4gBqFixIr179+bXX39Vd1dfuHCBtWvX0q5dOxo2bFjgOORyOStWrKBFixaULVuWvn374uzsTHBwMMePH8fMzIy9e/dq3bdWrVpYWlrSu3dvRowYgUwmY/369Vq7X/z9/dm6dSujRo2iatWqmJiY0Lp1a63HnT17Ni1atKBmzZr0799fvbyRubk5kyZNKvC1vq+ePXuybds2Bg8ezPHjx6lduzZKpZK7d++ybds2Dh06RJUqVWjWrBl6enq0bt2azz//nLi4OJYvX46dnR0vX77UOKa/vz8///wz06ZNw9PTEzs7uxzHZeVHq1atmDdvHs2bN6dbt268evWKJUuW4OnpyfXr17PlL1euHAEBARrLGwFaK2JZDRo0iF9++YU+ffpw6dIlXF1d+f333/n7779ZsGBBnie85aRHjx6sW7eOQ4cO0b17d43WbFtbW8aOHcvkyZNp3rw5bdq04d69eyxdupSqVavSo0ePHI/bsGFDevbsycKFC3nw4IG6C/r06dM0bNiQYcOG4eHhwbRp0xg7diyBgYG0a9cOU1NTnjx5ws6dOxk0aBCjR48u8LWZmZnx888/07NnTypXrsxnn32Gra0tQUFB7N+/n9q1a7N48WLMzMyoV68eP/74I6mpqTg7O3P48GGtPQcZE7rGjRvHZ599hq6uLq1bt1Z/qf3hhx8YMGAAVapU4dSpU9y/fz/P8ea1PI4dO8awYcPo1KkT3t7epKWlsX79ehQKBR07dszx+Bs3bkShUOT4BaFNmzaMGzeOLVu25Pjzo35+figUCmbNmkV0dDT6+vo0atQIOzu7PJX1h/Yxxztjxgzatm1L7dq16du3L5GRkSxevJhy5cppbZQQitGHneQu/Nvcv39fGjhwoOTq6irp6elJpqamUu3ataVFixZpLA+UmpoqTZ48WXJzc5N0dXUlFxeXXBdsf1tOS3BkuHLlitShQwfJ2tpa0tfXl0qVKiV17txZOnr0qDqPtuWN/v77b6lGjRqSoaGh5OTkJH377bfSoUOHsi3ZERcXJ3Xr1k2ysLDQWB4jp2VXjhw5ItWuXVsyNDSUzMzMpNatW+e4YHtYWJhGel5/KrMgC7anpKRIs2bNksqWLSvp6+tLlpaWkr+/vzR58mQpOjpanW/Pnj1ShQoV1D+jN2vWLGnVqlXZ4goJCZFatWolmZqaaiz2nNNi3zlds7ZrWblypeTl5SXp6+tLPj4+0urVq7UuuZJxnRs2bFDnr1SpUp5/Fi80NFTq27evZGNjI+np6Unly5fXuoxOfpY3ypCWliY5OjpKgHTgwAGteRYvXiz5+PhIurq6kr29vTRkyJA8LdielpYmzZ49W/Lx8ZH09PQkW1tbqUWLFtl+2nL79u1SnTp1JGNjY8nY2Fjy8fGRhg4dKt27dy/X2PO6YPvx48elgIAAydzcXDIwMJA8PDykPn36aCw39fz5c6l9+/aShYWFZG5uLnXq1El68eJFtqWJJEmSpk6dKjk7O0tyuVzjfktISJD69+8vmZubS6amplLnzp2lV69e5bi80dv3WF7L4/Hjx1K/fv0kDw8PycDAQLKyspIaNmwoHTlyJMcySElJkaytraW6devmWlZubm5SpUqVJEnSvryRJEnS8uXLJXd3d0mhUGR7DuWlrN/1XHjb+yxvVNTx5rZg+9u03UtbtmyRfHx8JH19falcuXLSnj17pI4dO0o+Pj65lIjwockkSYyuFQTh4yWTyRg6dGixtOgIgvDv4ufnh62trdYl4ITiIcZoCoIgCILwr5KampptrO2JEye4du0aDRo0KJ6gBK3EGE1BEARBEP5VgoODadKkCT169MDJyYm7d++ybNkyHBwcsi3mLxQvUdEUBEEQBOFfxdLSEn9/f1asWEFYWBjGxsa0atWKH374AWtr6+IOT8hCdJ0LgvBRkyRJjM8UBEGDubk5W7du5fnz5yQnJxMREcFvv/2mXuPz/4tTp07RunVrnJyckMlkOS4Rl9WJEyeoXLky+vr6eHp6smbNmiKNUVQ0BUEQBEEQ/oXi4+OpWLEiS5YsyVP+J0+e0KpVKxo2bMjVq1f56quvGDBgAIcOHSqyGMWsc0EQBEEQhH85mUzGzp07adeuXY55vvvuO/bv36/xE9OfffYZUVFRHDx4sEjiEi2agiAIgiAIH4nk5GRiYmI0ttx+tSo/zp49m+1nQwMCAjh79myhHF8bMRlIyGa/buniDiFfSt459e5MH4k0KeefS/zYmMpjizuEfNGVUoo7hDy7H+9a3CHki6tJSHGHkGdP4x2KO4Q88zB+Vtwh5MvLFPviDiHPavm+369+5Udhf2ZeHNc126+eTZw4sVB+eS4kJAR7e81/R3t7e2JiYkhMTMTQ0PC9z/E2UdEUBEEQBEEoIJmurFCPN3bs2Gw/Y6qvr1+o5/iQREVTEARBEAThI6Gvr19kFUsHBwdCQ0M10kJDQzEzMyuS1kwQFU1BEARBEIQCk+sUbotmUapZsyYHDhzQSPvzzz+pWbNmkZ1TVDQFQRAEQRAKSKZbfPOq4+LiePjwofrvJ0+ecPXqVaysrChZsiRjx44lODiYdevWATB48GAWL17Mt99+S79+/Th27Bjbtm1j//79RRajmHUuCIIgCILwL/TPP/9QqVIlKlWqBMCoUaOoVKkSEyZMAODly5cEBQWp87u5ubF//37+/PNPKlasyNy5c1mxYgUBAQFFFqNo0RQEQRAEQSig4uw6b9CgAbkth67tV38aNGjAlStXijAqTaKiKQiCIAiCUECFPev8v0Z0nQuCIAiCIAhFQrRoCoIgCIIgFNC/adZ5cRAVTUEQBEEQhAISXee5E13ngiAIgiAIQpEQLZqCIAiCIAgFJLrOcycqmoIgCIIgCAUkU4iKZm5ERVMoUlZ1quD+dX/MK5fDwMmOfzp+Qeieo0V6zj/27WDP9i1ERUZQys2D/oO/xKt0mRzznzl9nC0bVhIWGoKjkzM9+g6mctXMn+PaunEVf586xuuwV+jo6ODuWZquvQbi7aN5zEsXzvLb5jUEBT5CV1ePMuX9+G78jHfGe2jfdvbu2ER0ZAQl3Tzp+/lIPHOJ99xfx9i2YTlhoSE4OJWgW58hVKpaS/36hTMn+POPXTx5eI+42Bh+WLgaV3dvjWMsX/wjN65eJDIiHAMDI7x9y9Gtzxc4u5TKNdZ9e/ewffvvREZG4ubmzuAhX1C6dOkc858+fYoN69cRGhqKk5Mzffv1o2rVaurXIyMjWb16JVcuXyY+Pp6y5coxePAXODs7q/O8fPmClStWcOvWLVJTU/H392fwkC+wtLTMNVZt9uzbz2/bdxIRGYm7mxtDBw/Cp7S31ryBT4NYt2EjDx4+IvTVKwYP7E+Hdm1zPPaWbb+zau062rdtzZBBA/MdW15IksT+bUs5c3Q7ifGxuPv40WXA99g55vzv9vD2PxzZs4agJ3eIiQxj4OgFVKzWqNBj27d3Dzu2/0ZkZARubu58PmQopUv75Jj/r9On2LB+jfre6NNvQLZ7Y83qFVy5fOnNvVGezwcP1bg38kqSJPZvXcrfWcrts4G5lxvAyYNbOLJnDTFR4TiX8qZzv7G4epVXvx4W8oyd6+by6O4V0tJS8PWrTed+YzGzsFbnWfbDcJ4H3iM2JgIjYzNKl69Bux5fYWFll+f49+zdx+/btxP55r79YsjgHN93gU+fsn79Bh48fMirV6/4fNBA2rdrp5Fny9Zt/H3mDM+fP0dPT48yvr7069cXlxIl8hxTBkmS2LX5F07+uZOE+Di8fCrSc/AYHJxK5rrf0QPb+GPneqKjXlPS1YvuA7/B3buc1uPPn/olNy6fYfiYOVSu0QCAoCf3ObBjDfdvXyMuNgobO0caBHSkWeuu+b4GoWiJMZpCkVIYGxFz/R43R0z+IOf7+9RR1i5fQqduffhx4Qpc3TyZNn400VGRWvPfvX2DBT9OoXGzVsxeuIKqNevy47RxBAU+VudxcnZhwOCvmLdkDdNmL8HO3oFp478mOjpKnefc3ydYNHcaDZu2ZM7i1Uybs5S6DZq8M94zp46wfsUiPu3aj5k/raKUmyczJ4zKMd57d26w8MdJNGz6CT8sXE2VGnWZM30sz7LEm5SUhE+ZCnTrMyTH87p5lmbIV+OY+/Mm/jdlHkgSMyaMRKVU5rjPqZMnWb58Od269WDhosW4ubszfvw4oqKitOa/ffs2P876gWbNAli4aAk1a9Zk2tQpBAYGAukfINOmTibkZQjjJ0xk4aLF2NnZMe5/Y0lKSlJfy/fjxoEMZs78gTlz5pKWlsaUyRNRqVTvKF1NJ06d5pflK+nR7TOWLpyPu5sr/xs/kcgc4k9OTsbBwYF+fXph9Y5K7b37D9h/8CDubq75iim/juxezck/NvHZwPGMnrERPX1DlkwfTGpKco77JCcn4uxami79/1dkcZ06eYIVy3+ha7ce/LRoKW7u7kwY/z+icriP79y+xY+zZtC0WXMWLvqZGjVrMX3qJAIDnwAZ98YkQl6+5PsJk/lp0VLs7Oz4/n/fkZSUmO/4/ty9mhN/bOKzQeP5ZmZ6uS2elnu5Xfr7IDvWzqZlp8GMmbWVEqVKs3j6YGKjXwOQnJTA4mmfg0zGiInLGTV1Lcq0VJb9MFzj3vQuV43+o2Yz4ac9DBw9j/DQZ6yY+3WeYz958hTLly+nR7duLF60EHd3N8aNH5/j+y45ORkHRwf69e2T45exGzdv0PqTVsyfN5eZ06eRpkxj3Ljv1e+7/Diwcy1/7ttCr8FjGf/jGvQMDJg3eXiuZXv+r8NsWTWftp8NZNK8Dbi4ejN38nBioiKy5T28d5PWYwQ+uoOpuRWDRk5h2sKtfPJpP7avX8yR/VvzfQ3vS66QFer2XyMqmh+RgwcPUqdOHSwsLLC2tuaTTz7h0aNH6tfPnDmDn58fBgYGVKlShV27diGTybh69ao6z82bN2nRogUmJibY29vTs2dPwsPDi+Fq0oUdOsX9iQsI3X3kg5xv785tNGn+CY2atsSlpCuDhn2NvoEBxw5r/x3XA3t+x8+/Gm07dqVESVe69hyAm4c3f+zboc5Tt0FTKlSqgr2jEy6l3Og9cBgJCfE8fZL+b6NUprHql0X07DeEgJZtcXJ2waWkK7XqvrvVaP+urTQKaE2Dpq0oUdKNAUO/QU9fnxN/7tOa/48926joX53WHbvj7OJKl56DcPPw5tC+39V56jVqTseu/SjnVzXH8zZp3hbfcn7Y2Tvi5lmazj0H8ToslFevXua4z86dO2jevDlNmzWjZMlSDBs2HAN9fQ4fPqQ1/57du/D3r0LHTztRsmRJevbqjYeHJ/v27gHgRXAwd+/eZeiwYXh7l6ZECReGDh1OSkoyJ08cB+D27Vu8ehXKqFFf4+rmhqubG6O+Hs2DBw+4du3qu4pXw/adu2nRvBkBTZtQqmRJvhz2BfoG+hw6rP3eLO3txaD+fWlYvx66uro5HjcxMZEfZs9l5PBhmJiY5Cum/JAkieMHNhDQYSAVqjbEuZQ3vYZNJzoyjGsXj+W4X9lKdWn92XAqVmtcZLHt2rmdgOYtaNosgJIlSzF02Jfo6+vzZ673RlU6ftoZl5Il6dmrT7Z7497dO3wxbIT63vhi6Ig398aJfMUmSRLH92+geceBVHxTbr3zUG5H962jVuOO1GzYDkcXDz4bNB49PUPOHtsFwON7V3n96gU9h07FuZR3+r/H0GkEPb7F/ZsX1Mdp9ElP3LwrYm3rhHtpP5q160fgg+so01LzFP+OnTtp3rw5zZo1pVTJkgwfNgx9fQMOHT6sNX9pb28G9u9Pg/r1c7xvp0+dSrOmTXEtVQp3d3e+HjWKV2FhPHjwUGv+nEiSxJ97N9O6c38qV2+Ai6sXA7+cQmREGJfPn8hxv8O7N1KvWTvqNm6Ds4s7vYaMRU/fgNNH92jkC3p8j0O7N9J/+IRsx6jXpC3dB4zGp5w/dg4lqNWgJXUat+HSueP5uobCIJPLCnX7rxEVzY9IfHw8o0aN4p9//uHo0aPI5XLat2+PSqUiJiaG1q1bU758eS5fvszUqVP57rvvNPaPioqiUaNGVKpUiX/++YeDBw8SGhpK586di+mKPqzU1FQeP7xPBb8q6jS5XE55P3/u3b2ldZ/7d29Rwc9fI82vcjXu55A/NTWVP//Yg5GxCa5uHgA8fnifiNdhyORyRg/vz4Ae7Zg24RuNVlFt0lJTefLwHuWzVAjT463C/bs3te7z4O4tyme5PoCKlavnGG9eJCUlcuLIfuzsnbCxsdeaJzU1lYcPH+DnV0kjVj+/Sty9e0frPnfv3sGvUiWNtMr+/ur8qanpH7R6enoax9TV1eXW7VsaebJ+YOrp6SKTybh9K+/XnJqayoOHD6nk56dxrkp+Fblz926ej6PNop+XUa1qFSpX8ntn3vfx+lUwMVHh+FSooU4zNDLF1bM8gfevFem5c1Owe+O2lnujSp7ujdu3tb83cpJRbqXLZyk34/Rye3JPe7mlpaby7PEdjbKWy+X4VKjO4zdlnZaagkwmQ0c3M0YdPX1kMjmP7l7Wetz42Ggunj6Am7cfCp2cv7xkyPm+9Xvv+zarhPh4AExN8/dFKSw0mOjI15StkDnkwcjYBA/vcjy8d0PrPmmpqQQ+ukvZCtXVaXK5nDIVq/Hw3nV1WnJyEr/M+54eg77F3NImb9eREIeJiVm+rkEoemKM5kekY8eOGn+vWrUKW1tbbt++zV9//YVMJmP58uUYGBhQpkwZgoODGTgwcyzY4sWLqVSpEjNmzNA4houLC/fv38fbO/tYtOTkZJKTNbs4UiUVurJ/33eQ2JhoVCol5haa3UUWFlYEPwvSuk9UZAQWFlYaaeYWlkRFanbh/HPhDAtmTSY5OQlLK2smTJuLmbkFAKEh6a2A2zaups/AodjaObJ351Ymjv2Shb9uxNRU+4MvJibqTbxvn9+K4Oc5xftaa/7oqNda8+fm8P4dbFy9lOSkRJxKlOR/0+ajk0MLSExMDCqVCgtLC410CwsLnj17pnWfyMhILCyy54+MTO9OLeHigq2tHWtWr2bY8BEYGBiwa9dOwsPDiYxIL38fHx8MDAxYvWoVvXr3AWD16lWoVCoiIrN3s+UkI37Lt+KxtLDg2bPgPB/nbcdPnuLhw8csXjC3wMfIq5io9J4JU3NrjXRTc2tiCvDvX1gy742333eWPM/13ng7v4X6fZdxb6xdvYphw79E38CA3bt2EB4eTkRE3v/dIbPcso6bBDC1yLnc4mIjUamUWss6JDi9e9/VqwJ6+obs3jCfNt1GIEkSuzf+hEqlJDpSsxdp14b5nDy4mZTkJNy8KjB47OK8xV6A911+qVQqlv3yK2XKlMHV1TVf+2Y8d94uWzNzK6IjtZdtbGz6c8/s7eeYuRUhzwPVf29eORcPnwpUrt4gT7E8uHuNi38d5qvvf8r7BRQSmeLf93n5IYnS+Yg8ePCArl274u7ujpmZmfpNHxQUxL1796hQoQIGBgbq/NWqVdPY/9q1axw/fhwTExP15uOTPhg/axd8VjNnzsTc3Fxj26bK34P8/4NyFSoxe9FKps9Zil/lasz7YaJ6HKUkpY/H6tilJzVqN8DDqzRDR45BBpz968N34+RVnQbN+OGn1Uz8YQmOTi789MMEUnIZV1XYdHR0GPf9eIJfBPNZl050aN+W69evUaVKVWRvvuiYm1sw9n/jOH/+PJ92bE+nTzsQHxeHh6cn8mL+MvQqLIyff13OmG9GabS8FZaLp/czqmd19aZUphX6OT5W6ffGBIJfPOezLh3p2L41169fw79KVeSy3LsWL5zez8ge1dWbMq1oys3U3IoBX8/hxqWTjOpZg9G9a5MQH4uLmy+yt2Js0qYPY37cxrDvf0EmV7Bu0TgkSSqSuPJrydKfCXz6lLFjvntn3rMn/2DwZ3XVW1GV7ZULJ7lz4x+69c/bWNbnTx+ycMbXtOkykHKVarx7h0ImxmjmTrRofkRat25NqVKlWL58OU5OTqhUKsqVK0dKSkqe9o+Li6N169bMmjUr22uOjo5a9xk7diyjRo3SSDtm5a8178fO1MwcuVyRbSJNVFQEFpZWWvexsLQi6q0B6NFRkdnyGxgY4uhUAkenEnj7lGXYwK4cPbyfDp17YGmZ/m2+RElXdX5dXT3sHJwIf/Uqx3jNzCzexPv2+XOL11prfvO3WhTywsjYBCNjExydXfAqXZb+nzXn4tlT1K7fVEusZsjlcqIiozTSo6KisLTSPuHA0tIy24SFqKgojQkKXl5eLF68lPj4eNLSUjE3t2DkV1/i5eWlzlO5sj8rV60mOjoahUKBiYkJ3bt3xcHBIc/XmhH/2xN/IqOisHqrtSivHjx8RFRUNF+MGKlOU6lU3Lh5i91797N/13YUCkWBjg1QvkoDjRnOaanpz4HY6NeYW9qq02OjX1PCNeeZ/0Ut8954+30XiaWV9vs4/d54O3+Uxn3v6eXNosXLNO6NUV8Nx8tL+yoBGSpUaYCrZ5ZyS0svt5iot8otKudyMzG1RC5XqCf+qPeJfo2ZRWY3rm/FWkxefIC4mEjkCgVGxmaMGdAQf3vN2dsmZpaYmFli7+SKQwk3vh/cjCf3r+NZWXv5ZCjI+y4/liz9mfMXLjDnx1nY2ry7e9qvWj2NmeEZ92RM1GssrDL3j4mOwMVN+7+TqWn6c+/tiT/R0RGYvXmW3rn+D2EhzxnavaFGnsU/fou3rx9jpv+qTgt+9pjZE76gQbP2tOk84J3XIHx4okXzI/H69Wvu3bvH999/T+PGjfH19VV3MQKULl2aGzduaHRzX7x4UeMYlStX5tatW7i6uuLp6amxGRsbaz2vvr4+ZmZmGtu/sdsc0sfxuXt6c+PqJXWaSqXixtXLlPYpq3Ufb5+y3LimOZ7q2pWLeOeQP4Okkkh985B19yqNrq4eL7J0d6elpRH2KgRbO+1jHgF0dHVx8yzNzWv/aMR789olvH2yL/MB4OVTlptZrg/geh7ifRfpzX8Z1/Q2XV1dPD29uJplAo5KpeLq1av4+Phq3cfHx5drWSaqAVy5cllrfmNjY8zNLQgODubhwwfUqFkzWx5zc3NMTEy4dvUq0VFRVK+R95YLXV1dvDw9uXo1c0xeevzX8fXJeQme3FSqWIFflizi50U/qTdvL08aNajPz4t+eq9KJoCBoTG2DiXVm0MJD8wsbLh347w6T2JCHIEPb+DqXfG9zvU+Mu6Na2/dG9dyvTfKcPXqFY20vN4b1bXcG1kZGBpj51hSvTlmlNvN7OXmVlp7ueno6uLi7qtR1iqVins3zuOupaxNzCwxMjbj3o3zxMVEUKFKgxzjk1TpLZkZFeDcqO9bLe+7gt63kD6JZ8nSnzlz9iyzZs7I85c2Q0Nj7B1d1JuTizvmltbcvp75WZSYEMej+zfxLF1e6zF0dHVx9fDh9vXMCVMqlYo71y/iWboCAK069mbKgs1Mnr9RvQF07TeK/iMmqvcLDnrEj98PpnbDVnTsMTTf5VBYxGSg3IkWzY+EpaUl1tbW/Prrrzg6OhIUFMSYMWPUr3fr1o1x48YxaNAgxowZQ1BQEHPmzAFQd9MMHTqU5cuX07VrV7799lusrKx4+PAhW7ZsYcWKFe/9wVcQCmMjjD0z11MzciuBWUUfUiKiSXqW8wzngmrdvjOL583Ew6s0nt6+7N/9G8lJiTRs2hKAhXOnY21tQ/c+nwPQss2nTBwzgj07tuBftSZ/nTrK44f3GDz8GyB9osz2reupWr02llbWxERHc3D/TiJeh1OrTvq3bSMjY5q1bMPWjauxtrXD1s6BPds3A1CzTkMtUWZq1a4LP8+fjruXD57eZTiwexvJSUnUb9IKgCVzp2JlbUPXN0sVtWjTmSljhrJvx2YqVa3FmVNHePzwLoOGZXZ7xcXGEB4WQuTr9HFiGRVgC0trLCytCQ0J5uypo1SoXA0zMwtevw5jz2/r0dPTp1KVWuSkffsOzJs3By8vL7y9S7N7906SkpNo2rQZAHPnzMba2po+ffsB0KZtO8Z89w07dmynatVqnDp5gocPHjB8+JfqY54+fQpzc3Nsbe0IDAzk119+pkaNmlSunNmq/ufhw7iUdMHc3Jw7d+7w6y/LaNeuPSVKuORatm/r2L4ts+ctwMvLEx9vb3bs3kNSUhIBTdNnY/84dz7W1lb079MbSJ+IERSUPg4uNS2N8NcRPHr0GANDA5ydnDAyMsLNVXMdRgMDA8zMTLOlFwaZTEbDlj04uONXbB1LYm3nzP4tSzC3tKVi1cwVDhZOGUDFao2p3zx9PcHkpATCQjK/BL1+FczzwLsYmZhjZaO9pyO/2rXvyPx5s9/cGz7s3r2DpOQkmjQNAGDunB/f3Bv9gYx7YzQ7dvye5d64z7As98Zfp09hZm6Ona0dgYFP3twbtahcuYrWGHIik8lo2KoHB7f/ip1Dernt25q93H6anF5uDVqkl1vjT3qxbsn3lPQog6tneY7t30ByciI1GrZT73P2+C4cnN0wMbPiyf1r/L56Fg1b9cTe2Q2AJw+u8/ThLTx8KmFkYkZ4yDP2bV2Cjb0Lbt4VgdB3xt+hfXvmzJuHl5cXpb292bl7N0nJSTRrmt7zMHvOXKytrenXtw+Qcd+m/3unpaUR/vo1jx49wtDQECcnJwCWLF3K8RMnmThhPIaGhupxr8bGxujr6+erbJu27sre31Zi7+SCjZ0zOzf9jKWVrcbYyh/HD6FyjQY0adUFgGZtu7Pip0m4epbB3assh/duIjkpkTqNWwNgbmmjdQKQtY0Dtvbp66g+f/qQHycMoZxfDQLadlePi5XJFZiZv39rb378F7u7C5OoaH4k5HI5W7ZsYcSIEZQrV47SpUuzcOFCGjRoAKR3oezdu5chQ4bg5+dH+fLlmTBhAt26dVOP23RycuLvv//mu+++o1mzZiQnJ1OqVCmaN2+OXF48rZTm/uWoeXS9+u8yc9LX8nu2bgfX+48t9PPVrteYmOgotmxYRVRkBK7unoybMkfdJRceFqoxxsunTHm+/GYCW9avYNPa5Tg6l+Db76dT0tUdSP93CX72lJNHDxITHY2pmRkeXj5M/XERLqXc1Mfp2e8L5HIFi+ZOJyU5Ga/SZZg0YwEmpqa5xlurXhNioqP4bcOK9AXm3b0YM2WuRrxZv+GW9i3P8G8msXX9r2xZ9wsOTiUYPW4mLm/iBfjn/GmWLcicELbwx/QWgI5d+9Gpe390dfW4e+saf+zZRlxcLOYWVviWrciU2cuyTaTKql79+kTHRLNh/fr0haPd3ZkyZZq6Kzws7JVGrGXKlOGbb79j/bq1rF2zBmdnJ74fP0FjwkFkRAQrlv/6pkvdisaNG/NZ124a530e/Jw1a1cTFxuLnZ09Xbp8Rrv2HXItV20a1KtLdHQ06zZsUsc/fcokdfyvwsI0xta9johgyIiv1H//vmMnv+/YSYXy5Zjzw7sX4i8KTdr2JTk5kc2/TCExIRYPn0p88b+f0dXLrByEhz4nLiazN+Tpo1ssnNxf/feOdbMBqF6/DT2HTiuUuOrVb/Dm3liX5d6YrnFvyLPcG75lyvLNt2NZv24N69asxsnZiXHjJ+Hqmvmeioh4zYrly9T3RqPGTfisa/cCxde0bV9SkhLZlKXcho7LXm7xsZnl5l+7ObExkezbupTYqHCcXUszdNzPGhNfQoMD2b3xJxLiorG2cyagw0AafdJT/bqengHXzh/hwLalJCcnYm5hg69fbfqPnI2ubt7G9davX4/omGjWr9+gLttpU6Zo3rdyzft26PAR6r+3b9/B9u07KF++PLNn/QDAvv0HAPj2uzFkNWrkV+oKbF61bN+blKQk1iydQUJ8LN6+foyasFCjbF+FPCcuJkr9d/U6zYiNjmTX5mVER76mpJs3oyYuytcQoH/OHCU2OpKzJ//g7Mk/1OnWto7MWb43X9cgFC2Z9LGMSBbybePGjfTt25fo6GgMDQ0L7bj7dYtvvFdBlLxzqrhDyLM06cO3KheUqTy2uEPIF10pb2OZPwb3412LO4R8cTUJKe4Q8uxpfN7H7hY3D+PCmTn+obxMyXko0Memlm/uX/IL0z/1cx/OkV9VTp4t1OMVN9Gi+S+ybt063N3dcXZ25tq1a3z33Xd07ty5UCuZgiAIgiDknayYegz/LURF818kJCSECRMmEBISgqOjI506dWL69OnFHZYgCIIgCIJWoqL5L/Ltt9/y7bffFncYgiAIgiC88V+cKV6YREVTEARBEAShgMSs89yJgQWCIAiCIAhCkRAtmoIgCIIgCAUkus5zJyqagiAIgiAIBSRmnedOlI4gCIIgCIJQJESLpiAIgiAIQgGJrvPciYqmIAiCIAhCAYlZ57kTXeeCIAiCIAhCkRAtmoIgCIIgCAUkus5zJyqagiAIgiAIBSRmnedOVDSFbEreOVXcIeRLkG+94g4hz3y6ehZ3CHlmU969uEPIF7meXnGHkGdzLvcs7hDyZWe9HcUdQp45e5Ur7hDy7OWctcUdQr6Ej9hT3CEI/0KiGi4IgiAIglBAMrmsULf8WrJkCa6urhgYGFC9enUuXLiQa/4FCxZQunRpDA0NcXFxYeTIkSQlJRX08t9JtGgKgiAIgiAUUHGO0dy6dSujRo1i2bJlVK9enQULFhAQEMC9e/ews7PLln/Tpk2MGTOGVatWUatWLe7fv0+fPn2QyWTMmzevSGIULZqCIAiCIAj/QvPmzWPgwIH07duXMmXKsGzZMoyMjFi1apXW/GfOnKF27dp069YNV1dXmjVrRteuXd/ZCvo+REVTEARBEAShgAq76zw5OZmYmBiNLTk5Odt5U1JSuHTpEk2aNFGnyeVymjRpwtmzZ7XGWqtWLS5duqSuWD5+/JgDBw7QsmXLoikcREVTEARBEAShwGRyeaFuM2fOxNzcXGObOXNmtvOGh4ejVCqxt7fXSLe3tyckJERrrN26dWPKlCnUqVMHXV1dPDw8aNCgAf/73/+KpGxAVDQFQRAEQRA+GmPHjiU6OlpjGzt2bKEc+8SJE8yYMYOlS5dy+fJlduzYwf79+5k6dWqhHF8bMRlIEARBEAShgAr7Jyj19fXR19d/Zz4bGxsUCgWhoaEa6aGhoTg4OGjdZ/z48fTs2ZMBAwYAUL58eeLj4xk0aBDjxo1DXgRrgooWTUEQBEEQhAIqruWN9PT08Pf35+jRo+o0lUrF0aNHqVmzptZ9EhISslUmFQoFAJIkFeDq3020aAqCIAiCIPwLjRo1it69e1OlShWqVavGggULiI+Pp2/fvgD06tULZ2dn9RjP1q1bM2/ePCpVqkT16tV5+PAh48ePp3Xr1uoKZ2ETFU1BEARBEIQCKs6foOzSpQthYWFMmDCBkJAQ/Pz8OHjwoHqCUFBQkEYL5vfff49MJuP7778nODgYW1tbWrduzfTp04ssRlHRFARBEARBKKDiXLAdYNiwYQwbNkzraydOnND4W0dHh4kTJzJx4sQPEFk6MUZTEARBEARBKBL/byqaDRo04Kuvvvrg501JScHT05MzZ87ka78+ffrQrl27Ap93zJgxDB8+vMD7C4IgCILwbsX9W+cfu/9013mfPn2Iiopi165d7NixA11dXfVrrq6ufPXVVxqVzzVr1vDVV18RFRVVaDEsW7YMNzc3atWqle21zz//nBUrVrBlyxY6depUaOcEGD16NO7u7owcORJ3d/f3Pt4f+3awZ/sWoiIjKOXmQf/BX+JVukyO+c+cPs6WDSsJCw3B0cmZHn0HU7lq5iy4rRtX8fepY7wOe4WOjg7unqXp2msg3j6ax7x04Sy/bV5DUOAjdHX1KFPej+/Gz3jv69HGqk4V3L/uj3nlchg42fFPxy8I3XP03TsWMrPGrbBo2RGFuSUpz54Qvn4ZyY/va83rNHYmhr4VsqXHX71IyLxJACjMLLDq0hejcpWQGxmTdO8W4euXkRr6olDi1atUF/2qjZEZm6F8FUzS0d9RhjzNeQd9QwzqfoKuV0VkBkaoYiJJOradtCe301+u3hQdr4oorO2RUlNRvnhC0sndqCJfvXesuhVqo+ffAJmRKarwFySd2Ikq9JnWvDq+VTFs9plGmpSWStySMeq/ZUYm6Nf+BEVJb2T6hiiDH5N0cidSVPh7x5qhV3sHmjewxsRIwe0H8Sxc+4wXoSk55l87pwwOtnrZ0vccCWPJ+mAAWjSwpmENSzxdDTE2VNBhyA3iE5TvFefWyw9Ye+Eur+OT8Laz4LsmlSnnaK01754bT5j4h+ZP3ukp5Jz/OvM5OOHAefbeDNTIU8vNgSWd6r9XnABbj59n7aG/eB0dh7eLA991bUU5txJa8x69fIuVB07x7FUEaUolJe2s6dmsNp/U9MuMddUO9p69ohlrWU+WfNX7vWOFf9czQZIkDm9fzPnjv5EYH4urdyU69JuArYNrjvs8vvMPJ/avIvjJLWKiwug9ciHlqjTRyLNl2f+4dHqXRpp3hToM/O7X9475fRXnGM1/g/90RTMrKyurD3YupVKJTCZDJpOxePFipkyZki1PQkICW7Zs4dtvv2XVqlWFXtG0sbEhICCAn3/+mdmzZ7/Xsf4+dZS1y5cwaNjXeJUuw/5dvzFt/GgW/roRcwvLbPnv3r7Bgh+n0L3PIPyr1uT0ySP8OG0cP/60gpKu6ZVeJ2cXBgz+CnsHJ1JSktm3axvTxn/NohWbMTe3AODc3ydYtnA2XXsPonzFyiiVSp49ffxe15IbhbERMdfv8WzNdqr8vqTIzpMb4+p1sek2kLA1i0l6dA+LgHY4fjOVZ98OQhkbnS1/yMLpyHQyv0DJTUxxmbaY+At/qdMcvvoeKU1JyIKpqBITMG/eHsfvpvNszGCklOw/a5YfuqUrY9CgPYl/bkX58in6/g0w7vQFsSunIiXEZd9BrsC401CkhDgS9qxEFRuN3MwKKTlRnUXh4knKldPplVW5AoO6rTHuNJTY1dMhNecK1rvoePmhX7cNScd/RxUShK5fXYzaDSJ+3SykRC2xAlJyIvHrZmVN0Xjd8JO+SColiftWIyUnoVe5PkbtPyd+/WxIK3isGTq3tKNtU1vmLH9KSHgKvTs4MmO0BwP/d5fUVO1LkYyYfA95llYRV2cDfvjOk9MXM+8fAz05/9yI4Z8bMfTv7PTecR66E8Tc41cZ18yfco7WbPrnPl9sO8muAS2xMjbQuo+Jni47B7RQ/y2TZW/JqeXmwOQW1dR/6+m8/6zYQxdvMHfbH4zr0YZybiXYdOQsXyxYy66pX2JlZpItv7mxEQNa1sfV0QZdhQ6nr99j0pqdWJkaU6ucV2as5byY3Kd9llgL5+P13/ZMOLFvJX8d2kCXz2dgZVeCQ78tZMUPgxj941509bSvDZmSnIBTydJUrd+BdQtG5Hjs0hXq0PnzzEkrOrrZv1AJH5//N9XwrF3nDRo04OnTp4wcOVJdITxx4gR9+/YlOjpanTZp0iQAkpOTGT16NM7OzhgbG1O9enWNAbZr1qzBwsKCPXv2UKZMGfT19QkKCuLSpUs8evSIVq1aZYvnt99+o0yZMowZM4ZTp07x7Jn2VpXJkydja2uLmZkZgwcPJiUl88Pr999/p3z58hgaGmJtbU2TJk2Ij49Xv966dWu2bNny3mW3d+c2mjT/hEZNW+JS0pVBw75G38CAY4f3a81/YM/v+PlXo23HrpQo6UrXngNw8/Dmj3071HnqNmhKhUpVsHd0wqWUG70HDiMhIZ6nTx4BoFSmseqXRfTsN4SAlm1xcnbBpaQrteo2eu/ryUnYoVPcn7iA0N1Hiuwc72LRvD0xJw4Se/oIqS+eEbZmMVJyEqb1m2nNr4qPQxkdqd6MylVCSkkm7sJpAHQdnDDw9CVs7RKSnzwgNSSY8LVLkOvpYVLz/VuG9Ko0JOX6WVJvnkf1OoTEw1uRUlPQK6d9DTe98jWQGRqRsOtXlMFPkGIiUD5/iCosWJ0n4fefSb2VfjxVWDCJf2xAbm6Fwt7l/WKtXI/UW+dIu30RVUQoyce2I6Wlolu2Wq77SQmxWbbMCqnMwgaFoyvJx7ajCn2GFBVG8rHtoKOLbulK7xVrhnYBtmzeG8LZKzE8eZbEj78+xdpCl1qVzXPcJzpWSWR0mnqr7mfOi9Bkrt/NjH3n4TC27X/F3UcJhRLnhn/u0aGCO23Lu+NhY864gCoY6Oqw68aTnHeSgY2JoXqz1lIh1VMoNPKYGbx/xWLDn2foULcKbWtXxsPJjnE9WmOgp8uuvy9rzV+ltBuNKpfB3dEOFzsrujWpiVcJe6481Gy119NRYGNuqt7MjA3fO1b4dz0TJEni9MF1NG73OeWqNMapZGk+G/IDMVGvuHUp594hH796NO/8JeWrNskxD6RXLM0sbNWbkXHO74MPSXSd5+7/TUUzqx07dlCiRAmmTJnCy5cvefnyJbVq1WLBggWYmZmp00aPHg2kz+g6e/YsW7Zs4fr163Tq1InmzZvz4MED9TETEhKYNWsWK1as4NatW9jZ2XH69Gm8vb0xNTXNFsPKlSvp0aMH5ubmtGjRgjVr1mTLc/ToUe7cucOJEyfYvHkzO3bsYPLkyQC8fPmSrl270q9fP3WeDh06aCy4Wq1aNZ4/f05gYGCByyo1NZXHD+9Twa+KOk0ul1Pez597d29p3ef+3VtU8PPXSPOrXI37OeRPTU3lzz/2YGRsgqubBwCPH94n4nUYMrmc0cP7M6BHO6ZN+IagwKJr0Sx2Ch30XT1JuHU1M02SSLx9FQNPnzwdwrReM+LOnVK3SmS0bEhZWwIlCSk1FQPvsu8Xr1yBwsGFtKf3siRKpD29h8LJVesuOp7lUb4IxLBJZ0y/mI5Jn7HoV28GWlqzMsj00ysgUtJ7VIrkCuR2JVAGPciSKKEMuo/coVTO++nqYdx3HMb9xmPwSV/kVpm/KSxTpLdYSco0jWOiVKJwcit4rG842OphbaHL5VuZFcSERBV3Hyfg62mcp2PoKGQ0qmXJoVOv3zuenKQqldwJiaS6a2bZyGUyqpey5/qLnIcQJKak0WLZXpr/vIevdpzmUXj21rl/nr2i0eJdtFt+gOmH/yEq8f1a21LT0rjz9AXVfTOHE8nlcqr7enD9kfYv+1lJksT5O48IDAnH39tVM9Z7gTQa9QPtvl/A9A17iIorhEr8v+yZEBH2nNiocLzKZn7RNDQypaRHBZ4+uJrzjnn06M5FJg2pw4+jW7J91WTiY6Pe+5iFobB/6/y/5v9N13lWVlZWKBQKTE1NNX6mydzcHJlMppEWFBTE6tWrCQoKwskpvYtp9OjRHDx4kNWrVzNjRvp4wdTUVJYuXUrFihXV+z59+lS9T1YPHjzg3Llz7NiR3sLXo0cPRo0apV7fKoOenh6rVq3CyMiIsmXLMmXKFL755humTp3Ky5cvSUtLo0OHDpQqlf5BWb58eY3zZJz76dOnuLq6FqisYmOiUamU2brILSysCH4WpHWfqMgILCw0hyqYW1gSFRmhkfbPhTMsmDWZ5OQkLK2smTBtLmZvus1DQ14CsG3javoMHIqtnSN7d25l4tgvWfjrRkxNzQp0PR8zhakZMoUCZUyURnpadBSGju9uzdN390bfxZWwlT+p01JePic1/BXWnfoQtnoxquQkLJq3Q8faFh0twx7yQ2ZojEyuQEqI0UiXEmI1KmRZyc1tkJe0IvX2P8RvX4bCwhaDpp1BoSD5zB/azoJBo46kPX+EKvzle8eqSoh9K9Y4FFZ2WvdRRb4i6c+tqMJfItM3QK9yA4w6Dyd+w2ykuGhUka9QxUSgX6slScd+h9QU9CrVQ25qgcr4/e9PK/P0x3NUdKpGelRMqvq1d6nlb46JkYLDf0W8O3MBRSakoJQkrIw0WyStjQ0IjIjRuk8pK1MmtqiKt60FscmprL94jz4bjvJ7/+bYmxqlx+7mSCOvEjhbGPM8Ko5Fp24w7LdTrO3RGEUBP4wj4xJQqlTZusitzUwIDMm5UhybkETAt7NJTUtDLpMztvsn1CjjqX69VjlPGlX2xdnGkudhESzaeYRhP61j7dhBBY4V/n3PhNg3Y5NNzW000k3MrdWvFZRPxTqUr9oEK9sSvH4VxB9bF7Dyx88ZNnkTcnnRLDQuFI7/lxXN/Lhx4wZKpRJvb2+N9OTkZKytMwe66+npUaGC5gDsxMREDAyydwetWrWKgIAAbGzS34wtW7akf//+HDt2jMaNG6vzVaxYESMjI/XfNWvWJC4ujmfPnlGxYkUaN25M+fLlCQgIoFmzZnz66adYWmY+KAwN07tuEhJy/madnJxMcrJmK0FKcjJ6efid1fdVrkIlZi9aSWxMNEcO7mXeDxOZOe8XzC0skSQVAB279KRG7QYADB05hs97deTsX8dp1qJtkcf3b2NarxnJQU80JwkolYQsnI5d/y9xW7YVSakk8dZV4q9dREYxdNHIZEgJsSQe3gyShCr0GTJTc/SrNtZa0TRo2gmFjSNxmxZ88FBVIU9RZZnUlPgyEOOe36FbriYp5w6CSkXi/rUYNOmM6eBpSColyqAHpAXeKdD5Gta05Ms+mRNSxs97/9b7gHpWXLweQ0RU2rszf0AVnW2o6Gyj8XfHlX/w+9VHDK2b/oW5uW9J9etethZ42VrQ+tf9/PMsjOqltH+RKSrGBnpsmfAFiUkpnL/7mLnbDlLC1ooqpdNbrptXy3z2e5VwwKuEA63/N59/7j2huq/HB401q6J+Jlz+ey/bV05S/93vm2WFFHl2fjVbqv+/Y0lvHEuW5oeRATy6fQGvHIbqfDC59MgIoqL5TnFxcSgUCi5dupTt55lMTDK/FRsaGmYbzG5jY8ONGzc00pRKJWvXriUkJASdLIPFlUolq1at0qho5kahUPDnn39y5swZDh8+zKJFixg3bhznz5/HzS394RcRkd6KYWtrm+NxZs6cqe6OzzB4+Nd8MeIbAEzNzJHLFURHRWrkiYqKwMJS+wQrC0sroqI0W1CioyKz5TcwMMTRqQSOTiXw9inLsIFdOXp4Px0698DSMr0SX6Kkqzq/rq4edg5OhL96/9nHHyNlbAySUonCzEIjXcfcAmV0pPad3pDp6WNSox6ROzZkey0l8CHPxw9HbmgEOjqoYmNwnjiP5CcPtBwp76TEeCSVEpmRZuudzMgUKV57S5YUH42kUkGWIR6q16HITcxBrgBV5sxng8ad0HUvR9yWn5DiogolVrmRKSqNWE1QxcfmuJ8GlQplWDByi8wKkurVcxI2zQM9A2QKBVJiPEZdRqAMfZ7vGM9diebeo8wx1rq66S1hFua6RERnVhQtzHR5FJSYbf+32VnrUqmsKVMX5jJOshBYGumhkMmISEjSSH8dn6R13KU2ugo5pe0teBapfVIWQAkLEywM9XkWGVvgiqaliREKuZyIGM3zvI6Jw1rLRKAMcrmcknbpz6TSJR158jKMVQdOqSua2WK1tcLCxIhnryLeq6L5sT8TylRuREmPzEp22psJcLHR4ZhZZn7uxEW/xqlU3rr688razgVjU0vCQ4OKvaL5XxxXWZj+e4MB8khPTw+lUvnOtEqVKqFUKnn16hWenp4aW9Yudm0qVarE3bt3NcZNHjhwgNjYWK5cucLVq1fVW8YYzKxLK127do3ExMwPlHPnzmFiYoKLS3qXiUwmo3bt2kyePJkrV66gp6fHzp071flv3ryJrq4uZcvmPO5m7NixREdHa2wDPs+c9aerq4u7pzc3rl5Sp6lUKm5cvUxpH+3H9fYpy41rmgPrr125iHcO+TNIKonUN+OG3L1Ko6urx4vnmd3zaWlphL0Kwdbuw7ZmfDDKNJIDH2JU1i8zTSbDsIwfSQ/v5rqrSbW6yHR0iT1zPMc8qsQEVLEx6No7oe/mSfzlc+8Xr0qJMuQZOqWytvbL0CnljfJFoNZd0oKfvKmoZT6Y5Za2qOKis1cyvSoQv3URUnQhjC9UKVG9eo7CxStLogyFi5dGq2WuZDLk1o7aK9EpSUiJ8cgsbJDbuZD2+Ga+Q0xMUvHiVYp6exqcxOuoVCqVyawAGRnI8XE34s7D+FyOlK5ZXWuiYtI4f017pb+w6CoU+DpYcv5pqDpNJUlceBpKBSebXPbMpFSpeBgWjY1JzhXT0NgEohOTsXmPSTa6Ojr4lnLi/J3M1mKVSsWFO4+p4JH3yWaSJJGSlnMrcWhENNHxidiY51x5zZOP/JlgYGiMjUMp9Wbv7ImphQ0Pb2UeJykhjqBH1ynl5ZfzgQog6nUICXFRmFnk3JAifBz+37Zourq6curUKT777DP09fWxsbHB1dWVuLg4jh49qu629vb2pnv37vTq1Yu5c+dSqVIlwsLCOHr0KBUqVNA6ozxDw4YNiYuL49atW5QrVw5InwTUqlUrjbGcAGXKlGHkyJFs3LiRoUOHAumLvffv35/vv/+ewMBAJk6cyLBhw5DL5Zw/f56jR4/SrFkz7OzsOH/+PGFhYfj6+qqPefr0aerWravuQtdGX18f/be6yfX0NVtLWrfvzOJ5M/HwKo2nty/7d/9GclIiDZumd2UsnDsda2sbuvf5HICWbT5l4pgR7NmxBf+qNfnr1FEeP7zH4OHpraRJSYls37qeqtVrY2llTUx0NAf37yTidTi16jQEwMjImGYt27B142qsbe2wtXNgz/bNANR8k6ewKYyNMPbM7K4zciuBWUUfUiKiSXpW8PGB+RF1cCd2A0eR/OQBSY/vY96sLTJ9A2JP/QmA3aBRpEW+JuK3tRr7mdZvSvzls6jisrfOGVetgzI2mrTXYei5uGLTfRDxl86RePNKtrz5lfLPcQxb9kAZEoTy5VP0qjRApqtPys30DxrDlj1RxUaRfHpvev6rp9GvVBeDxh1JuXwSuaUd+jWakXL5pPqYBk06o+frT/zO5UipSciM0yfTSclJkJaaPYi8xnr5FAbNPkP56ln68kaV6iHT1SP1dvp6jgbNuqKKiyblzAEA9Ko1RRnyFFVUODJ9Q/T8GyI3syTp1nn1MXU8KyAlxqOKjURh44h+/XakPb6JMkj7Gof5tetQGF3b2BMcmkxIWPryRq+jUjlzOXPizA/fenDmcjR7jmSOgZPJoFldK478FYFKlf24luY6WJrr4mSfPovbrYQBCUkqwl6nEBuf//U0e1QpzYQD5ynjYPVmeaN7JKam0bZ8eovf9/vPYWdixIj66a1fv/x9iwpO1rhYmhCblMLaC/d4GZNA+wrpk3QSUlL55e9bNC5dAhtjQ55FxfHTiWu4WJpQyy33L/jvjLVpLSas2kEZV2fKuTmz6chZElNSaFu7cnqsK3/HztKMER3SZ3WvPHCSsq7OlLC1IiUtjb9uPGD/uauM7d46PdakZH7Ze5zGlctiY27Cs7AIfvr9MC62VtQq65VjHHn1b3omyGQy6jbvxdFdv2DjUAor2xIc+n0hZhZ2lPXP7K37ZUZfylVpQu1m3QFIToonPCSzUSEiLJjgwDsYmZhjaeNEclI8f+5YSvmqzTC1sOF1aBD7N8/F2r4kpSvUea+YC8N/cQJPYfpPVzRVKpVG93RWU6ZM4fPPP8fDw4Pk5GQkSaJWrVoMHjyYLl268Pr1ayZOnMikSZNYvXo106ZN4+uvvyY4OBgbGxtq1KjBJ598kuv5ra2tad++PRs3bmTmzJmEhoayf/9+Nm3alC2vXC6nffv2rFy5Ul3RbNy4MV5eXtSrV4/k5GS6du2qXnLJzMyMU6dOsWDBAmJiYihVqhRz586lRYvMdem2bNmizv8+atdrTEx0FFs2rCIqMgJXd0/GTZmj7goPDwtFnmXYgE+Z8nz5zQS2rF/BprXLcXQuwbffT1evoSmXywl+9pSTRw8SEx2NqZkZHl4+TP1xES6lMruievb7ArlcwaK500lJTsardBkmzViAiZZZ/IXB3L8cNY+uV/9dZs7/AHi2bgfX+48tknO+Lf78aV6bmmPZoQc65pYkBz3m5ewJ6skAOta2Gi3kALoOzhiWLseLWeO0HlPHwhKbbgNQmFuQFhVJ7N9Hidz1/steAaTeu4zMyASD2q2QGZuifBVM/O9Lkd5MupGbWmp0k0uxUcT/vhSDhh0w6TMWVVwUKZdOknzhT3Ue/Up1ATDp+qXGuRIObCA1SyUvv9IeXCXZ0Bj9GgHIjMxQhQeTsGu5eskimakF8iyxygwMMWjcCZmRGVJyQno3+bZFqCIyW+5kxmbo12uLzMgEKT6G1DuXSMlyLe9r24FXGOjL+bKPCyZGCm49iGfcnMcaa2g62uljZqL5nKtU1hR7Gz0OndI+CahVQxt6ts+ssM0dl14hmrM8iD8LMHEowLckkYnJ/PzXTV7HJ1HazoIlneqru85DYhI0nhGxSSlMOXSR1/FJmBno4WtvyZrujfGwSV+uRi6T8SAsmr23AolNSsXWxICarg58Ubf8e6+lGVC1PJGx8fy8+yivY+Io7eLIki97qbvOQyKikcsyKw5JyanM2LiXV5Ex6Ovq4upow7T+nxJQNX0sqVwu58HzUPaevUpsQhK2FqbULOPJF+0ao6f7/h+x/7ZnQoNP+pOSnMjvKyeSlBCLq3dlBnz3q8Yamq9DnxEfm9n1//zxLZZN76P+e++G9LVr/eu247PBM5DLFbwMus8/p3eTFB+DmaUd3uVrE9Bp+EexlqboOs+dTHr7Dv0Pad68OZ6enixevLjYYrh+/TpNmzbl0aNHGmM6i9off/zB119/zfXr13OsbOfkxsPQd2f6iAT51ivuEPLMp6vnuzN9JGzKv/8vSn1Icr3i/8DJq08v9yzuEPJlZ70d7870sfAqV9wR5NnLFWvfnekjcmvEnuIOIc/aVPlwM9Ffft2tUI/nODd7Y9S/2X+yvTcyMpJ9+/Zx4sQJmjTJfQHYolahQgVmzZrFkydFOyD/bfHx8axevTrflUxBEARBEPJOrKOZu/9kLaRfv35cvHiRr7/+mrZti38ZnD59+nzwc3766acf/JyCIAiC8P+N6DrP3X+yopl15rUgCIIgCIJQPP6TFU1BEARBEIQPQbRo5k5UNAVBEARBEArqPziusjCJ0hEEQRAEQRCKhGjRFARBEARBKKC3f35a0CQqmoIgCIIgCAX0X1ySqDCJ0hEEQRAEQRCKhGjRFARBEARBKCAx6zx3oqIpCIIgCIJQUKLrPFeidARBEARBEIQiIVo0BUEQBEEQCkh0nedOVDQFQRAEQRAKSCYTncO5ERVNIZs0SVHcIeSLT1fP4g4hz+5ufljcIeRZtZJ2xR1CvvyblhgxNDYq7hDyRe7673mPRdp5F3cIeWbh7ljcIeSLj2VwcYeQDyWLOwDhDVHRFARBEARBKCjRdZ4rUdEUBEEQBEEooH9Tb0pxEKUjCIIgCIIgFAnRoikIgiAIglBAYtZ57kSLpiAIgiAIQkHJ5IW75dOSJUtwdXXFwMCA6tWrc+HChVzzR0VFMXToUBwdHdHX18fb25sDBw4U9OrfSbRoCoIgCIIg/Att3bqVUaNGsWzZMqpXr86CBQsICAjg3r172NllXzkkJSWFpk2bYmdnx++//46zszNPnz7FwsKiyGIUFU1BEARBEIQCKs6u83nz5jFw4ED69u0LwLJly9i/fz+rVq1izJgx2fKvWrWKiIgIzpw5g66uLgCurq5FGqPoOhcEQRAEQfhIJCcnExMTo7ElJydny5eSksKlS5do0qSJOk0ul9OkSRPOnj2r9dh79uyhZs2aDB06FHt7e8qVK8eMGTNQKpVFdj2ioikIgiAIglBQcnmhbjNnzsTc3FxjmzlzZrbThoeHo1Qqsbe310i3t7cnJCREa6iPHz/m999/R6lUcuDAAcaPH8/cuXOZNm1akRQNiK5zQRAEQRCEApPJCrfrfOzYsYwaNUojTV9fv1COrVKpsLOz49dff0WhUODv709wcDCzZ89m4sSJhXKOt4mKpiAIgiAIwkdCX18/TxVLGxsbFAoFoaGhGumhoaE4ODho3cfR0RFdXV0Uisyfmvb19SUkJISUlBT09PTeL3gtRNe5IAiCIAhCQRVy13le6enp4e/vz9GjR9VpKpWKo0ePUrNmTa371K5dm4cPH6JSqdRp9+/fx9HRsUgqmfAfrmg2aNCAr776qrjDEARBEAThP0wmlxXqlh+jRo1i+fLlrF27ljt37jBkyBDi4+PVs9B79erF2LFj1fmHDBlCREQEX375Jffv32f//v3MmDGDoUOHFmqZZPWf6jrv06cPUVFR7Nq1ix07dqin7kP69P2vvvpKo/K5Zs0avvrqK6KioooknsDAQNzc3NR/W1lZ4e/vz6xZs6hUqRKQXiE+efIkkN5c7u7uzrBhw/jiiy/U+yUmJvLDDz+wefNmnj59iqmpKQ0bNmTSpEmULVtW43oybq4M+vr6JCUlvfe1HNq3nb07NhEdGUFJN0/6fj4Sz9Jlcsx/7q9jbNuwnLDQEBycStCtzxAqVa2lfv3CmRP8+ccunjy8R1xsDD8sXI2ru7fGMZYv/pEbVy8SGRGOgYER3r7l6NbnC5xdSuU7frPGrbBo2RGFuSUpz54Qvn4ZyY/va83rNHYmhr4VsqXHX71IyLxJACjMLLDq0hejcpWQGxmTdO8W4euXkRr6It+xFZRVnSq4f90f88rlMHCy45+OXxC65+i7dyxkBtUbYVSnBXITc9JCgojbt5G04Cc55pcZGGLcpCN6Zf2RGxqjjHpN/IHNpNy//iaDDKNG7TDwq4ncxBxVbBRJl/8i4cTe94+1WkMMazdPjzX0GfH7N70zVqPGHdAvUxmZoTGqqNfE/bGF1Ac3ALAcOQuFpU22/RLPHyN+/8b3jheg6yfWNK1tjrGhnLuPE1m2+RUvw1Jz3cfKXIde7W2oXMYYfT0ZIWGpLFwfwqOgzJmrJRz06NXOhrJehijkMp6FpDDr1xeER6YVKM4tJ/9h7Z/nCI+Jw7uEPWM6N6O8q7PWvEeu3GXlob95FhZJqlJFKTtLejauQevq5dV5EpJSWLD7GMev3Sc6PhFnawu6NqhC53r+BYovq537D7Fl114iIqPwdC3FiEF98fX21Jr3SdAzVm/axr1HTwh9FcbQ/r3o1KaVRh6lUsWaLb/x54nTRERFYWNlRfNG9enZuUOhjN/Tr9oQw9oBb95jz0j4Y/O779tG7dHzfXPfRr8m/uBW9X0LIDe1wKjpp+h6lkOmq4cy4hVxu1ejfPE0X7Ht37ubHdt/IzIyAjc3Dz4fMhTv0j455v/r9Ek2rF/Lq9AQnJyc6dNvAFWqVle/npiYyNrVKzh39gyxsTHY2zvQuk07WrRqrc4TGRHBqpW/cvXqZRITEnEuUYLOXbpRu07dfMX+X9ClSxfCwsKYMGECISEh+Pn5cfDgQfUEoaCgIORZWkldXFw4dOgQI0eOpEKFCjg7O/Pll1/y3XffFVmM/6mKZlZWVlYf7FxKpRKZTKbxj5nVkSNHKFu2LM+fP2fEiBG0aNGCu3fvqhdIHThwIFOmTCEhIYF169YxdOhQLC0t6dq1K8nJyTRp0oSgoCDmzp1L9erVCQ0NZebMmVSvXp0jR45Qo0YN9bnMzMy4d++e+u/CeMidOXWE9SsWMWDoN3iWLsOB3duYOWEU837ZjLmFZbb89+7cYOGPk+ja+3MqV6vNXycOM2f6WH5YsBoXV3cAkpKS8ClTgZp1GvHrollaz+vmWZo6DZphbWtPfGwMv29ayYwJI1m04jfkWcaXvItx9brYdBtI2JrFJD26h0VAOxy/mcqzbwehjI3Olj9k4XRkOplfUuQmprhMW0z8hb/UaQ5ffY+UpiRkwVRUiQmYN2+P43fTeTZmMFJK9mUoioLC2IiY6/d4tmY7VX5f8kHO+Tb9ctUwafEZsXvWkfbsMYa1mmLe52siFoxFio/NvoNCgXmfb1DFxxCzeQmqmEgUFjaokhLUWYzqtcSwWkNit68g7VUwOs5umHboh5SUSOK5IwWOVa9cVYybdyFu73rSnj/GsGZTzHqNJHLhuBxjNev9NVJ8LDFbf0YVE4ncwhopMTPWqF+manR16dg5Y95nNCm3/ilwnFm1b2rJJw0s+GldCKGvU+n2iQ0ThzszfMpTUtMkrfsYG8r5YbQLN+4nMHVJMNFxaTjZ6RGfkNlV5mCjy4xRLhw9G83mfa9JTFLh4qhHaqr2Y77LwX9uM2f7Eb7v2oLyrk5sPHaBIYu2sHvSYKxNjbPlNzc2ZEDz2rjZ26Cro+DUjQdMXL8XK1MjapfxAGDO9j+5cP8pM/q0xcnanLN3HjNjy0HsLExpUME72zHz6tjpMyxdtY5RQwbg6+3F73sP8M2kGaxfOh9LC/Ns+ZOTk3G0t6d+rRosWbVO6zE379jN7j/+ZOxXX+DqUoJ7Dx8za+HPGBsZ0bF1iwLHCqBXtirGAZ2J37eBtODHGNRogmmPr4ha/H3O923PUajiY4ndtgxVbCRyc2ukLO8xmYERZv3HkPrkHrEbf0IVH4vC2k7j3s6L0ydPsGL5LwwdNgJvH1/27NrBhPFjWfbrKiy0fDbcuX2L2bNm0LtPf6pWq87JE8eZPnUSCxYupZRreqPMyuXLuH7tKl9/MwY7e3uuXL7Ez0sWYmVtTfUa6Y0V8+bOIj4+nvETpmBmZs7JE8f48YdpzPtpCR4e2r8wFKkC/JpPYRo2bBjDhg3T+tqJEyeypdWsWZNz584VcVSZ/l90nTdo0ICnT58ycuRIZDIZMpmMEydO0LdvX6Kjo9VpkyZNAtIfLKNHj8bZ2RljY2OqV6+u8Y+1Zs0aLCws2LNnD2XKlEFfX5+goKAcY7G2tsbBwYEqVaowZ84cQkNDOX/+vPp1IyMjHBwccHd3Z9KkSXh5ebFnzx4AFixYwNmzZ9m3bx+dO3emVKlSVKtWje3bt+Pr60v//v2RpMwPB5lMhoODg3p7e9mDgti/ayuNAlrToGkrSpR0Y8DQb9DT1+fEn/u05v9jzzYq+lendcfuOLu40qXnINw8vDm073d1nnqNmtOxaz/K+VXN8bxNmrfFt5wfdvaOuHmWpnPPQbwOC+XVq5f5it+ieXtiThwk9vQRUl88I2zNYqTkJEzrN9OaXxUfhzI6Ur0ZlauElJJM3IXTAOg6OGHg6UvY2iUkP3lAakgw4WuXINfTw6Rm/XzF9j7CDp3i/sQFhO4ueOXrfRnWbkbSP6dIvvwXyrAXxO1Zh5SagoG/9pYFg8p1kRsZE7NxEWlBD1FFvSY18B7KkGfqPDouniTfvULK/euool6TcusfUh/eQqeE+/vFWqsZSZdOkXzlb5RhL4nbuz491sp1tMdaqQ5yQ2NiNi1Wx5oWeB9l6HN1HikhDikuRr3pla6I8nUoqYH3tB4zv1o3smTbwQguXI/naXAKP60Nwcpch+oVTXLcp0MzK8IjU1m0PpQHT5N49TqNq3cSCAnPbAXt3saay7fiWbsznCfPkwkJT+XijXii4wq2lt76Y+fpUNuPdjUr4uFoy/ddW2Kgp8OuM9e05q/qXYrGfj64O9rgYmtJ90bV8HK248qjzPvg6uNgWlcvT1XvUjhbW/Bpncp4O9tzM/D9eg1+272fVs0a06JJQ1xLlmDUkAEY6Otx4Mhxrfl9vDwZ0rcHjevV1ugly+rm3fvUqV6FmlUq42hvR4PaNahaqQJ3Hjx8r1gBDGo2JfnyaZKvpt+38fs2QGoK+pW037f6leogMzQmdssS0p69uW+fat63hnVaoIqOIH73atKCn6CKCif10W1UkWH5im3Xzu0ENG9Bk2bNKVmyFF8M+xJ9fX3+PHxIa/49u3dS2b8qHT7tjEvJUvTo1QcPD0/27d2tznPnzm0aNW5K+QoVsbd3oHmLVri5e3A/SwPK3Tu3+aR1W7xL++Dg6EiXrt0xNjbm4QPtvVRFTi4r3O0/5j9b0cxqx44dlChRgilTpvDy5UtevnxJrVq1WLBgAWZmZuq00aNHA+nfDs6ePcuWLVu4fv06nTp1onnz5jx48EB9zISEBGbNmsWKFSu4desWdnZ2TJo06Z0r7BsaGgLpC63mlifj9U2bNtG0aVMqVqyokUculzNy5Ehu377NtWuZD/O4uDhKlSqFi4sLbdu25datW/kqq7elpaby5OE9ymepEMrlcsr7VeH+3Zta93lw9xbl/apopFWsXJ37dwseS1JSIieO7MfO3gkbm3xUnhU66Lt6knDramaaJJF4+yoGnjl372RlWq8ZcedOqVsqM1o7pdQs/4aShJSaioF3WW2H+G9SKNBxciXlUZZ/V0ki9dFtdF20tyro+VQiNegRJq17YD1mAZbDp2JUvxVkaXlPe/YQPfcyKKzT/50VDi7olvIi5cH194vVsRSpj+5ki1WnhEcOsfqR+uwRJp90x+rbeVgMnYJhvZYasb59Dv0KNUi68pf21/PJ3loXK3Mdrt/NbGVKSFJxPzCJ0u4GOe5XrYIxD58m880AR9bMcmfe2JI0rZ3ZUieTQZVyJrx4lcLEYc6smeXOj9+4UL1i9pbHvEhNU3In6CU1SmcOE5LLZdTwceP6k+e57JlOkiTO331CYGgE/p4l1el+7s6cvP6A0KgYJEniwr1Anr6KoKZvwb9wpKamce/RY/wrZnbRy+Vy/CuW5/a9B7nsmbtyPt5cun6TZ8HpleCHTwK5cfse1Sv7FfiYwJv3WClSHt/OTJMkUh7fQTeHL156pf1Ie/4Y41bdsBw9D/MvJmNYV/O+1S1dkbQXTzHpNBjLb+Zh/vkE9Cvnr9s5NTWVhw/vU9GvsjpNLpfj51eZe3dva93n7t3b+FWqrJFWyb8Kd+9mvi99fctw/vxZXoeHI0kS169d5UXwcypVzhwy4eNbhtOnThIbG4NKpeLUyeOkpKRSvoLm56TwcfjPdp1nZWVlhUKhwNTUVGPKv7m5uboFMENQUBCrV68mKCgIJycnAEaPHs3BgwdZvXo1M2bMANLfZEuXLtWoANrY2ODhof1DC9J/yH7q1KmYmJhQrVq1bK8rlUo2b97M9evXGTRoEJA+G6xhw4Zaj+fr66vO4+fnR+nSpVm1ahUVKlQgOjqaOXPmUKtWLW7dukWJEiW0HiM5OTnbLw6kpCSjp5e+tEJMTBQqlRJzC82hCOYWVgQ/196KGxX5Wmv+6KjXWvPn5vD+HWxcvZTkpEScSpTkf9Pmo5NDq4I2ClMzZAoFypgojfS06CgMHV3eub++uzf6Lq6ErfxJnZby8jmp4a+w7tSHsNWLUSUnYdG8HTrWtuho6S76r5IbmSJTKFDFxWikq+Ki0bXRvrSGwsoWhYUvSdfPEr1uPgore0za9AS5DgnH01s1Ek4dQKZviOWXM0BSgUxO/JEdJF8reFePOtb4t2KNj0HX1lH7Ppa26Lr5knz9HNHrf0JhbYfJJz1ArkPiiT3Z8uv5VEJmYETylTMFjjMrC/P04SFRMZpjJqNjlFia5fzotrfRpXk9c/YcjeT3gxF4lTJgQCdb0tIkjp+PwdxUgaGBnA7NrNi4N5x1u8KpVMaI7wY6Mf6n59x6kJivOCPjElCqJKzNNCuq1qbGPAnN+T0fm5hE0/8tJDVViVwu43+fNdeoRI7pHMCUTQdo9r9F6MjlyOQyJnZrib9XyRyP+S7RMekVE6u3usgtLcwJel7wltJuHdsSn5BIr6GjkMvlqFQqBvToQtMG7zdmUGZkgkyuQHrrPSbFxyDL6T1maYPczYfk6+eI2fgTCis7jFt1B7mCxJN73+SxRVG1AYlnD5N4ej86zm4Yt+gKSiXJ1/J2/8bERKNSqbC01HzmWVhY8vzZM637REVGZvtNbQsLS6IiI9R/fz5kKIsXLqBPr64oFApkMjnDvxxJufKZ4+a/GzueH3+YRrcuHVEoFOjr6/O/8RNxctI+JrioyYq56/xj9/+iopkfN27cQKlU4u2tOQYoOTkZa2tr9d96enpUqKA5YSSncRK1atVCLpcTHx+Pu7s7W7du1ejSXrp0KStWrCAlJQWFQsHIkSMZMmSI+vWsXePaZCxJULNmTY0lDWrVqoWvry+//PILU6dO1brvzJkzmTx5skbaoGHfMHjEt7me80Op06AZ5f2qEhX5mn07NvHTDxOYPPtndUW4qJnWa0Zy0BPNiUNKJSELp2PX/0vclm1FUipJvHWV+GsXkfHf6/YoVDIZqvgY4natAUki7cVT5GYWGNZtoa5o6perin7FmsT+9gtpr16g4+iCSctuqGKjSL7y9wcM9U2se9aCJKF8+ZQEM0uMagdorWga+Ncl9eENVLFRBTpfvaqmDOma+VyY9nNwgeN+FJTEhj3plbwnz5Mp6aRHQF1zjp+PUTdsXbgex95jUeo8Pu6GBNQxz3dFs6CM9fXZNnYACckpnL8XyNztRyhhY0lV7/TJfptP/MP1J8H8NLgTTlbmXHoYxIyth7C1MKWGj9s7jv5hHf/rLEdO/sX3o4bjVtKFh08CWbxyLdZvJgV9UG/u2/i969T3rdzMAsNaAeqKJjIZaS8CSTy6EwBlyDMUds7oV6mf54pmUdm7Zzf37t5h/MQp2NrZc+vmdZYtXYSVlbW6NXTj+jXEx8UzbcYszMzMOXf2DD/OnMYPP87H1a0Y7o3/YHd3YRIVzbfExcWhUCi4dOmSxoKmACYmmeOiDA0N8zzRZuvWrZQpUwZra+ts3+YAunfvzrhx4zA0NMTR0VFjUpGXlxd37tzJtg+gTn+7UpxBV1eXSpUq8fBhzuOEtP0CwZ1nmQPMzcwskMsVREdFaOSJjorAwlL7hCsLS2ut+c0trLXmz42RsQlGxiY4OrvgVbos/T9rzsWzp6hdv2me9lfGxiAplSjMLDTSdcwtUEZH5rqvTE8fkxr1iNyxIdtrKYEPeT5+OHJDI9DRQRUbg/PEeSQ/KXj327+NKiEWSalEbmKmkS43Mc/WyqneJzYKVErI8uVJGfYShakFKBSgVGLcvAsJp/aTfONC+uuhz1FY2GBUr1WBK5rqWI3fitXYDJWWCWGQ3jIrKd+O9QXyLLGqj2Nuja57GWK3FHxS1oXrcdwPzFwhQlcn/fliYaZDZEzmuczNFDx5nvOEs8joNJ691Bya8zwkhZqVTAGIjVOSppS05vH1MMx33JYmRijkMl7HxGukv46Nx8Ys5+54uVxGSbv0Z4iPiwNPQsJZeegMVb1LkZSSysI9x5k/6FPqlfcCwLuEPfeeh7L2yLkCVzTNzcyQy+VERGn+m0dGRWNlaVGgYwIsW7ORbh3b0rhebQDcXUsSEhbGxt93vVdFU0qIQ1Ipkb31HpMZmyHF5XDfxkYjaXmPZb1vVbHRKMM0x7orw16i71uZvDIzM0culxMZqfkcjYqKxNJKe8+OhaVltlVeoqIi1Z8lycnJrF+7iv99P4mq1dJnoru5ufP40SN27vgNv0qVefnyBfv27mbxz8spVco1PY+7B7du3WD/vt0MHf5Vnq9B+DD+37T36unpZfvReG1plSpVQqlU8urVKzw9PTW2nFbafxcXFxc8PDy0VjIhvQvf09MTZ2fnbDPXu3btypEjRzTGYUL6oqzz58+nSpUqlCmjfZkhpVLJjRs3cHTU3jUI6csfmZmZaWxZWwt1dHVx8yzNzWuZs2hVKhU3r13C26ec1mN6+ZTl5tVLGmnXr1zE2+f9xi9Kb/5LTc15fGs2yjSSAx9iVNYvM00mw7CMH0kP7+a6q0m1ush0dIk9o32SAIAqMQFVbAy69k7ou3kSf/nDzeQrdkolaS8C0XPPcv/JZOi6+5L6TPuXm7Sghyis7DXGiylsHFDGRKorbjJdPY0PSQBJpcp5bGReY335FF1332yxpj1/pHWX1KCHKKzsNGO1dkgfhvHWc8Ogcm2k+JjMJZoKIClZIiQsVb09e5lCRHQaFUobqfMYGsjxdjXg3uOclyy7+zgRZ3vN4SVOdnqERaRPBkpTwsOnSTjb6+WYJz90dRT4lnTk/L1AdZpKJXH+XiAV3LQP2dFGJUmkpqW9iVFFmlKF/K2WovRu6YLNjAfQ1dWhtIc7l69nLvOjUqm4dP0mZUp7Ffi4ySnJ2WJVyOXv7I16J6WStBdP0XV7+771IfX5Y627pD7Tdt/ap3/Je3Pfpj17qB4DnTWPMjrvw5t0dXXx9PTm+rUr6jSVSsW1q1co7aP9M8nHpwzXrl7RSLt65TI+Pr5vLjeNtLS0bI04coVCvcB4clL6lyz523kKo7wLSCaXF+r2X/Pfu6IcuLq6curUKYKDgwkPD1enxcXFcfToUcLDw0lISMDb25vu3bvTq1cvduzYwZMnT7hw4QIzZ85k//79uZ5j8eLFNG7cuFDjHjlyJNWqVaN169b89ttvBAUFcfHiRTp27MiDBw9Yu3atOu+UKVM4fPgwjx8/5vLly/To0YOnT58yYMCA94qhVbsuHDu0l5NHDxD8LJCVS+eQnJRE/Sbpa8ktmTuVzWt+Vudv0aYz1y6fY9+OzQQ/e8pvG1fy+OFdAj75VJ0nLjaGwMf3CQ5KXwvuxfMgAh/fJyoy/UEXGhLMrm3rePzwLuGvQrh35wYLZn6Pnp4+larUIj+iDu7EtH4ApnUao+vkgk3vocj0DYg99ScAdoNGYdWpd7b9TOs3Jf7yWVRx2ZcQMa5aBwOf8ujYOmBUuQaO304j/tI5Em9eyZa3qCiMjTCr6INZxfRJTUZuJTCr6IOBS85fLApb4t+HMahSH/1KtVHYOmLSphcyPX2SLqVPiDHtOADjppn/7okXjiMzNMakZTcU1vboeVfAqH4rks4fU+dJuXsVo/qfoOddAbmFNXq+lTGqHUDK7cvvF+uZwxj410PfrxYKG0eMP+mRHuvl9FZSkw79MWrSQZ0/6U2sxi26Ire2R9e7Akb1WpJ04ZjmgWUy9CvVIenqGcjyaxuFYe+xSDq1sKJqeWNKOenxVW8HIqLTOH8tTp1nyogStKxvof57z7FIvN0M+TTACgdbXepVMaVZHXMOnIxS59n5ZyS1/U1pWtscB1tdWta3oGp5Y/44lZknP3o2qs6Ov6+w59x1Hr8MZ9qWP0hMTqVdzfThRePW7OGnXZlf2FYe/Juzdx7zPDySxy/DWXvkHPvP36RVtfQvryaG+lTxKsm8Hce4eP8pz8Oj2H32GvvO36CxX+kCxZihU9tW7Dt8jIPHTvL02XPmL1tBUlIyLZo0AGDG/MX8um6TOn9qahoPHgfy4HEgaalphL+O5MHjQJ6/DFHnqVnVn/W/7eTsP5d5GfqK02cvsG33furWyHlVjbxKOvtn+n1b8c1926oHMl19deu+Sft+GDXOvG+TL55AZmiMUfPP0u9br/IY1m1F0oXM8k88+yc6JdwxrNsSuZUdeuWrYeBfTyNPXrRr35FDBw9w9MhhngU9ZemShSQlJ9GkaQAA8+bMYu3qler8bdq25/Kli+zc8RvPngWxacM6Hj64zyet2wJgZGRMufIVWL1qOTeuXyMk5CVH/jzE8aN/UrNW+iz7Ei4uODo5sWTRT9y/d5eXL1+wc8dvXL1ymRo18/fZUGhkssLd/mP+U13nKpUKHR3tlzRlyhQ+//xzPDw8SE5ORpIkatWqxeDBg+nSpQuvX79m4sSJTJo0idWrVzNt2jS+/vprgoODsbGxoUaNGnzyySe5nj88PJxHj7S3jhSUgYEBR48eZebMmYwdO5anT5+SlpaGp6cnN2/e1JjkExkZycCBAwkJCcHS0hJ/f3/OnDmTY4tnXtWq14SY6Ch+27CCqMgISrl7MWbKXHV3R3hYqMavGZT2Lc/wbyaxdf2vbFn3Cw5OJRg9bqZ6DU2Af86fZtmCGeq/F/44EYCOXfvRqXt/dHX1uHvrGn/s2UZcXCzmFlb4lq3IlNnLtK7dmZv486d5bWqOZYce6Jhbkhz0mJezJ6gnCOlY22b7Jqzr4Ixh6XK8mDVO6zF1LCyx6TYAhbkFaVGRxP59lMhdW/IV1/sy9y9HzaPr1X+XmfM/AJ6t28H1/mNz2q1QJd+8gMzYFOPG7dIXk34ZRPTaeUhvJt3ILaw1WidV0RFEr52LScuuWA6biio2ksSzf5Jw6oA6T9y+jRg1aY9Jm55vurajSLx4Qj2Gs6BSbl4k3sgUo0btkJuYkRbyjJj189WxKsytNGONiSRm/XyMm3fB8ovJ6bGeO0Li6T80jqvrXgaFhTVJlwtntnlWO/+MxEBfzhfd7DE2knPnUSJTFgdrrKHpYKuLmUnmMJ+HT5P54ZcX9GxrQ+eWVoS+TmXl72Gcupj5hen8tTiWbQ6lY4AVAzrZ8iI0hVnLX3DnUcF+3KF5lTJExsWzdN9JwmPiKV3CnqXDPsPaLH24UUhktEaLX2JKKjO2HCQ0KhZ9XR3c7K2Z3qctzatkPqtm9WvPT7uPM3b1LmISknC0MmdYmwZ0qpv37l1tGtWtRVRMDKs3bUtfsN3NlR8njsXqTY9TaPhrjVal8IgIBo7MXMx66669bN21l4rlyvDT9PTn1pcD+7Jy01YWLFtJZHQ0NlZWtA5oQu8un/K+Um5dJMHYBMOGbdX3beyGBZnvMXNrjeeXKiaS2PXzMWreBYshk1DFRJJ0/giJf2Xet8oXgcRuXYpR4w4Y1m+NMjKc+INbSLlxPtv5c1O3fgOiY6LYuH4tkZGRuLt7MHnKDPUEobCwVxqfDb5lyjL627FsWLeGdWtW4+TszLjxk9RraAJ8+9041q5ZyZzZM4mLjcXWzp6evfrSomX656+Ojg6TJk9nzeqVTJ08nsTEJBydnPhq1DcaC78LHw+ZVFxtzUWgefPmeHp6snjx4uIOpUj98ccftG/fnjlz5uS4SOv7uPIgvNCPWZTMpmZvjfxY3d38/uvqfSjVvium1oEC+jd1OQ0IL7pf4SgKWzr+e4aERDpn/1Wvj5Xeln/XZ9XrnhOKO4Q88/Yo+OoE+ZWwZvK7M+WDUZ+JhXq84vbveTLnIjIykn379nHixAmaNGlS3OEUuRYtWvDHH38QERGhHgYgCIIgCEIxEF3nufpPdJ3369ePixcv8vXXX9O2bdviDueDaNiwYY7rawqCIAiCIHwM/hMVzZ07dxZ3CIIgCIIg/D/0bxq2Uxz+ExVNQRAEQRCEYiF+GShXonQEQRAEQRCEIiFaNAVBEARBEApK/ARlrkRFUxAEQRAEoYBkous8V6J0BEEQBEEQhCIhWjQFQRAEQRAKSnSd50pUNAVBEARBEApKdJ3nSpSOIAiCIAiCUCREi6YgCIIgCEJB/Qd/NrIwiYqmIAiCIAhCQYlfBsqVqGgK2ZjKY4s7hHyxKe9e3CHkWbWSdsUdQp5dmHWmuEPIF/taVsUdQp6Fm70s7hDyJ/7f80xI0TEs7hDyzNT233PPAqRIesUdgvAvJCqagiAIgiAIBSUmA+VKVDQFQRAEQRAKSixvlCtRDRcEQRAEQRCKhGjRFARBEARBKCjRdZ4rUdEUBEEQBEEoKLG8Ua5ENVwQBEEQBEEoEqJFUxAEQRAEoaDEOpq5EhVNQRAEQRCEghJd57kS1XBBEARBEAShSIgWTUEQBEEQhIISs85zJUpHEARBEAShoOTywt3yacmSJbi6umJgYED16tW5cOFCnvbbsmULMpmMdu3a5fuc+SEqmoIgCIIgCP9CW7duZdSoUUycOJHLly9TsWJFAgICePXqVa77BQYGMnr0aOrWrVvkMYqKpiAIgiAIQkHJZIW75cO8efMYOHAgffv2pUyZMixbtgwjIyNWrVqV4z5KpZLu3bszefJk3N3d3/fq36lYxmjKZDJ27tyZY3PtiRMnaNiwIZGRkVhYWHzQ2ATt9u3dw/btvxMZGYmbmzuDh3xB6dKlc8x/+vQpNqxfR2hoKE5OzvTt14+qVaupX4+MjGT16pVcuXyZ+Ph4ypYrx+DBX+Ds7KzO8/LlC1auWMGtW7dITU3F39+fwUO+wNLSMt/x61Wqi37VxsiMzVC+Cibp6O8oQ57mvIO+IQZ1P0HXqyIyAyNUMZEkHdtO2pPb6S9Xb4qOV0UU1vZIqakoXzwh6eRuVJG5f4vMC4PqjTCq0wK5iTlpIUHE7dtIWvCTHPPLDAwxbtIRvbL+yA2NUUa9Jv7AZlLuX3+TQYZRo3YY+NVEbmKOKjaKpMt/kXBi73vHmldWdarg/nV/zCuXw8DJjn86fkHonqMf7PwZbNt1xOGz7uhaWZHw6CHPfppH/N3bOea3+7QLdm3bo2fvQFp0FJEnjvN8+c9IKSkAyA2NcO4/CIu69dC1tCLhwX2CFs0n4e6dQou5f3dXWjdzwNRYhxt3Ypiz9AHPXybmmP+3FdVxtDfIlr5jfzDzlj0EwMpCly/6eVDVzxIjQwVBwQms2xbEyTPhBY5zy9/XWHvyMuGxCXg72jCmXX3Kl3R4535/XL3PmI0HaVjWnQV9PtGaZ+r2Y/x+7ibftKlLj7qVChxjhj379vPb9p1EREbi7ubG0MGD8CntrTVv4NMg1m3YyIOHjwh99YrBA/vToV1bjTx79x9g34E/CA1Nf/+XKlWS7l0/o1oV//eOFWDbtcesu/yQ1wnJeNmY8W39CpRz0P4c3HM7iMlHrmik6SnknB3aWv13Qkoai87c5sSjl0QnpeBkZsRnfu58Wt4t37H9sW8nu7dvISoyAlc3D/oP/hKv0r455j9z+jibN6wiLDQERydnevQdjH/VGlrz/rJ4Lof/2EPfgcP4pF0ndfrgvl0IexWikbd770F06Nw93/EXikIeo5mcnExycrJGmr6+Pvr6+hppKSkpXLp0ibFjx6rT5HI5TZo04ezZszkef8qUKdjZ2dG/f39Onz5dqLFrUyQtmmFhYQwZMoSSJUuir6+Pg4MDAQEB/P3333nav1atWrx8+RJzc/OiCO+DO3v2LAqFglatWmV7LTAwEJlMpt6sra1p1qwZV65oPihu3bpF586dsbW1RV9fH29vbyZMmEBCQoJGvs8//xwPDw8MDQ2xtbWlbdu23L17973iP3XyJMuXL6dbtx4sXLQYN3d3xo8fR1RUlNb8t2/f5sdZP9CsWQALFy2hZs2aTJs6hcDAQAAkSWLa1MmEvAxh/ISJLFy0GDs7O8b9byxJSUkAJCUl8f24cSCDmTN/YM6cuaSlpTFl8kRUKlW+4tctXRmDBu1JOvMHcet+RBUWjHGnL5AZmWjfQa7AuNNQ5GbWJOxZSezKaSQe2owqLlqdReHiScqV08RtmEv8b0vU+6Crl6/Y3qZfrhomLT4j/vhuIpdOIi3kGeZ9vkZmbKp9B4UC8z7fILe0IWbzEiIWjCVu1xqUMZHqLEb1WmJYrSFxezcQ8dP/iDv0G4Z1W2BYo8l7xZofCmMjYq7f4+aIyR/snG+zbNgYl6EjeLF2JbcH9iHx0QO85sxHx0L7B7ZVk2aUGDSEF2tXcbPXZwTOmoFlo8Y4DxyszuP67VjMqlTlyfQp3Orbg5iL5/GeuxBdG9tCibl7Rxc+/cSZOUsfMGj0FRKTlMybUh493ZxbPQaOukybnmfU21ffXwPg+F9h6jzfj/KhpLMhY6bepPewfzh1Jpwp35bByz2H98Q7HLx6nzl7T/N50+ps+eozSjvZMGTFbl7HJeS6X3BEDPP2naaym1OOeY7eeMSNpyHYmhkXKLa3nTh1ml+Wr6RHt89YunA+7m6u/G/8RCJzeJ4lJyfj4OBAvz69sMrhS66NjQ39+/RmyU/zWfzTPPwqVGDS1OkEPg1673gP3w9m3ulbDKpemo2f1cfbxpxhu88SkZCc4z7Gejoc6h+g3vb1barx+rzTNznz9BVTA/z5vWdjulXy4McTNzj5+GW+Yvv71DHWLF9C5269mb1wOaXcPJg6fjTRUZFa89+9fZP5P06lcbOWzFm4nGo16/LjtHEEBT7Olvf8mVPcv3sbK2sbrcf6rEc/Vqzfod5atumQr9g/ZjNnzsTc3FxjmzlzZrZ84eHhKJVK7O3tNdLt7e0JCQnJlh/gr7/+YuXKlSxfvrxIYtemSCqaHTt25MqVK6xdu5b79++zZ88eGjRowOvXr/O0v56eHg4ODsj+JWtTpbxp3cjJypUrGT58OKdOneLFixda8xw5coSXL19y6NAh4uLiaNGihboid+7cOapXr05KSgr79+/n/v37TJ8+nTVr1tC0aVON8/v7+7N69Wru3LnDoUOHkCSJZs2aoVQqC3x9O3fuoHnz5jRt1oySJUsxbNhwDPT1OXz4kNb8e3bvwt+/Ch0/7UTJkiXp2as3Hh6e7Nu7B4AXwcHcvXuXocOG4e1dmhIlXBg6dDgpKcmcPHEcgNu3b/HqVSijRn2Nq5sbrm5ujPp6NA8ePODatav5il+vSkNSrp8l9eZ5VK9DSDy8FSk1Bb1yNbXnL18DmaERCbt+RRn8BCkmAuXzh6jCgtV5En7/mdRb6cdThQWT+McG5OZWKOxd8hXb2wxrNyPpn1MkX/4LZdgL4vasQ0pNwcBf+zgag8p1kRv9H3v3Hd5U+TZw/JvRNt170FJaoAsoFErZsqeATFmyBVQUQVFEhiAgS5ElCopsZO+9t+xZ9igUSktLV7pH0uT9I5ISmpbSFpHf+3yu61wXOXnOkzuH9OQ+z4olyX/9gvrRPTTKeFTht8mJjtCXkXv6kHXrEtl3QtEo48m+fh7VvevIS7/+LpNnYvce48742cRsPfCvveaLXLv2IG7HNuJ37yTzYTgPf/4RTWYWTq2Nt6JZVapM6rWrJBzYR3Z0NMnnz5JwcD+WARUBkJiaYd+gEY8X/Epq6GWyIh8TtXQRWZGPcW7fsURi7tLOg+XrHnLiTDxh4Wn8MOsWjg5m1K9t/MsXQJmsIkGZu9Wt4cjjqAwuXcu9UQoMsGXjjkhu3k0hKiaTZesekZqmxt+naInmimOX6FQrkA41KlLe1ZGxnZqgMJGz5Wz+rcU5Gg2jV+1lcIvalHYw3qgQk5TKtK1HmPJBS0xkJfN1tXHzVt5t1YKWzZvhVaYMw4Z8ipnCjL37jH82/f18+WhAfxo3bICJiYnRMnVq1aRmjRA8PNwp7eFB/769MVcouFnMm3yAlZfu0THQi3YVvSjnaMPoJkEo5DK23si/R0YCOFkq9JujhWELd+iTBNpW8CSktBPuNhZ0CvTG18mG6zHKV4pt++Z1NGvVlibNW+NZxpuPh3yFmULBwX27jJbfuW0D1arXpEPnHpQu402P3gMoW96P3Ts2G5SLj4vlzwVzGTZiLDKZ8Y5Xc3ML7B0c9ZtCYf5KsZeoEu46HzVqFElJSQbb862WRZWSkkLv3r1ZuHAhTk75X0NKWoknmkqlkuPHjzN9+nQaN26Ml5cXNWvWZNSoUbRr105fLi4ujo4dO2JhYYGvry/btm3TP3fkyBEkEok+0Vq6dCl2dnbs2LEDf39/LCwseP/990lPT2fZsmV4e3tjb2/P0KFDDRIqb29vfvjhB/r06YOVlRVeXl5s27aN2NhY2rdvj5WVFVWqVOH8+fMG7+HEiRPUr18fc3NzPD09GTp0KGlpaQb1Tpo0iT59+mBjY8NHH32U7/lITU1l7dq1DB48mDZt2rB06VKj5RwdHXFzcyMkJIQZM2YQExPDmTNn0Gq1DBgwgAoVKrBp0yZq1qyJl5cXXbp0Yfv27Zw6dYpZs2bp6/noo49o0KAB3t7eBAcH88MPPxAREaFvTXxVKpWKe/fuUrVqbneVVCqlatVq3Mqne/DWrZtUrWbYvRVcvbq+vEqlAnQ3FM/XaWJiwvUb1w3KPH9hNzU1QSKRcOP69cK/AakMmZsn6oe3n9upRf3wNjJ3b6OHyH0qkxMVjnmzrlh/OhmrfqMwq9WiwLEzEjPdhVybWXALToFkMuTu3mSHPff+tFpUYTcw8fQxeohpQDVUj8Kweq8Xjt/Oxv7zSVg0bGMQqzriHqblKiJz1N31ytw8MfHyJftuaNFjfctI5HIs/fxJvnAud6dWS/KFc1hWCjR6TOr1q1j4+esTS9NS7tjWrkvSGV2XlEQmQyKXo3nhRlOTlYV15aBix+zuqsDJwYxzl3Nbh9LSc7hxJ5nAAJtC1SGXS2jR2JWdBwxbN67dSqJJfResreRIJNC0vjOmplIuXVW+cpwqdQ43I59S2zf3JksqlVDb15PQh/m3kP2+/yz2VuZ0qlnJ6PMajZYxq/fRr2F1fNwcXzkuo7GqVNy9d49qVas+F6uUalWDSiQpBN34t8NHj5GZmUnFCgHFqkuVo+HW0yRqeua2kEslEmp6OnP1ifFWQ4AMVQ5tluyj9eK9DN9+hrD4ZIPnq5Ry4Nj9aJ6mZqDVajkXEcsjZSq1yxS+JV6lUhF27w5VquYOD5BKpVSpWp07t4xfo+/cum5QHqBqcA1uP1deo9Ew9+fJtO/cnTJe+Xflb16/ir7d3+PrzwewZeNqcnLUhY69xJXwrHMzMzNsbGwMthe7zUHXki6TyYiJiTHYHxMTg5tb3mErYWFhhIeH89577yGXy5HL5Sxfvpxt27Yhl8sJCwt7PaenpCu0srLCysqKLVu25Blj8LwJEybQtWtXQkNDad26NT179iQhISHf8unp6cydO5c1a9awZ88ejhw5QseOHdm1axe7du1ixYoV/P7772zYsMHguFmzZlGvXj0uXbpEmzZt6N27N3369KFXr15cvHiR8uXL06dPH7RaLaD7j2jVqhWdO3cmNDSUtWvXcuLECYYMGWJQ74wZMwgKCuLSpUt89913gG7s6YuJ5Lp16wgICMDf359evXqxePFi/Wvlx9xcd2eWnZ3N5cuXuXHjBsOHD0f6wrIHQUFBNGvWjNWrVxutJy0tjSVLllC2bFk8PYvW0pacnIxGo8HO3s5gv52dHYkJxi90xsbW2tnZkZioK1/a0xNnZxeWLllCSkoKKpWK9evXERcXR+I/n4GAgAAUCgVLFi8mMzOTzMxM/vzzTzQaDQmJ+X9OXiQxt0QilaFNN7zQatNTkFga/7KW2jph4lcVJFLSNi4g69ReTGs0waxOq/xeBUWTzqgfh6GJe7WuJ4PXtbBGIpOhSTWMVZOahNTKeKwyB2fMKoWAVErS8lmkH96Oeb1WWDTKvalLP7aLrKtnsB82BacJC7H/9HvST+4n68rpIsf6tpHb2iGRy1G98NlRJyZg4mA8iUk4sI+oJQvxn7eA4IPHqbJmIymXLxK9chkAmox0Uq9dxb1Pf0wcnUAqxaF5S6wqBWLiWPzEyMFedyOWqFQZ7E9UZuufe5kGtZ2wspSz66Bhojlu+g3kMgm7V9fj8Kb6jPjMj9FTrhP5JPOV40xMyyBHo8XRysJgv6OVBXEpxm+8Lj6IYvO564zv0jTfepccOY9MKuGDd4qftD/z7Hpm/8L1yd7OjoREZbHqfhAeTrvOXWnToTNzf53P+LGj8SpTplh1KjOyyNFqcbQwTDIcLcyISzf+f+Vtb8W4ZlWZ2bYWk1pUR6PV0n/9cWJScsf1ftOwMmUdrHl38T5q/bqdz7eeZmSjKgR7FL6VKyU5CY0mB7sXhp7Y2tmjzOcarUxMwPaF8nYvlN+yYZVuqFm7zvm+dut2nfhy5DgmTJ1N83fbsWntSpYvXlDo2P9XmJqaUr16dQ4ezB3vrtFoOHjwIHXq5O2xCwgI4OrVq1y+fFm/tWvXjsaNG3P58uUi5wkvU+KTgeRyOUuXLmXQoEEsWLCA4OBgGjZsSPfu3alSpYq+XL9+/ejRowcAU6ZMYe7cuZw9e5ZWrYx/matUKubPn0/58uUBeP/991mxYgUxMTFYWVlRsWJFGjduzOHDh+nWrZv+uNatW/Pxxx8DMG7cOObPn0+NGjXo0kU3sHjkyJHUqVNHfwcwdepUevbsyRdffAGAr68vc+fOpWHDhsyfPx+FQtdy1aRJE7766iuDGP39/fOMK120aBG9evUCoFWrViQlJXH06FEaNWpk9H0qlUomTZqElZUVNWvW5MiRIwBUqGB8cHWFChU4ceKEwb7ffvuNb775hrS0NPz9/dm/f79B6+HzjA06zsrKMnr3VFLkcjljxn7HnDmz6N6ti66FtFo1QkJq6JNwW1s7Ro0ew6/z5rFt21YkEgkNGzaivI8P0te9OK5EgjY9hYx9q0GrRRMTgcTaFrMaTck6uTtPcUXzLsicSpG6avbrjSufWDVpyaRuWQpaLeqoh0ht7DCv/y7ph7cCYBZYA7OgOqSs/x310yjkpTyxav0BmhQlWZcKN276/yPrqtUo1bMvj2b9RNrNG5h5lMbz8y8o1ac/T5YvAeDB5Al4jxxD0KbtaNVq0u/eIeHgfiz8X70lq3lDF0Z8ljsh5ZuJV4v9Hto0d+PMhQTiEwxbXQf2LIu1pZxhY66QlKyifm0nJn5Tkc++vcz9h2n51FYy0jKzGbN6H+Pfb4q9pfHuzhuPn/LX8Sus+aL7WzOEqrSHB/N/mU1aWjrH//6bn2bOZsb0KcVONl9VlVIOVCnlYPD4/ZWH2HgtnE/r6L5H1oQ+4Fp0ArPa1qKUjTkXI+OZfiQUZ0sFtcq4/KvxPi/s7m12bt3IT3MXFvj/3q5j7ne8d9nyyOVyfp/3M736fYRJMcfJF4X2DX5Ghw8fTt++fQkJCaFmzZrMnj2btLQ0+vfvD0CfPn3w8PBg6tSpKBQKAgMNe3CeNQq9uL8kvZZZ5507d6ZNmzYcP36c06dPs3v3bn788Uf+/PNP+vXrB2CQdFpaWmJjY1Pguk8WFhb6JBN0g129vb2xsrIy2PdiHc+/zrMBs5UrV86z7+nTp7i5uXHlyhVCQ0P566+/9GW0Wi0ajYYHDx7oE76QkJA8Mb446eb27ducPXuWzZt140/kcjndunVj0aJFeRLNunXrIpVKSUtLo1y5cqxdu9ZggG9BraAvJpE9e/akefPmPHnyhBkzZtC1a1f+/vtvfZL8vKlTpzJhguEEjc8/H8rQYV8AYGNjg1QqRfnC3b5SqcTewfjAeHt7+zwThZRKpcFscV9fX+bN+420tDTUahW2tnZ8+cUwfH199WWCg6uzaPESkpKSkMlkWFlZ0bNnD6NdAvnRZqSh1eQgsTBsEZRYWKNNSzZ+TFoSWo0GnjvnmvgYpFa2IJWBJnd4hqJpF0zKBZK6Zg7aVKWR2gpPk56CNicnT+ul1Mo2Tyun/pgUpS6e52LNiX2CzNoOZDLIycGyVTfSj+0k66puEd+cmMfI7JywaNDm/02iqU5SolWrMbF3MNgvt3dAlWB87Lj7gI+I37eHuJ262fkZ98OQKhR4ff0tT1YsBa2WrKhIbg/7FKlCgczCElVCPOXGTyIrKtJonQU5cTaeG3dyh/GYmuhuqOztTIhPzE0U7e1MuXc/9aX1uTqbERJkz5ipht2Y7m4K3n/Pg96fnePBI12L473wNIIq2dKpjTszfrv7SnHbW5ojk0ryTPyJT03HydoiT/mI+CSiEpMZuiR31QPNP5/f4JG/sHVEby4+iCQhLZ1WU5boy+RotPy8/QR/Hb/M7tH9XynGZ55dz16c+JOoVOLwQq/NqzIxMcHDXTepyc/Xhzt37rF563a++PyzItdpZ26GTCIh/oWJP/HpWThZ5L2eG41LJsXf2ZbHSbobiEx1Dr+evMGMNjWpX1Z3LfV1suV2bBIrLoYVOtG0trFFKpWhfGHiT5IyEbsX/s7078feIc9EIeVz5W9eDyUpKZGP+3XVP6/R5LBs0W/s2LqBBUvWGq3X178iOTk5PI2JxqP0v5vYA2/0l4G6detGbGws48aNIzo6mqpVq7Jnzx59/vDo0aM8vaH/tte2vJFCoaB58+Y0b96c7777joEDBzJ+/Hh9ovnioGqJRFLgbGJj5QtTx/Nlnt0hGdv37LjU1FQ+/vhjhg4dmieGMs/dmVpavnwG5KJFi1Cr1bi7586o1Gq1mJmZMW/ePIPWz7Vr11KxYkUcHR0Nup2fJV43b96k2gvjHp/t9/MzXJbj2Sw1X19fateujb29PZs3b9a3ID9v1KhRDB8+3GBfxOPcCUsmJib4+Phy+cpl6tStC+jO1eXLl2n73nsYExBQgSuXL9OhQ+6EiEuXLhIQkLdV9tl5jIyM5N69u/Tu0ydPmWfn6crlyyQpldSqbXwpDKM0OeRERyD38kN979mYRAlyLz+yLxpf1kEd+QDTCtXRDanXfQFK7Z11s85fTDJ9q5C2Zi7apMJNdCtQTg7qqHBMy1Uk++Y/qw5IJJiUq0DGGeNLAakf3cOsSm3dmMx/vqxlTm66Wef/jFeWmJgaJKKALpF+S1qKSoJWrSbtzm2sq4egPHFMt1MiwSY4hKebNxg9RmqmQKt94Zr07Pry3PkG0GRmosnMRGZljU2NWjz+/ddXjjEjI4fIDMNJe3EJWYQE2XPvgS5JsDCXUdHPhi27jE8qfF6bZm4kJmVz6pzhZ1NhJjN4K8/kaLRIi/CRMJHLqODhwpl7ETQJLP9P3VrO3Iuge9283d5lXezZ8JXhMjS/7jlFWlY237RviJudNW2DA6jla5gwDF64hbbVA+gQUvHVg3wWq4kJvj4+XL58hXp1av8Tq4bLl0Np1zbvqiDFodFq9GPNi8pEJiXAxZZzEbE0Ll/qn3p1Yyq7BhVuKaIcjZZ78cm846VLPNQ5GtQaLdIX/v5lUok+4S9UbCYmlPfx4+rlC9Sqo5usqNFoCL18kXfbGp8M5xdQidArFwyWKgq9dB7/AN043YZNWuQZwzlp3AgaNG5Bk+bv5htL+P17SKVSbG1ffem7/wVDhgzJM7zvmWe9ovnJb95ISfrX1tGsWLEiW7Zs+bdersiCg4O5ceMGPj7GJ18UllqtZvny5fz888+0aNHC4LkOHTqwevVqPvkkd5kUT09PgxbbZ6pVq0ZAQACzZs2ie/fuBncmV65c4cCBA8ybNy/fOLRaLVqtNt/xssbW5jIzM/xi6tixEzNnzsDX1xc/P3+2bt1MZlYmzZvr3tfPM37C0dGRfv0/BKBd+w58O3IEmzZtpEaNmhw7eoR7d+/y+efD9HUeP34MW1tbnJ1dCA8P54/f51O7dh2Cg3MvMvv37cOzjCe2trbcvHmTP35fQIcOHSld+tXGkWSfP4x5617kRD8i58lDTEMaITExI/uaboyieeveum7k47oWluzLxzGrVh9F085kXzyK1N4Fs9otyL54VF+nollXTCtUJ23zQrSqTP3yQ9qsTFAX/csl4+99WHceiCoqHPXj+5jXbYHE1IzMC7rhEdadB6JJVpK2X5ccZZw9jKJWU6xaf0DG6QPIHF2xaNiGjFO5M2izb13GomFbNMp41E8jkZfywqJeSzIvvP71056RWVpg6ZObOFiULY1NUADZCUlkRhR9XOuriFm3mrKjviP91i3Sbl3H9f3uSM0VxO3eAYD36HGoYmOJXDgfgKSTJ3Dt2oP0u3dIu3EdRenSuH/4EUknT+izNJsatUAiIfPRQxSlS1P6kyFkPnpI/K4dJRLz+m2R9O1WhoioDJ7EZDKwlzfxCVkcP5273uXsH6pw7FQcm3bmJp8SCbRu5saeQzHkvJBQPnycTkRUOiM+8+XXxfdJSlHRoLYTNara883Ea0WKs3eDany3dj+VSrsS6OnKyuOXychW06GGLikcs3ofLraWDGtdDzMTOb4vTO6xVuiuQc/228nNsXuhW91EJsXJ2gJvl+IlE507tuenmbPx9fUhwM+PTVu3kZmZScvmuvGiP/48C0dHBwb06wvohm09eqRbxUGlVhMXn0BY2H0U5gp9C+aipcuoEVIdF2dnMjIyOHTkKKFXrzFl0vfFihWgVzUfxu+/SAVXOwJd7Vl1OYwMdQ7tKur+nsbtu4CzpTmf19Od6z/O3Kaymz2edpakZKlYcfEe0cnpdKikK29lZkJ1D0fmnLiOmVxGKWtzLkTGs/NmBF/Wf7Xu0/c6duWXmVMp7xuAr18AO7ZuICszQ58Uzv15Mg6OzvTqp5sw26bd+4z7dijbNq0luEZt/j52iLB7t/nk868BXSuptY3h8DOZTI69vYO+pfL2zWvcvX2TwCrVUJhbcOfWdZYsnEeDxs2xss5nGbjXTfzWeYFKPNGMj4+nS5cufPjhh1SpUgVra2vOnz/Pjz/+SPv27V9ewRs2cuRIateuzZAhQxg4cCCWlpbcuHGD/fv3F5jQgW6g7dSpU+nYsSM7duwgMTGRAQMG5Bm32blzZxYtWmSQaOZHIpHw559/0qJFCzp37syoUaNwc3PjzJkzfPXVV7Rs2VI/BvX+/fusXbuWFi1a4OzszOPHj5k2bRrm5ua0bt26yOekQcOGJCUnsXLFChITEylXrhwTJ/6g7wqPjX2K5LmmkIoVKzLim5GsWL6MZUuX4uHhztjvxuHt7a0vk5iQwJ8L//inS92Bpk2b0r3HBwav+zjyMUuXLSE1JQUXF1e6detOh46vvlaa6vZFJBZWKOq1QWJpTc7TSNI2/IY2PQUAqbW9QeuUNkVJ2obfUDTuhFW/UWhSlWRfOErW2f36MmbVdHfwVj2GGbxW+q6VqK6feeUYn8m6dhaJpTWWTTvoFmx/8oikZTP13fxSO0fDlrSkBJKW/YxV6x7YD5mEJiWRjFP7ST+Wu7xI6o6/sGjWEat2vZFa2qBJUZJx7oh+DOe/wbZ6IHUOrtA/rjhjNAARyzcROqD4y3YURuLhg8jt7HH/cCAmDo6k37vL3RFfov5nkpqZi6tBM1/UiqVotVo8BnyMqbMzKmUiSSf/JvLP3EkHMisrPAZ9gqmzC+qUZJRHjxD55wK0xVhO7Hl/bYxAoZDxzRA/rCzlXL2RxFfjr5Ktyv0MeLiZY2dj2LsTUtUeNxcFO/fnXUsvJ0fLiO+v8Um/skz/LhBzcxmRTzKYPPsWpy8UfqLd81pV9SMxLYPf9p4mLiUNf3dnfhvYHsd/us6jlSl5WtDelEYN6pOUlMTylav017PJE7/XX8+exsYajA+MT0hg8NAv9I83bNrMhk2bqVI5kBnTpgCgVCbx08+zSUhIwMLSknLe3kyZ9D3VjfRCvaoWfh4kZmSx4PQt4tOy8HO24Zf2tfVLFkWnZBjEm5KVzQ+HLhOfloWNwoQAFzsWd6lPOcfcITlTWoUw7+QNxu69QHJmNm42FnxapwLvV/Z+pdjqNWhCUpKSNSsXo0xMoGw5H8ZO/EnfFR4X+xTJc0lYQMVAvhjxHatXLOKvZQsp5VGab8ZOpox34ZdaMzEx5cSxQ6xdtRS1KhsX11K816EL73Xs+vKDX5M3OUbzbSDRvmwK9CvKysri+++/Z9++fYSFhaFSqfD09KRLly6MHj0ac3Nzo78MZGdnx+zZs+nXr1+eXwZaunQpX3zxhcG4v++//54tW7Zw+fJl/b5+/fqhVCr1Lafe3t588cUX+ok9kPdXicLDwylbtiyXLl2i6j9LXpw7d44xY8Zw6tQptFot5cuXp1u3bowePTrfep/VvWTJEvr168d7772HRqNh586dec7R2bNnqVWrFleuXMHGxibP6xtz9epVJkyYwOHDh/Wz84cMGcKsWbOQy3X3C1FRUQwcOJALFy6QmJiIq6srDRo0YNy4cQX+is+L7oXl/ys0/0XOm2a+6RAKLTvp5WPr/ivOTj/5pkN4Ja51jY8L+y/6wmbamw7hlRwYVLSWzjchpuK/90MExeW4d9GbDuGVhLcc/vJC/xGBPoUfy19c6UfXlGh9Fg27l2h9b1qJJ5rC66fRaBgwYAB79+7l6NGjBhNoSoJINF8fkWi+PiLRfH1Eovl6iETz9flXE81j60q0PosGb6519nUQAwveQlKplEWLFjFy5Mh/5XdKBUEQBEHIRwn/MtD/mn9tMpBQsqRSKcOGDXt5QUEQBEEQhDdEJJqCIAiCIAhF9YbXqfyvE4mmIAiCIAhCEYlZ5wUTabggCIIgCILwWogWTUEQBEEQhKISC7YXSCSagiAIgiAIRaQViWaBxNkRBEEQBEEQXgvRoikIgiAIglBUYjJQgUSiKQiCIAiCUESi67xg4uwIgiAIgiAIr4Vo0RQEQRAEQSgq0XVeIJFoCoIgCIIgFJXoOi+QSDSFPEy02W86hFciNTV90yEUmuQt+qky17oObzqEVxJzMuFNh1B4rd50AK8oM+NNR1BoZur0Nx1CoeWkvz3nFUCtlb3pEIS3kEg0BUEQBEEQikj8BGXBRKIpCIIgCIJQVKLrvEDi7AiCIAiCIAivhWjRFARBEARBKCItouu8ICLRFARBEARBKCKxYHvBxNkRBEEQBEEQXgvRoikIgiAIglBUokWzQCLRFARBEARBKCKxvFHBRBouCIIgCIIgvBaiRVMQBEEQBKGIxGSggolEUxAEQRAEoahE13mBRBouCIIgCIIgvBYlnmhKJBK2bNmS7/NHjhxBIpGgVCpL+qUFQRAEQRD+VVqJtES3/zWv3HUeGxvLuHHj2LlzJzExMdjb2xMUFMS4ceOoV6/eS4+vW7cuT548wdbWtkgB/9ecOnWKd955h1atWrFz506D58LDwylbtqz+sYODA9WrV2f69OlUq1ZNv//69etMmDCBw4cPk5ycjJeXF927d+fbb7/FwsJCX+6PP/5g1apVXLx4kZSUFBITE7GzszN4zcmTJ7Nz504uX76Mqanpa0vot+3YyfqNm0lITKRc2bJ89slHBPj7GS0b/vARy1f+xd17YcQ8fcongwbQqUP7fOtes24Di5ctp2P79xj80aASidekSj1MqzdCYmGNJi6KzCOb0cREGC0rr1AD8xbdDfZp1SpSf/1W/1hiYYVZvbbIyvghMTMnJ/I+mUc3o1XGFTtWRc3GmNdrhdTKFnVMBGk7V6GOfJBveYnCHIumnTCrGIzE3BKNMp7U3WtQ3b0KgP2X05HZO+U5LuPMIdJ2/lXseJ07dMate09MHBxID7tHxJyZpN26kW95l/e74dK+I6aubqiTlCQeOczjhfPRZmcDIDW3wGPAR9jVb4CJvQPpd+/w6JdZpN+6WexYC8vhnRDKfTUA2+BAFO4unO/8KTHbDv5rr/+8AT29ea+FG9aWcq7eTGbGb3d5/CQj3/Lr/6xFKVdFnv2bdkYyc8E9ABzsTPj0w/LUqGqPhbmMR5HpLF/3iKMni/75XXPmOstOhBKXmoGfmwPftqlL5dIuLz1ud2gY364/ROMAL2b3bKHfn56lYvb+sxy++ZCk9Ew87K3pUbsSXWtWLHKMz2zeuZc1W7aTkJhEee8yDPuoPxX8fIyWffAogsWr1nMn7D7RT+MYMqAPXdq1NiiTk6Nh6Zr17DtyggSlEicHe1o1aUifrp2QlEC3qmm1+pjVaIrE0oacp5FkHtxATvTD/A8wM0dRvy0mvkFIFBZokhPJPLQR9QPd36VZrebIfYOQObqiVanIiXpA5tGtaBKfvnJse3dsZPum1SgTE/AqW57+H3+Jj3/+/0enThxi3co/iY2Jxs29ND37DaZajToAqNVq1q74g0vnT/M0OgoLS0sCg0L4oN9gHBxzr2E/ThxJ+IO7JCuVWFpZU7lq3jL/JvHLQAV75dS5c+fOXLp0iWXLlnHnzh22bdtGo0aNiI+PL9TxpqamuLm5lcgf378h+58vv/wsWrSIzz//nGPHjhEVFWW0zIEDB3jy5Al79+4lNTWVd999V58Anj59mlq1apGdnc3OnTu5c+cOkydPZunSpTRv3tzg9dPT02nVqhWjR48uMN4uXbowePDgV3+zhXTk2HF+X7iIXh9057e5syhX1pvR340nMZ+kNisrCzc3Nz7s1wcHe/sC67595y479+yhXFnvEotX7lsVs/rtyDqzj/TVs8iJjcKiw0dIzK3yPUablUHqwu/1W9qSHwyeN2/bH4mtAxk7lpC2aiaalEQsOn4MctNixWoaWAPLVt1IP7IN5YIJ5ERHYNPnSySW1sYPkMmw6fsVMnsnktfOJ3HuGFK2LUOTnKgvovx9EvE/fqnfkpbOACD7+vlixQpg37gpnp8NJWrZIm4M6kdG2F18Z8xCbmf8/9mhWQtKfzSYqGWLudanO+HTp2DfpCkegz7Rl/H+ZhQ2ITV4MHki1/v3IvncGfx+nouJk3Ox4y0smaUFyaG3uTZ0wr/2msb07OzJ+209mPHbXT76+hIZmTnMnFgZU5P8r5+Dhl+kXe+T+u2LsVcAOHwiVl9m7PAAyniY8+2ka/Qdcp5jJ+OY+E1FfMvl/zdRkD1Xw5ix+zQfNw5mzeCO+Ls5MnjZbuJT80+IASITU5i59wzBXm55npux5zQn7z5myvuN2Dy0Cz3rBDJt50mO3CwgwSqEQ8dP8uviFfTt9j4LZ06lfFkvvv5+KonKJKPlM7OycXd14aPeH+Bgb2e0zKpNW9m6+wBffNyf5fN+5uM+H7B603Y27thTrFgBTPyDUTTqSObJ3aQu/xFNbCSWXT5FYpHP/5VUhmWXz5DaOJK+bREpi34gY+9qNKm570/m6UP2peOkrvyZtPW/6o/B5NWuXyePHWT5n/Po3KM/0+YswqusD1PGDSdJmWi0/O2bV5n74wQaN2/LtLmLqVG7Pj9NHsWj8PsAZGdl8iDsDp2792XanMUMHz2ZJ5GP+GnSSIN6KlUJ5ouRE5n1+yqGj/6BmCeRzJo69pVi/1/y66+/4u3tjUKhoFatWpw9ezbfsgsXLqR+/frY29tjb29Ps2bNCixfEl4p0VQqlRw/fpzp06fTuHFjvLy8qFmzJqNGjaJdu3b6cnFxcXTs2BELCwt8fX3Ztm2b/rkXu86XLl2KnZ0dO3bswN/fHwsLC95//33S09NZtmwZ3t7e2NvbM3ToUHJycvT1eHt788MPP9CnTx+srKzw8vJi27ZtxMbG0r59e6ysrKhSpQrnzxt+mZ44cYL69etjbm6Op6cnQ4cOJS0tzaDeSZMm0adPH2xsbPjoo4/yPR+pqamsXbuWwYMH06ZNG5YuXWq0nKOjI25uboSEhDBjxgxiYmI4c+YMWq2WAQMGUKFCBTZt2kTNmjXx8vKiS5cubN++nVOnTjFr1ix9PV988QXffvsttWvXzjemCRMm8OWXX1K5cuV8yxTXxs1bebdVC1o2b4ZXmTIMG/IpZgoz9u47YLS8v58vHw3oT+OGDTAxMcm33oyMDKb99DNffj4EK6uifeEZYxrcANX106hvnEOTEEPWoY1o1SpMKtUs8DhtespzW6p+v8TOCVkpb7IObUQTE4FWGUvWoY0gN8HEv1oBNb6ced0WZF44Rtalv8mJfULq9hVoVdkogt8xWl5R7R2k5pYkr5qH+tE9NMp41OF3yIl5/Nz7SEWbmqzfTP2DyImPQRV+u1ixArh27UHcjm3E795J5sNwHv78I5rMLJxatzVa3qpSZVKvXSXhwD6yo6NJPn+WhIP7sQzQtYBITM2wb9CIxwt+JTX0MlmRj4lauoisyMc4t+9Y7HgLK3bvMe6Mn03MVuOf6X9Ll3YeLF/3kBNn4gkLT+OHWbdwdDCjfu38W26UySoSlLlb3RqOPI7K4NK13EQjMMCWjTsiuXk3haiYTJate0Rqmhp/n6L93a04eZVOIQF0CPanvIs9Y997B4WJnC0X8/+M5Wg0jN5wmMFNgintkPdG6vKjGN6r6kuNsu542Fvzfo0K+Lk5ci3y1Vvdnrdu607atmhC62aN8C5Tmq8GD0RhZsquA0eMlq/gW57B/XvRtEFdTE2MdwJev3WHerWqUyckmFKuLjSqV5sa1apw625YsWIFMA1pTHboKVTXzqCJjyZj31q0qmxMA+sYL1+5NhJzC9K3/EFO5AO0yQnkPL6HJjZSXyZ9w3xU13X1aWIjydi9EqmtAzJXz1eKbeeWNTRt+R6Nm7ehdJmyDPxsBKZmCg7v32G0/O5t66lavRbtOn9AaU9vuvUeRNnyfuzdsREAC0srxv4wmzr1m+Jeugx+AYH0/2Q49+/dJu5ptL6eNh264RcQiLOLG/4VKtO+Sy/u3r6OWq1+pfhLypvsOl+7di3Dhw9n/PjxXLx4kaCgIFq2bMnTp8b/To4cOUKPHj04fPgwp06dwtPTkxYtWhAZGWm0fEl4pXdkZWWFlZUVW7ZsISsrK99yEyZMoGvXroSGhtK6dWt69uxJQkJCvuXT09OZO3cua9asYc+ePRw5coSOHTuya9cudu3axYoVK/j999/ZsGGDwXGzZs2iXr16XLp0iTZt2tC7d2/69OlDr169uHjxIuXLl6dPnz5otVoAwsLCaNWqFZ07dyY0NJS1a9dy4sQJhgwZYlDvjBkzCAoK4tKlS3z33XeAbuzpi4nkunXrCAgIwN/fn169erF48WL9a+XH3Nwc0LU8Xr58mRs3bjB8+HCkUsP/iqCgIJo1a8bq1asLrO/fplKpuHvvHtWqVtXvk0qlVKsaxM1bt4pV9y/zF1CzRgjB1aq+tGyhSWVIXUqT8+juczu15Dy6g9TNK//jTEyx7D8Gyw+/Q9G2P1IHV/1TEpnuy0ab8/xFTQs5Ocjcy1JkMhnyUl6owp7rItZqUYXdQF66vNFDTAOqoooIw6ptTxy+mYndZxMxb9A6/1mQMhlmVWqTeelE0eP8h0Qux9LPn+QL5wziTb5wDstKgUaPSb1+FQs/f31iaVrKHdvadUk6c0pXp0yGRC5H80JPgiYrC+vKQcWO+W3i7qrAycGMc5dzW4fS0nO4cSeZwACbQtUhl0to0diVnQeiDfZfu5VEk/ouWFvJkUigaX1nTE2lXLqqfOU4VeocbkbFUbuch36fVCqhdnkPQiPyTwp/P3wJe0sFnaoHGH2+ahlXjt5+SExyGlqtlrP3o3gYl0Qdn9KvHKM+VpWaO2EPqB6UeyMulUqpHlSZ67fvFLneSgF+XAy9RkSkrlfr3oOHXL1xm1rBVYtcpy44GTI3T9QPn0/Ytagf3kbm7m30ELlPZXKiwjFv1hXrTydj1W8UZrVaFDgzWmKmG2qhzUwvdGhqlYr79+5QuWpIbrhSKZWrhnD31nWjx9y5dY3A58oDBAXX4s6ta/m+Tnp6KhKJBAsr4706qSnJnDiyD78Kgcjlb2ghHYmkZLdXMHPmTAYNGkT//v2pWLEiCxYswMLCgsWLFxst/9dff/Hpp59StWpVAgIC+PPPP9FoNBw8+PqGBr3S/4pcLmfp0qUMGjSIBQsWEBwcTMOGDenevTtVqlTRl+vXrx89evQAYMqUKcydO5ezZ8/SqlUro/WqVCrmz59P+fK6L9P333+fFStWEBMTg5WVFRUrVqRx48YcPnyYbt266Y9r3bo1H3/8MQDjxo1j/vz51KhRgy5dugAwcuRI6tSpQ0xMDG5ubkydOpWePXvyxRdfAODr68vcuXNp2LAh8+fPR6HQ/bE1adKEr776yiBGf3//PONKFy1aRK9evQBo1aoVSUlJHD16lEaNGhl9n0qlkkmTJmFlZUXNmjU5cuQIABUqVDBavkKFCpw4UfyEoCBZWVl5bhqysrIxMzPehZKcnIxGo8H+hbGh9nZ2REQU/Y7o8NFj3Lt3n3mzfy5yHcZIzC2RSGVo0lMM9mvTU5E5GB8/pkl8Sub+tWjiniAxU2Aa3AiLrp+TtvIntKlJaBKfoklOwKxuazIPbQBVNqbVGiC1tkNjWbgEwBiphTUSmQxNWrJhPGnJmDiXMn6MvTMmZSuQFXqapBVzkDm6YNW2F0jlZBzZlqe8aUA1JAoLsi6dLHKcz8ht7ZDI5agSDW8i1YkJKMoYT+ITDuxDbmuL/7wFIJEglct5unUT0SuXAaDJSCf12lXc+/Tn/sNwVIkJODRtjlWlQLIiHxut83+Vg73ubzBRqTLYn6jM1j/3Mg1qO2FlKWfXQcNEc9z0G0z4piK7V9dDrdaQmaVh9JTrRD7JfOU4E9MzydFocbQyN9jvaGXOgzil0WMuPoxm88XbrPu0U771ftumLhO3HqfFT6uQSyVIJBLGt69PdW/jfwuFkZScTI5Gg72d4bXc3s6WR4+Lfv3q2bk96ekZ9P7sK6RSKRqNhoG9utG8kfGeiMJ6dv3SphteE7TpKQY3v8+T2johLeOA6sZ50jYuQGbnjKJ5V5DJyDq529iroGjSGfXjMDRxTwodW3JyEhpNDrZ2Dgb7be0ciHpsfHiDMjEBuxeG1dja2ZOkNN4QlZ2dxaol86nboBkWFpYGz/215Df27thEVlYmvv6VGDn+x0LH/l9n7HvZzMwMMzMzg33Z2dlcuHCBUaNG6fdJpVKaNWvGqVOnCvVa6enpqFQqHBwcXl64iIo0RjMqKopt27bRqlUrjhw5QnBwsEFr3/NJp6WlJTY2Nvk24wJYWFjok0wAV1dXvL29DbpPXV1d89Tx/Ou4uur+6J7vMn6279lxV65cYenSpfqWWSsrK1q2bIlGo+HBg9zJFiEhhndcALdu3aJjx9yuu9u3b3P27Fl9Qi2Xy+nWrRuLFi3Kc2zdunWxsrLC3t6eK1eusHbtWn1sQIGtoKamxRvz9zJTp07F1tbWYPvt999f62u+6GlsLPP/WMi3I4a/9vdbGJroh6hvXUATF0VO5H0ydi5Fm5GGybOuKo2GjJ3LkNo7Y/3JD1h9NhVZaR/U4TdBq/lXY5VIJGjSkkndtoycJw/JvnaO9GM7Ma/R0Gh5RfX6qO5dRZOi/FfjfMa6ajVK9ezLo1k/cXNQP+6N/Rbb2nUp1ae/vsyDyRNAIiFo03aq7z+Ka+euJBzc/9Legrdd84Yu7Fv3jn6Ty4s/jr1NczfOXEggPsGwhXhgz7JYW8oZNuYKA7+8yNotj5n4TUXKeVnmU1PJScvKZsyGw4xvXx97y7yTlp5Zffo6oRFPmdOzBasHd+SrVrWZsuMkp8NeXxdfUR0+cZr9R0/w3fDPWThzKqOGDWbtlh3sOXT03w9GIkGbnkLGvtVoYiJQ3b5I1um9mAYZn6yraN4FmVMp0rcv/XfjfAm1Ws3saePQAgM/+zrP8+91+oBpcxczZtIspDIpv8784Y1dI7RIS3Qz9r08derUPK8bFxdHTk6OQT4ButwnOjo6T3ljRo4cibu7O82aNSuRc2FMkdqZFQoFzZs3p3nz5nz33XcMHDiQ8ePH069fP4A84/AkEgkaTf5fwMbKF6aO58s8m1xkbN+z41JTU/n4448ZOnRonhjKlCmj/7el5csvtosWLUKtVuPu7q7fp9VqMTMzY968eQatn2vXrqVixYo4OjoazBL39fUF4ObNmwaz0J+5efMmfn7GZ3KXlFGjRjF8+HCDfdER+Q+2t7GxQSqV5pn4k6hU5jtQ/mXu3gtDqUzi06Ff6vdpNBquXrvO1u072bllIzKZrEh1azPS0GpykFpY8/ynR2JhhSYtJd/jDGg05MRGIrXLHRenefqY9FUzwVSBRCZDm5GGRbehBmMjX5UmPQVtTg7SF1pFpZY2aFKMT1TQpCahzcmB5y6wObFRSK3tQCaD58Y1S20dMSlXkZQ1vxY5xuepk5Ro1WpM7A3vhOX2DqgSjE8OdB/wEfH79hC3czsAGffDkCoUeH39LU9WLAWtlqyoSG4P+xSpQoHMwhJVQjzlxk8iK+q/l2CUpBNn47lxJ3dMuamJrh3A3s6E+MTcRNHezpR791PzHP8iV2czQoLsGTPVsBvT3U3B++950Puzczx4pOsqvReeRlAlWzq1cWfGb3eNVZcvewsFMqkkz8Sf+NQMnKws8pSPSEghSpnK0L/26vdp/vn8Bo//k63DuuJsbcHcA+eY1aM5Dfx112Y/N0duR8ez7EQotct75Km3MGxtbJBJpXkm/iQqk4p8/QKYv3QlPTu3p2mDugCU9y5DTGwcf23YSqsmxm/6CuPZ9UtiYXhNkFhYo32h50N/TFoSWo3G4JqgiY9BamULUhlocq8JiqZdMCkXSOqaOWhTla8Um42NLVKpLE9rZJIyATt7R6PH2Nk7oHxholCSMjFPq6guyfyO2KfRjJsyN09rJoCNrR02tna4e5TBw9OLT/t14u6t6/hVMD5s53Uq6d86N/a9/GJrZkmYNm0aa9as4ciRI/oe3dehRBZsqlixosGEmv+q4OBgbty4gY+PT57tVVrS1Go1y5cv5+eff+by5cv67cqVK7i7u+cZV+np6Un58uXzLEVUrVo1AgICmDVrVp4k+sqVKxw4cECfvL8uZmZm2NjYGGz5dZuDLpH39fHh8uUr+n0ajYbLl0OpEGB8rNXLVAuqwu+//sL8X+boNz9fH5o0asj8X+YUOcnUBZeD5uljZJ6+z+2UIPP0RVPQ8iDPk0iQOpYyfmHPzkSbkYbEzgmpiyfq+/mPNXqpnBzUTx5iUu65oRQSCSblKqB+bHxSgerRPd0QgOcudDJHN3KSlQZJJoAiuB7atGSy74QWPcbnaNVq0u7cxrr6cz0AEgk2wSGkXTd+HqRmCrQvtvo+++y/cLHWZGaiSohHZmWNTY1aKP8+XiJx/1dlZOQQ+SRTvz14lE5cQhYhQbldjRbmMir62XDtlvEk43ltmrmRmJTNqXOGSb/CTPf39OK9f45Gi7QI35cmchkV3J04cz/3RkCj0XLmfhRVPPMOTynrZMuGIZ1Z+2kn/dbI34saZd1Z+2kn3GwsUedoUOdokL7wmZBKJPqktChMTOT4lS/LhdDcz6dGo+Fi6DUq5bM8W2FkZWcjeeHkSaVSNMXt4dDkkBMdgdzr+dgkyL38yIkKN3qIOvLBPzfFufFI7Z11s85fTDJ9q5C29he0SYVbNeZ5chMTyvn4cfXKhdxwNRquXbmAb0Alo8f4BQRy7bLhBN2rl87hF5CbHD5LMp9EPea7ybOxtnn5Uojafz7MKlXBq8S8LYx/L+dNNJ2cnJDJZMTExBjsfzZcsCAzZsxg2rRp7Nu3z6B3+HV4pRbN+Ph4unTpwocffkiVKlWwtrbm/Pnz/Pjjj7Rvn/+6iP8VI0eOpHbt2gwZMoSBAwdiaWnJjRs32L9/P/PmzSvw2ICAAKZOnUrHjh3ZsWMHiYmJDBgwIM+4zc6dO7No0SI++eSTfGrKJZFI+PPPP2nRogWdO3dm1KhRuLm5cebMGb766itatmypH4MKEB0dTXR0NPfu6dbCu3r1KtbW1pQpU0Y/vuLRo0ckJCTw6NEjcnJyuHz5MgA+Pj4lNpO7c8f2/DRzNr6+PgT4+bFp6zYyMzNp2bwpAD/+PAtHRwcG9OsL6MbgPnqkW7NSpVYTF59AWNh9FOYKPNzdsbCwoKy34Zg+hUKBjY11nv1FkX3xGIoW3cl5GoEm+hEm1RogMTFFdUO3pIOiRQ80qUlkn9wFgGnN5uREP0SjjENiZo5p9cZIbezJvH5GX6fcpwrajDQ0KYnInEph1rAD6vvXyHlU9AkFABkn92HdcQDqqHDUjx+gqNMMiakZmRf/BsCq0wA0yYmkH9gEQObZwyhqNsHy3R5knDmIzNEViwatyTj9wsBuiQSzau+Qeflk3gyjGGLWrabsqO9Iv3WLtFvXcX2/O1JzBXG7dbNOvUePQxUbS+TC+QAknTyBa9cepN+9Q9qN6yhKl8b9w49IOnlCH5dNjVogkZD56CGK0qUp/ckQMh89JH6X8Zmsr4PM0gJLn9xeDouypbEJCiA7IYnMiMKPYyuu9dsi6dutDBFRGTyJyWRgL2/iE7I4fjp3vcvZP1Th2Kk4Nu3MXV5NIoHWzdzYcyiGnBf+ux8+TiciKp0Rn/ny6+L7JKWoaFDbiRpV7flmYtFulHrXrcx3m45SycOZQA9nVp66Rka2ig7BugRpzIbDuNhYMqxFTcxM5Pi6GrZgWZvrbm6f7TeRywjxLsXMvWcwM5FRys6KCw+i2XH5Ll+/m/+qG4XRtX0bps6ZT4BPOQJ8fdiwfRcZmVm820zX8jh51q84OzrwUR/dsCiVSk14xON//p1DXHwCd++HY26uoHQp3Zd53RrBrFy/BVdnJ7w9S3P3fjjrtu6kdbNGxYoVIPv8Ycxb9yIn+hE5Tx5iGtIIiYkZ2ddOA2DeujeaFCVZx3W9BNmXj2NWrT6Kpp3JvngUqb0LZrVbkH0xtxtf0awrphWqk7Z5IVpVpn75NG1WJqhVeYPIR5sO3flt1mTK+wZQ3q8Cu7auIyszg0bN2gAw7+dJODg680E/3ffhu+26MOHbIWzftJrgGnU5eewAYfduMWjIN4AuyZw1dSwPwu7wzbjpaDQalIm6JNjKyga5iQl3b18n7M4tAipVwdLKmpgnkaxd+SeupTzeSGsmvLnfOjc1NaV69eocPHiQDh06AOgn9rw4yfl5P/74I5MnT2bv3r1GhwqWtFdKNK2srKhVqxazZs0iLCwMlUqFp6cngwYNKnBtx/+KKlWqcPToUcaMGUP9+vXRarWUL1/eYIJRfm7fvk1Skq67ZdGiRTRr1szoovOdO3fmxx9/JDQ0FBubl08MqVevHqdPn2bChAm8++67+tn5Q4YMYdasWQateQsWLGDChNx1/Ro0aADAkiVL9C2f48aNY9myZfoyz7rkDx8+nO8kpVfVqEF9kpKSWL5yFYmJiZQrV47JE7/H/p81Mp/GxhqskxqfkMDgoV/oH2/YtJkNmzZTpXIgM6ZNKZGYCqK+e5ksc0vMardEYmGDJi6S9C0L9UsWSaztkD7XSiJRmKNo2gWJhQ3arHRdN/m6X9Ak5N41SixtMGvQHomFFdq0ZFQ3L5B9dn+xY82+do40C2ssmnRAamWDOjqC5BWz9K2pMlsHwy6x5ESSV8zCslU37D+dgCYlkYzTB8g4bjjo36RcRWR2jmReLNnJZYmHDyK3s8f9w4GYODiSfu8ud0d8iTpR1z1m5uJqkNhGrViKVqvFY8DHmDo7o1ImknTybyL/XKAvI7OywmPQJ5g6u6BOSUZ59AiRfy7QDRH4l9hWD6TOwRX6xxVn6K5vEcs3ETpgVH6Hlbi/NkagUMj4ZogfVpZyrt5I4qvxV8lW5X4GPNzMsbMxHGoUUtUeNxcFO/fnHaeVk6NlxPfX+KRfWaZ/F4i5uYzIJxlMnn2L0xfyXx2kIK0qlycxLZPfDl4gLjUd/1KO/NbnXRz/6TqPTkpD+orNpdO7NmHO/nOMWn+Y5IwsStlZMaRZCF1qGJ88WVhN6tdFmZzM4lXrSUhU4lPWi5/Gf4vDPz1OT+PiDGKNS0hg4Je5P9awZssO1mzZQdXACsyZPB6AYYP6s2jVOmYtWExiUhJODva0a9mMvt06FytWANXti0gsrFDUa4PE0pqcp5GkbfgN7T8THKXW9gbXBG2KkrQNv6Fo3AmrfqPQpCrJvnCUrOeuT2bV6gNg1WOYwWul71qJ6rkb6pep26ApyUlK1q38E2ViAt7lfBg18Wfs/hlOEx8bY7Ciin+Fynw+YjxrVyxkzfI/cHMvzYgxUynjXQ6AhPhYzp/RXaNGDu1v8FrjpsylUpVgzMwUnD11lPWrFpGVmYmdgyNVg2vRqdtETF5xHdCS8iYXbB8+fDh9+/YlJCSEmjVrMnv2bNLS0ujfX3f++vTpg4eHh36M5/Tp0xk3bhyrVq3C29tbP5bz2byV10Gi/V8fYf+W0Wg0DBgwgL1793L06FH9OM5/08N7xV9f8d/ksPOPNx1CoWXFK990CIUWfjT/X/f5L4o5WbQk6U2Y2urt+cwCHOh97uWF/iOUVZq+6RAKzXy78SVo/qsedBj3pkMotKq+/94PPDy5dblE6ysVUPWVys+bN4+ffvqJ6Ohoqlatyty5c6lVqxYAjRo1wtvbWz9h29vbm4cP8w4bGz9+PN9//30xIzfuDS06JeRHKpWyaNEifvnlF44fP/5GEk1BEARBEArnTf8++ZAhQ/LtKn+2jOIz4eHhrz+gF4hE8z9IKpUybNiwlxcUBEEQBOGNKulZ5/9r3mwaLgiCIAiCIPzPEi2agiAIgiAIRfQmJwO9DUSiKQiCIAiCUERveozmf504O4IgCIIgCMJrIVo0BUEQBEEQikh0nRdMJJqCIAiCIAhFJLrOCybOjiAIgiAIgvBaiBZNQRAEQRCEIhJd5wUTiaYgCIIgCEIRia7zgomzIwiCIAiCILwWokVTEARBEAShiETXecFEoinkcSfN+02H8EpmXOz9pkMoNHNLizcdQqHF2Tx50yG8mlZvOoDCG7Xnozcdwivp7rn9TYdQaC1t/N50CIW25lSPNx3CK5nZXvamQ/hPEr91XjDRdS4IgiAIgiC8FqJFUxAEQRAEoYi0WtGiWRCRaAqCIAiCIBSRVnQOF0icHUEQBEEQBOG1EC2agiAIgiAIRSRmnRdMJJqCIAiCIAhFJBLNgomuc0EQBEEQBOG1EC2agiAIgiAIRSRaNAsmEk1BEARBEIQiEolmwUTXuSAIgiAIgvBaiBZNQRAEQRCEIhILthdMJJqCIAiCIAhFJLrOCya6zgVBEARBEITXQrRoviH9+vVDqVSyZcsWg/1HjhyhcePGJCYmcvnyZRo3bkzFihUJDQ1FJpPpy9nZ2TF79mz69esHgLe3Nw8fPgTA3Nyc8uXLM2zYMAYOHPhvvSW0Wi071/3GyYMbyUhLoVxAVboNHItLKa98j7l34zwHti3l0YObJCfGMujr2QTVbPJa4uvT0Y1WjRyxspBx424ac5dFEBWTnW/5ZTMq4uZsmmf/tgOx/LoiEoB3GznSuLY9Pt7mWJrL6DT4KmnpOcWOtUdbR5rXs8XSXMqt+xksWP2UJ7GqAo9xsJXTp6MTwRUtMTOVEB2rYu6KaMIeZenLlHYzpU8HJyr5miOTSoiIzmb6H1HEJaqLFe+Ant6818INa0s5V28mM+O3uzx+kpFv+fV/1qKUqyLP/k07I5m54J7u/diZ8OmH5alR1R4LcxmPItNZvu4RR0/G/b+JtTAc3gmh3FcDsA0OROHuwvnOnxKz7eBrf11j3qbPrVar5fTuuVw9tZ6sjGTcywbTpMv32Lt453vM2f2/E3ZlHwlP7yM3UVCqbDXeee9rHFzL6cso4x5xfMt0ou5fIEedjVeF+jTq/B2WNk5FjhXgw+6etG3uipWFjKu3Upj5x30in2TmW37NgmBKueT93G7e/YTZCx/oH1fys2JgTy8q+Fqh0Wi59yCNryfdJDtbg1arJTV2CxmJR9HkpDN5bBAffvoNpdw9C4x1384N7Nj0F0mJCZQp60Pfj4fj41dJ/3x2dhZ/LZrLqeMHUKlUVKlWiw8Hj8DW3kFfJu5pNIvn/8SN0AsozC2o3+RduvcdjEymS1sSE+L4a9Fc7t+7RcyTx7R8rwt9Bn1pEEfv3r05e/ZsnvgaNmzIH3/8UeB7eBWiRbNgItF8C9y/f5/ly5fTv3//AstNnDiRQYMGkZ6ezvr16xk0aBAeHh68++67/0qcB7Yu4ejuVfT+7AccXTzYsXYev07+hLEzt2Biamb0mKysDDy8/anTpCMLZ3xptExJ6NrahfbNnZmx8CHRcdn07VSKKV+XZ9DoW6hUWqPHDJ1wG6k09wLi7aFg2kgfjp9L0u9TmEo5fzWZ81eTGdDVvURi7djcnraN7JizPJqYeBUftHVi/OcefD7xISq18VgtzaVM+9qTq3fSmfRrJEmpatxdTElL1+jLuDmZMGW4JwdPJbF6RzwZmRo8S5nm+/4Lq2dnT95v68Hk2bd4EpPJwJ7ezJxYmV6fniM7n7oHDb+I9Ln+lHJelsz+IYjDJ2L1+8YOD8DKUs63k66RlKyieUMXJn5TkYHDL3L3fur/fKyFJbO0IDn0NhFLNxKy4dfX+loFeds+t+cPLuTSsRW07DkNG4fSnNo1h80LBtBn1C7kJsavV5H3zlKlfk/cylRGo8nh7x0z2Tx/AH1G7cTEzAJVVjqbf/sQJ48AOg9ZBsDJXXPYtvATun+5Dom0aJ2IPTp60KlNKabOvcuTp1kM6FGGGd9VpO+wS/l+bj/+JhTZc9evsmUsmPl9JY6cjNfvq+RnxY/fVeSvTZHM+fM+OTlafLwt0Wp0dabF7SI9fj+2HgORmTqjUOxn2rgv+Om3VZjmc00/dfwAK/+cy4effYOPXyV2b1vLtHFf8vOCNdja6RLJFX/O4fK5kwwbORlzSyuWLviZWVO/5fsfdcmfJieHnyZ+ha29I9//9AfKhHjmz5qITC6ne5/BAKhVKqxt7enQrR+7t64xGssvv/yCSpV7o6NUKmnfvj2tWrUq7KkvFJFoFkx0nb8FPv/8c8aPH09WVlaB5aytrXFzc6NcuXKMHDkSBwcH9u/f/6/EqNVqObxrJS07DaJKjcZ4ePnRZ8hkkhJjuXLuUL7HVapWn/e6f05QzaavNb4OLZ1ZvT2aU5eSeRCRyY9/PMTRzoS6wbb5HpOUkkNiklq/1apqS1RMFqG3chOHzftiWbfzKbfC0kss1vea2LNuTwJnQ9N4GJnNnGXRONjKqRVkle8xnVo4EJeo4pcVMdx9mMnTeDWXb6YTHZd7ke3ZzpGL19NYtjmOB4+ziI5Tce5qGkmpxWuB7dLOg+XrHnLiTDxh4Wn8MOsWjg5m1K+dfwuOMllFgjJ3q1vDkcdRGVy6lpvEBwbYsnFHJDfvphAVk8mydY9ITVPj75P/efhfirWwYvce48742cRsPfDaX6sgb9PnVqvVcunocmq1GEz5ys1w9gigZa8fSUt6StjV/M9jx8GLqFSrE46lfHH2CKBFz2mkJEYRE3EdgKgHF0lOiKRFz2k4ufvj5O5Py57TiYm4RsTd00WOt0vbUqzY8Ji/zyVy/2E6U+bexdHBlHdqOuR7TFKy2uBzWyfEnsdPMrh8PVlf5rMPy7Jx1xNWbY4kPCKDiKhMDp+MR6XWotVqSU/Yj5XzeyhsgjFReDL4y3EoE+I4f/pYvq+7a8tqGrdsR6NmbSldpiwDPv0GMzMzju7fAUB6WipH9m+n18ChVAoKoZxPAB8PG8Odm1e5e+saAKGXzvI4IpzPhn+Pdzk/qobUoUuvQezfuRH1P4mjs2sp+n70JQ2atMbCwvhnzM7ODmdnZ/32999/o1AoSjzRFAomEs23wBdffIFareaXX34pVHmNRsPGjRtJTEzE1DRv1+/rEP80kmRlHAFVauv3mVtY4+1TmfA7V/6VGPLj5myKo50JF6/nJojpGRpu3U+ngo9loeqQyyQ0qWvP3mPxLy9cDK6OJjjYygm9lZu4pmdquBOeiX+5vN1gz9SsYsm9h1mMGFiKpdPLMXNUGZrXy02iJRIICbQi6mk244d4sHR6OX4c4UmtoMK9//y4uypwcjDj3OVE/b609Bxu3EkmMMCmUHXI5RJaNHZl54Fog/3XbiXRpL4L1lZyJBJoWt8ZU1Mpl64q/+djfdu8bZ/b5PjHpCfH4ulXV7/PzNwaN68gnjy4VOh6sjNSAFBY6GLOUWeDRIJMnnvdlZmYIZFIibx/oUixlnI1w9HelAtXlPp9aek53LybQiV/60LVIZdLaN7Amd2Hnur32dmaUMnPGmWSil+nBLJ5cQhzJlWicoCuzhxVLBp1EqaWuV3eFpZWlPerqE8IX6RWqXhw7zaBQTX0+6RSKYFVa3D3tu6YB/dukaNWG5Tx8PTGydmNu7euAnD31lXKeJU36EqvUq02GelpPH50v1Dv2ZiNGzfSpk0bLCwsilyHMVqtpES3/zUi0XyDduzYgZWVlcFmrJvbwsKC8ePHM3XqVJKSkozUpDNy5EisrKwwMzPj/fffx97e/l8bo5ms1I1Fs7Z1NNhvbetIsvL1Jmcv42CrGyGiTDIcK6ZMVumfe5m61W2xspCx70RCicf3PDtb3ThcZbLh2LOk5BzsbfKP1dXJhFYNbHnyNJsJv0Sy51gSA7s407iWLoGytZZhrpDSqYUDF2+kMeGXSE5fSWXkIHcq+ZoXOV4He90XaqLS8NwmKrP1z71Mg9pOWFnK2XXQMHkbN/0GcpmE3avrcXhTfUZ85sfoKdcLHJf2vxLr2+Zt+9ympeiGPVhaG16vLKwdSUsp3LharUbD0U1TcC8bjJO7HwBu3lUxMTXnxLafUGVnoMpK5/iW6Wg1OaQlx76kRuMc7HSfzYSkFz+3qkJ/buvXdMDKUm6QaLq76rq++3XzZMeBp3wz6SZ37qcxc0IlPEop0Kh13zVSueFNmK2dA0mJxq/pKclKNJocgwTx2THKf45RJsYjl5tgaWWYJNvY2ZOk1F1flcoEbOzy1qE7vmjX4NDQUO7cuUOXLl2KdHxBNEhKdPtfI8ZovkGNGzdm/vz5BvvOnDlDr1698pQdMGAAP//8M9OnT2fKlClG6xsxYgT9+vXjyZMnjBgxgk8//RQfH58CY8jKysrTJZ+dTb7jb545d3wnq/+YqH88eNSbGxv2osZ17BnWr7T+8Xczi34H/EzLBg6cC00mQVm8STMvalDDmsE9XPWPf5gfWaR6JBIJYY8yWblNdzF/8DiLMu6mtKxvy+EzyUj+uXadDU1l+yGlvkxAOXNavmPL9bv5T4Z5XvOGLoz4zE//+JuJV4sU7/PaNHfjzIUE4hMMJ2YN7FkWa0s5w8ZcISlZRf3aTkz8piKffXuZ+w/T/qdifdu8bZ/bW+e3cXDteP3j9h//XqR4n3dowwTiou/Sddgq/T4LKwfa9J/DoXXfc/nYCiQSKf7BbXApXQmJpHAJRLMGTnz1cXn9428n3yx2rK2bunD2YiLxibnJ6rN4tu+LYfehp2QoT3Fi1zImj5WSo9Fi7T6s2K/7X7Jhwwb8/PyoUqXKmw7l/x2RaL5BlpaWeRLBx48fGy0rl8uZPHky/fr1Y8iQIUbLODk54ePjg4+PD+vXr6dy5cqEhIRQsWLFfGOYOnUqEyZMMNjX6+Mx9Bn8XYGxVw5phLdvZf1jtUr3xZuSFI+tvbN+f0pSPKW9/Qusq6SdvpTE7bDcL3cTE13DvZ2tCQlJuYminY0JYY9e/kXl4mhCtUrWTJr74KVlX9XZ0FTuhOe2epnIJf/EJicxOXcMmq2NjAeP8x+jm5ikJuKJYfLzODqbOtV0rQYpqTmoc7RGy1QoX/iWoRNn47lx57z+sek/59bezoT4xNy67e1MuVeISTCuzmaEBNkzZup1g/3ubgref8+D3p+d48EjXXfsvfA0girZ0qmNOzN+u/s/Fevb5m373JYLbIKbV5D+cY5aV19aSjyWti76/ekp8Th7BLy0vsMbJvLg+hG6DF2JtZ2bwXNeAe/Qf9wBMlITkEjlKCxs+GNsPfwcWxcq1r/PJnDzTu7n0cREd24dbE1IeC5RtLcz4d6Dl9/EuDqbUb2KHd/9eMtg/7O/gfAI3WfWzLoqjubl+HxAWTQaLfMW61ZU0KiTkZnY6Y9LUibgVc4PY6xt7JBKZSS90OqYpEzAzl7Xemxn74harSItNcWgVTNZmahvtbSzc+D+nRt56tAdn/+41Pykp6ezc+dOhg4d+srHFsabngz066+/8tNPPxEdHU1QUBC//PILNWvWzLf8+vXr+e677wgPD8fX15fp06fTunXhPp9FIbrO3yJdunShUqVKeRJDYzw9PenWrRujRo0qsNyoUaNISkoy2LoP+Oal9SvMLXF2K6Pf3EqXx8bOidtXz+jLZKSnEn7vKt5+QQXUVPIyMjVEPc3Wbw8jM4lXqqhWMXfAuIVCSkA5C27ee/mFukV9R5TJas5cSX5p2VeVmaUlOlal3yKeZJOQpKaKf+4YInOFFD9vBbfv598Ne+t+Bh6uJgb73F1MiU3QfTGpc+Dew0w8XE3zLVMYGRk5RD7J1G8PHqUTl5BFSJC9voyFuYyKfjZcu/Xy89WmmRuJSdmcOmfYFacw03XFajSG5XM0WqSFvKa/TbG+bd62z62pwgo7Zy/95uDmg4WNMxF3TunLZGWmEv3wCqXKVsu3Hq1Wy+ENE7kXup/Ony3D1jH/ZX7MrRxQWNgQcecU6anxlAss3LJtGZkaIqMz9Vt4RAbxidkEV7HTl7Ewl1HB15rrt1NeWt+7TVxQJqs4fSHRYH/00yxi47Pw9NAl7FKZOXIzV6pX9UEjcUZu5o5Ubkt2Wm7Cl56eRtidG/gGBBp9LbmJCWV9/LkemnuDp9FouH7lPL7+umPK+gQgk8u5fiW3TNTjh8TFRuMboGu88A2ozKOHYfrkEuDq5bOYW1jiUabsS9/zi/bs2UN2djbt2rV75WML402O0Vy7di3Dhw9n/PjxXLx4kaCgIFq2bMnTp0+Nlj958iQ9evRgwIABXLp0iQ4dOtChQweuXTM+7rYkiETzLTNt2jQWL15MWtrLE6Rhw4axfft2zp8/n28ZMzMzbGxsDLaXdZsbI5FIaNy6F3s2/UHo+cNEPrrDinljsLV3JqhG7gV27sSBHN2zWv84KzOdx+G3eByuu9uOfxrJ4/BbJMQ9eeUYCrJlbyw92rlSu5oN3qUVjPjIi3ilipMXc8e8TvumPO2aGc4+lkigRX0HDpxIyJNIANjbyilXxhz3f74Iy5ZWUK6MOdaWsryFC2n7oUS6vOtAjcqWeLmb8kVfNxKS1Jy5ktvKMXFoaVo3tNM/3nYoEb+y5rzf0gE3ZxMahFjT4h1bdh1V6sts3p9IverWNK9ni5uzCa0b2lGjsiW7j+WWKYr12yLp260M9Wo6Us7LkrHDA4hPyOL46dyxbrN/qEKnNobLP0kk0LqZG3sOxZDzwrl9+DidiKh0RnzmSwVfa9zdFHTvUJoaVe05drroY37fplgLS2ZpgU1QADZBupY4i7KlsQkKQOFZ6rW/9vPeps+tRCKhWsM+nN03n7CrB4mLus3eld9gaetC+crN9OU2zuvL5WMr9Y8Pr5/AzfPbeLfPz5gqLElLjiUtORZ1dm4yff30Rp6EX0YZ94ib57ayc8kXBDfsZ7DW5qtav+MJfd4vTd0a9pQrY8HooT7EJ2Rz4mxuIjbz+4p0fNewdVUi0SWaew4/zfO5BVizNYrOrUvRsI4jHm4KPuzhSRkPc3YeiEEikWDh0JzU2O1kJl9ClRnB/JkTsXNwIqR2A30dk8cMYe+O9frHrTv04PDebRw7uJPIiHAW//YjmZmZNGzWFtBNKGrU/D1WLprL9dAL3L93i9/n/IBvQKA+ga1SrSalPb35beZEHj64y5WLp1m/8g+at+mMiUnuTUf4/TuE379DZmYGyUlKwu/f4fGjvD1PGzZsoFmzZtjb2+d57m03c+ZMBg0aRP/+/alYsSILFizAwsKCxYsXGy0/Z84cWrVqxYgRI6hQoQKTJk0iODiYefPmvbYYRdf5W6ZJkyY0adKEffv2vbRsxYoVadGiBePGjWPXrl2vPbZm7fuTlZXB6t8nkpGeQvmAanw6er7BGppxMY9JTc69s34Ydp25EwboH29a/hMAtRq2o/dnP5RYbOt2PUVhJmVYP0+sLGRcv5vGmBn3DdbiK+Viho2V4Z9EtUrWuDqZsveY8QHobRo70btj7sX95zG+AMxY+Ij9RZw4tHl/IgozKZ9+4IqlhZSbYRlMnBdpsBahm7MJNla5yey9h1lM+z2K3u2d6NragZh4FYs2xHLsXG6Lx5krqSxYHUPnlg4M7OJMVEw20xdGcTOseBNW/toYgUIh45shflhZyrl6I4mvxl81WN/Pw80cOxvDlquQqva4uSjYuT/6xSrJydEy4vtrfNKvLNO/C8TcXEbkkwwmz77F6QtFn5D1NsVaWLbVA6lzcIX+ccUZowGIWL6J0AEF92iUpLftcxvSdBDq7AwOrh2nW7C9XHU6fvKnwRqayvgIMtJyr1ehf+tukjf80tugruYfTKVSrU4AJD59wN87ZpKZnoSNgwc1W3xCtUb9ihXr6s2RmJtJ+fqT8rrP7c1kRky6YfC5dXdTYPvC57Z6FVvcnM3YddB469aGHU8wNZEypL831lZywsLT+GrCDaJidMMdLJ1ao9Vmk/xkKZqcdNysqvLthFkGjREx0ZGkJOfesNep34zkpEQ2/PUnysR4vMr58u2EWQYThHoPHIZUImH21FGoVSqqBNei/+AR+uelMhlfj5vB4t9+YvzXgzBTmNOgybt06TnIIP7Rw/rq//3g3i1OHt2Hk4sbcxdt1u+/f/8+Fy5cyDfxKgkl3XVubO6EmZkZZmaGjUDZ2dlcuHDBoOdSKpXSrFkzTp06hTGnTp1i+PDhBvtatmyZ58djSpJEq9UWb9Vb4X/O/isFr9f5XzNjZvEHy/9bzC1LdlmN1ykuomRblYVco/Z89KZDeCULB21/0yEUWst2xscP/het+ePkmw7hlcyc9vLxq/8V1f1efSxnUZ2/nfjyQq9gx+o5eYbIjR8/nu+//95gX1RUFB4eHpw8eZI6dero93/zzTccPXqUM2fO8CJTU1OWLVtGjx499Pt+++03JkyYQExMTIm+j2dEi6YgCIIgCMJ/xKhRo/K0Or7Ymvk2EYmmIAiCIAhCEZV017mxbnJjnJyckMlkeVoiY2JicHNzM3qMm5vbK5UvCWIykCAIgiAIQhG9qVnnpqamVK9enYMHD+r3aTQaDh48aNCV/rw6deoYlAfYv39/vuVLgmjRFARBEARBeAsNHz6cvn37EhISQs2aNZk9ezZpaWn0798fgD59+uDh4cHUqVMB3Wo0DRs25Oeff6ZNmzasWbOG8+fP88cff7y2GEWiKQiCIAiCUERGVo7613Tr1o3Y2FjGjRtHdHQ0VatWZc+ePbi66n6569GjR0iluZ3XdevWZdWqVYwdO5bRo0fj6+vLli1bCAw0vjZqSRCJpiAIgiAIQhG96iLrJW3IkCH5/mLgkSNH8uzr0qXLa/nN9/yIMZqCIAiCIAjCayFaNAVBEARBEIroTf/W+X+dSDQFQRAEQRCK6E13nf/Xia5zQRAEQRAE4bUQLZqCIAiCIAhFJLrOCyYSTUEQBEEQhCLSaN90BP9toutcEARBEARBeC1Ei6YgCIIgCEIRia7zgolEU8jD2yr6TYfwSjY32PSmQyg0qbfPmw6h8NJS3nQEryYz401HUGjdPbe/6RBeyaCF773pEAot5PNVbzqEQuvd4u36HBxIqvWmQyi06v/ia4lZ5wUTXeeCIAiCIAjCayFaNAVBEARBEIpIKyYDFUgkmoIgCIIgCEWkEWM0CyS6zgVBEARBEITXQrRoCoIgCIIgFJGYDFQwkWgKgiAIgiAUkRijWTDRdS4IgiAIgiC8FqJFUxAEQRAEoYjEgu0FE4mmIAiCIAhCEYnfOi+Y6DoXBEEQBEEQXgvRoikIgiAIglBEYtZ5wUSiKQiCIAiCUERi1nnBRNe5IAiCIAiC8FqIFs3XqF+/fixbtgwAuVxO6dKl6dKlCxMnTkShUAAgkeQ2udvY2BAYGMikSZNo0qSJ0TocHByoUqUKPXr0oF+/fkilufcK3t7ePHz4ME8cU6dO5dtvvy3R97Zj+zY2bVxPYmICZcuW4+PBn+HvH5Bv+RPHj7FyxVJiYmJwd/eg34cDqVGjpv75xMREli75k0sXL5CWlkalwMp8/MlneHh4lEi8ay/eZdnZW8SnZeLnYsfIZsEElnI0Wnbb1QeM333WYJ+pTMqZr7roH4/bdYbt18INytQt68avXRoWO9Y1R8+zbP9p4pJT8SvtyrddW1DZ2/h5OHDpFov2/k1EbCKqHA1eLvb0blqb92pV1pdJz8xm9tZDHL5yh6S0DDwc7ejRKISuDaoXO1aANX9fYdnRi8SlpONXyolvOzSkchm3lx63+/Idvv1rD40rlWN2v7ZGy0zaeIgNp68xol19etWvVvxYz1xn2YlQ4lIz8HNz4Ns2dalc2uXlsYaG8e36QzQO8GJ2zxb6/elZKmbvP8vhmw9JSs/Ew96aHrUr0bVmxWLH+kyPto40r2eLpbmUW/czWLD6KU9iVQUe42Arp09HJ4IrWmJmKiE6VsXcFdGEPcrSlyntZkqfDk5U8jVHJpUQEZ3N9D+iiEtUl1jseeJ6J4RyXw3ANjgQhbsL5zt/Ssy2g6/t9fKzadc+1mzeQYIyifLeZRg2qC8V/XyMln3w6DGLVq3nTtgDomPjGPJhb7q2e9egTHpGBn/+tZ7jZ86TmJSEb1lvhg7sQwXf8iUS77rLYSy/cJf4tEx8nW35pnEQgW4ORstuu/6QCfsuGOwzlUk5NbSD/nH1WZuMHjusfiB9QvyKFatWq2X/xnmcPbyejPQUvP2q0bH/OJzcvPM95v6t8xzbuZjHD66TooylzxdzqRTSLE+5mMgwdq+Zyf1b59BocnB1L0+vYbOxd3IvVszFJX6CsmAi0XzNWrVqxZIlS1CpVFy4cIG+ffsikUiYPn26vsySJUto1aoVcXFxjBkzhrZt23Lt2jXKlStnUEdOTg4xMTHs2bOHYcOGsWHDBrZt24ZcnvvfOHHiRAYNGmQQg7W1dYm+p2NHj/Dnwt/5bMhQ/AMC2LplE+O+G83vfyzCzs4+T/mbN67z4/Qp9O33ITVr1ubIkUNMnvQ9s+f+ird3WbRaLT9M+h65TMbYcROwsLBgy+aNjB09kvm/L0ShMC9WvHtvPuLnw5cZ06I6gaUcWXX+Dp+uO8qWga1xsFQYPcbK1ITNA3O/TJ6/IXimblk3JrybmyybymXFihNgz/kbzNh4gLE93qWytzt/HTrL4F/WsPX7T3C0tsxT3tbSnIGt6lHW1QkTuYxjV+8yfsV2HKwtqFdR9yU3Y+N+zt55yJR+7XF3tOXUzftMWbMHFztrGlUp3pfKnst3mLH9OGM7N6FyGVf+On6ZwX9uZes3vXG0ssj3uMiEZGbuOE5w2fy/IA5eDePqw2icbfK+7yLFejWMGbtPM7bdO1Qu7cJfp64xeNlutg7riqNV/p+xyMQUZu49Q7BX3uR5xp7TnL0fxZT3G+FuZ82pe4+ZsuNvXKwtaVTBq9gxd2xuT9tGdsxZHk1MvIoP2jox/nMPPp/4EJXaeH+dpbmUaV97cvVOOpN+jSQpVY27iylp6Rp9GTcnE6YM9+TgqSRW74gnI1ODZylTVKrX2wcos7QgOfQ2EUs3ErLh19f6Wvk5eOIUvy5eyVeDP6Sinw/rt+3m6wnT+OvXn7G3s81TPjMrC3c3FxrXq8Uvi1carXP6vIU8eBTBmC8G4+Rgz74jJxg+fgrLf/kJZ0fjCWFh7bv9mJnHrjK6aVUC3RxYdfEeQzb9zaZ+zXGwMH79sjSVs6lf7g3Ri1evvR+1Nnh8Mjyaifsu0sSn+Df2R3cs4u99K+n68RQcnEuzb8NcFk3/iOHTt2Niamb0mOysdEqV8SekQSdWzBlqtEx8zCMWTOpFjYadad75MxTmVsQ8voeJifE6/02i67xgouv8NTMzM8PNzQ1PT086dOhAs2bN2L9/v0EZOzs73NzcCAwMZP78+WRkZBiUeVaHh4cHwcHBjB49mq1bt7J7926WLl1qUJe1tTVubm4Gm6VlyXxRP7Nl80ZatnqX5i1aUqaMF58NGYaZmRn79+01Wn7b1i1Ur16Dzu93xbNMGXr36Uf58j7s2L4NgKjISG7fusmnQ4bi5+dP6dKefPrZULKzszh65Eix4115/jadqpSjfeVylHeyZUzLEBQmcrZcfZD/QRJwsjLXb45GElJTmcygjI3CtNixrjh0hk71qtKhThDlSzkztkdrFKZytpy8YrR8DT8vmlYNoFwpJzyd7enZpCa+Hi5cCovQl7l8P5L3alWmhp8XHo52vP9OMH4erlwLjyp+vMcu0alWIB1qVKS8qyNjOzXRnduzN/I9JkejYfSqvQxuUZvSDnm/2AFiklKZtvUIUz5oiYmsZC5TK05epVNIAB2C/SnvYs/Y997RxXrxdsGxbjjM4CbBlHbIe8N2+VEM71X1pUZZdzzsrXm/RgX83By5Fvm0RGJ+r4k96/YkcDY0jYeR2cxZFo2DrZxaQVb5HtOphQNxiSp+WRHD3YeZPI1Xc/lmOtFxua2gPds5cvF6Gss2x/HgcRbRcSrOXU0jKTWnROLOT+zeY9wZP5uYrQde6+sUZN3WXbRt0ZjWTRvh7VmarwYPQGFmxs6DR42Wr+Bbnk/79aRp/bqYyvO2zWRlZXPs1FkG9/2AqpUqULqUGx/2eB8PN1e27Cn++1x58S4dA71pV8mbco42jG5WDYVcxtZreXuvnpFIJDhZKvTbi9ev559zslRwJOwJIZ7OlLYr3neFVqvlxJ7lNGn/MZWqN6VUGX+6fjKNZOVTrl/Iv+U6IKgBLbsMI7BG3lbMZ/asn4N/UANa9/gaD++KOLqWoWL1JljZGu+ZEv47RKL5L7p27RonT57E1DT/hMTcXNeykp2dXWBdTZo0ISgoiE2bjHeBvC4qlYp79+5StWpuN6ZUKqVq1WrcunXT6DG3bt2gajXDbs/g6iH68iqV7gvw+fMilUoxMTHhxo1rxYs3J4eb0YnU8nbNrVsioZaXK6FRcfkel5Gt5t0F22k1fxtfbDpOWFxSnjLnI57SZN4WOizcxeR951FmZBmp6RViVedw89ETavuXzY1VKqF2QFlCHzx+6fFarZYztx4QHpNAdZ8y+v1Vy3lwNPQuMcpktFotZ2+H8/BpAnUqlCt+vJFPqe3raRivryehD5/ke9zv+89ib2VOp5qVjD6v0WgZs3of/RpWx8etZL5EVOocbkbFUbtcbouNVCqhdnkPQiPyTwp/P3wJe0sFnaobHxZStYwrR28/JCY5TXdu70fxMC6JOj6lix2zq6MJDrZyQm+l6/elZ2q4E56JfznjLVkANatYcu9hFiMGlmLp9HLMHFWG5vVyE3qJBEICrYh6ms34IR4snV6OH0d4UiuoZG9I/4tUKjV3wh4QUiVQv08qlVI9KJDrt+8Wqc4cTQ45Gg2mJiYG+83MTLl6I/+bmELFm6PhVoySmmVyh3dIJRJqlnHh6pOEfI/LyFbT5s/dtF64m+FbTxEWl5xv2fi0TE48iKZ9oHexYgVIiH1MSlIcvoF19PvMLazxLF+FR3cvF7lejUbDrctHcXLz5s/pg5j46TvMG9+N6+ff3A3L87RaSYlu/2tE1/lrtmPHDqysrFCr1WRlZSGVSpk3b57Rsunp6YwdOxaZTEbDhi8f6xcQEEBoaKjBvpEjRzJ27FiDfbt376Z+/fpG68jKyiIryzBBys7KwtTMeHdEcnIyGo0GO3vDLnI7O3seR0QYPSYxMTFPl7qdnR3KRN2FsrSnJ87OLixbspghnw/DTKFg65ZNxMXFkZCQ/8W0MBLTs8nRavN0MTlaKghPMH7x9XKwZvy7NfBztiMlS8WKc7fpt/IgGwa0wtVa1x1ct2wpmviWxsPOksfKVH45dpUh64+xrFdTZNKi3b8lpqaTo9Hi+EJXsaO1JQ9i4vM9LiUjk+aj56JS5SCVShjdvZVBEvlt15ZMXLWLFqN/QS6VIpFKGP9Ba6r7lsm3zkLFm5ahi/eFLnJHKwsePE00eszFB1FsPneddV9+kG+9S46cRyaV8ME7QcWKzyDW9Mx/YjXsIne0MudBnNJ4rA+j2XzxNus+7ZRvvd+2qcvErcdp8dMq5FIJEomE8e3rU927VLFjtrPVDcVQJhuOmUxKzsHeJv9Lt6uTCa0a2LLtYCIb9iTg66VgYBdn1Goth88kY2stw1whpVMLB/7aHsfyLXFUq2jByEHufDfnMdfvZhQ79v+qpJQUcjSaPF3kDra2PHpctBZ+C3NzKvn7smzdZrw8PbC3teXg8ZNcv30XD7eXj1UuiDIjixytFkcLw+uxo4UZ4YkpRo/xtrdiXItgfJ1sSc1WseL8XfqvPcL6Ps3016/n7bjxCEsTOU18ij/OMUWpu3m3snEy2G9l40hKUv439i+TlhxPdmY6R3b8Scv3h9K6+3BuXznBijnD+Gj0UspVqFGsuItLLNheMJFovmaNGzdm/vz5pKWlMWvWLORyOZ07dzYo06NHD2QyGRkZGTg7O7No0SKqVKny0rq1Wm2esYMjRoygX79+BvsKmlAzdepUJkyYYLBvyOfDGDrsy5e+fkmRy+WMGTuOOXNm0r1bZ10LabVgqofUeCODX4I8nAjycDJ43HnRbjZcDuOz+rpJNq0q5CZpvs52+Drb8d4fOzkfEUstL9c8db5OlmZmrBs1kPSsbM7cDufnjQco7WRPDT/dGMHVR84T+iCSOZ90wd3Blgv3HjFl7V6c7aypHVD2JbWXnLTMbMas3sf495tib2l8TOSNx0/56/gV1nzR3ei42H9LWlY2YzYcZnz7+tjnM44XYPXp64RGPGVOzxa421lxITyaKTtO4mxjSe3yrzberUENawb3yP3s/DA/skixSyQSwh5lsnKb7ubkweMsyrib0rK+LYfPJPPstJ4NTWX7IaW+TEA5c1q+Y/s/nWi+LmO/+JRp836n04efIZNK8S3vTdP6dbkdVsDwnNekirsjVdxzewKqlHLk/WX72Xj1AZ/WzduLsPV6OO9W8MSsCGPML/29nU2Lv9c/7v/1giLF/DLaf74HKgU3of67fQFw96rAw7uXOX1w7RtPNIWCiUTzNbO0tMTHRzebcfHixQQFBbFo0SIGDBigLzNr1iyaNWuGra0tzs7Oha775s2blC1rmCg4OTnpX68wRo0axfDhww32RTyOzre8jY0NUqkUZaJhi5VSmYi9g/FB7/b29iiVL5ZXYmefW97H149f5i0gLS0NtVqFra0dw7/4HF/f4k1WsbcwRSaRkJCeabA/Pi3T6LhLY0xkUvxd7YhITM23TGk7K+zMzYhITClyomlvZYFMKiE+Oc0w1pQ0nAqYECOVSijjojuXAZ5uPIiOY9Hek9Tw8yIzW8XcbYeZ9dH7NKjsC4BfaVduP45h2YHTxUo07S11s5XjU9MN9senpuNkpOUkIj6JqMRkhi7Zrt+n+ecLJHjkL2wd0ZuLDyJJSEun1ZQl+jI5Gi0/bz/BX8cvs3t0/6LFaqH4J1bDJCo+NQMnI5OWIhJSiFKmMvSv3HHH+ljH/8nWYV1xtrZg7oFzzOrRnAb+uhsPPzdHbkfHs+xE6CsnmmdDU7kTnvs5NZHrMkI7GzmJybljJ21tZDx4nP8wjcQkNRFPDIfePI7Opk413RjTlNQc1Dlao2UqlC/exLv/Oltra2RSKYlKw6EwCUlJONjbFblej1Ku/DJ5HBmZmaSlZ+DkYM/4n+bi7vryFQ0KYmduhkwiIT7d8P87Pj0Lp3wmAr3IRCbF38WOx8q0PM9dehzHw8RUprWpaeTIl6sY3ATP8rmNImq17jOVmhyHjX3ud1lqcjzuZfJfleRlLKztkMrkuHgYzuJ38ShH+O2LRa63pIjJQAUTiea/SCqVMnr0aIYPH84HH3ygH4/p5ub2SskhwKFDh7h69Spfflm8lkczMzPMXugmNzUz3u0JYGJigo+PL1euXKZO3XqAbvzMlcuXafteO6PHBARU5PLlS7TvkNsFeenSRQICKuQp+2ziUmRkJPfu3aVXn76v/J4M4pXJqOBmz5mHMTT21Y2b02i1nH0YQ7dg30LVkaPRcC82iXrl8u8OjUlJJykjC6d8WuoKFatcRoUypThzO5wmVf11sWq0nLkdTveGIYWuR6PVolLrulvVORrUORqkUsPWQalUiqaY/T0mchkVPFw4cy+CJoHlc+O9F0H3unm7vcu62LPhq54G+37dc4q0rGy+ad8QNztr2gYHUOuFLv3BC7fQtnoAHUKKvmSQiVxGBXcnztyPpElF79xY70fRvVbeess62bJhiGHPw68HzpOWreKb1nVws7EkS52jO7cvtLxKJRJ9UvoqMrO0RL+wbFFCkpoq/hb6xNJcIcXPW8GeY3nHDD9z634GHq6G4wXdXUyJTdDVrc6Bew8z8XA1zbfM/yoTEzl+5ctyIfQ69WvrWsE0Gg0XQ6/TsXWLlxz9cuYKBeYKBSmpqZy7FMonfXsUL16ZlABXO85FPKXxP13bGq2WcxFP6RpUuKWTcjRa7sUl8U7ZvN34W66HU8HFDj9nuyLFZ2ZuiZl57k2wVqvF2taJe9dP4+6lu75npqcSERZK7abdi/QaAHK5KaXLBRL7xLCFOO5J+Btf2ghAK5Y3KpBINP9lXbp0YcSIEfz66698/fXXhTomKyuL6Ohog+WNpk6dStu2benTp49B2ZSUFKKjDVskLSwssLGxKbH30KFjZ2bN/AlfX1/8/ALYunUTmVmZNGveEoCfZ/yIo6Mj/frrWm3bte/AtyO/ZtOmDdSoUZNjR49w7+4dhnw+TF/niePHsLG1xcXZhfDwB/zx+3xq165LcHDhE6z89ArxZ9yuM1R0c/hneaPbZKjUtK+sa80bu/M0LlYWDG2ouzP//e/rVHF3xNPeipTMbJadvc2T5HQ6VtGNe0zPVvH739dp6l8aJ0tzIpSpzDlyBU97K+oauZi/it5NavHd8m1U8ipFoJc7Kw+fJSNLRYc6utjGLN2Gi501wzo0BmDRnr+p6FUKT2d7slU5HL9+j51nrjGmRysArMzNCPEtw8xNhzAzMaGUgy0X7j5kx5mrfN05/xmehY63QTW+W7ufSqVdCfR0ZeXxy2Rkq+lQQ5e8jVm9DxdbS4a1roeZiRzfFyb3WCt0NznP9tvJzbF7IVk3kUlxsrbA2yXv0lmvFGvdyny36SiVPJwJ9HBm5alrZGSr6BCsazUfs+EwLjaWDGtRUxerq2ELvbW5LjF7tt9ELiPEuxQz957BzERGKTsrLjyIZsflu3z9bu1ixfrM9kOJdHnXgain2TyNV/HBe04kJKk5cyW3dX3i0NKcvpLKrqNKALYdSmTa12V4v6UDJy6m4OeloMU7tvy2KkZ/zOb9iXw9oBTX72Vw9U46wRUtqVHZkrGzjY+zLikySwssn5uoZlG2NDZBAWQnJJEZkf8EspLUtX1rps5ZgL9POSr4lmf99t1kZGbSuqluXPzk2b/h5OjAx711iZFKpSY8QjcZT6VWE5eQwN374ZibKyhdSvf3fvbSFbRa8PQoReSTGOYvXUWZ0u76OoujV7Av4/eep4KLPYFu9qy6dI8MVQ7tKumGxozbcx5nKwWfv6Ob4PTH6ZtULuWAp60VKVnZrLhwl+jkdDq8MNknNUvFgTuRfNmg8osvWWQSiYR3WvXh0JbfcXL1wt5Ft7yRjZ0Llao31Zf7Y0p/AkOaUbeF7sYzKzON+JhH+ucTYiOJengTc0tbfSLZsPWHrJo3nLIBIZSvUJM7oSe4eekIH41ZWmLxC6+HSDT/ZXK5nCFDhvDjjz8yePDgQh2zZ88eSpUqhVwux97enqCgIObOnUvfvn0NFmwHGDduHOPGjTPY9/HHH7NgQcmNnWnQsBFJyUmsXLGcxMREypUrx8SJk7H/Z4JQbOxTgxa0ChUrMeKbUaxYvpTlS5fg7uHOmO++x9s7t9s2ISGePxcuQKlUYm/vQJOmzejeo2ee1y6KlhXKkJiRxfwT14hPy8TfxY5fuzTUd51HJ6cbtEqlZGYzce854tMysVGYUsHVnqU9m1LeSTeBQCqRcDc2ie3Xw0nJVOFspaCOtxuf1q9c7LU0W4VUJDE1jd92HCUuOQ3/0q78NqQ7jja65WyiE5MMzm1Gtoopa/YQo0zBzEROWVdHJvdrT6vnWv+mf9iROVsPM2rJFpLTMynlYMuQdo3oUj+4WLECtKrqR2JaBr/tPU1cShr+7s78NrA9jv90nUcrU/K0+L0prSqXJzEtk98OXiAuNR3/Uo781udd/WSm6KS0PC2/LzO9axPm7D/HqPWHSc7IopSdFUOahdClRt7W+qLYvD8RhZmUTz9wxdJCys2wDCbOizRYQ9PN2QQbq9zP3b2HWUz7PYre7Z3o2tqBmHgVizbEcuxc7uSRM1dSWbA6hs4tHRjYxZmomGymL4ziZpjhEJOSZls9kDoHV+gfV5wxGoCI5ZsIHTDqtb72M03fqYMyKZnFqzeQkKjEp6wXM8Z/i8M/E4RiYuORSHKvq3EJiQwYPlr/eM2WnazZspOqlSowd/J3AKSmZfDHijXExidgbW1Fwzo1GNSzm8Eax0XVwr80iRlZLDh1g/j0LPycbfmlY73c61dKOs//iaVkqvhh/0Xi07OwMTMhwNWOxd0bUc7RsLFh3+3HaIGWAZ6UpIZtB5CdlcHGxePJTE/B2y+YD7/5w2ANzYSnEaSl5PacPb5/nT+m9NM/3vGXbp3p6vU70PXjKQAE1mhGxw/Hc3jbQrYtn4JzKW96DZtNWf+S+eGJ4hCTgQom0WrF6ALB0N2w/Ndn+y/yOLLoTYdQaFLvVxsi8UalGZ/V+p+V+fZMYul+xPgwk/+qQQvfe9MhFFpI6Ko3HUKhWR7d8KZDeCUHqv/wpkMotA41iv8DGoW1/rTm5YVeQZfar2flyYSEBD7//HO2b9+OVCqlc+fOzJkzBysr4+vyJiQkMH78ePbt28ejR49wdnamQ4cOTJo0CVtb42sgGyPW0RQEQRAEQfgf17NnT65fv87+/fvZsWMHx44d46OPPsq3fFRUFFFRUcyYMYNr166xdOlS9uzZYzCZuTBE17kgCIIgCEIRvQ39wjdv3mTPnj2cO3eOkBDd3IdffvmF1q1bM2PGDNzd806qCgwMZOPGjfrH5cuXZ/LkyfTq1Qu1Wl3ooSGiRVMQBEEQBKGINFpJiW5ZWVkkJycbbC/+sMqrOnXqFHZ2dvokE6BZs2ZIpVLOnDlT6HqSkpKwsbF5pfHHItEUBEEQBEH4j5g6dSq2trYG29SpU4tVZ3R0NC4uhuu6yuVyHBwc8qxUk5+4uDgmTZpUYHe7MSLRFARBEARBKCKttmS3UaNGkZSUZLCNGmV8VYZvv/0WiURS4Hbr1q1iv8fk5GTatGlDxYoV+f7771/pWDFGUxAEQRAEoYhKeoymsR9Syc9XX32V52enX1SuXDnc3Nx4+vSpwX61Wk1CQgJubgWv/5ySkkKrVq2wtrZm8+bNmJiYFFj+RSLRFARBEARBeAs5OzsX6qer69Spg1Kp5MKFC1Svrlt79NChQ2g0GmrVqpXvccnJybRs2RIzMzO2bduGQlG4nz59nug6FwRBEARBKCKNtmS316FChQq0atWKQYMGcfbsWf7++2+GDBlC9+7d9TPOIyMjCQgI4OzZs4AuyWzRogVpaWksWrSI5ORkoqOj9b9UWFiiRVMQBEEQBKGItNr/xq+fvcxff/3FkCFDaNq0qX7B9rlz5+qfV6lU3L59m/T0dAAuXryon5Hu42P4YyMPHjzA29u7UK8rEk1BEARBEIT/cQ4ODqxalf8vZ3l7e/P8j0U2atSIkvjxSJFoCoIgCIIgFNHbsGD7myQSTUEQBEEQhCJ6XeMq/1eIyUCCIAiCIAjCayFaNIU8HqYVvKbWf42Hb+CbDqHQEl383nQIhZYtN3/TIbwSM3X6mw6h0FravD2fA4CQz/Mf1/Vfc77KB286hEJrusv4Itz/VaKL2DhxXgomEk1BEARBEIQiEolmwUTXuSAIgiAIgvBaiBZNQRAEQRCEIhKTgQomEk1BEARBEIQiEl3nBRNd54IgCIIgCMJrIVo0BUEQBEEQikijedMR/LeJRFMQBEEQBKGIRNd5wUTXuSAIgiAIgvBaiBZNQRAEQRCEIhItmgUTLZqCIAiCIAjCayFaNAVBEARBEIpIrKNZMJFoCoIgCIIgFJG2xPvOJSVc35slus4FQRAEQRCE10K0aP5H9OvXj2XLlgEgl8txcHCgSpUq9OjRg379+iGV6u4JvL29efjwocGxHh4ePH78WP+4ZcuWHDhwgNOnT1OjRo0SiU+r1bJz7W/8fXAjGWkplAuoSvdBY3Ep5VXgcUf3rOHAtqUkK+Pw8PKj64ej8PatrH8+NjqCzct/JuzWJdTqbCpUrUfXD0dhY+eoL7Ng2uc8Dr9NSnICFpY2+FeuTYdeX2Dn4FLo+NcePsOyvSeIT0rFz9ONkT3aEFi2tNGyBy9eZ9GuY0Q8TUCdk0MZF0d6t6hH2zpV9WXGLd7E9lOXDI6rW8mHX7/oW+iY8rN5517WbNlOQqISH28vhn7Unwp+PkbLPngUwZJV67gd9oCYp7F8NqAPXdq1MSiTk6Nh6Zr17D9ynASlEicHB1o1aUjvrp2QSIp/57xtx07Wb9xMQmIi5cqW5bNPPiLA389o2fCHj1i+8i/u3gsj5ulTPhk0gE4d2huU2b5zFzt27SYm5ikAXl5l6NmjOzVDqhc71txzm0R57zIMe8m5XbxqPXfC7hP9NI4hA/rQpV1rgzLPzu2+Iyf+Obf2tGrSkD4ldG61Wi2nd8/l6qn1ZGUk4142mCZdvsfexTvfY87u/52wK/tIeHofuYmCUmWr8c57X+PgWk5fRhn3iONbphN1/wI56my8KtSnUefvsLRxKnKsm3btY83mHSQo/zm3g/pSMd9z+5hFq9ZzJ+wB0bFxDPmwN13bvWtQJj0jgz//Ws/xM+dJTErCt6w3Qwf2oYJv+SLH+Koc3gmh3FcDsA0OROHuwvnOnxKz7eC/9vrPrDl6gWUHzxCXnIqfhwvfdmlBZW93o2UPXL7Nor0niYhLRJWjwcvZ5QLmIQAAfL9JREFUnt5Na/JezdzrbnxyGrO3HubUzQekZGQS7OPJt11a4OXiUOxYtVot+zfN49zh9WSkp+DtV40O/cbh5Oad7zH3b53n2M7FRIZfJ0UZS+9hc6kU0sygzLe9Kxo99t3uX9GwzYBix10cYjJQwUSL5n9Iq1atePLkCeHh4ezevZvGjRszbNgw2rZti1qt1pebOHEiT5480W+XLuUmPI8ePeLkyZMMGTKExYsXl1hs+7cu4cjuVXT/6DtGTP0LUzNz5v3wCarsrHyPufD3HjYt+4nWXT7h2+lrKe3lz7zJn5CSFA9AVmY68374GCQSho5fyPBJy8hRq1gw7XM0z62A6xdYkwHDf2LcnG0M+nomcTER/PnzV4WOfe+5q/y8bjcfv9eYVd8Nxq+0G5/OXkZCcqrR8raWFgxs3ZBlowaxbvwQ2tcL5vulmzl57a5BubqBvuyf8Y1+mzqoa6Fjys+h4yf5bfFy+nXrzMKZ0yhf1osR308hUZlktHxWVhalXF35qHcPHOztjJZZvWkrW3fvZ9jHH7Js3kw+6vMBqzdtY9OOPcWO98ix4/y+cBG9PujOb3NnUa6sN6O/G0+iUplvvG5ubnzYrw8O9vZGyzg5OTGgX19+nTOLeXNmUrVKFb6fNJnwh4+KFeuh4yf5dfEK+nZ7n4Uzp1K+rBdffz8133ObmZWNu6sLH/X+IN9zu2rTVrbuPsAXH/dn+byf+bjPB6zetJ2NJXBuAc4fXMilYyto2vV7un+5DhNTczYvGIBalf/fXeS9s1Sp35PuX66j06dL0OSo2Tx/AKqsdABUWels/u1DkEjoPGQZXb9YTU6Oim0LP0FbxJWnD544xa+LV9Kveyf+nDkZH+8yfD1hWgHnNgt3Nxc+7tM933M7fd5Czl+5ypgvBrN0znRqVK3M8PFTiI1PKFKMRSGztCA59DbXhk74117zRXsu3GDG5oN8/O47rBn5If4ergz+dS3xKWlGy9taKBjYqi7Lv+rDhlEDaF+7CuNX7uTvG/cBXSL4xR8beBynZPbHnVn77YeUcrDl419Wk56VXex4j+5cxMl9K+nQfzyffb8GEzNzFv/4UYHfFaqsdEqV8ad93+/yLTPml6MG2/uDfkAikRBYo0WxYy4ujaZkt/81ItH8DzEzM8PNzQ0PDw+Cg4MZPXo0W7duZffu3SxdulRfztraGjc3N/3m7Oysf27JkiW0bduWwYMHs3r1ajIyMoodl1ar5fDOlbTqPIigGo3x8PKj75DJJCXGcuXcoXyPO7hjOXWbdqZO4w6U8ixP94++w9TUnFOHtgBw//Zl4p9G0fuzSXh4+eHh5Uefz37g0f3r3Ll2Vl9Pk7a9KesXhKOzO+X8q9Kiw4eE3w0lR60qVPwr95+kU/0Q2tcLpry7C2N6vYfC1IQtf180Wj7EvyxNgitSrpQLni4OfNCsDr6lXbl0z7Al2VQuw8nWWr/ZWJoXKp6CrN+6kzYtmvJus8Z4lynN8MEDUZiZsuvAYaPlA3x9GNy/F00b1MPExMRomWu37vBOrRDqhARTytWFRvVqU6NaFW7evVfseDdu3sq7rVrQsnkzvMqUYdiQTzFTmLF33wGj5f39fPloQH8aN2yQb7x1atWkZo0QPDzcKe3hQf++vTFXKLh561axYl23dSdtWzShdbNGeJcpzVf6c3vEaPkKvuX/Obd1MTUx3vlz/dYd6tWqnufc3robVqxYQfd3d+nocmq1GEz5ys1w9gigZa8fSUt6SthV4+cXoOPgRVSq1QnHUr44ewTQouc0UhKjiIm4DkDUg4skJ0TSouc0nNz9cXL3p2XP6cREXCPi7ukixbpu6y7atmhM66aN8PYszVeDB6AwM2PnwaNGy1fwLc+n/XrStH5dTOV5z21WVjbHTp1lcN8PqFqpAqVLufFhj/fxcHNly57833tJi917jDvjZxOz9d97zRetOHSWTnWD6FCnCuVLOTG2eysUpnK2nAo1Wr6GnxdNg/wp5+aEp7M9PRvXwNfdhUv3IwB4+DSB0PAoxnRvSaCXO96ujozt1opMlZo9F24UK1atVsvfe5bTpN3HVKrelFJl/On28TSSlU+5cSH/lmD/oAa07DKMwBdaMZ9nbedssN24cIhyFWri6OJZrJiF108kmv9xTZo0ISgoiE2bNr20rFarZcmSJfTq1YuAgAB8fHzYsGFDsWOIfxpJsjIO/8q19fvMLa3x9qnMg9tXjB6jVqmIuH+TgCq5x0ilUgKq1OL+nSv/lMlGIpEgNzHVl5GbmiGRSAm7ZTwJTEtJ4tzxXZT1q4pMbjxReZ5KrebmwyhqVcjtNpRKpdSqUJ7QsIiXHq/VajlzM4zw6Diq+3kbPHf+djhNhk+jw9jZTF65DWVq+kvrKzBWlZrbYfepHpTbxSWVSqkeVJkbt+8WcGTBAgP8uBB6jYjIKADuPQjn6o3b1AquWsx4Vdy9d49qVXPrkUqlVKsaVOyk8JmcnBwOHz1GZmYmFSsEFLkelUrNnbAHRs/t9dt3ilxvpQA/Lhqc24clcm4BkuMfk54ci6dfXf0+M3Nr3LyCePLgUgFHGsrOSAFAYWELQI46GyQSZPLcvzuZie7vLvL+hVeO89m5DakSqN+nO7eBXC/i5zZHk0OORoPpCzcjZmamXL1xu0h1vo1U6hxuRkRT27+sfp9UKqG2vzehDyJferxWq+XM7XDCnyZQvXwZfZ0AZs8l+FKpBFO5jEthj43WU1gJsY9JSYrDJ7COfp/CwhrPclV4eO9ysep+XkpSHLeuHKNGw84lVmdxaLUlu/2vEWM03wIBAQGEhubevY4cOZKxY8fqH0+ZMoWhQ4dy4MAB0tPTadmyJQC9evVi0aJF9O7du1ivn6yMAzAYNwlgbedIsjLe6DGpKYloNDlY275wjK0j0ZEPAPD2rYKpmTlbV86i3QdD0Wq1bP1rDhpNDkmJcQbHbVk5i6N7VpOdlUlZ3yp8MmpeoWJPTE0nR6PBwcbKYL+jjRXh0XH5HAUp6Zm0/OYnVGo1UomUUT3bUrti7nizuoE+NAmugIeTPY9jE/hl8wGGzFnOslEfIZMW7f4tKTkZjUaDg52twX57O1sePY4qUp0AH3RuT1p6Bn0+G45UKkWj0TCwVzeaN6pf5DoBkv+J197O7oV47YiIePmXYEEehIcz7KtvyM7OxtzcnPFjR+NVpkyR60tKTiZHo8He6Lkteqw9O7cnPT2D3p999cK5fafIdT6TlhILwP+1d9fRTWRtGMCfpG6pu7u3uHvR4sUXp8iy2MLChxdbZHH3FofFivviUhxaqFEDitTdUsl8fwTShqal1JLA+zsn5zCTmcnTELm5c+87KmrC7yFlNW1kZ5b92i2J4fFw238ZjCzrQceIP27WwKIO5OSVcO/sKjTvNg1gGNw7twYMrwjZGYk/nDM9M1Pkc6ulXvnXrbKSEpztbbHv2CmYmxpDU10d1+8+QHB4BIwNDCp1TGnE//xioK2mLLRem6OCmHjRn70AkJmbhw5zN6OgsAhsNgtzBnRCU0d+Y9XCQBuGmhxsPHsL8wd1hpK8PA7cfIz4tEwkposeTlRRWV++K1TVhcf6qqprIyu9Yq/Zinh+9wwUFJXh3KBDtR2zKqi8UfmooSkFGIYRmlgwY8YMjBgxQrCso8N/U/v5+WHAgAGQ/fJLddCgQZgxYwaioqJgbS16AD2XywWXKzx2JuDmJRzzXS5Y/mP2lur6U4SoqWth9F+r8e+uv3Hr0mGwWGzUb94FppaOpSZStO8xAk3b9UZK4mdcPL4d+zfNxfjZm6tlwoUoKory+NfnD+Tm5eNRWDTWHLsME10tNPjSs9C5kZtgW1sTA9iaGKD7nHV4Gh6Dxo61N1mhIm7eC8B/t+9h3rRJsDQzRWTMW2z23QftL5OCJJGJsTG2bVqP7Owc3L1/H6vWrsfqf5ZVqbFZE27ee4hrt+9h/rRJsDAz+fLc7hdMCvoRYU/P4vrRBYLlnuN2VDnfjROLkBQXgf5TDgvWKatqoevIDbhxbCFe3jkAFosN+3pdoWfiXGPvp8qY9+cfWLF5B7xGTYAMmw1bawt4tGyG8KgYcUeTeCoKCjg2exRyuAV4FP4Wa/yvw0RbAw3tzCEnI4O1Y7yw8NBFtPzfesiwWWhsb4EWTlb40fbSi/vncGrPQsHyiL+2V+vfUZand/xRp1k3yMkr1MrjkaqhhqYUCA0NhaVl8akTHR0d2NgIz+ZMSUnBqVOnUFBQgG3btgnWFxUVwc/PD0uXLhV57OXLl2PRIuGB7r+N/h9mrzouWC4s5A8Qz0hLhrpm8XjQzLRkmFjYizyuqpom2GwZwcQfwT7pyeBoFP/adXRvhkWbLyIrIxVsGRkoq3Awa3Rb1NcXnhGuytGEKkcT+kYWMDCxxLzfOyLmTRCs7N1FPv5XmqrKkGGzS038Sc7IgvY3vZwlsdlsmOnxe5LszQwR8zkRfhfvCBqa3zLR1YKGqjJiE1Iq3dBU53DAZrOR8s0EitS09DInTFTE9r2H8FufnvBo1RwAYGVhhrjERBw6cbpKDU3Ol7zfTvxJTUurUl4AkJOTg7ERf1atna0N3ryJxKkz5/DnpAmVOp46hwMZNrvU5JSqPrfb9h7E4D494dGKf3rb2sIM8YlJOHTizA8/t1Yu7WBgXvx6LvryvsvOTIaKenGFhZzMZOgaf38Ywc0TixETfAv9Jh+EmoZwL6C5QwuM9PkPuVkpYLFloajMwc55zWGn7VnG0cqmrqYm8rlNSa/ac2tsqI9NS32Qm5eH7Jxc6GhpYsGqjTDSr3i1CWnH//xiITlTeFhOckY2dMr9/GLBTJc/g9zBRB8xccnwvRqAhnb8KiFOZoY4Ntsbmbl5KCjkQUtNGYNX7YWzmeEP5XOq1w6mNsU/uosK+K/ZrPQkcDSKvyuy0pNhaF75oS8lxYQ/ReLnGAyasKZajlcdfsbT3dWJxmhKuBs3buDVq1fo06f8sSiHDh2CiYkJAgMD8fLlS8FtzZo12Lt3L4qKikTuN3v2bKSnpwvdBo+bCz1DM8HN0MQaHA0dhL9+JNgvNycLbyNfwbKMhp6snBxMrRwR/qp4Hx6Ph/BXj2BlV3ofVY4mlFU4CH/1CFkZKXBr0KbMv5X5cp7iawO4PHKysnA0N8Kj0GihHI9Do+FmXfFB5AzDIL/EzP9vxaekIz07FzrqZX/4fzernCzsra3wPOiVUNZnQa/hZG9b6eNy87lgs4V7qmTY7CoXGZaTk4OtjQ1eviwep8vj8fDyZRAcHarnS0VwXIaHgoKKTf4SRU5OFnbWlngW9Lr4mDwenge9hnMZpZgqgpufD9Y3zy2bzQaP+fGpo/KKqtDQNRfctAxsoMzRReybgOLHy8tC3LtAGFrWLfM4DMPg5onFiAy6hj4T9kFdu+zXuZKqFhSVOYh9E4CcrGRYubT74dzFz22wYB3/uQ2GcxVet4KMiorQ0dJEZlYWnrwIQotGVS9zJS3kZGXgaGqAR+FvBet4PAaP3ryDm6VxhY/DYxjB2MyS1JQUoaWmjHcJKQh5H4c2bj/2/6WgpAIdfXPBTc/YBmrqOogMLp5UlpebhdjoIJjb1PmhY5flyS1/GFs6w6iaGq7VgeEx1Xr72VCPpgThcrmIi4tDUVER4uPjcfnyZSxfvhzdunXDsGHDyt3X19cXffv2hYuLi9B6U1NTzJ49G5cvX0bXrl1L7aegoAAFBeHTD/LywqfSWSwW2nYdgssnd0LPwAzaesY4f3QL1DV14d6w+Itpw6LRcG/kgTZdBgEAPLoNw/4t82Bm7QQLG1fcuHAQXG4umrTtJdgn4OZpGBhbQpWjhZg3gTix5x+07ToU+sb8nsOYiCC8iwyGtUNdKKtykBQXi/NHt0BH3xSWIhqsogzp0Aw+fv5wsjCGi6UxDv8XgNz8fPRsXg8AMM/3BPQ0OZjsxS+T4XvxNpwtjGGiq4X8wkLcexWBCw9fYvbg7gCAnDwudpy7CY96ztBRV0VsYgo2nLgKU10tNHOu2hdrv55dsXzDVtjbWMPR1honzl1EXh4XXdq3AQAsW7cZOtpaGDvsNwD8iRhvY/kD+AsLCpGUnIqI6LdQUlKEiSG/F6tpw/o4cPwU9HR1YGFqgsjotzh25gI827etUlYA6NO7J1atXQ9bWxs42NnB/8xZ5OXloVMHDwDAyjXroK2tBe8Rw7/kLcD79/xJWAWFhUhKTkFUVDQUlRQFPZi+e/ehYYP60NPVRW5uLm7cuo2gV6+xbMnCKmXt37Mrlm/YBgcbKzjY2uDEuYvIzeOiS3t+z+PSdVugq62FscMGfcla/NwWFBQhKTml1HPbrGE9HDx+GvpfntsIwXPbpkpZAf77rm7rYXh8dRs0dM2hrm2CBxc3QEVdD9auxbNzT24eDmu3DqjTaggA4ObxRQh7fh49Rm+FvKKKYNylgqIaZOUVAQDBD09Cy8AaSqpa+BzzArf9l6Fe6xFCtTZ/RP+enli+YTvsbazgaGuN4+cuITcvD54eX57b9Vuho62FcUMHAvjmuS0sRFJK6ef28YtAMAxgamyIj5/jsW3vYZiZGAmOWRtkVJShYlM8XEPZ0gQcdwfkp6QjL/ZzrWQY2q4R5h84D2czA7hYGOHgzSfI5RagVxN+T+Lc/eegp66GKT3bAAB8rzyAk5khTHU1kF9YhLvBUbjw+DXmDuwkOObV56HQVFWGoRYHEZ8SsfLEf2jrZodmjpX7//+KxWKheedhuHFmB3QMzKGla4KrJzaCo6EHp/oegu12LR8J5wbt0azDYAAANy8byfHF5ctSEj/i07tQKKuoQ0OnuF5oXm4WXj2+gq6/zahSTlK7qKEpQS5fvgxDQ0PIyspCU1MT7u7u2LhxI4YPHy4o2C7Ks2fPEBgYiF27dpW6T11dHR4eHvD19RXZ0KyoDj1HIj8vF4d3LEZuTiasHepiwtxtQmNkkuI/IDszVbBcv3lnZGak4vzRrchMS4KxhT0mzN0mNKko/uNbnDm0ATlZ6dDWM0YnrzFo16148pK8vCICH/2Hi8e2gsvNhbqGDhzrNIf31FWQKzFbvTydGroiNTMb285cR3JGFuxNDbFlyjDBqfO4lHSwWcXPbx63AMsOnUNCagYU5ORgYaiDv737olND/oxlNpuNiA/xOBfwEpk5edDVUENTJxv80cujzDI4FdWuZTOkZWRgz+Fj/ILtlhZYuWA2tL5MuIlPSgarxGshKSUFY6bOFCwfPX0OR0+fg7uLEzYs5Y/3mzJmJHwPH8X67b5ITU+HjpYWundqj+ED+lYpKwC0adUS6enp2H/wMFJTU2FlZYWlixdC80uNzITERKFxf8kpKRg/+U/B8gn/Uzjhfwpuri5YvWIZACAtLR2r1qxHSkoKlFVUYGVhgWVLFqJ+3bJ78Sri63Prd/j4l+fWHKsWzBI8twlJSUI9v0kpKRg9dZZg+d/T5/Hv6fOo4+L4zXN7DOu2+315bjXRo1N7DB9QPbNhG3iMQWF+Lq4f9eEXbLeqj96/74asXPH7Li05FrnZxe+7oPtHAAAnNglPAuzw23I4N/YCAKQmxOD++bXIy0kHR8sYjTr+jrptRlQ6p0eLpkhLz4DfkROC53b1glmCiW3xiclgsUq+blPhPW2OYPnf0xfw7+kLqOPsiI1L+bUUs7JzsfPAv0hMToGamipaN22IMYOLx6DXBvX6Lmh6/YBg2Wk1P3Psfn8Eec+ulQyd6zshNSsHWy/cRVJmNuyN9bB1Qn9oc1QAAHEpGWCXeI/l5hdg2bEriE/LhIKcLCz1tbF0eHd0rl9c8DwxIwur/a8jOTMbuhxVdGvsgnGdqz6BDQBad/VGPjcX/n4LkJeTCQu7ehg5Y6fQd0VyQqzQd8WHmGDsWjZCsHzh8D8AgHoteqH/uGWC9YEBFwEwqNO08t9lNeEn7ISsViym+i/SSaTcf0FlF9aVRM3Szog7QoWl61X+NG1ty5etel3Q2qRQWLXyUrXp1NuqNZprm5f5j5c9Epenbr+JO0KFeVysncZqdbmkXrUKJrWpdyOZWnusf05Ub5X1mX1/rlGNP9dfQwghhBBCJAadOieEEEIIqSQenTsvFzU0CSGEEEIqiQYglo9OnRNCCCGEkBpBPZqEEEIIIZVEPZrlox5NQgghhJBK4jFMtd5qSkpKCgYPHgwOhwMNDQ14e3sjK6ti17dnGAZdunQBi8XC6dOnf+hxqaFJCCGEEPKTGzx4MIKDg3Ht2jWcP38ed+7cwdixYyu07/r164VqIv8IOnVOCCGEEFJJlbjibLm4XC64XOF61qKu4vcjQkNDcfnyZTx58gQNGjQAAGzatAmenp5YvXo1jIyMytz36+Wsnz59CkNDwx9+bOrRJIQQQgipJIZhqvW2fPlyqKurC92WL19epYwBAQHQ0NAQNDIBoH379mCz2Xj06FGZ++Xk5OC3337Dli1bYGBgUKnHph5NQgghhBAJMXv2bEybNk1oXVV6MwEgLi4Oenp6QutkZWWhpaWFuLi4MvebOnUqmjVrhp49e1b6samhSQghhBBSSbxqPnX+I6fJZ82ahX/++afcbUJDQyuV4+zZs7hx4wZevHhRqf2/ooYmIYQQQkglMWKsb/TXX39hxIgR5W5jZWUFAwMDJCQkCK0vLCxESkpKmafEb9y4gaioKGhoaAit79OnD1q2bIlbt25VKCM1NAkhhBBCpJCuri50dXW/u13Tpk2RlpaGZ8+eoX79+gD4DUkej4fGjRuL3GfWrFkYPXq00DpXV1esW7cO3bt3r3BGamgSQgghhFSSNFzq3NHREZ07d8aYMWOwfft2FBQUYOLEiRg4cKBgxvnHjx/h4eGB/fv3o1GjRjAwMBDZ22lmZgZLS8sKPzY1NEkp1iqx4o7wQz6v3ifuCBWmYfXjpSHERU1XS9wRfkhRTq64I1TYvwGDxB3hhwzteE7cESrM4+JscUeosOueVZtJXNtUn0rT61am1h6JkYaWJoBDhw5h4sSJ8PDwAJvNRp8+fbBx40bB/QUFBQgPD0dOTk61Pi41NAkhhBBCfnJaWlo4fPhwmfdbWFh8d7xpZcajUkOTEEIIIaSS6Frn5aOGJiGEEEJIJfGk5NS5uNCVgQghhBBCSI2gHk1CCCGEkEoSZx1NaUANTUIIIYSQSmKq+cpAPxs6dU4IIYQQQmoE9WgSQgghhFQSj06dl4samoQQQgghlURjNMtHp84JIYQQQkiNoB5NQgghhJBKojqa5aOGJiGEEEJIJdGZ8/LRqXNCCCGEEFIjqEdTgowYMQL79u0DAMjJycHMzAzDhg3DnDlzcO/ePbRt21awrZ6eHlq0aIFVq1bBysoKAGBhYYF3796VOu7y5csxa9asas169tx5nDh5EqmpqbCytMQf43+Hvb29yG3fvnuHAwcOIiIyEgkJCRg3dgx69+oltM2/R4/h/oMH+PDhA+Tl5eHk6IhRo0bC1MSkWvJyPLpCw7MPZNQ1kR8bg6QD28GNfiNyW6PZy6Hk6FZqffbLJ4hbuxAAIMPRgNaAkVB2qQu2sgrywoORdGA7CuI/VTmrQsO2UGreCWxVdRTGxSLn0hEUfowpc3uWohKU2/WGvGM9sJRUwEtPRvbloyiIeCXYhq2mAeUOfSFn4wKWnDyKUhKQdWYPij6Vfr38qGOB0dj/PBLJOVzY6nDwv9ZucDHQFLnt2ZD3WPTfC6F18jJsBEzoLljOyS/EpgchuBX1Gel5+TDiKGNgHSv0dbWsclb5ui2h0NADLBUOihI+Iu/6CRTFlfMcKChBsWU3yNm6g6WoDF5GKvJunERhTAj/7sYdIGvrDhltfTAFBSj6FIO822fAS02octavRg00RbcO+lBVlsGrsEys3RmNj5/zytz+3+31YKinWGr9qUufsX5X8evI2U4Vowebw9FWFTweg8iYbExfEor8/MoVBTz2Mgr7n0UgOTsPtrrq+F9bd7gYaInc9mzwOyy6+kxonbwMGwGTewmW66/zF7nvlJYuGNbArlIZv/r39jPsu/4ISRlZsDPWw6x+HeFqYSRy2/9ehsP3ygPEJqWioIgHc11NDPVohO6NXAXbJGdkY/2ZmwgIjUFmbh7q2ZhiVr+OMNcT/ffXBK0WDWD1lzfU67lA0UgPT/v8gfiz12vt8cvDMAwuHNuKB9dPIjc7E1YOdTBg9DzoGZqXuU9kyFP8d3Yv3seEIiM1EWOmr4d7o3a1mLriGDp1Xi5qaEqYzp07Y8+ePeByubh48SImTJgAOTk5NG3aFAAQHh4ONTU1REREYOzYsejevTuCgoIgIyMDAFi8eDHGjBkjdEw1NbVqzXj79h3s2rULkyZOhL2DPU6fPo258+dj986d0NDQKLU9l8uFgaEBWrZsgR07d4k85qvXr9C9W1fY2dmBV1SEPfv2Ye7cedi5YzsUFUt/af4IlcYtofPbGCTu3Yy8qHBodOoFwxlLEPu/sSjKTC+1fdzGpWDJygmW2apqMP17M7If3xOsM/hzHpjCIsStXwJebg7UO/eG4cyliJ31O5h8bqWzyjs3hEqn/sg+fxCFH6Oh2KQ91Ib8ibTN88BkZ5beQUYGnKHTwMvOROax7eBlpoKtrg0mL0ewCUtRGRzvWSiICUfmoQ3gZWdCRlsPTG5O6eP9oKtvPmLt3WDMaecGF31NHH4ZjYlnAuA/1ANaygoi91GRl4X/UI/ifCzh+9fefY0nH5KwpFN9GHGU8fB9AlbcDIKuiiJaWxlWOqucfT0otumN3GtHUfT5HRTqt4FKvz+Q6bsETE5W6R3YMlDpNwFMThZyzvqCl5kONkcLDDdXsImMqQ3yX9zlN1bZMlBs2R0q/SYgc89SoCC/0lm/GtTbGF5dDbF8YwQ+J3DhPcgMq+c7YfiUF8gvEP3lNu5/QZBhFz+plmbKWLvQGbceJAvWOdupYuV8Jxzy/4gNu6NRVMTAxkKl0l+YV8M/YO2dV5jjUQcuBlo4/DwSE/3vw39EB2gpi37/qsjLwn9ER8HyNy8DXBnrKbT84G0cFl99jnY2xpXK+NXlZyFYfeo65g3oDFcLIxy6+QTjtxzFGZ+x0FZTKbW9urIiRnduBkt9bcjJyODO60gsOHgBWqoqaO5kBYZh8OfOE5CVkcH6cX2gqqiA/TceY9ymI/CfNwbKCvJVyltRMirKyAgKR+zek2hwYkutPGZF/XdmD25fOoyhE/6Gtp4xzh/djC1Lf8e8tachJy/6c4LLzYWxhT2atuuNXaun1nLiH0PljcpHp84ljIKCAgwMDGBubo7x48ejffv2OHv2rOB+PT09GBoaolWrVvDx8UFISAgiIyMF96upqcHAwEDopqJS+sOzKvxPnULnzp3RsWMHmJuZYdLEiVBQUMSVq1dFbm9vZ4cx3t5o07o15OTkRG6zdMkSdOzQARbm5rCyssJf06YhITERERGRIrf/ERqdeyPj1mVk3v0PBZ9ikbh3MxhuHtRadxS5PS87C0XpqYKbsktdMPlcZD2+CwCQMzCCoo0jEvdtATcmAgVxH5G0bwvY8vJQbdq6SlkVm3YA9/ldcF/eR1HiZ2SfPwgU5EOhbguR2yvUbQGWkgoy/92CwthI8NKSUfjuDYriPwi2UWrRBbz0FGSf2YPCjzHgpSWhICoEvNTEKmUFgIMvItHbxRw9nMxhpc3BnHbuUJSVwZmQsnsJWQB0VBQFN+1vGiJBn1PQzdEUDUx0YMRRhpeLBWx1OAiOT6tSVvkGbZEfFICC14/AS45D7tWjYAryIe/SVPT2rk3AUlJGzumdKPoYAyYjBUUfIsFL/CjYJufENhQE84/HS/yI3EsHwVbXgoy+aZWyftWvmyEOnPiA+09SEf0uB8s2RkBbSx4tGpXdU5aeUYiUtALBrWkDTXz4nIuXwRmCbSaMssTJi59x+NRHvI3NReynPNx8kIyCwsp9YR58HoHeLhbo4WzBfx20r8t/Hbwu53XAYgm/DlSEXwcl79NRUcStqM9oYKoLE42qfZ4duPEYXs3c0aupG6wNdTBvYGcoysvidECQyO0b2pnDw90eVgY6MNXVxOC2DWFrpIcX0bEAgHcJKQh6+wlzB3aCi7kRLPS1MW9AZ+QVFOLys5AqZf0RiVfu4M2C9Yg/81+tPWZFMAyDmxcPopPXGLg1bAtjczsMm7gU6amJCHxyo8z9nOu2RPeBk+DeyKPMbYh0oIamhFNSUkJ+vuieESUlJQAo8/6aUFBQgIjISNStU0ewjs1mo26dOggNC6u2x8nJzgYAqKmpVu1AMrJQsLBBTvDL4nUMg9yQl1C0cajQIdRadUTWwzuCnsqvvZ1MyR4rhgFTUABFO+cqZJWBrJE58qNLfDkxDPKjQyFnYiVyF3n7Oij8EA2Vrr9Bc/paqP+xCEotPYW6CeXs3VH46R1U+/0OzRlroT7OBwr1WlY+5xcFRTyEJaSjkamuYB2bxUIjU128+pxa5n65BUXouucqPP2uYNq5R4hKzhC6381QC3ei45CQlQuGYfAkNhHv07LQxEy3jCNWAFsGMgamKHwXXmIlg8J34ZAxshC5i6yNK4o+vYVS+/5Q+2MpVEfMhkLjjqW7YEtgKfAbSyV7lCvLUF8B2pryeBaYJliXnVOE0IhMONtX7CyFrCwLHVrp4tKN4lP5GupycLZTQ1p6AbYsc8EpvwbYsMQZrg6VO/NRUMRDWHwaGpnpCdaxWSw0MtPDq88pZe6Xm1+IrrsvwXPXJUw7E4CopIwyt03OzsO9mDj0dLGoVEZB1sIihMbGoYl98TAMNpuFJvYWCIr5WM6efAzD4FH4W7xNSEF9azPBMQFAQbb4BCGbzYK8rAxeRH0QeZxfSXLCR2SkJcHBrYlgnZKyGixsXPH2TaAYk1UfhsdU6+1nQw1NCcUwDP777z9cuXIF7dqVHpfy+fNnrF69GsbGxkJjI2fOnAlVVVWh2927d8t8HC6Xi4yMDKEbl1v2qd+MjAzweDxoaGoIrdfQ0EBqStmNix/B4/GwfcdOODk5wcLCokrHklHjgCUjg6KMNKH1helpkFEXPY6wJAUrOyiYWiDj9hXBuvzPH1CQlADtfiPAVlYFZGSh0bUvZLV1Iavx/WOWhaWsChZbBkyW8Bcuk50Blqq6yH1kNHUg71QfYLGRcWgDcm+fh2LTjlBq1a3ENrpQbNgGRSnxyDiwDnlPb0GlyyAouDerdFYASMvloohhoP3NKXJtZQUk5YgeQ2ihqQqf9nWwtltjLOlYHzyGwcjjdxGfWXw6+n+tXWGppYYuflfReMs5TDrzEDPbuKGesU6ls7KUVPjPbc43z21OJlgqHJH7sNV1IGdXB2CxkX1yO7gBVyDfsB0UmnYu61Gg2K4PCj9EgZf0udJZv9LS4J9yTUkvEFqfmlYALc2KnY5t2UgLqiqyQg1NI33+/9eIAaY4/18C/rckFG+is7F2kTOMDX98mEqlXwcd62Ftj6ZY0qUB/3Vw9BbiM0U30M+HvIeKnCza2YgeR1lRqVk5KOIx0FZTFs7KUUFShojhE19k5uahybTVaDBlJSZuO4ZZ/TqgqSO/sWphoA1DTQ42nr2FjJxcFBQWwe9aAOLTMpGYXvYxfxUZaUkAADV1baH1aurayEhLFrWL1KGGZvlojKaEOX/+PFRVVVFQUAAej4fffvsNCxcuxJMnTwAAJiYmYBgGOTk5cHd3x8mTJyEvX/ylM2PGDIwYMULomMbGZY9pWr58ORYtWiS0bvKkSfhzyuTq+6N+0Jat2/D23TusWb1KbBm+UmvVEdz3McITh4qKELdxKfS8p8By+1EwRUXIDX6J7MAnYJUaaVbDWCzwsjOQfW4/wDAo+vwObI4GlJp1Qu7tc4JtCj+9Re71U/z4cbGQ0TOGQoPW4AY+qNW4boZacDPUElrue/AGTr5+iz+aOgIA/g2Kweu4FKzr1hiGHCU8/5iMf27xx2g2LtFrVuNYLDA5mci9egRgGPDiY8FSU4dCQw9wH1wqtblih36Q0TFE1uH1lXq49q108Nc4a8HyrKWhlU0u4Omhh8fPU5GcWtxYZX3pkT13NV7QAI2IyUZ9V3V4ttPDrkPvq/y43+NmpA03o+KGh5uhNvruu4aTr2LwR7PSZwXOBL9FF0dTKMjK1Hg2UVQUFHBs9ijkcAvwKPwt1vhfh4m2BhramUNORgZrx3hh4aGLaPm/9ZBhs9DY3gItnKzw8zUZvu/J3Qs4snOxYHn8bMkaL0pqHzU0JUzbtm2xbds2yMvLw8jICLKywv9Fd+/eBYfDgZ6enshJPjo6OrCxsanw482ePRvTpk0TWvfpQ2yZ23M4HLDZbKSlpgmtT0tLg6ZW5XvzvtqydRsePX6M1Sv/ga5O5XuwvirKzABTVAQZjobQell1DRSll98Dy5JXgGqTVkj1P1jqvvy3kfgwfxLYSsqArCx4mRkwXrAW3JiISmdlcrLA8IrAUhXuYWOpcMBklZ60BAC8zHQwvCKhQm5FiZ/BVtMAZGSAoiLwMtNRlCjcw1aU+BkKjvUqnRUANJQUIMNiITlHuAc8OYcLnTImgHxLToYNe111fEjnD5XIKyzClgchWN21EVpaGgAAbHXUEZ6YjgPPoyrd0GRys/nPrfI3z62yGphs0adsmex0MDye0HPLS44HW1UdYMsAvCLBekWPfpCzckHWvxvAZKVVKuP9xykIfVPcAyYnx28QaqnLIaVEQ1FTQw6RMdnfPZ6+rgLqu2lg/krhIS3JqfwhH29jhXsP333Mhb6u6IkZ5am214GeBj6klf67XnxIwrvULKzo2uiHs31LU1UZMmwWkr/pOU3OyIYOp+xhOmw2C2a6/B9IDib6iIlLhu/VADS048+adjIzxLHZ3sjMzUNBIQ9aasoYvGovnM0qP3lNWrk2aAML2+IZ+YVfhhhlpidDXbN4+EtmejJMLERXKpE2P2EnZLWiU+cSRkVFBTY2NjAzMyvVyAQAS0tLWFtbV9tMcgUFBXA4HKGbgkLZXzZycnKwtbHBy8CXgnU8Hg8vX76Eo0PFxjyKwjAMtmzdhgcBAfhn+TIYGBhU+lhCigrBfRsJZec6xetYLCg51UFeZPljSlUbtQRLVg6ZD26WuQ0vNwe8zAzI6RtBwdIG2c8fViFrEQo/vYOcpaNQVjkrBxR8iBa5S0FsJGS09ITGDcpo64OXmQYU8RtChbGRkNHWF9pPRlsfRelVO20lJ8OGg546nsQWTyrifRlT6WpYsR8dRTwGkckZggZJYREPhTwG7G/GQcqwWVWb2ckrQlFcLGTNS5bFYUHW3A5Fn96K3KXwYwzYGjooOR+arakLXlZ66UamrRuyj24CU4XnNDePh49xeYLb29hcJKfmo56bhmAbZSUZONqqIThcRAWCb3Rpp4e0jAI8fCb8gyougYvEZC5MjZWE1psaKiI+8ccrJsjJsOGgr4EnscWn5/mvgwS4GlasvE8Rj0FkUjp0VEo3TE8Hv4WjngbsdDV+OFuprLIycDQ1wKPwt8VZeQwevXkHN8uKz2bnMYxgbGZJakqK0FJTxruEFIS8j0MbN9sqZ5Y2ikoq0DUwE9wMTKzB0dBB+KtHgm1yc7LwNvIVLOzcxZi0+tCp8/JRj+ZPJjMzE3FxcULrlJWVweGIHodWGV69e2P12rWwtbWFvZ0dTp05gzxuHjp26AAAWLV6DbS1tTFq5AgA/AlE79/zT8cVFhYiKTkZUVFRUFJSgpERf8zVlq1bcfPWbSzwmQ8lJSWkpPAnEaioqJTb8K2ItMunoDdmGrgxEciLfgP1jj3BUlBE5p1rAAC9sdNQmJqMlOP7hPZTa90B2c8DwMsq/aWu0rAFijLTUZicCHlTC+gMHovsZw+R+/pFqW1/RF7ANaj2HoWiT+9Q+DEGik3agyWnAO6L+wAA1d6jwMtIQ851fo1B7pNbUGzUDsqdByLv8Q3IaOlBqWVX5D0qrp+XG3AN6t6zoNTSE9zgp5A1toBi/VbIOre/SlkBYEhdGyy49hyO+hpfyhtFIbewCD2c+BMlfK4+g66KEiY1dwIA7HwUDlcDTZhqqCCTW4ADzyMRl5GDXs787VUV5FDfWBsb7gVDQVYGhmpKePYxGRdCYzG1pUuVsuY/vQklzyEoinuPos/vIN+gDVhyCsh/zf9xoOQ5FLzMNHDv8occ5L+8C4W6LaHo0Qf5z2+DrakHhSYdkf/8tuCYiu37Q96xPrJP7QJTkAeWCv8HIMPNAwoLSof4QcfPf8awvib48DkXcfFcjBpkiuSUfNx7XDzJZu1CJ9x9lIJTl4rf9ywWv6F5+WYCikSUxfz3zCeMHGCKqLc5iIzJRqe2ujAzVoLPqvDSG1fAkHq2WHDlKRz1NOFioInDLyKRW1CEHs78Hj+fy0+hq6qISS34/4c7H4bC1VALpuqqyOTm48CzCP7r4JvJPlncAvz35iOmtnL99iErbWi7Rph/4DyczQzgYmGEgzefIJdbgF5N+LVz5+4/Bz11NUzp2QYA4HvlAZzMDGGqq4H8wiLcDY7ChcevMXdgJ8Exrz4PhaaqMgy1OIj4lIiVJ/5DWzc7NHMUPYmvJsioKEPFxkywrGxpAo67A/JT0pEXW/Uxw5XFYrHQ1nMILvvvhK6hGbT1jHHh3y1Q19SFe8Pi+QcbF4+GeyMPtO48CADAzctBYlzxMI7khI/48DYMyqrq0NL59XqKpRk1NH8yPj4+8PHxEVo3btw4bN++vdoeo3XrVkjPSMeBAwf5BdutrPD34sXQ1OT3YiUkJoJVoo5fckoKJkwqHvN58qQ/Tp70h6urK1b9swIAcP7CRQDA/2YKF5afNvVPQQO2srIf3UWymjo0vYZAVl0T3PfR+LzKRzBBSFZbF8w3vWVyBsZQsnfBp3/mijymrIYmdH4bDRl1DRSmpSLz/nWknv63SjkBID/4CXJUVKHUtifYqhwUxsUi8+B6weldtrq2UFZeRioyD6yDcucB0Bi/kF9Q/NF/yL1XPIaw6NNbZB7dCmUPLyi17o6i1CRkX/4X+SV6GCqro50xUnO52P4wDMnZXNjpcrCpZxNByaK4zFzBmEAAyOTm4+8bL5GczQVHUQ4Oehrw69cSVtrFP4SWdW6AzQ9CMO/KM2Tk5cOAo4w/mjqir6tFlbIWhD8HS1kVis27gqWihqKEj8g+sRVMDv+HBFtNU+g0OZOZhuwTW6HY1guqI2aDl5WG/Ge3wX18TbCNQl3+7H3VQVOEHivn4kEUBFf9+T1y6iOUFNiY/rs1VFVk8So0AzOWhAjV0DQyUIQ6R7hsWH03dRjoKuDiddGF40+c/wx5OTYmjrSAmqosot5m469FIfgUX7kasB3tTfivg4AQJOdwYaerjk29mwtKFsVl5ghN1s/MK8Df154jOYcLjoIcHPQ14DewjdDrAODX52QAdHKonnJRANC5vhNSs3Kw9cJdJGVmw95YD1sn9Ic2h182KS4lQ6hHPTe/AMuOXUF8WiYU5GRhqa+NpcO7o3N9J8E2iRlZWO1/HcmZ2dDlqKJbYxeM6yy6JFlNUa/vgqbXDwiWnVbPAQDE7vdHkPfsWs3yrfY9R4LLzcWRHYuRm5MJa4e6+GPONqEamknxH5CVUdz7/i4qGBsXeQuW/ffzx+w3bt0DQyf8XXvhK+Db7w8ijMXQM0S+ERNV9dqVtYm3aMr3N5IQGlUoOF7bFHRr76om1aEoJ/f7G0mIHgGDxB3hh1zoeE7cESpM1lp6xv1d91wu7gg/RP6p6FqjkqiDe9XOhP2IMcuqd/b8rjna399IitAYTUIIIYQQUiPo1DkhhBBCSCXRieHyUUOTEEIIIaSSfsaZ4tWJTp0TQgghhJAaQT2ahBBCCCGVRD2a5aOGJiGEEEJIJVXpYhK/ADp1TgghhBBCagT1aBJCCCGEVBKdOi8fNTQJIYQQQiqJyhuVj06dE0IIIYT85FJSUjB48GBwOBxoaGjA29sbWVlZ390vICAA7dq1g4qKCjgcDlq1aoXc3IpfiY0amoQQQgghlcTjMdV6qymDBw9GcHAwrl27hvPnz+POnTsYO3ZsufsEBASgc+fO6NixIx4/fownT55g4sSJYLMr3nykU+eEEEIIIZUkDWM0Q0NDcfnyZTx58gQNGjQAAGzatAmenp5YvXo1jIyMRO43depUTJ48GbNmzRKss7e3/6HHph5NQgghhBAJweVykZGRIXTjcrlVOmZAQAA0NDQEjUwAaN++PdhsNh49eiRyn4SEBDx69Ah6enpo1qwZ9PX10bp1a9y7d++HHpt6NEkpn/P1xR3hhyRNPivuCBXmoPlR3BEqLJ+RF3eEH1LIyIg7QoWt7Sk9WQHgv/TG4o5QYdI0L0P16SBxR/gh+Q3cxB2h4grCa+2hqnsy0PLly7Fo0SKhdQsWLMDChQsrfcy4uDjo6ekJrZOVlYWWlhbi4uJE7hMdHQ0AWLhwIVavXo06depg//798PDwwOvXr2Fra1uhx6YeTUIIIYSQSmJ4vGq9zZ49G+np6UK32bNni3zsWbNmgcVilXsLCwur1N/F4/EAAOPGjcPIkSNRt25drFu3Dvb29vDz86vwcahHkxBCCCFEQigoKEBBQaFC2/71118YMWJEudtYWVnBwMAACQkJQusLCwuRkpICAwMDkfsZGhoCAJycnITWOzo64v379xXKB1BDkxBCCCGk0mpypvj36OrqQldX97vbNW3aFGlpaXj27Bnq168PALhx4wZ4PB4aNxY9NMbCwgJGRkYIDxcehvDmzRt06dKlwhnp1DkhhBBCSCUxDFOtt5rg6OiIzp07Y8yYMXj8+DHu37+PiRMnYuDAgYIZ5x8/foSDgwMeP34MAGCxWJgxYwY2btyIEydOIDIyEvPnz0dYWBi8vb0r/NjUo0kIIYQQ8pM7dOgQJk6cCA8PD7DZbPTp0wcbN24U3F9QUIDw8HDk5OQI1v3555/Iy8vD1KlTkZKSAnd3d1y7dg3W1tYVflwWQ9dOIt94EJop7gg/JClbWdwRKoxmndccaZp1XiRFWQEgNl1d3BEqTJq+0VQVCsUd4YdI06zzrrU467zf1JhqPd7xdZbVejxxox5NQgghhJBKkoaC7eJEYzQJIYQQQkiNoB5NQgghhJBK4jE8cUeQaNTQJIQQQgipJDp1Xj46dU4IIYQQQmoE9WgSQgghhFQS9WiWjxqahBBCCCGVRFUiy0cNTVIhDMPg9JEduH3tFHKys2Dr4I6hv8+CgZFZuftdv3gMl04dQHpaMswsbDF4zAxY2bmIPP66JVPw6vkDTJq1GvWatAEAvI95g4v+e/EmJBBZmWnQ0TNEm0590LH7oB/Of/XkZjy6eRy52ZmwsKsLr1E+0DWwKHOf6NCnuHXBDx9jgpGRlojhUzfCpUF7oW3+3T4Hz+6eFlpn59YCY2burHC2C+fOwP/kcaSmpsDS0hrjxk+Anb1Dmdvfu3sbBw/sQ0J8HIyMjDFi1Gg0aFh8CbHc3Fzs27MbDwMeIDMzA/r6Bujeoxe6dO0u2CY1JQV+vjvx8uVz5ObkwtjEBP0H/IbmLVqWm/XS+VM4c/JfpKWmwMLSGt6/T4GtvWOZ2z+4exNHDvohMT4OhkbGGDLyd9Rv2ETktjs2r8HVS2cxcsxEdOvVT7D+95EDkJgQJ7Tt4OFj4dV/cLlZAeDK+ZM4538EaakpMLe0xshxU2Fj71Tm9gH3buDYwd1IjI+DgZEJBo8Yj7oNmwLgXxf46IGdePH0IRLiPkFZRQUu7g3w24jx0NLWERxj5eKZeBsTgYy0NKioqsG1Dn8bTS1tHD/ki+tXziE7OxN2jm4Y9cf/YGhkWu7fcPXCCZz3P4T01BSYWdpg+LhpsLFzFtyfn8/FId+NCLj7HwoKCuBWtzFGjZ8BdU0twTZJCXHw27YKIUHPoKikjJbtumDg8PGQkeF/BaSmJOGQ70ZER4Yh/vMHdOreD8PGTBXKsePv4YgOe1Iqn4N7K4ycsR0Mw+Dayc14fPM4cnP477HeI32gU957LOwp7lzww4eYYGSmJWLYnxvh/M17DADiP0bh0r9rER32BDxeEfSNrDFkynpo6hiV+9yVh2EYXPPfjCcl8vYaUbG8H9/y8w6dUjrvrKGiX19dBv6F1l0rfjWV72W/cGwrHlw/idzsTFg51MGA0fOgZ2he5j6RIU/x39m9eB8TiozURIyZvh7ujdpVS57K0GrRAFZ/eUO9ngsUjfTwtM8fiD97XWx5SM2hMZqkQi6e2odr5//FsN9nY/7KvZBXVMTaRZNQkM8tc59H967iX7916DlwDBauPQhTCzusWTQJGWkppba9eu6wyGO8jQqFmroWxk5djL83HkW3vqNw8sBm/Hfh6A/lv3XeF/euHITXyAWYtPhfyCsoYfeKseXmz+fmwMjMHr1GzC/32PZuLTB/y23BbfDEVRXOdff2LezetQODfhuC9Zu2wdLKCj7zZyMtLVXk9qEhwVj1zzJ07NgZGzZtQ5OmzbF0yUK8e1tcMNh313Y8f/YUf82Yha07fNGjlxe2b9uMRw8fCLZZu+YffPz4AfN9FmPz1p1o1qwFVq74G1FRkWVmvX/nBvbu2oL+vw3Hqo27YG5pjSXzpyO9jKxhIa+xbuUSeHT0xOqNu9CoaUus/Hsu3r+NLrXtowd38CYsRKjBVtLAIaOw+4C/4ObZw6vMnF89uHMd+3dvRp9BI7Figy/MLW2wzGdamXnDQ19h48pFaNuhG1Zs9EPDJi2xaulsQd58bh5iot6gz8DhWLHBD9PmLMXnj++xaslMoeM4u9XDnzMXY92Ow5g252/Ef/6Idcvn4ezJQ7h07gRGT5iOJat9oaiohBU+fyK/nNdgwN3/cHD3RngN8sbS9XthZmmLFT5TkV7iPXRg9wY8f3wfU2YuxfzlW5GakoR1y2cJ7ucVFWHV4r9QWFiAhat24vc/5+PO9Ys4fmiXYJvCggKoqWui14ARMLO0EZll6J8bMG/zbcFt6oozYLNl4Nq4EwDg9nlf3L96EL1HLcDERfz3mO8/33+PGZrZo9fwst9jyfHvsX3JEOgZWWLc3L2YuuwUPHr9Djk5hTL3qYjbF3zx4OpB9Bq5ABMW/gs5BSX4rSw/b8GXvD3LyTt3022hW98xf4PFYsGlYccq5S3pvzN7cPvSYQwcMx/Tlx2CvIIStiz9vdzsXG4ujC3sMcB7TrXlqAoZFWVkBIXj9eRF4o5SZTwer1pvPxtqaEqoEydOwNXVFUpKStDW1kb79u2RnZ0NANi9ezccHR2hqKgIBwcHbN26VbDfqFGj4ObmBi6X/4GTn5+PunXrYtiwYZXOwjAMrp07gu79vVGvcRuYWthizJTFSE1JxPNHt8rc7+qZQ2jVsRdaevSAsakVho2fDXkFRdy9flZou/fR4bhy5hC8J/mUOkar9j0xePR0OLjUh56BCZq18UQLjx549vDmD+W/e3k/PHqNg0sDDxiZ2WPg+BXISEtA8LOyf0E71GmFzv2nwLVh6R6WkmTl5MHR0BXclFUqfhWV06dOolPnLmjfsTPMzMzxx8QpUFBQwLWrV0Ruf/bMKdSr3xBeffvD1MwcQ4aNgLW1Dc6fOyPYJjQ0BO08OsDVzR36+gbo3KUrLK2s8Sa8+EoZYaEh6Na9J+zsHWBgaIgBgwZDRUUFkRFvysx67tQxtO/cDe06eMLUzALjJv4FBUVFXL96UeT2F86eQN36jdCrzyCYmFlg0FBvWFrb4dL5U0LbJSclYvf2jZgyY56gh+1bSkrK0NTSFtwUFZXKzCl4/NP/wqNTd7Tt0BUmZpYYPWEG5BUUcfPaeZHbXzp7HHXqN0aPPr/BxNQCA4aOgaW1Ha6cPwkAUFZRxby/16NpSw8YmZjBzsEFI3+fhujIcCSV6HHt2msA7BxcoKtnAHtHV/TsNwRvwl7j4plj8BowDA2btISZpQ3GT/VBWkoSnj68U+bfcPH0EbTt1ANt2neDiZklvP/4HxQUFHD7y9+Qk52FW9fOYcjoyXB2bwArGweMmzIXb0JfISLsNQAg6MVjfIh9iwnTFsLCyg51GjRFvyFjcO3CSRQWFAAAdPUNMXzsVLRq5wllZVWRWZRVNaCmoSu4RbwOgJy8ItwadQLDMLh3eT/a9RwH5/oeMDSzR//fK/Aec2+FTv2mwKWc99jl4xtg794KnoOmw9jCCdr6ZnCq3w6q6tpl7vM9DMPg/uX9aNejOO+Acfy8IeXktf+aV0Sv61clnyM1DV2EPLsBK8dG0NYrv+f6R7LfvHgQnbzGwK1hWxib22HYxKVIT01E4JMbZe7nXLclug+cBPdGHtWSo6oSr9zBmwXrEX/mP3FHqTKGx1Tr7WdDDU0J9PnzZwwaNAijRo1CaGgobt26BS8vLzAMg0OHDsHHxwdLly5FaGgoli1bhvnz52Pfvn0AgI0bNyI7OxuzZvF7NObOnYu0tDRs3ry50nkS4z8iPTUZzm6NBOuUVVRhbeeCyPBXIvcpLCjA26gwOLsVn9Jls9lwcm+EyPAgwTouNw871s7DkLH/g7qm6N6sb+XkZEFVlVPh/CmJH5CZlgRb56aCdUrKajCzdsO7iJcVPk5ZokKfYOH4Flg53RMn/RYhOzOtQvsVFBQgMvIN3OvUE6xjs9moU6cewsNCRO4TFhaCOnXrCa2rW78BwsJCBcuOjk549CgAyUlJYBgGQYEv8enjB9StV1+wjYOjE+7euY3MzAzweDzcuX0T+fkFcHVzLzNrVOQbuNUpPgabzYZbnfp4ExYscp83YcFC2wNAnXoNEV5iex6Ph41rlqJnn4EwMy/7smunjh/G8IHdMX2SN06fPIKiovIv3VdYUIDoyDdwrdNAKK9rnQaIKDPva7iU2B4A3Os1xpsvDTZRcnKywGKxoKyqJvL+rMwM3Lt1FZY29khLTYFrnYaC+/jvISdBg1DU3xATGQ4X9+J92Gw2XOo0REQ4f5+YyDAUFRYKbWNsagEdXQNEhPHfmxFhr2Bmbi10Kt2tbhPk5mTjw/vSvcsV9fTWSbg39YS8ojL/PZaeBFsX4feYqbUb3lfhPcbj8RD28jZ0DCyw+58xWPxHC2xeMADBT6vWOPma16ZEXkVlNZhaueFdZOXzfiszPQlhgXfQsHWfajtmcsJHZKQlwcGteAiKkrIaLGxc8fZNYLU9DiHVhcZoSqDPnz+jsLAQXl5eMDfnj7lxdXUFACxYsABr1qyBlxf/1KGlpSVCQkKwY8cODB8+HKqqqjh48CBat24NNTU1rF+/Hjdv3gSHU/GG2bfS05IBABwN4R4EjroW0lOTRe6TmZkGHq8IHA0tofXq6lqI+/BWsHzEdw2sHdxQr3GbCmWJCAvEk3tX8ee8DRXOn5mWBABQUxduyKqqawvuqywH9xZwbdgeWromSE54j0tH18N35ThMXHQYbHb517POyEgHj8eDpqam0HoNDU18iI0VuU9aaio0NDRKbZ+WWnwqddz4Cdi8cT1GDBsEGRkZsFhsTJoyFS6uxdcpnjl7Plau+Bu/DegDGRkZKCgoYM78BTAyMhb5uJkZ6eDxiqChIZxVXUMTH2Pfl5E1Beoapf+2kllPnzgMGRkZdO1R9hexZw8vWFnbQVWNg/DQ1zi0dydSU5IxcszEMvfJ+JJX/dvXn4YWPn14V2ZeUX9fuoihHgB/bOThPdvQrFV7KCurCN13aM9WXDnvDy43D7b2zhgwZDRWLJxR6vlQ1yjnPZTBfw+VbCB++zekpSZDVlYOKt80dDklcqelpZR+H35ZLvl/8SNio4IQ9yECfccs4Wf98j5S5XzzHuNoIzO98u+x7Ixk5Ofl4Nb53ejUdzI8B05DeOA9HNgwBWPn7IWVY8PvH0SErK95RXwmZFUh77ee3z0DBUVlODfoUG3HzBB8ngl/HqupayMjTfRridQshgq2l4samhLI3d0dHh4ecHV1RadOndCxY0f07dsX8vLyiIqKgre3N8aMGSPYvrCwEOrqxadrmzZtiunTp2PJkiWYOXMmWrRoUeZjcblcwWn2r+5eP4dDu4rHGf45b331/XElvHh8G6GvnmLR2kMV2v7Du0hsXPYXegwYA5e6oieUAMDz++dw0nehYHnUjO1VjVqmOk09Bf82NLODoZk9VkzthKiQx0K9O7Xp3NkzCA8LxfwFi6Grp4/g10HYvnUTtLS0Bb2hhw7sRXZWNv5e9g84HHU8DHiAlcv/xoqV62BhWXbPYnWKigjHhTMnsWrjLrBYrDK369F7gODfFpbWkJWVxY7NazBkxFjIycnXRtRSCgsLsX6FDxgAoydML3W/rr4hGDCQk5dH5JtQnDiyp/ZD1qCL/64Fi8XCzmUjAQAjp9fMe+zrbF7neu3QsstwAICRuSPeRbzEw+tHK9zQfHH/HE7tWShYHvFXzX0mlPT0jj/qNOsGOfnKjyd9cvcCjuxcLFgeP3tLdUQj1ehnPN1dnaihKYFkZGRw7do1PHjwAFevXsWmTZswd+5cnDt3DgCwa9cuNG7cuNQ+X/F4PNy/fx8yMjKIjCx7cgcALF++HIsWCQ/GHjb2LyxaVzw5p7AgHwCQkZYMDa3iHoCM9BSYWtqJPK6amgbYbJlSE3/S01PA0eT/Eg8NeorEuA+YMLit0DabV/4Pdo51MGtp8cztj7HRWOXzB9p07I0e/UeX+zc51WsHM+vi3rvCQn7+zPQkcDR1Beuz0pNhZF727O7K0NYzhYqaJpLi33+3ocnhqIPNZiM1VXhySlpaKjS1NEXuo6GpibS0tFLba3zp9eJyuTiwzw9z5i1Ew0b814ilpRWio6Jwyv846tSth8+fP+H8uTPYvG0XzM0t+NtYWSM4+BUunD+DCZP+LPW4ahx1sNkypSYppZd47NJZtUpNvCmZNTQ4COnpqRg3or/gfh6vCPt8t+L8mRPYvkf0hC9beycUFRUhIT4Oxiaiqx5wvuT9tjcyPS0FGpqix/ZpaGqJ/Pu+7RXlNzLnIzEhDj7LNpbqzQSAlm06we3LafLU1GQsnDlBcDzNEu+h9LQUmFuV8R7i8N9D6all/w0amtooLCxAdlamUK9mRoncGhpaiH4TUuoYX//mH5Wfl4MPMcFo22Ms6rfsBaD4PZaV8c17LCMZRmaVf48pq2mALSMLPWNrofV6xlZ4G/68wsdxqtcOpjbFnwlFXz7TstKTwNEQ/kwwrKbPhJjwp0j8HINBE9ZU6TiuDdrAwtZVsPz18zgzPRnqJZ7rzPRkmFjYV+mxCKkJNEZTQrFYLDRv3hyLFi3CixcvIC8vj/v378PIyAjR0dGwsbERulmW6IVatWoVwsLCcPv2bVy+fBl79pTdmzJ79mykp6cL3Ub+MQv6hqaCm5GpFdQ1tRESVFzaJDcnC1FvXsPG3lXkcWXl5GBh7YCQoMeCdTweD6FBT2Bjz//A79pnOBavP4JF6w4JbgAwaNQ0eE9eINjv4/sorJz3O5q37Yo+QyZ897lTVFKBjoG54KZvbAM1DR1EBj8UbJOXk4X3UUEwt63z3eP9iLTkOORkpQl9eZVFTk4ONjZ2CAp8IVjH4/EQ+PIF7B1El0hxcHBC4MsXQutevngOBwd+iaGiokIUFhaW6iFky8gIZjNy8/g92Oxvt2Gzy6wHJycnB2sbO7x6+Uwoa9DL57BzcBa5j52DM4ICnwmtC3rxFPZftm/driPWbvbDmk27BTctbR308BqI+UvKnrn/NjoSbDYb6uqiG+MA//VnZWOHV4HCeV8HPoNtmXld8PrlU6F1r148gZ1DcTmur43Mz58+YP7S9VDjiJ74paSsDAMjExgYmUBXVx8AoKrGwasSx8/JyUbUmxDYOpQu9/X1b7C0sUdwUPE+PB4PwYFPYWvP38fSxgEysrIIDize5tOHd0hKjIOtA/+9aevgivfvooQa3a9ePoaSsgqMzX689zro8RXwigrQovNw4feYeun3WGxUEMyq8B6TlZWHiZULEj/HCK1P+vz2h0obKSipQEffXHDTE5U3Nwux0UEwt6l83pKe3PKHsaVzlX/MKiqpQNfATHAzMLEGR0MH4a8eCbbJzcnC28hXsLATPcaa1CyaDFQ+6tGUQI8ePcL169fRsWNH6Onp4dGjR0hMTISjoyMWLVqEyZMnQ11dHZ07dwaXy8XTp0+RmpqKadOm4cWLF/Dx8cGJEyfQvHlzrF27FlOmTEHr1q1hZWVV6rEUFBSgoCB8WkdePlNomcVioUP3QTh33Bf6RqbQ0TPGqcPboKmlKzS2cuX88ajXpA3ad+Wf6uzYczB2b1gICxsnWNk64+q5w+Dm5aKFB7+eo7qmjsgJQNo6BtDV548V/PAuEit9xsOlThN06jkY6an88Ukstgw45TQ0vs3fsvMwXD+9AzoG5tDSNcGVExvB0dCDc/3iGZg7lo2ES4P2aN6RX5+Rm5eNpLji8YcpiR/x8W0olFXVoaljBG5eNq75b4Vrw45Q09BBcvx7XDiyBtr6ZrB3K3u4Qkm9evfBurUrYWNrBzs7e5w5cwp53Dy078AvGbN29T/Q1tbB8JH8+ns9evbG7Jl/4ZT/cTRo2Bh3b99CZMQbTPzSC6msrAIXVzfs8dsFBQUF6Orp4fWrINy8fg3eY34HAJiYmsLQyAhbNm3AqNFjocbh4GHAfbx88Rw+C5eUmbV77/7YtHY5rG0dYGvngPNnToCbl4t2HboAADauWQotbV0MGTEWANC1R1/4zJqMs/5HUa9hE9y/cwNRkeH4fRL/VLMaR71UQ01GRhaamlqCnsrw0NeICA+Fi1tdKCop401YMPbs2oxWbTtAVU30BJyvuvYaiK3rlsLa1gHWdo64eOYYuHm5aNO+KwBg85ol0NLWxW8j+M9Llx79sGjWRJzzP4J6DZvhwZ3/EBUZhjET/weA38hct3weYqLe4H8+/4DH4yHty/hKVVUOZOXkEBEejKg3YXBwdoOKqhriP3/E0YO7oW9ojDbtPXHq6D4YGptCW88Yxw/ugoaWDho0aSXIvHTuRDRo2hqduvHriHr2GoTt65bAysYB1nbOuHTmX+Tl5aF1+278/28VVbTp0B0HfTdCRY0DJWUV7NuxBrYOLoIGrFvdRjAxtcDWtYvx28gJSEtNxvGDO9Ghax+hoQdvo/kVB/LycpGRnoa30W8gKysHk28ao09unYRTfQ+oqGkI1rFYLLToPAw3Tu+Ajr45NPVMcFXEe2znl/dYsxLvseR44ffYp3ehUFJRFzQkW3uOwuHN02Dp0ADWjo3wJugeQl/cwti5e8v9/y8Pi8VC887DcONM8WfC17xOJfLuWj4Szg3ao1mH8vMqq6hDo0TDNy83C68eX0HX32ZUOmN52dt6DsFl/53QNTSDtp4xLvy7BeqaunBvWFwXc+Pi0XBv5IHWnQd9yZ6DxBKfZ8kJH/HhbRiUVdWhpWNY7Tm/R0ZFGSo2xWcklC1NwHF3QH5KOvJiP9d6nqrg0RjNclFDUwJxOBzcuXMH69evR0ZGBszNzbFmzRp06cL/QldWVsaqVaswY8YMqKiowNXVFX/++Sfy8vIwZMgQjBgxAt278xtzY8eOxYULFzB06FDcuXNH6BT7j/DsPRz5eXnYu3UZcrIzYedYB9N8NgqNPUqI+4CsjDTBcuMWHZGZnorTR7YjPTUZZpZ2mLZgE9Q1Kl6W5OmD68hMT0XA7UsIuH1JsF5b1xCrd52r8HHadPNGPjcXJ3wXIC8nExZ29TB65k6h/MnxscjOLD51+iE6GNuXjhAsnzv4DwCgfsteGPj7MrDZMvj8/g2e3j2DvOwMcDT1YOfaHJ36TYJsBccOtmzdBukZaTh0YB9SU1NhZWWNRYuXCSYIJSYmgMUu7nl0dHLG9P/NxsH9e7F/7x4YGRtj7vyFMLcobgz8b+Zc7Nvri9WrliMrMxO6evoYOmwkunjyGyeysrJYuGgp9u7xxZJF85GbmwdDIyP8OW2GUOH3bzVv1Q7p6Wn496Af0lJTYGllg3mLVwlOvyYlJoDFKj5J4uDkgj9nzMeRA744tG8XDI1N8L95S2FmUfoHT1nk5ORx784NHD28F4UF+dDTN0T3Xv3QvXf/7+7brJUHMtLTcOzgbn6BeSsbzF68RpA3OTEebHZxXntHV0yasQBHD+zCv/t3wsDIBDPmLhfkTUlOxNNH9wAAMyePFHosn2Ub4exWDwoKingccBvHD/uCm5cHDS1t1KnXGF4DFkNTWweFBQXYuWklcrKzYOfkhlmL1kG+xGswPu4jMjPSBctNW7ZHRnoqThzajbTUZJhb2WLWonVCE4SGjp4CNouF9ctno7CgAG71GmPk+OIGDltGBtN9VsNv6yosmD4GCopKaNWuC/oNLh7nDQBzpgwX/DsmMgwPbl+Fjp4BNvoWl6NK/BSDt2+ew3vm7lLPd+sv77GTfsXvsVH/E36PpSSUfo/tXDZCsHz+UPF7rP+4ZQAAl4bt0XvUAtw8uwtn9y+DrqEFhkxZD0t74YoGP6p1V35e/xJ5R8745jPh27wxwdhVIu+Fw/y89VoU5wWAwICLABjUadq1ShnL0r7nSHC5uTiyYzFyczJh7VAXf8zZJpQ9Kf4DsjKKs7+LCsbGRcUF4/33888aNG7dA0Mn/F0jOcujXt8FTa8fECw7rebX94zd748g79m1nofUHBZD104i33gQmvn9jSRIUrayuCNUmIPmR3FHqLB8RjwTbSqrkKncjyhxKJKirAAQm17x2rDiJk3faKoK5ZfpkjT5Ddy+v5GE6FoQ/v2NqknHoS++v9EPuHqgbrUeT9yoR5MQQgghpJKYn/BqPtWJJgMRQgghhJAaQT2ahBBCCCGV9DPOFK9O1NAkhBBCCKkkujJQ+ejUOSGEEEIIqRHUo0kIIYQQUkk8OnVeLmpoEkIIIYRUEs06Lx+dOieEEEIIITWCejQJIYQQQiqJZp2XjxqahBBCCCGVRLPOy0enzgkhhBBCSI2gHk1CCCGEkEqiU+flo4YmIYQQQkgl0azz8tGpc0IIIYQQUjMYQmpBXl4es2DBAiYvL0/cUb5LmrIyjHTllaasDCNdeSlrzZGmvNKUlWGkLy/5cSyGYWhwAalxGRkZUFdXR3p6OjgcjrjjlEuasgLSlVeasgLSlZey1hxpyitNWQHpy0t+HJ06J4QQQgghNYIamoQQQgghpEZQQ5MQQgghhNQIamiSWqGgoIAFCxZAQUFB3FG+S5qyAtKVV5qyAtKVl7LWHGnKK01ZAenLS34cTQYihBBCCCE1gno0CSGEEEJIjaCGJiGEEEIIqRHU0CSEEEIIITWCGpqEEEIIIaRGUEOTEEIIIYTUCFlxByA/v8jISERFRaFVq1ZQUlICwzBgsVjijiXk/fv3ePfuHXJycqCrqwtnZ2eJLrfx6dMn7NixA5GRkTA0NMTo0aPh4OAg7liEEFJpkvjdQKqOejRJjUlOTkb79u1hZ2cHT09PfP78GQDg7e2Nv/76S8zpgLdv32LmzJkwNzeHpaUlWrdujS5duqBBgwZQV1dHhw4dcPz4cfB4PHFHhbKyMhITEwEAISEhcHJywuHDh1FQUIALFy6gfv36CAoKEnPK0hiGQUxMDAoLCwEA+fn5OHr0KPbv34+kpCQxp/sx2dnZuHPnjrhjVMjz58/RrVs3ccf46TAMg0uXLqFv377ijiK1RowYgezs7FLr3759i1atWokhEalp1NAkNWbq1KmQlZXF+/fvoaysLFg/YMAAXL58WYzJgMmTJ8Pd3R0xMTH4+++/ERISgvT0dOTn5yMuLg4XL15EixYt4OPjAzc3Nzx58kSsefPy8vC15O2cOXPQqlUrhIaG4tixYwgODkaPHj0wd+5csWb8Vnh4OCwtLWFjYwNHR0fExMSgWbNm8Pb2xvjx4+Ho6IiIiAhxx6ywyMhItG3bVtwxBK5cuYLp06djzpw5iI6OBgCEhYWhV69eaNiwoUT8QPpq5cqVyM3NFSzfv38fXC5XsJyZmYk//vhDHNEqJCYmBvPnz4eZmRl69+6NvLw8cUcCIJ3Pa2BgINzc3BAQECBYt2/fPri7u0NHR0eMyUiNYQipIfr6+szLly8ZhmEYVVVVJioqimEYhomKimJUVFTEGY2ZNWsWk5SUVKFtL126xJw8ebKGE5WPxWIx8fHxDMMwjKmpKXPnzh2h+58/f84YGhqKI1qZevbsyfTo0YMJCgpi/vzzT8bR0ZHp2bMnk5+fz+Tl5THdu3dnhgwZIu6YFfby5UuGzWaLOwbDMAyze/duhsViMdra2gybzWZ0dXWZAwcOMBoaGsy4ceOYkJAQcUcUwmazBa9fhmEYNTU1wecBwzBMXFycxDy3X+Xl5TEHDx5k2rZty8jJyTFsNptZu3Ytk56eLu5oAtL4vObn5zPTp09n5OXlmdmzZzP9+vVjVFVVmZ07d4o7Gqkh1NAkNUZVVZV58+aN4N9fPwCfPHnCaGlpiTOa1GGz2UxCQgLDMAxjbm7OBAYGCt0fHR3NKCoqiiNamXR1dZkXL14wDMMwWVlZDIvFYu7evSu4//79+4yZmZmY0pWmqalZ7o3D4UjMl7arqyuzcuVKhmEY5sSJEwyLxWKaNm3KxMbGijmZaCV/KDGM8OcBw0hWg+jp06fM+PHjGQ0NDaZBgwbMhg0bmLi4OEZWVpYJDg4Wdzwh0vS8fsvHx4dhsViMnJwc8+DBA3HHITWIJgORGtOyZUvs378fS5YsAQCwWCzweDysXLlSok5B5ubmgmEYwen9d+/e4dSpU3B0dESnTp3EnI6PYRjY2dmBxWIhKysLQUFBcHNzE9wfGRkJAwMDMSYsLSsrC1paWgAAFRUVqKiowNDQUHC/qakp4uPjxRWvFC6Xi/Hjx8PV1VXk/e/evcOiRYtqOZVoUVFR6NevHwDAy8sLsrKyWLVqFUxMTMScTPo1btwYkyZNwsOHD2Fvby/uOD+dgoICzJo1C1u2bMHs2bNx7949eHl5wdfXF56enuKOR2oANTRJjVm5ciU8PDzw9OlT5Ofn43//+x+Cg4ORkpKC+/fvizueQM+ePeHl5YXff/8daWlpaNy4MeTk5JCUlIS1a9di/Pjx4o6IPXv2CC3b2NgILT98+BC9e/euzUjfZWRkhPfv38PMzAwA//Wgp6cnuD8xMRGampriildKnTp1YGpqiuHDh4u8PzAwUGIamrm5uYIfRiwWCwoKCkKNeFJ5Hh4e8PX1RUJCAoYOHYpOnTrRTOhq1KBBA+Tk5ODWrVto0qQJGIbBypUr4eXlhVGjRmHr1q3ijkiqGTU0SY1xcXHBmzdvsHnzZqipqSErKwteXl6YMGGCRH0pPn/+HOvWrQMAnDhxAvr6+njx4gVOnjwJHx8fiWholtX4+Wr+/Pm1lKTi2rdvj7CwMLRo0QIASj2PV69eRb169cQRTaSuXbsiLS2tzPu1tLQwbNiw2gv0Hbt374aqqioAoLCwEHv37i01mWLy5MniiCZSeXkzMzPFGU3IlStXEBsbiz179mD8+PHIzc3FgAEDAEAiG5zS8rx+1aBBA2zcuBEqKioA+M/pzJkz0bFjRwwdOlTM6UhNYDHMl6mshPyilJWVERYWBjMzM/Tv3x/Ozs5YsGABYmNjYW9vj5ycHHFHFBIUFIQ3b95AXl4ednZ2Uls/MyYmBoqKihL1o0NaWFhYfLfRw2KxBLPRxa0ieQH+a0LSXLt2DXv27MGpU6dgamqKvn37om/fvhLxI0man1dRuFyuRNcvJpVDDU1SY8qq68hisaCoqAgzMzOJ+FBxc3PD6NGj0bt3b7i4uODy5cto2rQpnj17hq5duyIuLk7cEQEAjx8/hre3N0JCQgSljlgsFho2bIh9+/YJxpOlpKQIxkYSQqpHamoqDh48CD8/PwQFBaGoqEjckaTWgQMHsH37dsTExCAgIADm5uZYv349LC0t0bNnT3HHI9WM6miSGlOnTh3UrVsXdevWRZ06dQTLderUgYODA9TV1TF8+HCx16Tz8fHB9OnTYWFhgUaNGqFp06YA+Kd269atK9ZsX4WEhMDDwwNKSko4ePAgnj9/jufPn+PAgQMoKipC06ZN8enTJ2zdulWixjgVFhZi1apVqFevHlRVVaGlpYUmTZpgx44dkLbfuKmpqdi/f7+4Y5AaZmZmhuTkZMHy5s2bkZGRAU1NTUyaNAkvXrwQe13dkhiGQUREBIKDgwUXRpBk27Ztw7Rp0+Dp6Ym0tDRBg11DQwPr168XbzhSI6hHk9SYM2fOYObMmZgxYwYaNWoEgN8rt2bNGixYsACFhYWYNWsWBgwYgNWrV4s1a1xcHD5//gx3d3ew2WxBVg6HIxGnpvv374/CwkKcPHmy1KkyhmHg5eWFkJAQxMbG4tKlS2jdurWYkhbLzc1Fhw4dEBAQgPbt28PR0REAEBoaiv/++w9dunTB2bNnERMTg7t372LEiBHiDfwdgYGBqFevnkT0ZFW0wStJY0p5PB727t0Lf39/vH37FiwWC5aWlujbty+GDh0qMeMf2Ww24uLiBBPXOBwOXr58CSsrKzEnKy0mJgY9evRASEgIAMDY2BgnT55Ew4YNxZysbE5OTli2bBl69eoFNTU1BAYGwsrKCq9fv0abNm2k7ophpALEUlSJ/BIaNmzIXL58udT6y5cvMw0bNmQYhmFOnTrFWFlZ1Xa0Mr1//555//69uGOUoqOjwzx58qTM+x8/fsywWCyRz7e4+Pj4MGZmZqVqfjIMv/i5mZkZM3nyZMbY2JjZuHGjGBIKS09PL/d29+5dialJqKGhUeZNU1OTkZeXl5isDMMwPB6P6dq1K8NisZg6deowAwcOZAYMGMC4ubkxLBaL6dmzp7gjCnyvNqUk6dOnD+Pg4MAcPnyY8ff3Z5o1a8bUq1dP3LHKpaioyLx9+5ZhGOHn9s2bNxJXC5hUD2pokhqjqKjIhIaGllofGhoq+ECJiYlhlJSUajuakIKCAmbevHmCgtxsNpvhcDjM3Llzmfz8fLFm+0pBQaHcBvD79+8ZeXn5Wkz0fXZ2dsyJEyfKvP/YsWMMi8ViRo0aVYupysZisQT//6JuX++XZJ8+fWLGjRvHyMnJMZ06dRJ3HAE/Pz9GTU2NuXHjRqn7rl+/zqipqTH79u0TQ7LSpKmhqa+vL3QRhE+fPjFsNpvJysoSY6ryOTo6MqdPn2YYRvi53bhxI1O3bl1xRiM1hMobkRrj4OCAFStWYOfOnZCXlwfAL9a7YsUKwenojx8/Ql9fX5wxMWnSJPj7+2PlypWC8ZkBAQFYuHAhkpOTsW3bNrHmAwBzc3M8fvwYpqamIu9/9OgRzM3NazlV+d69eycYMiFKkyZNwGKx4OvrW4upyqampoa5c+eicePGIu+PiIjAuHHjajlVxWRmZuKff/7Bhg0b4OzsjCtXrkjURRGOHDmCOXPmiMzUrl07zJo1C4cOHZKYU/3SUjoqISEBtra2gmVDQ0MoKSkhISEBlpaWYkxWtmnTpmHChAnIy8sDwzB4/Pgxjhw5guXLl2P37t3ijkdqgrhbuuTndf/+fUZbW5vR1dVlPDw8GA8PD0ZPT4/R1tZmAgICGIZhmP379wsupScuHA6HuXjxYqn1Fy5cYDgcjhgSlfb1NPSrV69K3RcUFMSYm5sz8+fPF0Oysunq6jJPnz4t8/7Hjx8zOjo6tZiofG3atGH++eefMu9/+fIlw2KxajHR9+Xn5zNr1qxhtLW1GTs7O+b48ePijiSSvr6+4HKkojx//pzR19evvUDlMDc3ZywsLMq9WVpaijsmwzD8S9NGRkYKDfFQU1NjAgMDhdZJmoMHDzI2NjYMi8ViWCwWY2JiwuzevVvcsUgNoclApEZlZmbi0KFDePPmDQDA3t4ev/32G9TU1MScrJienh5u374tmKzyVWhoKFq1aoXExEQxJSuWl5cHDw8PPHr0CB06dICjoyMYhhFMrGnUqBGuX78OJSUlcUcVGDBggGACkyh9+vSBjIwMjh07VsvJRNu1axdyc3PL7KmKj4/H9u3bsWDBglpOVhrDMNi/fz98fHxQWFiIBQsWwNvbGzIyMuKOJpK8vDzevXtXZs3UT58+wdLSElwut5aTSTc2my1ycuDXdV//LQkT2L4qecnfnJwcvH79Gvfv34eTk5PEXPKXVC9qaJIaFxISgvfv3yM/P19ofY8ePcSUSNjixYsRFhaGPXv2COp6crlceHt7w9bWViIaFgCQn5+PdevW4ciRI4KGu62tLQYNGoSpU6dKRE3SkkJCQtC4cWM4Oztj2rRpcHBwEDSO161bh5CQEDx8+BDOzs7ijip1XF1dER0djUmTJuHPP/8UXI7yWxwOp5aTiSYjI4O4uDjo6uqKvD8+Ph5GRkYS0yASNUPeysoKffr0kagZ8rdv367QdpJQheKrjh07Cl3y18HBQeIu+UuqFzU0SY2Jjo5G79698erVK7BYLKFf2gDE+qXi5eUltPzff/9BQUEB7u7uAPilbPLz8+Hh4QF/f39xRPwpPHz4EN7e3ggNDRXqZXFwcMDu3bvRrFkzMSeUTl9LcAGiL4soaT1ZbDYbXbp0KfPHEJfLxeXLlyUiL8Mw6NatGy5dugR3d3ehH0ivXr1Cjx49cPr0aXHHBMD/DF29ejXOnj0r+LxasGCBRJ3Z+JaOjg5u374NZ2dn7N69G5s2bRK65G9oaKi4I5JqRpOBSI2ZMmUKLC0tcf36dVhaWuLRo0dISUnBX3/9Jfa6merq6kLLffr0EVoua9KNOB09elToC+X3338Xd6TvatKkCYKDg/Hy5UuhXlhJKYT/rcLCQqFe46+X+Rw5ciTGjh0rMT1ZN2/eFHeEHzJ8+PDvbiMpE4H27t2Lu3fv4vr166UmL924cQO9evXC/v37JSLvsmXLsHDhQrRv3x5KSkrYsGEDEhIS4OfnJ+5oZcrJyREMnbp69Sq8vLzAZrPRpEkTvHv3TszpSI2o9VGh5Jehra0tqKHI4XCYsLAwhmH45Uzq1KkjzmhSZ+vWrQyLxWLs7OwYd3d3hsViMdOnTxd3rO9KT09nrl69ypw/f55JSEgQd5xy5eTkMM2bN2fYbDbTsWNHZsqUKcyUKVOYjh07Mmw2m+natStTVFTEREZGMnv27BFr1u/V/JTUSSDSoEOHDszy5cvLvH/p0qVMx44dazFR2WxsbJjt27cLlq9du8bIy8szRUVFYkxVPldXV2bDhg3M+/fvGQ6Hwzx48IBhGIZ5+vSpxEwII9WLGpqkxmhoaDDR0dEMwzCMlZWVoIZeZGSk2Gtnfk96ejqzdetWpn79+uKOwjAMwzg5OTELFy4ULB84cIBRVlYWY6Lve/HiBWNoaCiYWcrhcCSqoPy3pKnA/Pdqfn69SZKYmBhm586dzObNm5nXr1+LO06ZpGmGvLy8fKn6ugoKCkxsbKyYEn3f8ePHGTk5OYbNZjMdOnQQrF+2bBnTuXNnMSYjNYVOnZMa4+LigsDAQFhaWqJx48ZYuXIl5OXlsXPnTom8nBvAPyXp5+cHf39/qKuro3fv3uKOBIA/3rXk6cfffvsN3t7e+Pz5c5kzecVt5syZsLS0xMmTJ6GoqIglS5Zg4sSJiIiIEHc0kf7991+sXbsWbm5upe5zd3fH6tWrMWDAAIwcORKTJk0SQ8JiJU+dMwwDT09P7N69G8bGxmJMVbabN2+iW7duyM3NBQDIysrCz88PQ4YMEXOy0lJSUsqt7auvr4/U1NRaTFS2wsJCKCoqCq2Tk5NDQUGBmBJ9X9++fdGiRQvBJX+/8vDwkJjPW1K9aDIQqTFXrlxBdnY2vLy8EBkZiW7duuHNmzfQ1tbG0aNH0a5dO3FHBMAvGr93717s2bMHaWlpSE1NxeHDh9G/f3+JGZPHZrMRHx8vNGu35HWCJZGOjg6uXr2KevXqAQDS0tKgpaWFtLQ0iZkNXZKioiIiIiLKHJ8bGxsLCwsLiZiw8i1Jfy20aNECOjo62LZtGxQVFTFv3jycOnUKnz59Ene0UqRphryoSVbnzp1Du3btoKKiIlhHExqJOFGPJqkxJWui2djYICwsDCkpKdDU1JSIBtzJkyfh6+uLO3fuoEuXLlizZg26dOkCFRUVuLq6SkTGkubPny9UxiY/Px9Lly4Vmti0du1acUQTKSUlBSYmJoJlDQ0NqKioIDk5WSIbmhwOBwkJCWU2NOPi4qClpVXLqX4Or1+/xoMHDwS976tWrcKOHTuQnJwMbW1tMacTxjAMRowYUe4MeUkhapKVJPYSk18bNTRJrZKkL+oBAwZg5syZOHr0qEQVkBelVatWCA8PF1rXrFkzREdHC5YlrWEM8GtpxsXFCZaZL2ViMjMzBetEnaoWh7Zt22LZsmVlFphfsWKFRF3WUZpkZGQIXcJRWVkZSkpKSE9Pl7iGpjTNkN+zZ4+4IxDyXXTqnPyyxo0bh6NHj8LZ2RlDhw7FgAEDoKmpCTk5OQQGBsLJyUncEaXa16uWiPqIKVlXVRJOQQLSXWBeTU0NQUFBEnt9azabjX379gn1vg8aNAjr168XGg8pKRdxIIRUH2pokl9abm4ujh07Bj8/Pzx69AidOnXChQsX8PLlS7i4uIg7npCMjAw8evQI+fn5aNSoUZljyCRFRWvimZub13CSipOWAvPfXnBA1Lg8QHLG5pUsMF8WSfrRQQipPtTQJOSLiIgI7NmzB/v27UNWVha6du2Kvn37lvpSF4eXL1/C09NTcBpaTU0Nx44dk/prA79+/VriGvQAJL7A/MiRIyu0nTSdWs3JySnzUpqEEOlFDU1CvsHj8XDhwgX4+vri0qVLEjH4v1OnTsjKysLq1asFpYJevXolsaWCypOZmYkjR45g9+7dePbsmUT1Yklbr/HPgMvlYsuWLVi5cqXQeF5CyM+BGpqElCMhIQF6enrijiF1pYJEuXPnDnx9fXHy5EkYGRnBy8sLffr0QcOGDcUdDcDP22ssCbhcLhYuXIhr165BXl4e//vf/9CrVy/4+flh3rx5kJGRwcSJEzFz5kxxRyWEVDNqaJJf0sOHD9GkSZMKbZuTk4OYmBixTgJhs9mIi4sTavRK+gQQgF8SaO/evfD19UVGRgb69++P7du3S+Rkq5+p11jSzJw5Ezt27ED79u3x4MEDJCYmYuTIkXj48CHmzJmDfv36QUZGRtwxCSE1gMobkV/S0KFDYWVlhdGjR8PT07PUJAqAPwv54MGD2LNnD/755x+xzzaWplJBANC9e3fcuXMHXbt2xfr169G5c2fIyMhg+/bt4o4m0rNnz4R6jf38/KClpYWMjAyp6TWWVMePH8f+/fvRo0cPvH79Gm5ubigsLERgYKBEluUihFQf6tEkv6SCggJs27YNW7ZsQXR0NOzs7GBkZARFRUWkpqYiLCwMWVlZ6N27N+bMmQNXV1ex5pW2UkEA/zKDkydPxvjx42FraytYL6nlo6S111gayMvLIyYmRnCJTCUlJTx+/Fjs7ytCSM2jHk3yS5KTk8PkyZMxefJkPH36FPfu3cO7d++Qm5sLd3d3TJ06FW3btpWYAvMxMTHijvDD7t27B19fX9SvXx+Ojo4YOnQoBg4cKO5Y5ZK2XmNpUVRUBHl5ecGyrKwsVFVVxZiIEFJbqEeT/PLev38PXV1dKCkpiTvKTyk7OxtHjx6Fn58fHj9+jKKiIqxduxajRo2SqCsySWOvsbT49prckl73kxBSfaihSX5pPB4PioqKCA4OFjq9S2pGeHg4fH19ceDAAaSlpaFDhw44e/asuGMBkM4C89LiZ6z7SQipGGpokl+es7MzfH19KzwLXdwsLS1hY2ODa9euCda1b98e0dHRQtc+l2RFRUU4d+4c/Pz8JKahWRGSWmCeEEIk1fevC0bIT27FihWYMWMGXr9+Le4oFTJ8+HD06tVLaF3v3r0xfPhw8QSqBBkZGfTq1UsqGpmZmZnYuXMnGjVqBHd3d3HHIYQQqUI9muSXp6mpiZycHBQWFkJeXr7UWM2UlBQxJRPGMAxiY2Olcjzp/v37oa6ujp49ewrWnTlzBhkZGRg6dKgYk5VN0gvME0KINKCGJvnl7du3r9z7JaWnUJrHk7LZbDg4OCAkJESwzsHBARERERI1uUaaCswTQog0oIYmIVJE2saTSpOSBeYHDx4sKDAvqXU/CSFEGtAYTUIAREVFYd68eRg0aBASEhIAAJcuXUJwcLCYkwmTtvGkBQUFGDVqlFTUAb106RK8vb2xaNEidO3alS6JSAgh1YAamuSXd/v2bbi6uuLRo0fw9/dHVlYWACAwMBALFiwQczphw4YNw+PHj+Hu7g4lJSVoaWkJ3SSNnJwcTp48Ke4YFXLv3j1kZmaifv36aNy4MTZv3oykpCRxxyKEEKlGp87JL69p06bo168fpk2bBjU1NQQGBsLKygqPHz+Gl5cXPnz4IO6IAtIynrSk4cOHo06dOpg6daq4o1SItBSYJ4QQaUANTfLLU1VVxatXr2BpaSnU0Hz79i0cHByQl5cn7ohS7e+//8aaNWvg4eGB+vXrl7oazOTJk8WU7PskucA8IYRIA2pokl+eiYkJjh07hmbNmgk1NE+dOoXp06cjKipK3BGFREVFYc+ePYiKisKGDRugp6eHS5cuwczMDM7OzuKOV4qlpWWZ97FYLKkoMi+tBeYJIUTcqKFJfnnTp0/Ho0ePcPz4cdjZ2eH58+eIj4/HsGHDMGzYMIkap3n79m106dIFzZs3x507dxAaGgorKyusWLECT58+xYkTJ8QdkRBCCBGgyUDkl7ds2TI4ODjA1NQUWVlZcHJyQqtWrdCsWTPMmzdP3PGEzJo1C3///TeuXbsGeXl5wfp27drh4cOHYkz2ffn5+QgPD0dhYaG4o3zX/v37cebMGaF1Z86cwYEDB8SUiBBCpBP1aBLyRWxsLF69eoWsrCzUrVtXIouiS+N40pycHEyaNEkwkenNmzewsrLCpEmTYGxsjFmzZok5YWnSUmCeEEIkHfVokl/e4sWLkZOTA1NTU3h6eqJ///6wtbVFbm4uFi9eLO54QjQ0NPD58+dS61+8eAFjY2MxJPq+2bNnIzAwELdu3YKioqJgffv27XH06FExJisbj8cTamQCQFhYGDUyCSHkB1FDk/zyFi1aJKidWVJOTg4WLVokhkRlGzhwIGbOnIm4uDiwWCzweDzcv38f06dPx7Bhw8QdT6TTp09j8+bNaNGiBVgslmC9s7OzxE20kqYC84QQIg2ooUl+eQzDCDWAvgoMDJS4IujSNJ70q8TEROjp6ZVan52dLfJ5FydpKjBPCCHSgBqa5JelqakJLS0tsFgs2NnZCV1hR11dHR06dED//v3FHVOIvLw8du3ahejoaJw/fx4HDx5EWFgYDhw4ILGXTGzQoAEuXLggWP7auNy9ezeaNm0qrlhl6tWrF06fPi3uGIQQ8lOQFXcAQsRl/fr1YBgGo0aNwqJFi6Curi64T15eHhYWFhLXEFq8eDGmT58OU1NTmJqaCtbn5uZi1apV8PHxEWM60ZYtW4YuXbogJCQEhYWF2LBhA0JCQvDgwQPcvn1b3PFKsbW1xeLFi3H//n2pKzBPCCGShmadk1/e7du30bx5c8jKSv7vLhkZGXz+/LnUqejk5GTo6elJ7GSVqKgorFixAoGBgcjKykK9evUwc+ZMuLq6ijtaKT9DgXlCCJEU1NAkvzxparyx2WzEx8dDV1dXaP2NGzcwYMAAJCYmiikZIYQQUprkd+EQUsPK+q3F5XKFiqKLk6amJlgslmA8aclJNEVFRcjKysLvv/8uxoTfl5CQgISEBPB4PKH1bm5uYkpUvvz8fMTExMDa2loqersJIUQS0acn+WVt3LgRAP906O7du6Gqqiq4r6ioCHfu3IGDg4O44gmRxvGkXz179gzDhw9HaGhoqUY9i8WSqB5jQDoLzBNCiKSiU+fkl/V1LN67d+9gYmIiNGv7a+Nt8eLFaNy4sbgiliJN40m/cnd3h7W1NWbOnAl9ff1SJY3Mzc3FlEy0KVOm4P79+1i/fj06d+6MoKAgWFlZ4cyZM1i4cCFevHgh7oiEECI1qKFJfnlt27aFv78/NDU1xR3lu6RpPOlXampqePHiBWxsbMQdpULMzc1x9OhRNGnSROgyn5GRkahXrx4yMjLEHZEQQqQG1dEkv7ybN29KRSMTkI7xpN/y8PBAYGCguGNUmDQVmCeEEEknPeffCKlG06ZNw5IlS6CiooJp06aVu+3atWtrKVXZpGk86bd2796N4cOH4/Xr13BxcYGcnJzQ/T169BBTMtG+FpifNGkSAMkvME8IIZKMGprkl/TixQsUFBQAAJ4/fy7xPVXr1q0DwO/R3L59u8jxpNu3bxdXvHIFBATg/v37uHTpUqn7JHEykLQVmCeEEElGYzQJkSLSNJ70KwsLC3Tr1g3z58+Hvr6+uONUiDQVmCeEEElGDU3yy/Ly8vruNrKysjAwMECHDh3QvXv3Wkj181FTU8PLly9hbW0t7iiEEEJqGZ06J7+skrUoy8Lj8RAREYHdu3dj+vTpWLx4cS0kEyZt40m/5eXlhZs3b0pdQ1PaCswTQogkooYm+WXt2bOnwtueP38ef/zxh1gamtI2nvRbdnZ2mD17Nu7duwdXV9dSk4EmT54spmSiSVuBeUIIkWR06pyQCkhLS8OoUaPg7+8v7ihS52thfFFYLBaio6NrMc33SVuBeUIIkWTU0CRECtB40tojbQXmCSFEklHBdkKkgLq6+ndvSkpKiIiIwIABA+Dj4yPuyFJL2grME0KIJKMeTUJ+Ml/Hk75//17cUQDwa3+eOHECN2/eFDm5RtKGIyQlJWH48OFo1KiRVBSYJ4QQSUaTgQj5ybRo0QINGjQQdwyBP//8Ezt27EDbtm1FjnmUNNJWYJ4QQiQZ9WgSQmqUlpYWDh48CE9PT3FHqRBpLDBPCCGSisZoEkJqlLq6OqysrMQdo8KSk5MxdepUamQSQkg1oIYmIaRGLVy4EIsWLUJubq64o1TI1wLzhBBCqo7GaBJCalT//v1x5MgR6OnpwcLCotTkmufPn4spmWjSVmCeEEIkGY3RJITUqP79++PmzZvo27evyMlACxYsEFMy0aStwDwhhEgyamgSQmqUiooKrly5ghYtWog7CiGEkFpGYzQJITXK1NQUHA5H3DEIIYSIAfVoEkJq1IULF7Bp0yZs374dFhYW4o7zXdJWYJ4QQiQZNTQJITVKU1MTOTk5KCwshLKycqnJNSkpKWJKJtqUKVPKLTC/Z88eMSUjhBDpQ7POCSE1av369eKO8EMOHDgAf39/qSkwTwghkowamoSQGjV8+HBxR/gh0lZgnhBCJBmdOieEVLuMjAzBBKCMjIxyt5W0iUL79u3D5cuX4efnByUlJXHHIYQQqUYNTUJItZORkcHnz5+hp6cHNptdapwjwJ90w2KxUFRUJIaEZcvNzUXv3r1x//59qSgwTwghkoxOnRNCqt2NGzegpaUFAFJ3Ocfhw4fj2bNnGDJkiMjJQIQQQiqOejQJIaQEKjBPCCHVh3o0CSE1Li0tDY8fPxZZl3LYsGFiSiUaFZgnhJDqQz2ahJAade7cOQwePBhZWVngcDhCp6JZLJbE1dGUtgLzhBAiyaihSQipUXZ2dvD09MSyZcugrKws7jjfJW0F5gkhRJLRqXNCSI36+PEjJk+eLBWNTED6CswTQogko4YmIaRGderUCU+fPpWaIujSVmCeEEIkGTU0CSHV7uzZs4J/d+3aFTNmzEBISAhcXV1LnYru0aNHbccrRZoLzBNCiCSjMZqEkGrHZrMrtJ2kFGyX5gLzhBAiyahHkxBS7b4tYSTppLnAPCGESDLq0SSE1IiAgAAkJyejW7dugnX79+/HggULkJ2djV69emHTpk1QUFAQY0pCCCE1iXo0CSE1YtGiRWjbtq2gofnq1St4e3tjxIgRcHR0xKpVq2BkZISFCxeKN6gI0lRgnhBCJBn1aBJCaoShoSHOnTuHBg0aAADmzp2L27dv4969ewCA48ePY8GCBQgJCRFnzFKkrcA8IYRIsoqN2CeEkB+UmpoKfX19wfLt27fRpUsXwXLDhg0RGxsrjmjl+uuvvzBq1ChkZWUhLS0Nqampghs1Mgkh5MdQQ5MQUiP09fURExMDAMjPz8fz58/RpEkTwf2ZmZmlSh1JAmkrME8IIZKMGpqEkBrh6emJWbNm4e7du5g9ezaUlZXRsmVLwf1BQUGwtrYWY0LRvhaYJ4QQUnU0GYgQUiOWLFkCLy8vtG7dGqqqqti3bx/k5eUF9/v5+aFjx45iTFhM2grME0KItKDJQISQGpWeng5VVVXIyMgIrU9JSYGqqqpQ41NcpK3APCGESAtqaBJCCCGEkBpBYzQJIQT8AvPnz58XWrd//35YWlpCT08PY8eOBZfLFVM6QgiRTtTQJIQQ8AvMBwcHC5a/Fphv3749Zs2ahXPnzmH58uViTEgIIdKHTp0TQgikt8A8IYRIMurRJIQQSG+BeUIIkWTU0CSEEEhvgXlCCJFk1NAkhBBIb4F5QgiRZFSwnRBCIF0F5gkhRFrQZCBCCClBGgrME0KItKCGJiGEEEIIqRE0RpMQQgghhNQIamgSQgghhJAaQQ1NQgghhBBSI6ihSQghhBBCagQ1NAkhhBBCSI2ghiYhhBBCCKkR1NAkhBBCCCE14v9xgfD/5xp1LAAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "width": 666,
       "height": 550
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/85dbbf48-6f21-4f03-892f-66e0c7b6bf4f",
   "content_dependencies": null
  },
  {
   "cellId": "d98ac0cb39d2434ea2491e167eb722c0",
   "cell_type": "code",
   "metadata": {
    "source_hash": "aeb21cd4",
    "execution_start": 1753983987856,
    "execution_millis": 1,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "d98ac0cb39d2434ea2491e167eb722c0",
    "deepnote_cell_type": "code"
   },
   "source": "'''sns.pairplot(X, corner=True, plot_kws={\"s\": 10})\nplt.suptitle(\"All Feature vs Feature Pairwise Plots\", y=1.02)\nplt.show()'''",
   "block_group": "d9cb1ddea72741d8bf1f80dc4b4744b8",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "'sns.pairplot(X, corner=True, plot_kws={\"s\": 10})\\nplt.suptitle(\"All Feature vs Feature Pairwise Plots\", y=1.02)\\nplt.show()'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/5d179823-f952-414d-9e63-aa4615fa19aa",
   "content_dependencies": null
  },
  {
   "cellId": "46147b8b26a44b53afa191c150f4ea19",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "46147b8b26a44b53afa191c150f4ea19",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Final Optimized Drop Columns",
   "block_group": "f9019a5aa6484957bbfdaf8cdbe2ab68"
  },
  {
   "cellId": "3ba7908f943144ebaf5a8ca5c396e92b",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "3ba7908f943144ebaf5a8ca5c396e92b",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Drops Age along with other redundant features",
   "block_group": "9d36e849d1594e258275b4e045b7f4b6"
  },
  {
   "cellId": "6dbe78ac0e374395a5b80f7195c39f58",
   "cell_type": "code",
   "metadata": {
    "source_hash": "7896f5fb",
    "execution_start": 1753983987906,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "6dbe78ac0e374395a5b80f7195c39f58",
    "deepnote_cell_type": "code"
   },
   "source": "\n'''features_to_drop = [\n    'Jitter(%)', 'Jitter:RAP', 'Jitter:DDP',\n    'Shimmer(dB)', 'Shimmer:APQ5', 'Shimmer:DDA',\n    'Shimmer', 'NHR', 'age'  \n]\nX_filtered = X.drop(columns=[col for col in features_to_drop if col in X.columns])\n'''",
   "block_group": "dfcc736e65f7429b87f6a3df19b2c076",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "\"features_to_drop = [\\n    'Jitter(%)', 'Jitter:RAP', 'Jitter:DDP',\\n    'Shimmer(dB)', 'Shimmer:APQ5', 'Shimmer:DDA',\\n    'Shimmer', 'NHR', 'age'  \\n]\\nX_filtered = X.drop(columns=[col for col in features_to_drop if col in X.columns])\\n\""
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/426d4c52-8c22-4523-8cfb-614bc7a49c76",
   "content_dependencies": null
  },
  {
   "cellId": "47d2f016afe541d4bd2ea0a71f05ce80",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "47d2f016afe541d4bd2ea0a71f05ce80",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Target Selection + Data Split",
   "block_group": "a2a8d5d62c834d32adcd9bbd5d9b6215"
  },
  {
   "cellId": "e0988f85f714499180c5d7463ef6d881",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "e0988f85f714499180c5d7463ef6d881",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Used Total_UPDRS because it can be applied more universally",
   "block_group": "d66feadb700d4b97a7ab35a4fd14e155"
  },
  {
   "cellId": "69be8811a0414d66a8ea776b064b5e09",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "69be8811a0414d66a8ea776b064b5e09",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "POSSIBLE IMPROVEMENT: Data split should probably utilize the patient label so that validation and testing cols can have patients that the model did not see yet",
   "block_group": "f4f8d8c666e840999440fe304f2760b4"
  },
  {
   "cellId": "defe953852de47eb865af2a468edb6c3",
   "cell_type": "code",
   "metadata": {
    "source_hash": "9d377f63",
    "execution_start": 1753983987966,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "defe953852de47eb865af2a468edb6c3",
    "deepnote_cell_type": "code"
   },
   "source": "print(df['total_UPDRS'].describe())\n",
   "block_group": "6d954be55bc84661bae17c41222035b6",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "text": "count    5875.000000\nmean       29.018942\nstd        10.700283\nmin         7.000000\n25%        21.371000\n50%        27.576000\n75%        36.399000\nmax        54.992000\nName: total_UPDRS, dtype: float64\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/61a2e751-4fcb-4a38-aa9f-55d084001495",
   "content_dependencies": null
  },
  {
   "cellId": "192a27f0b7d242759eca02f6dbedf8d0",
   "cell_type": "code",
   "metadata": {
    "source_hash": "61af330d",
    "execution_start": 1753983988016,
    "execution_millis": 0,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "192a27f0b7d242759eca02f6dbedf8d0",
    "deepnote_cell_type": "code"
   },
   "source": "y_target = y[\"total_UPDRS\"]\n#y_target = y[\"motor_UPDRS\"]\n\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X_filtered, y_target, test_size=0.5, random_state=seed\n)\n\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, random_state=seed\n)",
   "block_group": "95e0de4821eb49e6bc075a7961f6cf07",
   "execution_count": 16,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "86159a2ac68548dca4694f60ddff1b31",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "86159a2ac68548dca4694f60ddff1b31",
    "deepnote_cell_type": "text-cell-h1"
   },
   "source": "# Train & Optimize Random Forest Regression Model",
   "block_group": "545de8d11ccb40f2abafa53ab66a3735"
  },
  {
   "cellId": "63a567822c334a6bbbb3b6b9da51e097",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "63a567822c334a6bbbb3b6b9da51e097",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Baseline Model",
   "block_group": "d5daf4227ca84bf4ac17fef8a8f7b03f"
  },
  {
   "cellId": "9a3ec2c2c32547d19620d9368b2a5017",
   "cell_type": "code",
   "metadata": {
    "source_hash": "763aabfe",
    "execution_start": 1754051619116,
    "execution_millis": 1051,
    "execution_context_id": "534cffb7-45bc-4477-87bd-c9d8cf8b506a",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "9a3ec2c2c32547d19620d9368b2a5017",
    "deepnote_cell_type": "code"
   },
   "source": "model = RandomForestRegressor(n_estimators=100, random_state=seed)\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_val)\n\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\nprint(f\" RMSE: {rmse:.3f}\")\nprint(f\" R^2 Score: {r2:.3f}\")\n",
   "block_group": "816c80307f7a41b98aecdb630fd1678d",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "text": " RMSE: 3.215\n R^2 Score: 0.906\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/e5e2b5a0-aa89-4da7-82d0-7b7b2cf81686",
   "content_dependencies": null
  },
  {
   "cellId": "72109204ccda4d6a84cd2d969494481e",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "72109204ccda4d6a84cd2d969494481e",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Check for overfitting",
   "block_group": "ac9854f523b14a8393e3884db3f8a641"
  },
  {
   "cellId": "c4c584931c5e41229e8b491437f872e6",
   "cell_type": "code",
   "metadata": {
    "source_hash": "5e357459",
    "execution_start": 1753835400598,
    "execution_millis": 173,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "c4c584931c5e41229e8b491437f872e6",
    "deepnote_cell_type": "code"
   },
   "source": "importances = pd.Series(model.feature_importances_, index=X_filtered.columns)\nimportances.sort_values().plot(kind='barh', figsize=(8, 6))\nplt.title(\"Feature Importances\")\nplt.xlabel(\"Importance Score\")\nplt.show()",
   "block_group": "8bb90f348f844b00927e0d20655ecccc",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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Ikmtu0AUAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACbFH9VCgRpGr5WT1aO0ywAAACjzUmZ0Le0SCsXMPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShP0yZM2aNbrvvvvk6+srPz8/devWTUePHrW/v23bNt19991yc3NTs2bN9Mknn8hisWj37t32Nvv371fnzp1ls9nk7++vxx57TGfPni2FqwEAAEBpI+yXIRcvXtSYMWO0a9curV+/Xk5OTurVq5dyc3OVnp6u7t27q1GjRvruu+80ZcoUjRs3zuH4CxcuqEOHDgoLC9OuXbu0Zs0anT59Wn379i30nFlZWUpPT3fYAAAAYA4upV0A/k/v3r0dXi9cuFBVqlTRwYMHtWXLFlksFs2fP19ubm5q0KCBfvrpJw0dOtTefs6cOQoLC9O0adMc+ggMDFRycrJCQ0PznXP69OmKiYkpvosCAABAqWFmvww5fPiw+vXrp1q1asnb21vBwcGSpBMnTujQoUNq3Lix3Nzc7O2bN2/ucPyePXu0YcMG2Ww2+1avXj1JclgOdLXx48crLS3Nvp08ebJ4Lg4AAAAljpn9MqR79+4KCgrS/PnzVb16deXm5qphw4bKzs6+oeMzMjLUvXt3vfLKK/neq1atWoHHWK1WWa3Wv1Q3AAAAyibCfhlx7tw5HTp0SPPnz1fr1q0lSVu2bLG/X7duXb333nvKysqyh/OdO3c69NG0aVN99NFHCg4OlosLv1oAAIDbHct4yoiKFSvKz89Pb7/9to4cOaKvvvpKY8aMsb//97//Xbm5uRo2bJiSkpK0du1azZw5U5JksVgkSU8++aR+/fVX9evXTzt37tTRo0e1du1aDRo0SDk5OaVyXQAAACg9hP0ywsnJSYsXL9a3336rhg0b6plnntFrr71mf9/b21ufffaZdu/erbvvvlsTJkzQpEmTJMm+jr969eraunWrcnJy9MADD6hRo0YaPXq0fH195eTErxoAAOB2YzEMwyjtInBz3n//fQ0aNEhpaWlyd3e/JX2mp6fLx8dHgaM/lJPV45b0CQAAYGYpM7qW6Pny8lpaWpq8vb2v2ZaF3eXIO++8o1q1aqlGjRras2ePxo0bp759+96yoA8AAABzIeyXI6dOndKkSZN06tQpVatWTX369NHUqVNLuywAAACUUYT9cuT555/X888/X9plAAAAoJzgrk0AAADApJjZR4H2x0Rc94YPAAAAlG3M7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkXEq7AJRNDaPXysnqUdploAApM7qWdgkAAKCcYGYfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsF9GREVFyWKxyGKxqEKFCvL391enTp20cOFC5ebm2tsFBwfb2+Vtd9xxh0NfERERcnZ21s6dO0v6MgAAAFCGEPbLkMjISKWmpiolJUWrV69W+/bt9fTTT6tbt266fPmyvd3kyZOVmppq3xITE+3vnThxQtu2bdPIkSO1cOHC0rgMAAAAlBH8Bd0yxGq1KiAgQJJUo0YNNW3aVPfee6/uv/9+xcfHa8iQIZIkLy8ve7s/i4uLU7du3TRixAjde++9+te//iV3d/cSuwYAAACUHczsl3EdOnRQkyZNtHz58uu2NQxDcXFxGjBggOrVq6eQkBAtW7bsmsdkZWUpPT3dYQMAAIA5EPbLgXr16iklJcX+ety4cbLZbPZt9uzZkqQvv/xSmZmZioiIkCQNGDBAsbGx1+x7+vTp8vHxsW+BgYHFdh0AAAAoWYT9csAwDFksFvvr5557Trt377ZvAwcOlCQtXLhQjzzyiFxcrqzO6tevn7Zu3aqjR48W2vf48eOVlpZm306ePFm8FwMAAIASw5r9ciApKUk1a9a0v65cubJCQkIc2vz666/6+OOPdenSJc2bN8++PycnRwsXLtTUqVML7NtqtcpqtRZP4QAAAChVzOyXcV999ZX27dun3r17X7Pd+++/rzvuuEN79uxxmPV//fXXFR8fr5ycnBKqGAAAAGUFM/tlSFZWlk6dOqWcnBydPn1aa9as0fTp09WtWzf7Up3CxMbG6uGHH1bDhg0d9gcGBmr8+PFas2aNunbtWpzlAwAAoIxhZr8MWbNmjapVq6bg4GBFRkZqw4YNmj17tj799FM5OzsXety3336rPXv2FDj77+Pjo/vvv/+6N+oCAADAfCyGYRilXQTKjvT09CtP5Rn9oZysHqVdDgqQMoNvaAAAuJ3l5bW0tDR5e3tfsy0z+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBTP2UeB9sdEXPfubgAAAJRtzOwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApFxKuwCUTQ2j18rJ6lHaZZQrKTO6lnYJAAAADpjZBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYL6OWLVumRo0ayd3dXX5+furYsaMuXrwoSVqwYIHq168vNzc31atXT3PnzrUf98QTT6hx48bKysqSJGVnZyssLEwDBw4slesAAABA6SHsl0Gpqanq16+fnnjiCSUlJSkhIUEPPfSQDMPQ+++/r0mTJmnq1KlKSkrStGnTNHHiRC1atEiSNHv2bF28eFEvvPCCJGnChAm6cOGC5syZU+C5srKylJ6e7rABAADAHFxKuwDkl5qaqsuXL+uhhx5SUFCQJKlRo0aSpOjoaL3++ut66KGHJEk1a9bUwYMH9dZbb+nxxx+XzWbTe++9p7Zt28rLy0tvvPGGNmzYIG9v7wLPNX36dMXExJTMhQEAAKBEWQzDMEq7CDjKyclRRESEduzYoYiICD3wwAN6+OGH5erqKpvNJnd3dzk5/d+XMpcvX5aPj49Onz5t3/fiiy9q+vTpGjdunGbMmFHoubKysuxLfiQpPT1dgYGBChz9oZysHsVzgSaVMqNraZcAAABuA+np6fLx8VFaWlqhE7p5mNkvg5ydnbVu3Tpt27ZNX3zxhd58801NmDBBn332mSRp/vz5atGiRb5j8uTm5mrr1q1ydnbWkSNHrnkuq9Uqq9V66y8CAAAApY41+2WUxWJRq1atFBMTo8TERLm6umrr1q2qXr26jh07ppCQEIetZs2a9mNfe+01ff/999q4caPWrFmjuLi4UrwSAAAAlBZm9sugb775RuvXr9cDDzygqlWr6ptvvtEvv/yi+vXrKyYmRqNGjZKPj48iIyOVlZWlXbt26fz58xozZowSExM1adIkLVu2TK1atdK//vUvPf3002rbtq1q1apV2pcGAACAEkTYL4O8vb21adMmvfHGG0pPT1dQUJBef/11de7cWZLk4eGh1157Tc8995w8PT3VqFEjjR49Wn/88YcGDBigqKgode/eXZI0bNgwff7553rssce0adMmh+U+AAAAMDdu0IWDvBs+uEG36LhBFwAAlISi3KDLmn0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKR2+iQPtjIq57dzcAAADKNmb2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFIupV0AyqaG0WvlZPUo7TIkSSkzupZ2CQAAAOUSM/sAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKE/VISFRWlnj175tufkJAgi8WiCxcu2H++6667lJOT49DO19dX8fHx9tfBwcGyWCyyWCzy8PBQo0aNtGDBgmK+CgAAAJRlhP1y4NixY3rnnXeu227y5MlKTU3V/v37NWDAAA0dOlSrV68ugQoBAABQFhH2y4GnnnpK0dHRysrKumY7Ly8vBQQEqFatWho3bpwqVaqkdevWXfOYrKwspaenO2wAAAAwB8J+OTB69GhdvnxZb7755g21z83N1UcffaTz58/L1dX1mm2nT58uHx8f+xYYGHgrSgYAAEAZQNgvRStXrpTNZnPYOnfunK+dh4eHoqOjNX36dKWlpRXa37hx42Sz2WS1WvXwww+rYsWKGjJkyDVrGD9+vNLS0uzbyZMn//J1AQAAoGwg7Jei9u3ba/fu3Q5bYTfVDh48WH5+fnrllVcK7e+5557T7t279dVXX6lFixaaNWuWQkJCrlmD1WqVt7e3wwYAAABzcCntAm5nnp6e+cL4jz/+WGBbFxcXTZ06VVFRURo5cmSBbSpXrqyQkBCFhIRo6dKlatSokZo1a6YGDRrc8toBAABQ9jGzX4706dNHd911l2JiYq7bNjAwUI888ojGjx9fApUBAACgLGJmv5yZMWOGIiIibqjt008/rYYNG2rXrl1q1qxZMVcGAACAsoaZ/XKmQ4cO6tChgy5fvnzdtg0aNNADDzygSZMmlUBlAAAAKGsshmEYpV0Eyo709PQrj+Ac/aGcrB6lXY4kKWVG19IuAQAAoMzIy2tpaWnXfbgKM/sAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIpHb6JA+2Mi+Gu6AAAA5Rwz+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApl9IuAGVTw+i1crJ6lPh5U2Z0LfFzAgAAmBUz+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoT9YhQVFSWLxSKLxaIKFSqoZs2aev755/XHH3/Y2+S9b7FY5OPjo1atWumrr74qtA9/f3916tRJCxcuVG5ursP5goODHfrL22bMmFFi1wwAAICyg7BfzCIjI5Wamqpjx45p1qxZeuuttxQdHe3QJi4uTqmpqdq6dasqV66sbt266dixY/n6SElJ0erVq9W+fXs9/fTT6tatmy5fvuzQ1+TJk5WamuqwPfXUUyVyrQAAAChbCPvFzGq1KiAgQIGBgerZs6c6duyodevWObTx9fVVQECAGjZsqHnz5un33393aJPXR40aNdS0aVO9+OKL+vTTT7V69WrFx8c79OXl5aWAgACHzdPTsyQuFQAAAGUMYb8E7d+/X9u2bZOrq2uhbdzd3SVJ2dnZ1+yrQ4cOatKkiZYvX/6XasrKylJ6errDBgAAAHMg7BezlStXymazyc3NTY0aNdKZM2f03HPPFdg2MzNT//znP+Xs7Ky2bdtet+969eopJSXFYd+4ceNks9kcts2bNxfax/Tp0+Xj42PfAgMDi3R9AAAAKLtcSrsAs2vfvr3mzZunixcvatasWXJxcVHv3r0d2vTr10/Ozs76/fffVaVKFcXGxqpx48bX7dswDFksFod9zz33nKKiohz21ahRo9A+xo8frzFjxthfp6enE/gBAABMgrBfzDw9PRUSEiJJWrhwoZo0aaLY2FgNHjzY3mbWrFnq2LGjfHx8VKVKlRvuOykpSTVr1nTYV7lyZfv5boTVapXVar3h9gAAACg/WMZTgpycnPTiiy/qn//8p37//Xf7/oCAAIWEhBQp6H/11Vfat29fvm8JAAAAgDyE/RLWp08fOTs76z//+c8NH5OVlaVTp07pp59+0nfffadp06apR48e6tatmwYOHOjQ9rffftOpU6ccNm66BQAAuD0R9kuYi4uLRo4cqVdffVUXL168oWPWrFmjatWqKTg4WJGRkdqwYYNmz56tTz/9VM7Ozg5tJ02apGrVqjlszz//fHFcCgAAAMo4i2EYRmkXgbIjPT39ylN5Rn8oJ6tHiZ8/ZUbXEj8nAABAeZKX19LS0uTt7X3NtszsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADAp/qgWCrQ/JuK6N3wAAACgbGNmHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACblUtoFoGxqGL1WTlaPYus/ZUbXYusbAAAAVzCzDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJO6bcJ+u3btNHr06BI/b3Z2tkJCQrRt27YiHRcVFaWePXve9HlfeOEFPfXUUzd9PAAAAMo/U4f9qwPz8uXLNWXKFPt7wcHBeuONNxzax8fHy9fX95bW8N///lc1a9ZUeHh4vveGDx8uZ2dnLV269JaeU5LGjh2rRYsW6dixY7e8bwAAAJQPpg77V6tUqZK8vLxK5Fw5OTnKzc2VYRiaM2eOBg8enK9NZmamFi9erOeff14LFy685TVUrlxZERERmjdv3i3vGwAAAOXDbRP2r17G065dO/3www965plnZLFYZLFYlJCQoEGDBiktLc2+76WXXpIkZWVlaezYsapRo4Y8PT3VokULJSQk2PvO+0ZgxYoVatCggaxWq06cOKFvv/1WR48eVdeu+f9a7NKlS9WgQQO98MIL2rRpk06ePFlg3TExMapSpYq8vb31j3/8Q9nZ2fb3li1bpkaNGsnd3V1+fn7q2LGjLl68aH+/e/fuWrx48V8fPAAAAJRLt03Yv9ry5ct1xx13aPLkyUpNTVVqaqrCw8P1xhtvyNvb275v7NixkqSRI0dq+/btWrx4sfbu3as+ffooMjJShw8ftveZmZmpV155RQsWLNCBAwdUtWpVbd68WaGhoQV+oxAbG6sBAwbIx8dHnTt3Vnx8fL4269evV1JSkhISEvTBBx9o+fLliomJkSSlpqaqX79+euKJJ+xtHnroIRmGYT++efPm+vHHH5WSklLoWGRlZSk9Pd1hAwAAgDnclmG/UqVKcnZ2lpeXlwICAhQQECBXV1f5+PjIYrHY99lsNp04cUJxcXFaunSpWrdurdq1a2vs2LG67777FBcXZ+/z0qVLmjt3rsLDw1W3bl15eHjohx9+UPXq1fOd//Dhw/r666/1yCOPSJIGDBiguLg4h6AuSa6urlq4cKHuuusude3aVZMnT9bs2bOVm5ur1NRUXb58WQ899JCCg4PVqFEj/b//9/9ks9nsx+ed+4cffih0LKZPny4fHx/7FhgY+JfGFgAAAGXHbRn2i2Lfvn3KyclRaGiobDabfdu4caOOHj1qb+fq6qrGjRs7HPv777/Lzc0tX58LFy5URESEKleuLEnq0qWL0tLS9NVXXzm0a9KkiTw8POyvW7ZsqYyMDJ08eVJNmjTR/fffr0aNGqlPnz6aP3++zp8/73C8u7u7pCvfOhRm/PjxSktLs2+FLScCAABA+eNS2gWUdRkZGXJ2dta3334rZ2dnh/eunkV3d3eXxWJxeL9y5crat2+fw76cnBwtWrRIp06dkouLi8P+hQsX6v7777+hupydnbVu3Tpt27ZNX3zxhd58801NmDBB33zzjWrWrClJ+vXXXyVJVapUKbQfq9Uqq9V6Q+cEAABA+XLbhn1XV1fl5ORcd19YWJhycnJ05swZtW7dukjnCAsL07x582QYhv2DwKpVq/Tbb78pMTHR4cPD/v37NWjQIF24cMH++M89e/bo999/t8/Qf/3117LZbPalNhaLRa1atVKrVq00adIkBQUF6eOPP9aYMWPsfVaoUEF33XVXkeoGAACAOdy2y3iCg4O1adMm/fTTTzp79qx9X0ZGhtavX6+zZ88qMzNToaGh6t+/vwYOHKjly5fr+PHj2rFjh6ZPn67PP//8mudo3769MjIydODAAfu+2NhYde3aVU2aNFHDhg3tW9++feXr66v333/f3jY7O1uDBw/WwYMHtWrVKkVHR2vkyJFycnLSN998o2nTpmnXrl06ceKEli9frl9++UX169e3H79582a1bt3a/mEBAAAAtxdTh/3c3FyHpTJXmzx5slJSUlS7dm37Mpfw8HD94x//0COPPKIqVaro1VdflSTFxcVp4MCBevbZZ1W3bl317NlTO3fu1J133nnN8/v5+alXr172AH/69Gl9/vnn6t27d762Tk5O6tWrl2JjY+377r//ftWpU0dt2rTRI488ogcffND+OFBvb29t2rRJXbp0UWhoqP75z3/q9ddfV+fOne3HL168WEOHDr3xAQMAAICpWIw/PwLGRCIjIxUSEqI5c+aUWg179+5Vp06ddPToUYc1/sVt9erVevbZZ7V3795CP/AUJD09/cpTeUZ/KCerx/UPuEkpM/L/7QEAAABcX15eS0tLk7e39zXbmnJm//z581q5cqUSEhLUsWPHUq2lcePGeuWVV3T8+PESPe/FixcVFxdXpKAPAAAAczFlEnziiSe0c+dOPfvss+rRo0dpl6OoqKgSP+fDDz9c4ucEAABA2WLKsP/xxx+XdgkAAABAqTPlMh4AAAAAJp3Zx1+3Pybiujd8AAAAoGxjZh8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAm5VLaBaBsahi9Vk5Wj1vWX8qMrresLwAAANwYZvYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKRKJexbLBZ98sknhb6fkJAgi8WiCxculFhNAAAAgNkUS9j/5ZdfNGLECN15552yWq0KCAhQRESEtm7dekPHh4eHKzU1VT4+PsVRXonbvn27nJ2d1bVr/j8slZKSIovFYt/8/Pz0wAMPKDEx0aHdgQMH1LdvX1WpUkVWq1WhoaGaNGmSMjMzHdoNHz5ctWvXlru7u6pUqaIePXro+++/L9brAwAAQNlULGG/d+/eSkxM1KJFi5ScnKwVK1aoXbt2Onfu3A0d7+rqqoCAAFksluIo75bLzs6+5vuxsbF66qmntGnTJv38888Ftvnyyy+VmpqqtWvXKiMjQ507d7Z/s/H111+rRYsWys7O1ueff67k5GRNnTpV8fHx6tSpk8P577nnHsXFxSkpKUlr166VYRh64IEHlJOTc8uuFwAAAOXDLQ/7Fy5c0ObNm/XKK6+offv2CgoKUvPmzTV+/Hg9+OCD9nZnz55Vr1695OHhoTp16mjFihX29/68jCc+Pl6+vr5auXKl6tatKw8PDz388MPKzMzUokWLFBwcrIoVK2rUqFEOoTY4OFgvv/yyBg4cKJvNpqCgIK1YsUK//PKLevToIZvNpsaNG2vXrl0O17Blyxa1bt1a7u7uCgwM1KhRo3Tx4kWHfqdMmaKBAwfK29tbw4YNK3Q8MjIytGTJEo0YMUJdu3ZVfHx8ge38/PwUEBCgZs2aaebMmTp9+rS++eYbGYahwYMHq379+lq+fLmaN2+uoKAg9enTR5999pm2b9+uWbNm2fsZNmyY2rRpo+DgYDVt2lQvv/yyTp48qZSUlALPm5WVpfT0dIcNAAAA5nDLw77NZpPNZtMnn3yirKysQtvFxMSob9++2rt3r7p06aL+/fvr119/LbR9ZmamZs+ercWLF2vNmjVKSEhQr169tGrVKq1atUrvvvuu3nrrLS1btszhuFmzZqlVq1ZKTExU165d9dhjj2ngwIEaMGCAvvvuO9WuXVsDBw6UYRiSpKNHjyoyMlK9e/fW3r17tWTJEm3ZskUjR4506HfmzJlq0qSJEhMTNXHiRElX7kX4c5j/8MMPVa9ePdWtW1cDBgzQwoUL7ecqjLu7u6Qr3xjs3r1bBw8e1JgxY+Tk5PjratKkiTp27KgPPvigwH4uXryouLg41axZU4GBgQW2mT59unx8fOxbYe0AAABQ/tzysO/i4qL4+HgtWrRIvr6+atWqlV588UXt3bvXoV1UVJT69eunkJAQTZs2TRkZGdqxY0eh/V66dEnz5s1TWFiY2rRpo4cfflhbtmxRbGysGjRooG7duql9+/basGGDw3FdunTR8OHDVadOHU2aNEnp6en629/+pj59+ig0NFTjxo1TUlKSTp8+LelK+O3fv79Gjx6tOnXqKDw8XLNnz9Y777yjP/74w95vhw4d9Oyzz6p27dqqXbu2JKlu3br57jOIjY3VgAEDJEmRkZFKS0vTxo0bC73OCxcuaMqUKbLZbGrevLmSk5MlSfXr1y+wff369e1t8sydO9f+oWv16tVat26dXF1dCzx+/PjxSktLs28nT54stDYAAACUL8W2Zv/nn3/WihUrFBkZqYSEBDVt2tRh1rtx48b2nz09PeXt7a0zZ84U2qeHh4c9VEuSv7+/goODZbPZHPb9uY+rz+Pv7y9JatSoUb59ecft2bNH8fHx9rBss9kUERGh3NxcHT9+3H5cs2bN8tX4/fffq1evXvbXhw4d0o4dO9SvXz9JVz4IPfLII4qNjc13bHh4uGw2mypWrKg9e/ZoyZIl9tokXfPbgD8H+f79+ysxMVEbN25UaGio+vbt6/BB5WpWq1Xe3t4OGwAAAMzBpbg6dnNzU6dOndSpUydNnDhRQ4YMUXR0tKKioiRJFSpUcGhvsViUm5tbaH8Ftb+RPq5uk3fDb0H78o7LyMjQ8OHDNWrUqHw13HnnnfafPT09C601T2xsrC5fvqzq1avb9xmGIavVqjlz5jh8C7BkyRI1aNBAfn5+8vX1te+vU6eOJCkpKUlhYWH5zpGUlKTQ0FCHfXlLcurUqaN7771XFStW1Mcff2z/0AEAAIDbQ4k9Z79BgwYON7mWVU2bNtXBgwcVEhKSbytsKUxBLl++rHfeeUevv/66du/ebd/27Nmj6tWr51tnHxgYqNq1azsEfUkKCwtTvXr1NGvWrHwfZPbs2aMvv/zS/gGqIIZhyDCMa94/AQAAAHO65WH/3Llz6tChg9577z3t3btXx48f19KlS/Xqq6+qR48et/p0t9y4ceO0bds2jRw5Urt379bhw4f16aef5rtBtyD16tXTxx9/LElauXKlzp8/r8GDB6thw4YOW+/evQtcylMQi8WiBQsW6ODBg+rdu7d27NihEydOaOnSperevbsiIiI0fPhwSdKxY8c0ffp0ffvttzpx4oS2bdumPn36yN3dXV26dLn5QQEAAEC5VCxP42nRooVmzZqlNm3aqGHDhpo4caKGDh2qOXPm3OrT3XKNGzfWxo0blZycrNatWyssLEyTJk1yWIpTmEOHDiktLU3SlSU8HTt2LPAPg/Xu3Vu7du3Kd9NyYVq1aqWvv/5azs7O6ty5s4KCgtS3b1/16NFDn332mZydnSVdWTq1efNmdenSRSEhIXrkkUfk5eWlbdu2qWrVqkUYBQAAAJiBxbjecyBR5uTm5mrw4MFau3atNm7caF/Xfyukp6dfeQTn6A/lZPW4Zf2mzMj/14MBAABQdHl5LS0t7boPVymxNfu4dZycnBQbG6tx48Zp8+bNpV0OAAAAyqhiexoPipeTk5Oefvrp0i4DAAAAZRgz+wAAAIBJEfYBAAAAk2IZDwq0PyaCv6YLAABQzjGzDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJNyKe0CUDY1jF4rJ6vHTR2bMqPrLa4GAAAAN4OZfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYb8MiYqKksVikcVikaurq0JCQjR58mRdvnxZCQkJ9vcsFov8/f3Vu3dvHTt2zH58cHCwQ5u8bcaMGaV4VQAAACgtPGe/jImMjFRcXJyysrK0atUqPfnkk6pQoYJatmwpSTp06JC8vLx0+PBhDRs2TN27d9fevXvl7OwsSZo8ebKGDh3q0KeXl1eJXwcAAABKH2G/jLFarQoICJAkjRgxQh9//LFWrFhhD/tVq1aVr6+vqlWrpkmTJql///46cuSI6tatK+lKsM87HgAAALc3wn4Z5+7urnPnzhX6niRlZ2ffdP9ZWVnKysqyv05PT7/pvgAAAFC2sGa/jDIMQ19++aXWrl2rDh065Hs/NTVVM2fOVI0aNeyz+pI0btw42Ww2h23z5s2Fnmf69Ony8fGxb4GBgcVyPQAAACh5zOyXMStXrpTNZtOlS5eUm5urv//973rppZe0c+dOSdIdd9whwzCUmZmpJk2a6KOPPpKrq6v9+Oeee05RUVEOfdaoUaPQ840fP15jxoyxv05PTyfwAwAAmARhv4xp37695s2bJ1dXV1WvXl0uLo6/os2bN8vb21tVq1Yt8MbbypUrKyQk5IbPZ7VaZbVa/3LdAAAAKHsI+2WMp6fnNcN6zZo15evrW3IFAQAAoNwi7JvMb7/9plOnTjns8/DwkLe3dylVBAAAgNLCDbomM2nSJFWrVs1he/7550u7LAAAAJQCZvbLkPj4+ELfa9eunQzDuObxKSkpt7YgAAAAlGvM7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFI8jQcF2h8TwbP5AQAAyjlm9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuwDAAAAJkXYBwAAAEyKsA8AAACYFGEfAAAAMCnCPgAAAGBSLqVdAMqmhtFr5WT1KPJxKTO6FkM1AAAAuBnM7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkbnnYt1gs+uSTTwp9PyEhQRaLRRcuXLjVpwYAAABwlSKH/V9++UUjRozQnXfeKavVqoCAAEVERGjr1q03dHx4eLhSU1Pl4+NT5GLLou3bt8vZ2Vldu+Z/vnxKSoosFot98/Pz0wMPPKDExESHdgcOHFDfvn1VpUoVWa1WhYaGatKkScrMzHRo9/bbb6tdu3by9vYu9APT1KlTFR4eLg8PD/n6+t7KSwUAAEA5U+Sw37t3byUmJmrRokVKTk7WihUr1K5dO507d+6Gjnd1dVVAQIAsFkuRiy0N2dnZ13w/NjZWTz31lDZt2qSff/65wDZffvmlUlNTtXbtWmVkZKhz5872oP7111+rRYsWys7O1ueff67k5GRNnTpV8fHx6tSpk8P5MzMzFRkZqRdffPGa9fbp00cjRowo+sUCAADAVIoU9i9cuKDNmzfrlVdeUfv27RUUFKTmzZtr/PjxevDBB+3tzp49q169esnDw0N16tTRihUr7O/9eRlPfHy8fH19tXLlStWtW1ceHh56+OGHlZmZqUWLFik4OFgVK1bUqFGjlJOTY+8nODhYL7/8sgYOHCibzaagoCCtWLFCv/zyi3r06CGbzabGjRtr165dDtewZcsWtW7dWu7u7goMDNSoUaN08eJFh36nTJmigQMHytvbW8OGDSt0PDIyMrRkyRKNGDFCXbt2VXx8fIHt/Pz8FBAQoGbNmmnmzJk6ffq0vvnmGxmGocGDB6t+/fpavny5mjdvrqCgIPXp00efffaZtm/frlmzZtn7GT16tF544QXde++9hdYUExOjZ555Ro0aNSq0zdWysrKUnp7usAEAAMAcihT2bTabbDabPvnkE2VlZRXaLiYmRn379tXevXvVpUsX9e/fX7/++muh7TMzMzV79mwtXrxYa9asUUJCgnr16qVVq1Zp1apVevfdd/XWW29p2bJlDsfNmjVLrVq1UmJiorp27arHHntMAwcO1IABA/Tdd9+pdu3aGjhwoAzDkCQdPXpUkZGR6t27t/bu3aslS5Zoy5YtGjlypEO/M2fOVJMmTZSYmKiJEydKunIvwp/D/Icffqh69eqpbt26GjBggBYuXGg/V2Hc3d0lXZmB3717tw4ePKgxY8bIycnxV9GkSRN17NhRH3zwwTX7+6umT58uHx8f+xYYGFis5wMAAEDJKVLYd3FxUXx8vBYtWiRfX1+1atVKL774ovbu3evQLioqSv369VNISIimTZumjIwM7dixo9B+L126pHnz5iksLExt2rTRww8/rC1btig2NlYNGjRQt27d1L59e23YsMHhuC5dumj48OGqU6eOJk2apPT0dP3tb39Tnz59FBoaqnHjxikpKUmnT5+WdCXY9u/fX6NHj1adOnUUHh6u2bNn65133tEff/xh77dDhw569tlnVbt2bdWuXVuSVLdu3Xz3GcTGxmrAgAGSpMjISKWlpWnjxo2FXueFCxc0ZcoU2Ww2NW/eXMnJyZKk+vXrF9i+fv369jbFZfz48UpLS7NvJ0+eLNbzAQAAoOTc1Jr9n3/+WStWrFBkZKQSEhLUtGlTh1nvxo0b23/29PSUt7e3zpw5U2ifHh4e9lAtSf7+/goODpbNZnPY9+c+rj6Pv7+/JDksX8nbl3fcnj17FB8fb/+GwmazKSIiQrm5uTp+/Lj9uGbNmuWr8fvvv1evXr3srw8dOqQdO3aoX79+kq58EHrkkUcUGxub79jw8HDZbDZVrFhRe/bs0ZIlS+y1SbrmtwGurq6FvncrWK1WeXt7O2wAAAAwB5ebOcjNzU2dOnVSp06dNHHiRA0ZMkTR0dGKioqSJFWoUMGhvcViUW5ubqH9FdT+Rvq4uk3eDb8F7cs7LiMjQ8OHD9eoUaPy1XDnnXfaf/b09Cy01jyxsbG6fPmyqlevbt9nGIasVqvmzJnj8C3AkiVL1KBBA/n5+Tk8IadOnTqSpKSkJIWFheU7R1JSkkJDQ69bCwAAAFCQW/Kc/QYNGjjc5FpWNW3aVAcPHlRISEi+rSgz6JcvX9Y777yj119/Xbt377Zve/bsUfXq1fOtsw8MDFTt2rXzPQozLCxM9erV06xZs/J9kNmzZ4++/PJL+wcoAAAAoKiKFPbPnTunDh066L333tPevXt1/PhxLV26VK+++qp69OhRXDXeMuPGjdO2bds0cuRI7d69W4cPH9ann36a7wbdgtSrV08ff/yxJGnlypU6f/68Bg8erIYNGzpsvXv3LnApT0EsFosWLFiggwcPqnfv3tqxY4dOnDihpUuXqnv37oqIiNDw4cPt7U+dOqXdu3fryJEjkqR9+/Zp9+7dDjc/nzhxQrt379aJEyeUk5Nj/yCSkZFRlKECAACACRT5aTwtWrTQrFmz1KZNGzVs2FATJ07U0KFDNWfOnOKq8ZZp3LixNm7cqOTkZLVu3VphYWGaNGmSw1Kcwhw6dEhpaWmSrizh6dixY4F/GKx3797atWtXvpuWC9OqVSt9/fXXcnZ2VufOnRUUFKS+ffuqR48e+uyzz+Ts7Gxv+9///ldhYWEaOnSoJKlNmzYKCwtzeLTppEmTFBYWpujoaGVkZCgsLExhYWH5HkEKAAAA87MY13tWJEpUbm6uBg8erLVr12rjxo32df0lJT09/cojOEd/KCerR5GPT5mR/y8JAwAA4NbJy2tpaWnXfbjKLVmzj1vHyclJsbGxGjdunDZv3lza5QAAAKAcu6mn8aB4OTk56emnny7tMgAAAFDOMbMPAAAAmBRhHwAAADAplvGgQPtjIvhrugAAAOUcM/sAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJMi7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKZfSLgBlU8PotXKyehTpmJQZXYupGgAAANwMZvYBAAAAkyLsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUqYN++3atdPo0aNLuwwAAACg1Jgq7EdFRalnz56SpOXLl2vKlCn294KDg/XGG284tI+Pj5evr2+x1ZOSkiKLxWLf/Pz89MADDygxMdHepl27dvb33dzc1KBBA82dO9ehn99//13R0dEKDQ2V1WpV5cqV1adPHx04cCDf9Vx9vrw+AQAAcHsyVdi/WqVKleTl5VUi58rJyVFubm6h73/55ZdKTU3V2rVrlZGRoc6dO+vChQv294cOHarU1FQdPHhQffv21ZNPPqkPPvhAkpSVlaWOHTtq4cKFevnll5WcnKxVq1bp8uXLatGihb7++muHc3l7eys1NdW+/fDDD8VyzQAAACj7TBv2r17G065dO/3www965pln7DPeCQkJGjRokNLS0uz7XnrpJUlXAvbYsWNVo0YNeXp6qkWLFkpISLD3nfeNwIoVK9SgQQNZrVadOHGi0Fr8/PwUEBCgZs2aaebMmTp9+rS++eYb+/seHh4KCAhQrVq19NJLL6lOnTpasWKFJOmNN97Q9u3btXLlSvXt21dBQUFq3ry5PvroI9WvX1+DBw+WYRj2viwWiwICAuybv7//rRtUAAAAlCumDftXW758ue644w5NnjzZPuMdHh6uN954w2EmfOzYsZKkkSNHavv27Vq8eLH27t2rPn36KDIyUocPH7b3mZmZqVdeeUULFizQgQMHVLVqVb300ksKDg6+Zi3u7u6SpOzs7Gu2yXv/f//7nzp16qQmTZo4tHFyctIzzzyjgwcPas+ePfb9GRkZCgoKUmBgoHr06JFvqc+fZWVlKT093WEDAACAOdwWYb9SpUpydnaWl5eXfcbb1dVVPj4+DjPhNptNJ06cUFxcnJYuXarWrVurdu3aGjt2rO677z7FxcXZ+7x06ZLmzp2r8PBw1a1bVx4eHqpcubJq165daB0XLlzQlClTZLPZ1Lx583zv5+Tk6L333tPevXvVoUMHSVJycrLq169fYH95+5OTkyVJdevW1cKFC/Xpp5/qvffeU25ursLDw/Xjjz8WWtP06dPl4+Nj3wIDA68/oAAAACgXXEq7gLJm3759ysnJUWhoqMP+rKws+fn52V+7urqqcePGDm1GjhypkSNH5uszPDxcTk5OunjxomrVqqUlS5Y4LK+ZO3euFixYoOzsbDk7O+uZZ57RiBEj7O9fvUynIK6urpKkli1bqmXLlg7nrV+/vt566y2Hm5WvNn78eI0ZM8b+Oj09ncAPAABgEoT9P8nIyJCzs7O+/fZbOTs7O7xns9nsP7u7u8tisdxQn0uWLFGDBg3k5+dX4NN/+vfvrwkTJsjd3V3VqlWTk9P/feFSp04dJSUlFdhv3v4/fzDJU6FCBYWFhenIkSOF1ma1WmW1Wm/oOgAAAFC+3DZh39XVVTk5OdfdFxYWppycHJ05c0atW7e+JecODAy85vIeHx8fhYSEFPhev379NGHCBO3Zs8dh3X5ubq5mzZqlZs2aqUGDBgUem5OTo3379qlLly5/7QIAAABQLt0Wa/alK8/Z37Rpk3766SedPXvWvi8jI0Pr16/X2bNnlZmZqdDQUPXv318DBw7U8uXLdfz4ce3YsUPTp0/X559/fs1zzJkzR/fff/8trfuZZ55R8+bN1b17dy1dulQnTpzQzp071bt3bx0+fFiLFi2yt508ebK++OILHTt2TN99950GDBigH374QUOGDLmlNQEAAKB8MFXYz83NlYtLwV9WTJ48WSkpKapdu7aqVKki6cqa9n/84x965JFHVKVKFb366quSpLi4OA0cOFDPPvus6tatq549e2rnzp268847r3n+s2fP6ujRo7f0mtzc3LR+/XoNHDhQ48ePV+3atdW8eXPt379f+/fvd5jVP3/+vIYOHar69eurS5cuSk9P17Zt2wqd+QcAAIC5WYzr3f1ZjkRGRiokJERz5swp7VKK1erVq9WrVy/NnDmzwBuC/4r09PQrT+UZ/aGcrB5FOjZlRtdbWgsAAADyy8traWlp8vb2vmZbU8zsnz9/XitXrlRCQoI6duxY2uUUu86dO2v16tX69ddf7UuSAAAAgD8zxQ26TzzxhHbu3Klnn31WPXr0KO1ySkT79u3Vvn370i4DAAAAZZgpwv7HH39c2iUAAAAAZY4plvEAAAAAyM8UM/u49fbHRFz3hg8AAACUbczsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AcAAABMyqW0C0DZYhiGJCk9Pb2UKwEAAEBB8nJaXm67FsI+HJw7d06SFBgYWMqVAAAA4Fp+++03+fj4XLMNYR8OKlWqJEk6ceLEdf/x4NZIT09XYGCgTp48KW9v79Iu57bAmJc8xrxkMd4ljzEvebfzmBuGod9++03Vq1e/blvCPhw4OV25jcPHx+e2+x9OafP29mbMSxhjXvIY85LFeJc8xrzk3a5jfqOTstygCwAAAJgUYR8AAAAwKcI+HFitVkVHR8tqtZZ2KbcNxrzkMeYljzEvWYx3yWPMSx5jfmMsxo08swcAAABAucPMPgAAAGBShH0AAADApAj7AAAAgEkR9gEAAACTIuzfhv7zn/8oODhYbm5uatGihXbs2HHN9kuXLlW9evXk5uamRo0aadWqVSVUqXkUZcwPHDig3r17Kzg4WBaLRW+88UbJFWoiRRnz+fPnq3Xr1qpYsaIqVqyojh07Xvd/F3BUlPFevny5mjVrJl9fX3l6euruu+/Wu+++W4LVmkNR/1ueZ/HixbJYLOrZs2fxFmhCRRnz+Ph4WSwWh83Nza0EqzWHov47v3Dhgp588klVq1ZNVqtVoaGht31uIezfZpYsWaIxY8YoOjpa3333nZo0aaKIiAidOXOmwPbbtm1Tv379NHjwYCUmJqpnz57q2bOn9u/fX8KVl19FHfPMzEzVqlVLM2bMUEBAQAlXaw5FHfOEhAT169dPGzZs0Pbt2xUYGKgHHnhAP/30UwlXXj4VdbwrVaqkCRMmaPv27dq7d68GDRqkQYMGae3atSVceflV1DHPk5KSorFjx6p169YlVKl53MyYe3t7KzU11b798MMPJVhx+VfUMc/OzlanTp2UkpKiZcuW6dChQ5o/f75q1KhRwpWXMQZuK82bNzeefPJJ++ucnByjevXqxvTp0wts37dvX6Nr164O+1q0aGEMHz68WOs0k6KO+dWCgoKMWbNmFWN15vRXxtwwDOPy5cuGl5eXsWjRouIq0VT+6ngbhmGEhYUZ//znP4ujPFO6mTG/fPmyER4ebixYsMB4/PHHjR49epRApeZR1DGPi4szfHx8Sqg6cyrqmM+bN8+oVauWkZ2dXVIllgvM7N9GsrOz9e2336pjx472fU5OTurYsaO2b99e4DHbt293aC9JERERhbaHo5sZc/w1t2LMMzMzdenSJVWqVKm4yjSNvzrehmFo/fr1OnTokNq0aVOcpZrGzY755MmTVbVqVQ0ePLgkyjSVmx3zjIwMBQUFKTAwUD169NCBAwdKolxTuJkxX7FihVq2bKknn3xS/v7+atiwoaZNm6acnJySKrtMIuzfRs6ePaucnBz5+/s77Pf399epU6cKPObUqVNFag9HNzPm+GtuxZiPGzdO1atXz/dBF/nd7HinpaXJZrPJ1dVVXbt21ZtvvqlOnToVd7mmcDNjvmXLFsXGxmr+/PklUaLp3MyY161bVwsXLtSnn36q9957T7m5uQoPD9ePP/5YEiWXezcz5seOHdOyZcuUk5OjVatWaeLEiXr99df18ssvl0TJZZZLaRcAAGXJjBkztHjxYiUkJHAzXTHy8vLS7t27lZGRofXr12vMmDGqVauW2rVrV9qlmc5vv/2mxx57TPPnz1flypVLu5zbRsuWLdWyZUv76/DwcNWvX19vvfWWpkyZUoqVmVdubq6qVq2qt99+W87Ozrrnnnv0008/6bXXXlN0dHRpl1dqCPu3kcqVK8vZ2VmnT5922H/69OlCbwQNCAgoUns4upkxx1/zV8Z85syZmjFjhr788ks1bty4OMs0jZsdbycnJ4WEhEiS7r77biUlJWn69OmE/RtQ1DE/evSoUlJS1L17d/u+3NxcSZKLi4sOHTqk2rVrF2/R5dyt+G95hQoVFBYWpiNHjhRHiaZzM2NerVo1VahQQc7OzvZ99evX16lTp5SdnS1XV9dirbmsYhnPbcTV1VX33HOP1q9fb9+Xm5ur9evXO8w+XK1ly5YO7SVp3bp1hbaHo5sZc/w1Nzvmr776qqZMmaI1a9aoWbNmJVGqKdyqf+O5ubnKysoqjhJNp6hjXq9ePe3bt0+7d++2bw8++KDat2+v3bt3KzAwsCTLL5duxb/znJwc7du3T9WqVSuuMk3lZsa8VatWOnLkiP3DrCQlJyerWrVqt23Ql8TTeG43ixcvNqxWqxEfH28cPHjQGDZsmOHr62ucOnXKMAzDeOyxx4wXXnjB3n7r1q2Gi4uLMXPmTCMpKcmIjo42KlSoYOzbt6+0LqHcKeqYZ2VlGYmJiUZiYqJRrVo1Y+zYsUZiYqJx+PDh0rqEcqeoYz5jxgzD1dXVWLZsmZGammrffvvtt9K6hHKlqOM9bdo044svvjCOHj1qHDx40Jg5c6bh4uJizJ8/v7Quodwp6pj/GU/jKbqijnlMTIyxdu1a4+jRo8a3335rPProo4abm5tx4MCB0rqEcqeoY37ixAnDy8vLGDlypHHo0CFj5cqVRtWqVY2XX365tC6hTCDs34befPNN48477zRcXV2N5s2bG19//bX9vbZt2xqPP/64Q/sPP/zQCA0NNVxdXY277rrL+Pzzz0u44vKvKGN+/PhxQ1K+rW3btiVfeDlWlDEPCgoqcMyjo6NLvvByqijjPWHCBCMkJMRwc3MzKlasaLRs2dJYvHhxKVRdvhX1v+VXI+zfnKKM+ejRo+1t/f39jS5duhjfffddKVRdvhX13/m2bduMFi1aGFar1ahVq5YxdepU4/LlyyVcddliMQzDKK1vFQAAAAAUH9bsAwAAACZF2AcAAABMirAPAAAAmBRhHwAAADApwj4AAABgUoR9AAAAwKQI+wAAAIBJEfYBAAAAkyLsAwAAACZF2AeA21BUVJR69uxZ2mUUKiUlRRaLRbt37y7tUm7IL7/8ohEjRujOO++U1WpVQECAIiIitHXr1tIuDcBtzqW0CwAA4GrZ2dmlXUKR9e7dW9nZ2Vq0aJFq1aql06dPa/369Tp37lyxnTM7O1uurq7F1j8Ac2BmHwCgdu3a6amnntLo0aNVsWJF+fv7a/78+bp48aIGDRokLy8vhYSEaPXq1fZjEhISZLFY9Pnnn6tx48Zyc3PTvffeq/379zv0/dFHH+muu+6S1WpVcHCwXn/9dYf3g4ODNWXKFA0cOFDe3t4aNmyYatasKUkKCwuTxWJRu3btJEk7d+5Up06dVLlyZfn4+Kht27b67rvvHPqzWCxasGCBevXqJQ8PD9WpU0crVqxwaHPgwAF169ZN3t7e8vLyUuvWrXX06FH7+wsWLFD9+vXl5uamevXqae7cuYWO3YULF7R582a98sorat++vYKCgtS8eXONHz9eDz74oEO74cOHy9/fX25ubmrYsKFWrlz5l8ZJkrZs2aLWrVvL3d1dgYGBGjVqlC5evFhovQBuMwYA4Lbz+OOPGz169LC/btu2reHl5WVMmTLFSE5ONqZMmWI4OzsbnTt3Nt5++20jOTnZGDFihOHn52dcvHjRMAzD2LBhgyHJqF+/vvHFF18Ye/fuNbp162YEBwcb2dnZhmEYxq5duwwnJydj8uTJxqFDh4y4uDjD3d3diIuLs587KCjI8Pb2NmbOnGkcOXLEOHLkiLFjxw5DkvHll18aqampxrlz5wzDMIz169cb7777rpGUlGQcPHjQGDx4sOHv72+kp6fb+5Nk3HHHHcb//vc/4/Dhw8aoUaMMm81m7+PHH380KlWqZDz00EPGzp07jUOHDhkLFy40vv/+e8MwDOO9994zqlWrZnz00UfGsWPHjI8++sioVKmSER8fX+BYXrp0ybDZbMbo0aONP/74o8A2OTk5xr333mvcddddxhdffGEcPXrU+Oyzz4xVq1b9pXE6cuSI4enpacyaNctITk42tm7daoSFhRlRUVFF+NcAwMwI+wBwGyoo7N93333215cvXzY8PT2Nxx57zL4vNTXVkGRs377dMIz/C/uLFy+2tzl37pzh7u5uLFmyxDAMw/j73/9udOrUyeHczz33nNGgQQP766CgIKNnz54ObY4fP25IMhITE695HTk5OYaXl5fx2Wef2fdJMv75z3/aX2dkZBiSjNWrVxuGYRjjx483atasaf9A8me1a9c2/ve//znsmzJlitGyZctC61i2bJlRsWJFw83NzQgPDzfGjx9v7Nmzx/7+2rVrDScnJ+PQoUMFHn+z4zR48GBj2LBhDvs2b95sODk5Gb///nuh9QK4fbCMBwAgSWrcuLH9Z2dnZ/n5+alRo0b2ff7+/pKkM2fOOBzXsmVL+8+VKlVS3bp1lZSUJElKSkpSq1atHNq3atVKhw8fVk5Ojn1fs2bNbqjG06dPa+jQoapTp458fHzk7e2tjIwMnThxotBr8fT0lLe3t73u3bt3q3Xr1qpQoUK+/i9evKijR49q8ODBstls9u3ll192WObzZ71799bPP/+sFStWKDIyUgkJCWratKni4+Pt57zjjjsUGhpa4PE3O0579uxRfHy8Q60RERHKzc3V8ePHC60XwO2DG3QBAJKUL/xaLBaHfRaLRZKUm5t7y8/t6el5Q+0ef/xxnTt3Tv/+978VFBQkq9Wqli1b5rupt6Bryavb3d290P4zMjIkSfPnz1eLFi0c3nN2dr5mbW5uburUqZM6deqkiRMnasiQIYqOjlZUVNQ1z1kUfx6njIwMDR8+XKNGjcrX9s4777wl5wRQvhH2AQB/yddff20PlufPn1dycrLq168vSapfv36+x09u3bpVoaGh1wzPeU+ZuXpWO+/YuXPnqkuXLpKkkydP6uzZs0Wqt3Hjxlq0aJEuXbqU70OBv7+/qlevrmPHjql///5F6vfPGjRooE8++cR+zh9//FHJyckFzu7f7Dg1bdpUBw8eVEhIyF+qFYB5sYwHAPCXTJ48WevXr9f+/fsVFRWlypUr25/h/+yzz2r9+vWaMmWKkpOTtWjRIs2ZM0djx469Zp9Vq1aVu7u71qxZo9OnTystLU2SVKdOHb377rtKSkrSN998o/79+xd51nzkyJFKT0/Xo48+ql27dunw4cN69913dejQIUlSTEyMpk+frtmzZys5OVn79u1TXFyc/vWvfxXY37lz59ShQwe999572rt3r44fP66lS5fq1VdfVY8ePSRJbdu2VZs2bdS7d2+tW7dOx48f1+rVq7VmzZq/NE7jxo3Ttm3bNHLkSO3evVuHDx/Wp59+qpEjRxZpTACYF2EfAPCXzJgxQ08//bTuuecenTp1Sp999pl9Zr5p06b68MMPtXjxYjVs2FCTJk3S5MmTFRUVdc0+XVxcNHv2bL311luqXr26PTTHxsbq/Pnzatq0qR577DGNGjVKVatWLVK9fn5++uqrr5SRkaG2bdvqnnvu0fz58+2z/EOGDNGCBQsUFxenRo0aqW3btoqPj7c/DvTPbDabWrRooVmzZqlNmzZq2LChJk6cqKFDh2rOnDn2dh999JH+9re/qV+/fmrQoIGef/55+zcXNztOjRs31saNG5WcnKzWrVsrLCxMkyZNUvXq1Ys0JgDMy2IYhlHaRQAAyp+EhAS1b99e58+fl6+vb2mXAwAoADP7AAAAgEkR9gEAAACTYhkPAAAAYFLM7AMAAAAmRdgHAAAATIqwDwAAAJgUYR8AAAAwKcI+AAAAYFKEfQAAAMCkCPsAAACASRH2AQAAAJP6/wASt1AGtX1tJQAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "image/png": {
       "width": 763,
       "height": 547
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/5288b19b-6e48-4f1e-9edb-b2f8d9891974",
   "content_dependencies": null
  },
  {
   "cellId": "fea63cafe75840f5ba678692c26efeb4",
   "cell_type": "code",
   "metadata": {
    "source_hash": "9be8057e",
    "execution_start": 1753977191010,
    "execution_millis": 462,
    "execution_context_id": "8461e406-4300-4741-b32b-56a5ea06317c",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "fea63cafe75840f5ba678692c26efeb4",
    "deepnote_cell_type": "code"
   },
   "source": "print(df['age'].value_counts().sort_index())\n",
   "block_group": "9af886c2cd064838866b7065c62ab1ee",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalue_counts()\u001b[38;5;241m.\u001b[39msort_index())\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/1a0e7d9a-90c5-4768-a917-06de6bd45fca",
   "content_dependencies": null
  },
  {
   "cellId": "22f564bfd2624b2d9a7de542c0fd17b9",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "22f564bfd2624b2d9a7de542c0fd17b9",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Overfitting, so removing Age (as it acts as an id) & Split Data Again",
   "block_group": "d52b7070f033458186488e3150b31a92"
  },
  {
   "cellId": "c50e9e4126b74c08b3a9e178e2044c89",
   "cell_type": "code",
   "metadata": {
    "source_hash": "d0ac982d",
    "execution_start": 1754058190811,
    "execution_millis": 35473,
    "is_output_hidden": false,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "deepnote_app_is_output_hidden": true,
    "cell_id": "c50e9e4126b74c08b3a9e178e2044c89",
    "deepnote_cell_type": "code"
   },
   "source": "if 'age' in X.columns:\n    X_filtered = X_filtered.drop(columns=[\"age\"])\n\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X_filtered, y_target, test_size=0.5, random_state=seed\n)\n\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, random_state=seed\n)",
   "block_group": "20a362521822483a920e47fd49f6f388",
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "\"['age'] not found in axis\"",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[52], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mage\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m X\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m----> 2\u001b[0m     X_filtered \u001b[38;5;241m=\u001b[39m \u001b[43mX_filtered\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m X_train, X_temp, y_train, y_temp \u001b[38;5;241m=\u001b[39m train_test_split(\n\u001b[1;32m      5\u001b[0m     X_filtered, y_target, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, random_state\u001b[38;5;241m=\u001b[39mseed\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      8\u001b[0m X_val, X_test, y_val, y_test \u001b[38;5;241m=\u001b[39m train_test_split(\n\u001b[1;32m      9\u001b[0m     X_temp, y_temp, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, random_state\u001b[38;5;241m=\u001b[39mseed\n\u001b[1;32m     10\u001b[0m )\n",
      "File \u001b[0;32m~/venv/lib/python3.10/site-packages/pandas/core/frame.py:5344\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5196\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdrop\u001b[39m(\n\u001b[1;32m   5197\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5198\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5205\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   5206\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5207\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   5208\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5209\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5342\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5343\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5344\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5345\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5346\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5347\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5348\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5349\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5350\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5351\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5352\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/venv/lib/python3.10/site-packages/pandas/core/generic.py:4711\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4709\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4710\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4711\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4713\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4714\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[0;32m~/venv/lib/python3.10/site-packages/pandas/core/generic.py:4753\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4751\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4752\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4753\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4754\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4756\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4757\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:7000\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   6999\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 7000\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   7001\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   7002\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['age'] not found in axis\""
     ]
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/6ab42c59-db69-406b-b7f6-9b960aee6082",
   "content_dependencies": null
  },
  {
   "cellId": "9ebb7595f1d24361bbedf9ba569e6de6",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "9ebb7595f1d24361bbedf9ba569e6de6",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Check for overfitting again",
   "block_group": "b2b74699505d418892aef156bc6df7fc"
  },
  {
   "cellId": "b9933d8db01d4d5e93cb54989c694c4f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "763aabfe",
    "execution_start": 1753835400908,
    "execution_millis": 1599,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "b9933d8db01d4d5e93cb54989c694c4f",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score\n\n# Train\nmodel = RandomForestRegressor(n_estimators=100, random_state=seed)\nmodel.fit(X_train, y_train)\n\n# Predict\ny_pred = model.predict(X_val)\n\n# Metrics\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nmae  = mean_absolute_error(y_val, y_pred)\nr2   = r2_score(y_val, y_pred)\nev   = explained_variance_score(y_val, y_pred)\n\nprint(f\"RMSE:               {rmse:.3f}\")\nprint(f\"MAE:                {mae:.3f}\")\nprint(f\"R² Score:           {r2:.3f}\")\nprint(f\"Explained Variance: {ev:.3f}\")\n",
   "block_group": "a934118825454a60848884225e4a9376",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "text": " RMSE: 8.394\n R^2 Score: 0.358\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/44fdf06f-70ae-48c6-b474-f55a817a985e",
   "content_dependencies": null
  },
  {
   "cellId": "e5b678db12c74c6b94cbb17ac50cbbde",
   "cell_type": "code",
   "metadata": {
    "source_hash": "45832093",
    "execution_start": 1753835402559,
    "execution_millis": 133,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "e5b678db12c74c6b94cbb17ac50cbbde",
    "deepnote_cell_type": "code"
   },
   "source": "importances = pd.Series(model.feature_importances_, index=X_train.columns)\nimportances.sort_values().plot(kind='barh', figsize=(8, 6))\nplt.title(\"Feature Importances\")\nplt.xlabel(\"Importance Score\")\nplt.show()",
   "block_group": "a5958a44e97a40f28480421e9af8fcde",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 763,
       "height": 547
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/b77b462f-5f03-4210-a049-7904a8808919",
   "content_dependencies": null
  },
  {
   "cellId": "2efb1e781f5d42da8076af17debaedd7",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "2efb1e781f5d42da8076af17debaedd7",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Looks like there are no overfitting and the importance appears to be evenly distributed. Did try dropping sex columns but it lowered the R^2 score",
   "block_group": "eaa1d8a6aa364f8eb26c0628915fec40"
  },
  {
   "cellId": "44ce0e10c28c4f6aa50b7a63c9cc383d",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "44ce0e10c28c4f6aa50b7a63c9cc383d",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Hyper-parameter  Optimization",
   "block_group": "35fea1e434da4860bd097d183eb5406a"
  },
  {
   "cellId": "40d38097e9e6432a83bc6c9e1f453d33",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "40d38097e9e6432a83bc6c9e1f453d33",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": " No need to run the Optimization Blocks Again",
   "block_group": "4c983899c9e64eb8a4e1e97ebc4028ac"
  },
  {
   "cellId": "e65e269a243345e2972b2d401f93f1bf",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "e65e269a243345e2972b2d401f93f1bf",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Grid Search",
   "block_group": "7ec0f7ffcda54e9daf54a357c4bb5b3a"
  },
  {
   "cellId": "43808334973c4c1080c645fd2af72286",
   "cell_type": "code",
   "metadata": {
    "source_hash": "7bfc0e97",
    "execution_start": 1753835402738,
    "execution_millis": 1,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "43808334973c4c1080c645fd2af72286",
    "deepnote_cell_type": "code"
   },
   "source": "'''param_grid = {\n    \"n_estimators\": [100, 200],\n    \"max_depth\": [None, 10, 20],\n    \"min_samples_split\": [2, 5],\n    \"min_samples_leaf\": [1, 2],\n    \"max_features\": [1.0, \"sqrt\"]\n}\n\nbest_r2 = -float(\"inf\")\nbest_model = None\nbest_params = None\n\nfor params in ParameterGrid(param_grid):\n    model = RandomForestRegressor(**params, random_state=42)\n    model.fit(X_train, y_train)\n    val_pred = model.predict(X_val)\n    r2 = r2_score(y_val, val_pred)\n\n    if r2 > best_r2:\n        best_r2 = r2\n        best_model = model\n        best_params = params\n\nprint(\"Best R² on validation set:\", best_r2)\nprint(\"Best hyperparameters:\", best_params)'''\n",
   "block_group": "378d8e78442647c0ba013907334e0d76",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "'param_grid = {\\n    \"n_estimators\": [100, 200],\\n    \"max_depth\": [None, 10, 20],\\n    \"min_samples_split\": [2, 5],\\n    \"min_samples_leaf\": [1, 2],\\n    \"max_features\": [1.0, \"sqrt\"]\\n}\\n\\nbest_r2 = -float(\"inf\")\\nbest_model = None\\nbest_params = None\\n\\nfor params in ParameterGrid(param_grid):\\n    model = RandomForestRegressor(**params, random_state=42)\\n    model.fit(X_train, y_train)\\n    val_pred = model.predict(X_val)\\n    r2 = r2_score(y_val, val_pred)\\n\\n    if r2 > best_r2:\\n        best_r2 = r2\\n        best_model = model\\n        best_params = params\\n\\nprint(\"Best R² on validation set:\", best_r2)\\nprint(\"Best hyperparameters:\", best_params)'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/6b9a59a6-4924-4025-953b-da344bdd52a1",
   "content_dependencies": null
  },
  {
   "cellId": "d3fbfa7eec71451d8d8801f67d4a13fa",
   "cell_type": "code",
   "metadata": {
    "source_hash": "6605cd13",
    "execution_start": 1753835402808,
    "execution_millis": 0,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "d3fbfa7eec71451d8d8801f67d4a13fa",
    "deepnote_cell_type": "code"
   },
   "source": "'''param_grid = {\n    \"n_estimators\": [200, 400, 600,800,1000],\n    \"max_depth\": [None],\n    \"min_samples_split\": [2],\n    \"min_samples_leaf\": [2],\n}\n\nbest_r2 = -float(\"inf\")\nbest_model = None\nbest_params = None\n\nfor params in ParameterGrid(param_grid):\n    model = RandomForestRegressor(**params, random_state=42)\n    model.fit(X_train, y_train)\n    val_pred = model.predict(X_val)\n    r2 = r2_score(y_val, val_pred)\n\n    if r2 > best_r2:\n        best_r2 = r2\n        best_model = model\n        best_params = params\n\nprint(\"Best R² on validation set:\", best_r2)\nprint(\"Best hyperparameters:\", best_params)\n'''",
   "block_group": "378d8e78442647c0ba013907334e0d76",
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "text/plain": "'param_grid = {\\n    \"n_estimators\": [200, 400, 600,800,1000],\\n    \"max_depth\": [None],\\n    \"min_samples_split\": [2],\\n    \"min_samples_leaf\": [2],\\n}\\n\\nbest_r2 = -float(\"inf\")\\nbest_model = None\\nbest_params = None\\n\\nfor params in ParameterGrid(param_grid):\\n    model = RandomForestRegressor(**params, random_state=42)\\n    model.fit(X_train, y_train)\\n    val_pred = model.predict(X_val)\\n    r2 = r2_score(y_val, val_pred)\\n\\n    if r2 > best_r2:\\n        best_r2 = r2\\n        best_model = model\\n        best_params = params\\n\\nprint(\"Best R² on validation set:\", best_r2)\\nprint(\"Best hyperparameters:\", best_params)\\n'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/2569dd60-f67f-444d-b5e4-c0f6df59df08",
   "content_dependencies": null
  },
  {
   "cellId": "86db12d118374546bfa99133fb4207a3",
   "cell_type": "code",
   "metadata": {
    "source_hash": "c60ea500",
    "execution_start": 1753835402860,
    "execution_millis": 1,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "86db12d118374546bfa99133fb4207a3",
    "deepnote_cell_type": "code"
   },
   "source": "'''param_grid = {\n    \"n_estimators\": [1000, 1500, 2000, 2500],\n    \"max_depth\": [None],\n    \"min_samples_split\": [2],\n    \"min_samples_leaf\": [2],\n}\n\nbest_r2 = -float(\"inf\")\nbest_model = None\nbest_params = None\n\nfor params in ParameterGrid(param_grid):\n    model = RandomForestRegressor(**params, random_state=42)\n    model.fit(X_train, y_train)\n    val_pred = model.predict(X_val)\n    r2 = r2_score(y_val, val_pred)\n\n    if r2 > best_r2:\n        best_r2 = r2\n        best_model = model\n        best_params = params\n\nprint(\"Best R² on validation set:\", best_r2)\nprint(\"Best hyperparameters:\", best_params)'''\n",
   "block_group": "378d8e78442647c0ba013907334e0d76",
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 22,
     "data": {
      "text/plain": "'param_grid = {\\n    \"n_estimators\": [1000, 1500, 2000, 2500],\\n    \"max_depth\": [None],\\n    \"min_samples_split\": [2],\\n    \"min_samples_leaf\": [2],\\n}\\n\\nbest_r2 = -float(\"inf\")\\nbest_model = None\\nbest_params = None\\n\\nfor params in ParameterGrid(param_grid):\\n    model = RandomForestRegressor(**params, random_state=42)\\n    model.fit(X_train, y_train)\\n    val_pred = model.predict(X_val)\\n    r2 = r2_score(y_val, val_pred)\\n\\n    if r2 > best_r2:\\n        best_r2 = r2\\n        best_model = model\\n        best_params = params\\n\\nprint(\"Best R² on validation set:\", best_r2)\\nprint(\"Best hyperparameters:\", best_params)'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/cac8bfc8-ddbd-4ed7-9f4e-2578d8f9e563",
   "content_dependencies": null
  },
  {
   "cellId": "915983eb032943d5b96ac48f83bc024f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "4c847014",
    "execution_start": 1753835402937,
    "execution_millis": 0,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "915983eb032943d5b96ac48f83bc024f",
    "deepnote_cell_type": "code"
   },
   "source": "'''param_grid = {\n    \"n_estimators\": [1250, 1500, 1750],\n    \"max_depth\": [None],\n    \"min_samples_split\": [2],\n    \"min_samples_leaf\": [2],\n}\n\nbest_r2 = -float(\"inf\")\nbest_model = None\nbest_params = None\n\nfor params in ParameterGrid(param_grid):\n    model = RandomForestRegressor(**params, random_state=42)\n    model.fit(X_train, y_train)\n    val_pred = model.predict(X_val)\n    r2 = r2_score(y_val, val_pred)\n\n    if r2 > best_r2:\n        best_r2 = r2\n        best_model = model\n        best_params = params\n\nprint(\"Best R² on validation set:\", best_r2)\nprint(\"Best hyperparameters:\", best_params)'''\n",
   "block_group": "378d8e78442647c0ba013907334e0d76",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "'param_grid = {\\n    \"n_estimators\": [1250, 1500, 1750],\\n    \"max_depth\": [None],\\n    \"min_samples_split\": [2],\\n    \"min_samples_leaf\": [2],\\n}\\n\\nbest_r2 = -float(\"inf\")\\nbest_model = None\\nbest_params = None\\n\\nfor params in ParameterGrid(param_grid):\\n    model = RandomForestRegressor(**params, random_state=42)\\n    model.fit(X_train, y_train)\\n    val_pred = model.predict(X_val)\\n    r2 = r2_score(y_val, val_pred)\\n\\n    if r2 > best_r2:\\n        best_r2 = r2\\n        best_model = model\\n        best_params = params\\n\\nprint(\"Best R² on validation set:\", best_r2)\\nprint(\"Best hyperparameters:\", best_params)'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/413e7664-d90a-4a2d-845a-25202320fa46",
   "content_dependencies": null
  },
  {
   "cellId": "a25c420c4ada4537b23c828217d47194",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "a25c420c4ada4537b23c828217d47194",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Best Random Forest Regression Model",
   "block_group": "2d4f167fba1945bcadf0943f5d8e9605"
  },
  {
   "cellId": "8a7840f527144c6ab11b9f9b514bf531",
   "cell_type": "code",
   "metadata": {
    "source_hash": "4e8de567",
    "execution_start": 1754058276445,
    "execution_millis": 9871,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "8a7840f527144c6ab11b9f9b514bf531",
    "deepnote_cell_type": "code"
   },
   "source": "model = RandomForestRegressor(\n    n_estimators=1250,\n    max_depth=None,\n    max_features='sqrt',\n    min_samples_leaf=2,\n    min_samples_split=2,\n    random_state=seed\n)\n\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_val)\n\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "d6f51cba920e4a79bdf3c4bbb010def0",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "text": "RMSE: 8.307\nR^2 Score: 0.371\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/852ee695-8fd0-4db8-bc7c-c9615dccb090",
   "content_dependencies": null
  },
  {
   "cellId": "441d41dc17f647ea90e44cb5607c7b6f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "99d72d29",
    "execution_start": 1753970761080,
    "execution_millis": 791,
    "execution_context_id": "b7f7cbe8-df9c-498b-8dd5-d32bc8fe1470",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "441d41dc17f647ea90e44cb5607c7b6f",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n\n# Predict (no argmax)\ny_test_pred = model.predict(X_test).flatten()\n\n# Evaluation\nprint(\"RMSE:\", mean_squared_error(y_test, y_test_pred, squared=False))\nprint(\"MAE:\", mean_absolute_error(y_test, y_test_pred))\nprint(\"R² Score:\", r2_score(y_test, y_test_pred))\n\nplt.figure(figsize=(6, 6))\nplt.scatter(y_test, y_test_pred, alpha=0.5)\nplt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--', color='gray')\nplt.xlabel(\"True Values\")\nplt.ylabel(\"Predicted Values\")\nplt.title(\"True vs. Predicted (Regression)\")\nplt.tight_layout()\nplt.show()\n\nerrors = y_test_pred - y_test\n\nplt.figure(figsize=(14, 3))\nplt.plot(errors, marker='o', linestyle='-', markersize=2)\nplt.axhline(0, color='gray', linestyle='--')\nplt.title(\"Prediction Error per Sample (Regression)\")\nplt.xlabel(\"Sample Index\")\nplt.ylabel(\"Prediction Error\")\nplt.tight_layout()\nplt.show()\n\n# Compute prediction error\nerrors = y_test_pred - y_test\nabs_errors = np.abs(errors)\n\n# Find the index of the largest error\noutlier_idx = np.argmax(abs_errors)\n\n# Remove the outlier from predictions and ground truth\ny_test_no_outlier = np.delete(y_test, outlier_idx)\ny_pred_no_outlier = np.delete(y_test_pred, outlier_idx)\nerrors_no_outlier = y_pred_no_outlier - y_test_no_outlier\n\nplt.figure(figsize=(6, 6))\nplt.scatter(y_test_no_outlier, y_pred_no_outlier, alpha=0.6)\nplt.plot([y_test_no_outlier.min(), y_test_no_outlier.max()],\n         [y_test_no_outlier.min(), y_test_no_outlier.max()],\n         'k--', lw=2)\nplt.xlabel(\"True Values\")\nplt.ylabel(\"Predicted Values\")\nplt.title(\"True vs. Predicted (Regression, Outlier Removed)\")\nplt.tight_layout()\nplt.grid(True)\nplt.show()\n\nplt.figure(figsize=(12, 3.5))\nplt.plot(errors_no_outlier, linestyle='-', marker='o', markersize=2, alpha=0.8)\nplt.axhline(0, color='gray', linestyle='--')\nplt.title(\"Prediction Error per Sample (Outlier Removed)\")\nplt.xlabel(\"Sample Index\")\nplt.ylabel(\"Prediction Error\")\nplt.tight_layout()\nplt.grid(True)\nplt.show()",
   "block_group": "359a851aaeb345fbab3c9a0e4d2e4edc",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "text": "RMSE: 8.53154285953858\nMAE: 6.6600981418705585\nR² Score: 0.35952284526494127\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 590,
       "height": 590
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x300 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1389,
       "height": 290
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 590,
       "height": 590
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x350 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1190,
       "height": 340
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/b116a946-5650-43a2-9355-3427c0a25c0c",
   "content_dependencies": null
  },
  {
   "cellId": "4d804bebe0c24cd182284de656552bcb",
   "cell_type": "code",
   "metadata": {
    "source_hash": "21044a34",
    "execution_start": 1754058295209,
    "execution_millis": 2161,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "4d804bebe0c24cd182284de656552bcb",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score\n\n# Train\nmodel = RandomForestRegressor(n_estimators=100, random_state=seed)\nmodel.fit(X_train, y_train)\n\n# Predict\ny_pred = model.predict(X_val)\n\n# Metrics\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nmae  = mean_absolute_error(y_val, y_pred)\nr2   = r2_score(y_val, y_pred)\nev   = explained_variance_score(y_val, y_pred)\n\nprint(f\"RMSE:               {rmse:.3f}\")\nprint(f\"MAE:                {mae:.3f}\")\nprint(f\"R² Score:           {r2:.3f}\")\nprint(f\"Explained Variance: {ev:.3f}\")\n",
   "block_group": "29011a1e637c45f5888b6612bc0e88d2",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "text": "RMSE:               8.394\nMAE:                6.477\nR² Score:           0.358\nExplained Variance: 0.358\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/38906eaf-245c-483a-afe0-76fc49e56eae",
   "content_dependencies": null
  },
  {
   "cellId": "cfb3f64ce29141848800fee45c0ca256",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "cfb3f64ce29141848800fee45c0ca256",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Second validation for overfitting",
   "block_group": "a75f3147407d460f822ac22d8dc4591e"
  },
  {
   "cellId": "3f6598bd50c24bd1a4182a5861bf831a",
   "cell_type": "code",
   "metadata": {
    "source_hash": "5e357459",
    "execution_start": 1753835410918,
    "execution_millis": 200,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "3f6598bd50c24bd1a4182a5861bf831a",
    "deepnote_cell_type": "code"
   },
   "source": "importances = pd.Series(model.feature_importances_, index=X_filtered.columns)\nimportances.sort_values().plot(kind='barh', figsize=(8, 6))\nplt.title(\"Feature Importances\")\nplt.xlabel(\"Importance Score\")\nplt.show()",
   "block_group": "315a5644f22146258632bada5be2b245",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 763,
       "height": 547
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/4acd5d9b-fd81-4806-81a1-b5c7e6d421c0",
   "content_dependencies": null
  },
  {
   "cellId": "4c47019e5d4f4512b86b40a4fe74b40e",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "4c47019e5d4f4512b86b40a4fe74b40e",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Not overfitting, but the model performance is not that great",
   "block_group": "f805be9459d34d28955795015925a5a4"
  },
  {
   "cellId": "a6b520154b954a65a6f71a225ff5c30f",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "a6b520154b954a65a6f71a225ff5c30f",
    "deepnote_cell_type": "text-cell-h1"
   },
   "source": "# Train Other Model Architectures",
   "block_group": "3a8a85f1ad6f4cc0992512ad2409dde9"
  },
  {
   "cellId": "1c198433c370467db9fa3c214870c12a",
   "cell_type": "code",
   "metadata": {
    "source_hash": "c4fd9283",
    "execution_start": 1753835411168,
    "execution_millis": 1,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "1c198433c370467db9fa3c214870c12a",
    "deepnote_cell_type": "code"
   },
   "source": "# Takes too long so no need to run again\n'''models = {\n    \"Linear Regression\": LinearRegression(),\n    \"Ridge Regression\": Ridge(alpha=1.0),\n    \"Lasso Regression\": Lasso(alpha=0.1),\n    \"ElasticNet\": ElasticNet(alpha=0.1, l1_ratio=0.5),\n    \"Support Vector Regressor\": SVR(kernel='rbf', C=1.0, epsilon=0.2),\n    \"MLP Regressor\": MLPRegressor(hidden_layer_sizes=(64, 64), max_iter=1000, random_state=seed),\n    \"KNN Regressor\": KNeighborsRegressor(n_neighbors=5),\n    \"Random Forest\": RandomForestRegressor(\n        n_estimators=1500, max_depth=None, max_features='sqrt',\n        min_samples_leaf=2, min_samples_split=2, random_state=seed\n    ),\n    \"Gradient Boosting\": GradientBoostingRegressor(n_estimators=300, learning_rate=0.1, random_state=seed),\n    \"XGBoost Regressor\": XGBRegressor(n_estimators=300, learning_rate=0.1, random_state=42)\n}\n\nresults = []\nfor name, model in models.items():\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_val)\n    r2 = r2_score(y_val, y_pred)\n    rmse = mean_squared_error(y_val, y_pred, squared=False)\n    results.append((name, r2, rmse))\n\nresults_df = pd.DataFrame(results, columns=[\"Model\", \"Validation R²\", \"Validation RMSE\"])\nresults_df.sort_values(by=\"Validation R²\", ascending=False, inplace=True)\n\nprint(results_df)'''",
   "block_group": "6648f4fd76f34574b41ddd2be38f80d9",
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 26,
     "data": {
      "text/plain": "'models = {\\n    \"Linear Regression\": LinearRegression(),\\n    \"Ridge Regression\": Ridge(alpha=1.0),\\n    \"Lasso Regression\": Lasso(alpha=0.1),\\n    \"ElasticNet\": ElasticNet(alpha=0.1, l1_ratio=0.5),\\n    \"Support Vector Regressor\": SVR(kernel=\\'rbf\\', C=1.0, epsilon=0.2),\\n    \"MLP Regressor\": MLPRegressor(hidden_layer_sizes=(64, 64), max_iter=1000, random_state=seed),\\n    \"KNN Regressor\": KNeighborsRegressor(n_neighbors=5),\\n    \"Random Forest\": RandomForestRegressor(\\n        n_estimators=1500, max_depth=None, max_features=\\'sqrt\\',\\n        min_samples_leaf=2, min_samples_split=2, random_state=seed\\n    ),\\n    \"Gradient Boosting\": GradientBoostingRegressor(n_estimators=300, learning_rate=0.1, random_state=seed),\\n    \"XGBoost Regressor\": XGBRegressor(n_estimators=300, learning_rate=0.1, random_state=42)\\n}\\n\\nresults = []\\nfor name, model in models.items():\\n    model.fit(X_train, y_train)\\n    y_pred = model.predict(X_val)\\n    r2 = r2_score(y_val, y_pred)\\n    rmse = mean_squared_error(y_val, y_pred, squared=False)\\n    results.append((name, r2, rmse))\\n\\nresults_df = pd.DataFrame(results, columns=[\"Model\", \"Validation R²\", \"Validation RMSE\"])\\nresults_df.sort_values(by=\"Validation R²\", ascending=False, inplace=True)\\n\\nprint(results_df)'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/d92cc227-1ceb-4f96-bb9e-48dd2abe5edd",
   "content_dependencies": null
  },
  {
   "cellId": "fa42749378424c1ba6db11bb86358458",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "fa42749378424c1ba6db11bb86358458",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Random Forest is still the best model",
   "block_group": "0118bfb4d6734c6c9a58d7ed6d8d041c"
  },
  {
   "cellId": "165362c476ff479fb0dcceceace42d64",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "165362c476ff479fb0dcceceace42d64",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Train Neural Network",
   "block_group": "1a10e8d6ca564e729830ec00e4e9e905"
  },
  {
   "cellId": "7159a3b7754c4111963598303faf52ed",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "7159a3b7754c4111963598303faf52ed",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Process data for NN",
   "block_group": "553f6cf315a54f62b3ed0b23d7256aed"
  },
  {
   "cellId": "b4406baaadf14d36ba35338bca33aa67",
   "cell_type": "code",
   "metadata": {
    "source_hash": "3fbfbc25",
    "execution_start": 1753835411278,
    "execution_millis": 1084,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "b4406baaadf14d36ba35338bca33aa67",
    "deepnote_cell_type": "code"
   },
   "source": "num_features = X_train.shape[1]\nnum_cols = 3  # Number of plots per row\nnum_rows = math.ceil(num_features / num_cols)\n\nfig, axes = plt.subplots(num_rows, num_cols, figsize=(16, 4 * num_rows))\naxes = axes.flatten()\n\nfor i, col in enumerate(X_train.columns):\n    sns.boxplot(x=X_train[col], ax=axes[i], fliersize=2, linewidth=0.8, color=\"skyblue\")\n    axes[i].set_title(col)\n\n# Hide unused subplots\nfor j in range(i + 1, len(axes)):\n    axes[j].set_visible(False)\n\nplt.tight_layout()\nplt.show()",
   "block_group": "5e9dac162ba0405c807763ebeee89f4e",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1200 with 9 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1588,
       "height": 1190
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/ce9433de-0746-4eb8-922b-a7cb19e9e4a5",
   "content_dependencies": null
  },
  {
   "cellId": "110b5d7b86214e11824a7040e2a13950",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "110b5d7b86214e11824a7040e2a13950",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### No Extreme Outliers",
   "block_group": "1715398145fd4a61a338a7575ab493c0"
  },
  {
   "cellId": "1885ce409ee04e12a210e678aead3e70",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "1885ce409ee04e12a210e678aead3e70",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Standard Scale",
   "block_group": "f9d8aa8e5a1644c4957bb1c6489f933d"
  },
  {
   "cellId": "50bc09d31a2d4189b9cb7e7441fc2c38",
   "cell_type": "code",
   "metadata": {
    "source_hash": "568f3432",
    "execution_start": 1754180762866,
    "execution_millis": 0,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "50bc09d31a2d4189b9cb7e7441fc2c38",
    "deepnote_cell_type": "code"
   },
   "source": "scaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)",
   "block_group": "76bbbea4ba274a4caa6f3ad389ef310e",
   "execution_count": 3,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "b1004dea4f934cdbac0ec4c2cc44fa28",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "b1004dea4f934cdbac0ec4c2cc44fa28",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Robust Scale",
   "block_group": "377db7d48d4d4c0ca4442488e43c597e"
  },
  {
   "cellId": "f8134c4b635b4343bc57fce9d9ccaff9",
   "cell_type": "code",
   "metadata": {
    "source_hash": "1679f37c",
    "execution_start": 1753835412488,
    "execution_millis": 0,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "f8134c4b635b4343bc57fce9d9ccaff9",
    "deepnote_cell_type": "code"
   },
   "source": "'''scaler = RobustScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)'''\n#Tried and performs worse",
   "block_group": "dfdba5239de545d78ad2d15a6ccf87eb",
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 29,
     "data": {
      "text/plain": "'scaler = RobustScaler()\\nX_train_scaled = scaler.fit_transform(X_train)\\nX_val_scaled = scaler.transform(X_val)'"
     },
     "metadata": {}
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/e0cf7a8a-3ff7-4e32-9f8e-d8dba9add0bc",
   "content_dependencies": null
  },
  {
   "cellId": "a9b6354536ed41edbfdfa9c21674a5c7",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "a9b6354536ed41edbfdfa9c21674a5c7",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Baseline? NN",
   "block_group": "f789df66c1b3402f8621909d88c1564c"
  },
  {
   "cellId": "b602f4c5b4534eba9b027776e1adc9aa",
   "cell_type": "code",
   "metadata": {
    "source_hash": "ade478db",
    "execution_start": 1753835412538,
    "execution_millis": 29759,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "b602f4c5b4534eba9b027776e1adc9aa",
    "deepnote_cell_type": "code"
   },
   "source": "# Build model\nmodel = Sequential([\n    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(64, activation='relu'),\n    Dropout(0.3),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse')\n\n# Train\nmodel.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=100,\n    batch_size=32,\n    verbose=1\n)\n\n# Evaluate\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")\n",
   "block_group": "1b79f19273b244f58dbd5508c11b29c6",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/100\n92/92 [==============================] - 1s 5ms/step - loss: 458.9861 - val_loss: 522.9216\nEpoch 2/100\n92/92 [==============================] - 0s 3ms/step - loss: 151.3707 - val_loss: 343.0474\nEpoch 3/100\n92/92 [==============================] - 0s 3ms/step - loss: 133.2045 - val_loss: 205.1878\nEpoch 4/100\n92/92 [==============================] - 0s 3ms/step - loss: 140.3216 - val_loss: 136.0538\nEpoch 5/100\n92/92 [==============================] - 0s 3ms/step - loss: 125.7388 - val_loss: 95.8794\nEpoch 6/100\n92/92 [==============================] - 0s 2ms/step - loss: 118.8792 - val_loss: 94.8557\nEpoch 7/100\n92/92 [==============================] - 0s 3ms/step - loss: 124.4692 - val_loss: 80.9595\nEpoch 8/100\n92/92 [==============================] - 0s 3ms/step - loss: 115.7263 - val_loss: 84.1983\nEpoch 9/100\n92/92 [==============================] - 0s 3ms/step - loss: 116.0064 - val_loss: 77.0318\nEpoch 10/100\n92/92 [==============================] - 0s 3ms/step - loss: 114.6971 - val_loss: 75.1258\nEpoch 11/100\n92/92 [==============================] - 0s 3ms/step - loss: 109.5688 - val_loss: 78.4954\nEpoch 12/100\n92/92 [==============================] - 0s 3ms/step - loss: 112.5707 - val_loss: 76.9274\nEpoch 13/100\n92/92 [==============================] - 0s 3ms/step - loss: 107.3336 - val_loss: 77.2956\nEpoch 14/100\n92/92 [==============================] - 0s 3ms/step - loss: 109.6112 - val_loss: 77.6799\nEpoch 15/100\n92/92 [==============================] - 0s 4ms/step - loss: 107.3034 - val_loss: 76.3978\nEpoch 16/100\n92/92 [==============================] - 0s 3ms/step - loss: 106.3569 - val_loss: 70.9348\nEpoch 17/100\n92/92 [==============================] - 0s 4ms/step - loss: 103.3846 - val_loss: 72.3830\nEpoch 18/100\n92/92 [==============================] - 0s 3ms/step - loss: 100.9387 - val_loss: 71.5408\nEpoch 19/100\n92/92 [==============================] - 0s 4ms/step - loss: 100.7458 - val_loss: 73.5242\nEpoch 20/100\n92/92 [==============================] - 0s 3ms/step - loss: 102.6849 - val_loss: 68.8589\nEpoch 21/100\n92/92 [==============================] - 0s 3ms/step - loss: 106.8131 - val_loss: 75.3465\nEpoch 22/100\n92/92 [==============================] - 0s 3ms/step - loss: 97.5184 - val_loss: 74.8204\nEpoch 23/100\n92/92 [==============================] - 0s 4ms/step - loss: 98.8281 - val_loss: 78.4428\nEpoch 24/100\n92/92 [==============================] - 0s 3ms/step - loss: 101.4627 - val_loss: 70.0363\nEpoch 25/100\n92/92 [==============================] - 0s 3ms/step - loss: 101.1452 - val_loss: 72.5694\nEpoch 26/100\n92/92 [==============================] - 0s 3ms/step - loss: 102.7027 - val_loss: 71.6096\nEpoch 27/100\n92/92 [==============================] - 0s 3ms/step - loss: 94.9195 - val_loss: 69.0815\nEpoch 28/100\n92/92 [==============================] - 0s 3ms/step - loss: 95.0526 - val_loss: 71.0179\nEpoch 29/100\n92/92 [==============================] - 0s 3ms/step - loss: 99.2918 - val_loss: 70.6478\nEpoch 30/100\n92/92 [==============================] - 0s 3ms/step - loss: 95.0288 - val_loss: 67.4462\nEpoch 31/100\n92/92 [==============================] - 0s 3ms/step - loss: 96.1744 - val_loss: 70.7584\nEpoch 32/100\n92/92 [==============================] - 0s 3ms/step - loss: 95.5105 - val_loss: 71.8206\nEpoch 33/100\n92/92 [==============================] - 0s 3ms/step - loss: 96.0203 - val_loss: 69.8745\nEpoch 34/100\n92/92 [==============================] - 0s 4ms/step - loss: 91.6296 - val_loss: 72.1586\nEpoch 35/100\n92/92 [==============================] - 0s 3ms/step - loss: 93.6840 - val_loss: 73.0005\nEpoch 36/100\n92/92 [==============================] - 0s 3ms/step - loss: 93.8386 - val_loss: 67.2500\nEpoch 37/100\n92/92 [==============================] - 0s 3ms/step - loss: 92.2254 - val_loss: 67.1302\nEpoch 38/100\n92/92 [==============================] - 0s 3ms/step - loss: 93.0898 - val_loss: 69.0927\nEpoch 39/100\n92/92 [==============================] - 0s 3ms/step - loss: 92.8168 - val_loss: 68.4694\nEpoch 40/100\n92/92 [==============================] - 0s 3ms/step - loss: 87.9172 - val_loss: 66.5314\nEpoch 41/100\n92/92 [==============================] - 0s 3ms/step - loss: 91.3476 - val_loss: 68.3317\nEpoch 42/100\n92/92 [==============================] - 0s 3ms/step - loss: 92.1781 - val_loss: 71.8563\nEpoch 43/100\n92/92 [==============================] - 0s 3ms/step - loss: 90.3704 - val_loss: 71.1520\nEpoch 44/100\n92/92 [==============================] - 0s 3ms/step - loss: 89.4581 - val_loss: 66.2467\nEpoch 45/100\n92/92 [==============================] - 0s 3ms/step - loss: 89.4541 - val_loss: 66.5209\nEpoch 46/100\n92/92 [==============================] - 0s 3ms/step - loss: 90.5080 - val_loss: 66.3300\nEpoch 47/100\n92/92 [==============================] - 0s 3ms/step - loss: 91.6637 - val_loss: 65.5959\nEpoch 48/100\n92/92 [==============================] - 0s 3ms/step - loss: 92.2819 - val_loss: 67.0672\nEpoch 49/100\n92/92 [==============================] - 0s 3ms/step - loss: 89.1266 - val_loss: 67.2814\nEpoch 50/100\n92/92 [==============================] - 0s 4ms/step - loss: 90.1518 - val_loss: 68.2446\nEpoch 51/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.3722 - val_loss: 70.4450\nEpoch 52/100\n92/92 [==============================] - 0s 3ms/step - loss: 90.1076 - val_loss: 68.2794\nEpoch 53/100\n92/92 [==============================] - 0s 4ms/step - loss: 86.1905 - val_loss: 70.4594\nEpoch 54/100\n92/92 [==============================] - 0s 3ms/step - loss: 88.5141 - val_loss: 65.0862\nEpoch 55/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.6998 - val_loss: 66.9735\nEpoch 56/100\n92/92 [==============================] - 0s 3ms/step - loss: 82.7410 - val_loss: 66.3069\nEpoch 57/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.3816 - val_loss: 67.1917\nEpoch 58/100\n92/92 [==============================] - 0s 3ms/step - loss: 89.2572 - val_loss: 70.0197\nEpoch 59/100\n92/92 [==============================] - 0s 3ms/step - loss: 85.3218 - val_loss: 65.1483\nEpoch 60/100\n92/92 [==============================] - 0s 2ms/step - loss: 87.9330 - val_loss: 70.1210\nEpoch 61/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.6801 - val_loss: 65.9183\nEpoch 62/100\n92/92 [==============================] - 0s 3ms/step - loss: 85.2968 - val_loss: 64.5118\nEpoch 63/100\n92/92 [==============================] - 0s 3ms/step - loss: 88.2494 - val_loss: 64.5227\nEpoch 64/100\n92/92 [==============================] - 0s 3ms/step - loss: 87.9251 - val_loss: 64.5586\nEpoch 65/100\n92/92 [==============================] - 0s 3ms/step - loss: 82.8835 - val_loss: 65.4219\nEpoch 66/100\n92/92 [==============================] - 0s 3ms/step - loss: 87.4219 - val_loss: 69.1882\nEpoch 67/100\n92/92 [==============================] - 0s 3ms/step - loss: 85.3276 - val_loss: 65.0665\nEpoch 68/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.7370 - val_loss: 66.4804\nEpoch 69/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.7771 - val_loss: 66.0651\nEpoch 70/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.5625 - val_loss: 64.7762\nEpoch 71/100\n92/92 [==============================] - 0s 3ms/step - loss: 83.5573 - val_loss: 67.1499\nEpoch 72/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.5833 - val_loss: 66.0231\nEpoch 73/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.8944 - val_loss: 65.9201\nEpoch 74/100\n92/92 [==============================] - 0s 3ms/step - loss: 81.1090 - val_loss: 68.8146\nEpoch 75/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.8845 - val_loss: 65.5114\nEpoch 76/100\n92/92 [==============================] - 0s 3ms/step - loss: 83.3440 - val_loss: 66.5499\nEpoch 77/100\n92/92 [==============================] - 0s 3ms/step - loss: 85.4546 - val_loss: 64.4792\nEpoch 78/100\n92/92 [==============================] - 0s 3ms/step - loss: 86.8288 - val_loss: 67.9967\nEpoch 79/100\n92/92 [==============================] - 0s 2ms/step - loss: 82.2458 - val_loss: 63.5751\nEpoch 80/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.2963 - val_loss: 64.6513\nEpoch 81/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.7878 - val_loss: 64.8684\nEpoch 82/100\n92/92 [==============================] - 0s 2ms/step - loss: 83.6496 - val_loss: 69.7522\nEpoch 83/100\n92/92 [==============================] - 0s 3ms/step - loss: 81.3464 - val_loss: 64.1476\nEpoch 84/100\n92/92 [==============================] - 0s 4ms/step - loss: 81.6330 - val_loss: 64.5574\nEpoch 85/100\n92/92 [==============================] - 0s 3ms/step - loss: 82.5008 - val_loss: 65.8906\nEpoch 86/100\n92/92 [==============================] - 0s 3ms/step - loss: 80.1101 - val_loss: 64.1343\nEpoch 87/100\n92/92 [==============================] - 0s 2ms/step - loss: 80.7272 - val_loss: 64.9204\nEpoch 88/100\n92/92 [==============================] - 0s 3ms/step - loss: 80.1220 - val_loss: 71.3739\nEpoch 89/100\n92/92 [==============================] - 0s 3ms/step - loss: 84.5305 - val_loss: 63.9975\nEpoch 90/100\n92/92 [==============================] - 0s 2ms/step - loss: 83.6104 - val_loss: 63.9817\nEpoch 91/100\n92/92 [==============================] - 0s 2ms/step - loss: 80.0505 - val_loss: 64.6673\nEpoch 92/100\n92/92 [==============================] - 0s 3ms/step - loss: 79.6411 - val_loss: 64.6895\nEpoch 93/100\n92/92 [==============================] - 0s 3ms/step - loss: 81.0761 - val_loss: 67.7248\nEpoch 94/100\n92/92 [==============================] - 0s 2ms/step - loss: 81.6071 - val_loss: 63.1328\nEpoch 95/100\n92/92 [==============================] - 0s 2ms/step - loss: 79.8617 - val_loss: 63.6084\nEpoch 96/100\n92/92 [==============================] - 0s 2ms/step - loss: 80.9743 - val_loss: 67.3338\nEpoch 97/100\n92/92 [==============================] - 0s 2ms/step - loss: 79.1733 - val_loss: 63.3244\nEpoch 98/100\n92/92 [==============================] - 0s 2ms/step - loss: 77.3888 - val_loss: 66.7595\nEpoch 99/100\n92/92 [==============================] - 0s 2ms/step - loss: 79.6863 - val_loss: 62.7287\nEpoch 100/100\n92/92 [==============================] - 0s 3ms/step - loss: 79.4921 - val_loss: 64.1403\n46/46 [==============================] - 0s 2ms/step\nRMSE: 8.009\nR^2 Score: 0.416\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/d92b98e7-c011-4dd5-915c-81a5769949bf",
   "content_dependencies": null
  },
  {
   "cellId": "8e8b008c75014bfaafe4ef91ac2bb9ff",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "8e8b008c75014bfaafe4ef91ac2bb9ff",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Neural Network Optimization",
   "block_group": "80457ceed69e40178a1fbd51853a33ae"
  },
  {
   "cellId": "f2600d11d34747bd9858b33553558086",
   "cell_type": "code",
   "metadata": {
    "source_hash": "c1afc00f",
    "execution_start": 1753835442350,
    "execution_millis": 124169,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "f2600d11d34747bd9858b33553558086",
    "deepnote_cell_type": "code"
   },
   "source": "model = Sequential([\n    Dense(256, input_shape=(X_train_scaled.shape[1],)),\n    LeakyReLU(),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(128),\n    LeakyReLU(),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(64),\n    LeakyReLU(),\n    Dropout(0.2),\n\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\nearly_stop = EarlyStopping(monitor='val_loss', patience=45, restore_best_weights=True)\ncheckpoint = ModelCheckpoint('best_model.keras', monitor='val_loss', save_best_only=True)\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1)\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=32,\n    callbacks=[early_stop, checkpoint, reduce_lr],\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")\n",
   "block_group": "17ca61ad37f44c5f9e5e77e4a9fc8ac8",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n92/92 [==============================] - 2s 10ms/step - loss: 530.3774 - mae: 19.7700 - val_loss: 461.9943 - val_mae: 18.9398 - lr: 0.0010\nEpoch 2/300\n92/92 [==============================] - 1s 10ms/step - loss: 175.3035 - mae: 10.4858 - val_loss: 199.6366 - val_mae: 11.3594 - lr: 0.0010\nEpoch 3/300\n92/92 [==============================] - 1s 8ms/step - loss: 140.5132 - mae: 9.5113 - val_loss: 124.2510 - val_mae: 8.7054 - lr: 0.0010\nEpoch 4/300\n92/92 [==============================] - 1s 9ms/step - loss: 140.7338 - mae: 9.4814 - val_loss: 99.9614 - val_mae: 7.8797 - lr: 0.0010\nEpoch 5/300\n92/92 [==============================] - 1s 7ms/step - loss: 132.7072 - mae: 9.2162 - val_loss: 84.1910 - val_mae: 7.3865 - lr: 0.0010\nEpoch 6/300\n92/92 [==============================] - 1s 8ms/step - loss: 128.8177 - mae: 9.1021 - val_loss: 82.6977 - val_mae: 7.2462 - lr: 0.0010\nEpoch 7/300\n92/92 [==============================] - 1s 8ms/step - loss: 121.9469 - mae: 8.8494 - val_loss: 80.9912 - val_mae: 7.1715 - lr: 0.0010\nEpoch 8/300\n92/92 [==============================] - 1s 9ms/step - loss: 124.6049 - mae: 8.9217 - val_loss: 79.4932 - val_mae: 7.1293 - lr: 0.0010\nEpoch 9/300\n92/92 [==============================] - 1s 7ms/step - loss: 118.6902 - mae: 8.7017 - val_loss: 76.4962 - val_mae: 6.9609 - lr: 0.0010\nEpoch 10/300\n92/92 [==============================] - 0s 3ms/step - loss: 112.3521 - mae: 8.4645 - val_loss: 76.7576 - val_mae: 6.9205 - lr: 0.0010\nEpoch 11/300\n92/92 [==============================] - 0s 3ms/step - loss: 112.8022 - mae: 8.4783 - val_loss: 77.0756 - val_mae: 6.9787 - lr: 0.0010\nEpoch 12/300\n92/92 [==============================] - 1s 8ms/step - loss: 113.3292 - mae: 8.4552 - val_loss: 75.5167 - val_mae: 6.8775 - lr: 0.0010\nEpoch 13/300\n92/92 [==============================] - 0s 3ms/step - loss: 111.3965 - mae: 8.4230 - val_loss: 76.6327 - val_mae: 6.9169 - lr: 0.0010\nEpoch 14/300\n92/92 [==============================] - 0s 3ms/step - loss: 108.0789 - mae: 8.2943 - val_loss: 76.0096 - val_mae: 6.9203 - lr: 0.0010\nEpoch 15/300\n92/92 [==============================] - 1s 8ms/step - loss: 105.6176 - mae: 8.2316 - val_loss: 74.0177 - val_mae: 6.8174 - lr: 0.0010\nEpoch 16/300\n92/92 [==============================] - 1s 8ms/step - loss: 110.8678 - mae: 8.4086 - val_loss: 73.4071 - val_mae: 6.7351 - lr: 0.0010\nEpoch 17/300\n92/92 [==============================] - 0s 3ms/step - loss: 107.1818 - mae: 8.3373 - val_loss: 74.5900 - val_mae: 6.8645 - lr: 0.0010\nEpoch 18/300\n92/92 [==============================] - 1s 7ms/step - loss: 101.2195 - mae: 7.9956 - val_loss: 72.3595 - val_mae: 6.7362 - lr: 0.0010\nEpoch 19/300\n92/92 [==============================] - 0s 3ms/step - loss: 104.5988 - mae: 8.1809 - val_loss: 72.9454 - val_mae: 6.7418 - lr: 0.0010\nEpoch 20/300\n92/92 [==============================] - 0s 3ms/step - loss: 102.3594 - mae: 8.0997 - val_loss: 72.5623 - val_mae: 6.7148 - lr: 0.0010\nEpoch 21/300\n92/92 [==============================] - 1s 8ms/step - loss: 100.5460 - mae: 8.0255 - val_loss: 71.1535 - val_mae: 6.6460 - lr: 0.0010\nEpoch 22/300\n92/92 [==============================] - 0s 3ms/step - loss: 101.4855 - mae: 8.0166 - val_loss: 72.6737 - val_mae: 6.7055 - lr: 0.0010\nEpoch 23/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.7940 - mae: 7.7708 - val_loss: 74.7172 - val_mae: 6.7748 - lr: 0.0010\nEpoch 24/300\n92/92 [==============================] - 0s 3ms/step - loss: 98.6884 - mae: 7.8594 - val_loss: 72.9090 - val_mae: 6.7620 - lr: 0.0010\nEpoch 25/300\n92/92 [==============================] - 1s 8ms/step - loss: 97.8321 - mae: 7.9378 - val_loss: 69.9075 - val_mae: 6.5639 - lr: 0.0010\nEpoch 26/300\n92/92 [==============================] - 0s 3ms/step - loss: 101.1776 - mae: 7.9950 - val_loss: 70.7625 - val_mae: 6.6814 - lr: 0.0010\nEpoch 27/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.0472 - mae: 7.7377 - val_loss: 70.9180 - val_mae: 6.5832 - lr: 0.0010\nEpoch 28/300\n92/92 [==============================] - 0s 3ms/step - loss: 93.4816 - mae: 7.7228 - val_loss: 73.9359 - val_mae: 6.7830 - lr: 0.0010\nEpoch 29/300\n92/92 [==============================] - 0s 3ms/step - loss: 95.5565 - mae: 7.7791 - val_loss: 70.4425 - val_mae: 6.6047 - lr: 0.0010\nEpoch 30/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.0982 - mae: 7.6788 - val_loss: 71.1244 - val_mae: 6.6430 - lr: 0.0010\nEpoch 31/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.5284 - mae: 7.6372 - val_loss: 71.2422 - val_mae: 6.6272 - lr: 0.0010\nEpoch 32/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.9831 - mae: 7.6702 - val_loss: 71.0189 - val_mae: 6.5860 - lr: 0.0010\nEpoch 33/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.6614 - mae: 7.7752 - val_loss: 70.7318 - val_mae: 6.6454 - lr: 0.0010\nEpoch 34/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.4191 - mae: 7.7090 - val_loss: 70.9525 - val_mae: 6.6448 - lr: 0.0010\nEpoch 35/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.7690 - mae: 7.6472 - val_loss: 75.1267 - val_mae: 6.8417 - lr: 0.0010\nEpoch 36/300\n92/92 [==============================] - 1s 8ms/step - loss: 90.0764 - mae: 7.5464 - val_loss: 69.5437 - val_mae: 6.5617 - lr: 0.0010\nEpoch 37/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.0668 - mae: 7.6097 - val_loss: 70.2816 - val_mae: 6.6072 - lr: 0.0010\nEpoch 38/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.1701 - mae: 7.5008 - val_loss: 71.7241 - val_mae: 6.7073 - lr: 0.0010\nEpoch 39/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.5392 - mae: 7.4710 - val_loss: 69.8673 - val_mae: 6.5390 - lr: 0.0010\nEpoch 40/300\n92/92 [==============================] - 1s 9ms/step - loss: 88.8534 - mae: 7.4748 - val_loss: 68.5574 - val_mae: 6.5201 - lr: 0.0010\nEpoch 41/300\n92/92 [==============================] - 1s 8ms/step - loss: 89.4348 - mae: 7.5563 - val_loss: 68.3998 - val_mae: 6.5369 - lr: 0.0010\nEpoch 42/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.4273 - mae: 7.3898 - val_loss: 70.4464 - val_mae: 6.6489 - lr: 0.0010\nEpoch 43/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.4213 - mae: 7.5463 - val_loss: 70.6719 - val_mae: 6.5786 - lr: 0.0010\nEpoch 44/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.8681 - mae: 7.3375 - val_loss: 70.3113 - val_mae: 6.5932 - lr: 0.0010\nEpoch 45/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.6257 - mae: 7.4238 - val_loss: 68.4632 - val_mae: 6.5301 - lr: 0.0010\nEpoch 46/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.4680 - mae: 7.4591 - val_loss: 68.5550 - val_mae: 6.5218 - lr: 0.0010\nEpoch 47/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.2741 - mae: 7.3278 - val_loss: 68.6309 - val_mae: 6.5312 - lr: 0.0010\nEpoch 48/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.8223 - mae: 7.3438 - val_loss: 70.1330 - val_mae: 6.6482 - lr: 0.0010\nEpoch 49/300\n92/92 [==============================] - 1s 10ms/step - loss: 87.4816 - mae: 7.4127 - val_loss: 68.3571 - val_mae: 6.5198 - lr: 0.0010\nEpoch 50/300\n92/92 [==============================] - 1s 9ms/step - loss: 87.8394 - mae: 7.4293 - val_loss: 68.2247 - val_mae: 6.5291 - lr: 0.0010\nEpoch 51/300\n92/92 [==============================] - 0s 5ms/step - loss: 86.8574 - mae: 7.3788 - val_loss: 70.7486 - val_mae: 6.6176 - lr: 0.0010\nEpoch 52/300\n92/92 [==============================] - 1s 8ms/step - loss: 86.9216 - mae: 7.4401 - val_loss: 67.9646 - val_mae: 6.4530 - lr: 0.0010\nEpoch 53/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.1378 - mae: 7.3153 - val_loss: 68.5281 - val_mae: 6.4947 - lr: 0.0010\nEpoch 54/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.2694 - mae: 7.2740 - val_loss: 68.3633 - val_mae: 6.4767 - lr: 0.0010\nEpoch 55/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.0823 - mae: 7.4447 - val_loss: 68.9071 - val_mae: 6.5141 - lr: 0.0010\nEpoch 56/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.5170 - mae: 7.2848 - val_loss: 69.3392 - val_mae: 6.5209 - lr: 0.0010\nEpoch 57/300\n92/92 [==============================] - 0s 5ms/step - loss: 82.7827 - mae: 7.2206 - val_loss: 70.1948 - val_mae: 6.5984 - lr: 0.0010\nEpoch 58/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.8810 - mae: 7.3785 - val_loss: 69.8824 - val_mae: 6.5742 - lr: 0.0010\nEpoch 59/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.9453 - mae: 7.2899 - val_loss: 68.7711 - val_mae: 6.5253 - lr: 0.0010\nEpoch 60/300\n92/92 [==============================] - 1s 8ms/step - loss: 84.6609 - mae: 7.2953 - val_loss: 67.1444 - val_mae: 6.4356 - lr: 0.0010\nEpoch 61/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.9061 - mae: 7.1930 - val_loss: 67.7834 - val_mae: 6.4532 - lr: 0.0010\nEpoch 62/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.7980 - mae: 7.1698 - val_loss: 67.8263 - val_mae: 6.4459 - lr: 0.0010\nEpoch 63/300\n92/92 [==============================] - 1s 9ms/step - loss: 82.4301 - mae: 7.2624 - val_loss: 66.9679 - val_mae: 6.3965 - lr: 0.0010\nEpoch 64/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.2607 - mae: 7.3492 - val_loss: 68.5296 - val_mae: 6.5114 - lr: 0.0010\nEpoch 65/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.4261 - mae: 7.2272 - val_loss: 67.0085 - val_mae: 6.4238 - lr: 0.0010\nEpoch 66/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.9247 - mae: 7.2781 - val_loss: 68.9233 - val_mae: 6.5270 - lr: 0.0010\nEpoch 67/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.3356 - mae: 7.3029 - val_loss: 68.7160 - val_mae: 6.4842 - lr: 0.0010\nEpoch 68/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.1536 - mae: 7.3318 - val_loss: 67.8611 - val_mae: 6.4672 - lr: 0.0010\nEpoch 69/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.7302 - mae: 7.3366 - val_loss: 68.7871 - val_mae: 6.5653 - lr: 0.0010\nEpoch 70/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.1971 - mae: 7.1204 - val_loss: 67.0582 - val_mae: 6.4212 - lr: 0.0010\nEpoch 71/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.5559 - mae: 7.1988 - val_loss: 68.6712 - val_mae: 6.5047 - lr: 0.0010\nEpoch 72/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.1780 - mae: 7.2330 - val_loss: 67.0869 - val_mae: 6.4156 - lr: 0.0010\nEpoch 73/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.3927 - mae: 7.2420 - val_loss: 67.7794 - val_mae: 6.4451 - lr: 0.0010\nEpoch 74/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.4892 - mae: 7.1730 - val_loss: 70.2005 - val_mae: 6.5431 - lr: 0.0010\nEpoch 75/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.3765 - mae: 7.1996 - val_loss: 67.5354 - val_mae: 6.4511 - lr: 0.0010\nEpoch 76/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.8639 - mae: 7.1978 - val_loss: 67.1075 - val_mae: 6.4125 - lr: 0.0010\nEpoch 77/300\n92/92 [==============================] - 1s 8ms/step - loss: 86.0651 - mae: 7.2878 - val_loss: 66.6908 - val_mae: 6.4732 - lr: 0.0010\nEpoch 78/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.5903 - mae: 7.2261 - val_loss: 68.0424 - val_mae: 6.4788 - lr: 0.0010\nEpoch 79/300\n92/92 [==============================] - 1s 10ms/step - loss: 83.1479 - mae: 7.2176 - val_loss: 66.2202 - val_mae: 6.4051 - lr: 0.0010\nEpoch 80/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.8159 - mae: 7.2425 - val_loss: 66.9956 - val_mae: 6.4482 - lr: 0.0010\nEpoch 81/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.7173 - mae: 7.2223 - val_loss: 66.6344 - val_mae: 6.4083 - lr: 0.0010\nEpoch 82/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.4946 - mae: 7.0877 - val_loss: 69.7467 - val_mae: 6.5261 - lr: 0.0010\nEpoch 83/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.8659 - mae: 7.0716 - val_loss: 67.8967 - val_mae: 6.4818 - lr: 0.0010\nEpoch 84/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.2523 - mae: 7.1968 - val_loss: 67.1896 - val_mae: 6.4472 - lr: 0.0010\nEpoch 85/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.3139 - mae: 7.1155 - val_loss: 66.5906 - val_mae: 6.3941 - lr: 0.0010\nEpoch 86/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.4347 - mae: 7.1391 - val_loss: 66.2437 - val_mae: 6.3663 - lr: 0.0010\nEpoch 87/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.9648 - mae: 7.1202 - val_loss: 68.7197 - val_mae: 6.5565 - lr: 0.0010\nEpoch 88/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8574 - mae: 7.1677 - val_loss: 69.7248 - val_mae: 6.5556 - lr: 0.0010\nEpoch 89/300\n92/92 [==============================] - 1s 9ms/step - loss: 79.2658 - mae: 7.0518 - val_loss: 65.7914 - val_mae: 6.3685 - lr: 0.0010\nEpoch 90/300\n92/92 [==============================] - 1s 10ms/step - loss: 81.1590 - mae: 7.1571 - val_loss: 65.6300 - val_mae: 6.3661 - lr: 0.0010\nEpoch 91/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.5414 - mae: 7.1645 - val_loss: 65.7219 - val_mae: 6.3344 - lr: 0.0010\nEpoch 92/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.7013 - mae: 7.1226 - val_loss: 67.7316 - val_mae: 6.4571 - lr: 0.0010\nEpoch 93/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.7411 - mae: 7.1138 - val_loss: 66.1084 - val_mae: 6.3745 - lr: 0.0010\nEpoch 94/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.9568 - mae: 7.0948 - val_loss: 65.7888 - val_mae: 6.3596 - lr: 0.0010\nEpoch 95/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.3393 - mae: 7.0134 - val_loss: 66.7539 - val_mae: 6.4064 - lr: 0.0010\nEpoch 96/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.7509 - mae: 7.1223 - val_loss: 66.4576 - val_mae: 6.3596 - lr: 0.0010\nEpoch 97/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.6606 - mae: 6.9726 - val_loss: 67.4145 - val_mae: 6.4654 - lr: 0.0010\nEpoch 98/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.7199 - mae: 7.1011 - val_loss: 69.9558 - val_mae: 6.5776 - lr: 0.0010\nEpoch 99/300\n92/92 [==============================] - 1s 9ms/step - loss: 83.2337 - mae: 7.1995 - val_loss: 64.8641 - val_mae: 6.3139 - lr: 0.0010\nEpoch 100/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.9806 - mae: 7.2115 - val_loss: 65.4915 - val_mae: 6.3091 - lr: 0.0010\nEpoch 101/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.4046 - mae: 7.2284 - val_loss: 65.1960 - val_mae: 6.3058 - lr: 0.0010\nEpoch 102/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.4115 - mae: 7.0229 - val_loss: 65.5719 - val_mae: 6.3574 - lr: 0.0010\nEpoch 103/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.9853 - mae: 6.9750 - val_loss: 67.7961 - val_mae: 6.4528 - lr: 0.0010\nEpoch 104/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.2831 - mae: 7.1127 - val_loss: 65.9715 - val_mae: 6.3870 - lr: 0.0010\nEpoch 105/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1011 - mae: 7.0111 - val_loss: 66.8067 - val_mae: 6.3540 - lr: 0.0010\nEpoch 106/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.4213 - mae: 6.9799 - val_loss: 65.1816 - val_mae: 6.3257 - lr: 0.0010\nEpoch 107/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.1782 - mae: 7.0941 - val_loss: 65.0628 - val_mae: 6.2845 - lr: 0.0010\nEpoch 108/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.3651 - mae: 7.1026 - val_loss: 65.7286 - val_mae: 6.3280 - lr: 0.0010\nEpoch 109/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.6090 - mae: 7.0829 - val_loss: 66.0165 - val_mae: 6.3322 - lr: 0.0010\nEpoch 110/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.0063 - mae: 6.9274 - val_loss: 69.1643 - val_mae: 6.5464 - lr: 0.0010\nEpoch 111/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8108 - mae: 7.0672 - val_loss: 66.4404 - val_mae: 6.3613 - lr: 0.0010\nEpoch 112/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.4956 - mae: 7.0945 - val_loss: 66.7949 - val_mae: 6.3802 - lr: 0.0010\nEpoch 113/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.1199 - mae: 7.0316 - val_loss: 66.1318 - val_mae: 6.4148 - lr: 0.0010\nEpoch 114/300\n92/92 [==============================] - 1s 9ms/step - loss: 80.4676 - mae: 7.1211 - val_loss: 64.7039 - val_mae: 6.2896 - lr: 0.0010\nEpoch 115/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.3727 - mae: 7.0584 - val_loss: 66.3044 - val_mae: 6.3582 - lr: 0.0010\nEpoch 116/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.3342 - mae: 7.0136 - val_loss: 66.8449 - val_mae: 6.4003 - lr: 0.0010\nEpoch 117/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.9920 - mae: 7.0114 - val_loss: 66.4812 - val_mae: 6.4019 - lr: 0.0010\nEpoch 118/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.1115 - mae: 6.9944 - val_loss: 67.4187 - val_mae: 6.4057 - lr: 0.0010\nEpoch 119/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.1750 - mae: 6.9210 - val_loss: 67.0085 - val_mae: 6.3863 - lr: 0.0010\nEpoch 120/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.6270 - mae: 7.0496 - val_loss: 65.3853 - val_mae: 6.3349 - lr: 0.0010\nEpoch 121/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.5475 - mae: 6.9994 - val_loss: 64.7851 - val_mae: 6.2656 - lr: 0.0010\nEpoch 122/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.2241 - mae: 7.1021 - val_loss: 67.0554 - val_mae: 6.3876 - lr: 0.0010\nEpoch 123/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.2403 - mae: 7.0388 - val_loss: 65.6585 - val_mae: 6.3411 - lr: 0.0010\nEpoch 124/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.8230 - mae: 6.9417 - val_loss: 65.6773 - val_mae: 6.3187 - lr: 0.0010\nEpoch 125/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.9228 - mae: 7.0521 - val_loss: 65.8076 - val_mae: 6.3475 - lr: 0.0010\nEpoch 126/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.1284 - mae: 6.9792 - val_loss: 66.0301 - val_mae: 6.3653 - lr: 0.0010\nEpoch 127/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.9739 - mae: 6.9030 - val_loss: 65.3996 - val_mae: 6.3024 - lr: 0.0010\nEpoch 128/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1307 - mae: 7.0202 - val_loss: 66.3165 - val_mae: 6.3385 - lr: 0.0010\nEpoch 129/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.7437 - mae: 6.9512 - val_loss: 65.1537 - val_mae: 6.2953 - lr: 0.0010\nEpoch 130/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.0936 - mae: 7.0229 - val_loss: 65.0949 - val_mae: 6.2859 - lr: 0.0010\nEpoch 131/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.9321 - mae: 6.9892 - val_loss: 65.5027 - val_mae: 6.3101 - lr: 0.0010\nEpoch 132/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.4461 - mae: 6.9736 - val_loss: 67.1689 - val_mae: 6.4095 - lr: 0.0010\nEpoch 133/300\n92/92 [==============================] - 1s 9ms/step - loss: 81.2667 - mae: 7.0851 - val_loss: 64.3309 - val_mae: 6.2272 - lr: 0.0010\nEpoch 134/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.7657 - mae: 7.0243 - val_loss: 64.7739 - val_mae: 6.2814 - lr: 0.0010\nEpoch 135/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.2085 - mae: 6.9543 - val_loss: 65.8108 - val_mae: 6.3249 - lr: 0.0010\nEpoch 136/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.9202 - mae: 6.9984 - val_loss: 65.7600 - val_mae: 6.3357 - lr: 0.0010\nEpoch 137/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.8618 - mae: 7.0941 - val_loss: 64.6879 - val_mae: 6.2590 - lr: 0.0010\nEpoch 138/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.7909 - mae: 6.9392 - val_loss: 65.3470 - val_mae: 6.3073 - lr: 0.0010\nEpoch 139/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.4804 - mae: 6.9237 - val_loss: 65.1805 - val_mae: 6.3015 - lr: 0.0010\nEpoch 140/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.3580 - mae: 6.9869 - val_loss: 64.9552 - val_mae: 6.2973 - lr: 0.0010\nEpoch 141/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.5929 - mae: 7.0343 - val_loss: 65.5887 - val_mae: 6.2957 - lr: 0.0010\nEpoch 142/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.5043 - mae: 6.8247 - val_loss: 66.8997 - val_mae: 6.3956 - lr: 0.0010\nEpoch 143/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.7894 - mae: 6.9742 - val_loss: 65.7654 - val_mae: 6.3473 - lr: 0.0010\nEpoch 144/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.5653 - mae: 7.0161 - val_loss: 66.3882 - val_mae: 6.3446 - lr: 0.0010\nEpoch 145/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.7885 - mae: 6.8764 - val_loss: 67.9214 - val_mae: 6.3951 - lr: 0.0010\nEpoch 146/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.6931 - mae: 6.9026 - val_loss: 65.3915 - val_mae: 6.3256 - lr: 0.0010\nEpoch 147/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.0878 - mae: 6.9020 - val_loss: 64.7551 - val_mae: 6.2773 - lr: 0.0010\nEpoch 148/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.8074 - mae: 6.8804 - val_loss: 66.5728 - val_mae: 6.3360 - lr: 0.0010\nEpoch 149/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.5093 - mae: 6.8504 - val_loss: 65.4127 - val_mae: 6.3182 - lr: 0.0010\nEpoch 150/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.3735 - mae: 7.0514 - val_loss: 66.5060 - val_mae: 6.3442 - lr: 0.0010\nEpoch 151/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.5061 - mae: 7.0703 - val_loss: 64.9269 - val_mae: 6.2581 - lr: 0.0010\nEpoch 152/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.5239 - mae: 6.9868 - val_loss: 64.8434 - val_mae: 6.2821 - lr: 0.0010\nEpoch 153/300\n92/92 [==============================] - 0s 5ms/step - loss: 78.9506 - mae: 7.0239 - val_loss: 66.2468 - val_mae: 6.3822 - lr: 0.0010\nEpoch 154/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.8686 - mae: 6.9010 - val_loss: 64.9977 - val_mae: 6.2927 - lr: 0.0010\nEpoch 155/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.1127 - mae: 6.9067 - val_loss: 64.4033 - val_mae: 6.2423 - lr: 0.0010\nEpoch 156/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.5612 - mae: 6.8951 - val_loss: 64.8251 - val_mae: 6.2707 - lr: 0.0010\nEpoch 157/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.8240 - mae: 6.9621 - val_loss: 65.4487 - val_mae: 6.3096 - lr: 0.0010\nEpoch 158/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.4392 - mae: 6.9357 - val_loss: 64.3604 - val_mae: 6.2220 - lr: 0.0010\nEpoch 159/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.9745 - mae: 6.8593 - val_loss: 64.4626 - val_mae: 6.2518 - lr: 0.0010\nEpoch 160/300\n92/92 [==============================] - 2s 20ms/step - loss: 77.9106 - mae: 6.9164 - val_loss: 64.0238 - val_mae: 6.2488 - lr: 0.0010\nEpoch 161/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.7521 - mae: 6.9523 - val_loss: 65.1322 - val_mae: 6.2900 - lr: 0.0010\nEpoch 162/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.2192 - mae: 7.0487 - val_loss: 67.4268 - val_mae: 6.3956 - lr: 0.0010\nEpoch 163/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.3611 - mae: 6.9904 - val_loss: 64.1553 - val_mae: 6.2162 - lr: 0.0010\nEpoch 164/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.4581 - mae: 6.9253 - val_loss: 64.7320 - val_mae: 6.2548 - lr: 0.0010\nEpoch 165/300\n92/92 [==============================] - 1s 9ms/step - loss: 76.8283 - mae: 6.9010 - val_loss: 63.9295 - val_mae: 6.2056 - lr: 0.0010\nEpoch 166/300\n92/92 [==============================] - 1s 7ms/step - loss: 76.5968 - mae: 6.8636 - val_loss: 63.3516 - val_mae: 6.1895 - lr: 0.0010\nEpoch 167/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.3924 - mae: 6.8878 - val_loss: 67.1688 - val_mae: 6.3627 - lr: 0.0010\nEpoch 168/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8677 - mae: 7.0298 - val_loss: 63.9993 - val_mae: 6.2619 - lr: 0.0010\nEpoch 169/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.0633 - mae: 6.9991 - val_loss: 64.6199 - val_mae: 6.2330 - lr: 0.0010\nEpoch 170/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.0573 - mae: 6.9191 - val_loss: 64.8535 - val_mae: 6.2388 - lr: 0.0010\nEpoch 171/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.9375 - mae: 6.9507 - val_loss: 65.7546 - val_mae: 6.2970 - lr: 0.0010\nEpoch 172/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.8697 - mae: 6.9251 - val_loss: 63.9232 - val_mae: 6.2125 - lr: 0.0010\nEpoch 173/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.0243 - mae: 6.9168 - val_loss: 67.2375 - val_mae: 6.4164 - lr: 0.0010\nEpoch 174/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.5117 - mae: 6.8882 - val_loss: 64.0586 - val_mae: 6.2296 - lr: 0.0010\nEpoch 175/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.4412 - mae: 6.8537 - val_loss: 65.6232 - val_mae: 6.3486 - lr: 0.0010\nEpoch 176/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.5608 - mae: 6.9155 - val_loss: 64.5192 - val_mae: 6.2711 - lr: 0.0010\nEpoch 177/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2141 - mae: 6.8894 - val_loss: 65.6043 - val_mae: 6.3310 - lr: 0.0010\nEpoch 178/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.6606 - mae: 6.9020 - val_loss: 64.4007 - val_mae: 6.2460 - lr: 0.0010\nEpoch 179/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.6250 - mae: 6.8799 - val_loss: 65.9313 - val_mae: 6.3098 - lr: 0.0010\nEpoch 180/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.8427 - mae: 6.7958 - val_loss: 64.4788 - val_mae: 6.2805 - lr: 0.0010\nEpoch 181/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.7869 - mae: 6.8912 - val_loss: 63.5309 - val_mae: 6.1859 - lr: 0.0010\nEpoch 182/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.0603 - mae: 6.8354 - val_loss: 64.2646 - val_mae: 6.2722 - lr: 0.0010\nEpoch 183/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.7172 - mae: 6.8432 - val_loss: 64.7111 - val_mae: 6.2385 - lr: 0.0010\nEpoch 184/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.2761 - mae: 6.7836 - val_loss: 65.4604 - val_mae: 6.2843 - lr: 0.0010\nEpoch 185/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.3746 - mae: 6.8966 - val_loss: 65.3315 - val_mae: 6.2629 - lr: 0.0010\nEpoch 186/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.7742 - mae: 6.8052 - val_loss: 64.1755 - val_mae: 6.2182 - lr: 0.0010\nEpoch 187/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.0177 - mae: 6.9023 - val_loss: 63.8659 - val_mae: 6.1971 - lr: 0.0010\nEpoch 188/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.4195 - mae: 6.8150 - val_loss: 63.4942 - val_mae: 6.1943 - lr: 0.0010\nEpoch 189/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.7763 - mae: 6.9309 - val_loss: 64.3070 - val_mae: 6.2281 - lr: 0.0010\nEpoch 190/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.6155 - mae: 6.9761 - val_loss: 63.9288 - val_mae: 6.2275 - lr: 0.0010\nEpoch 191/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.4248 - mae: 6.8898 - val_loss: 63.5337 - val_mae: 6.1954 - lr: 0.0010\nEpoch 192/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.8669 - mae: 6.7883 - val_loss: 66.2358 - val_mae: 6.3453 - lr: 0.0010\nEpoch 193/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.9249 - mae: 6.8719 - val_loss: 63.8025 - val_mae: 6.2135 - lr: 0.0010\nEpoch 194/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.4620 - mae: 6.9723 - val_loss: 63.8616 - val_mae: 6.2183 - lr: 0.0010\nEpoch 195/300\n92/92 [==============================] - 1s 11ms/step - loss: 75.9328 - mae: 6.9509 - val_loss: 63.1254 - val_mae: 6.1703 - lr: 0.0010\nEpoch 196/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.3943 - mae: 6.7861 - val_loss: 63.8164 - val_mae: 6.1966 - lr: 0.0010\nEpoch 197/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8877 - mae: 6.8830 - val_loss: 64.9195 - val_mae: 6.2737 - lr: 0.0010\nEpoch 198/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.9587 - mae: 6.8291 - val_loss: 64.3828 - val_mae: 6.2282 - lr: 0.0010\nEpoch 199/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.3780 - mae: 6.8562 - val_loss: 67.2624 - val_mae: 6.3614 - lr: 0.0010\nEpoch 200/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.5714 - mae: 6.9375 - val_loss: 63.9783 - val_mae: 6.1826 - lr: 0.0010\nEpoch 201/300\n92/92 [==============================] - 0s 5ms/step - loss: 76.3285 - mae: 6.8891 - val_loss: 64.8071 - val_mae: 6.2373 - lr: 0.0010\nEpoch 202/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8751 - mae: 6.8769 - val_loss: 64.3624 - val_mae: 6.2326 - lr: 0.0010\nEpoch 203/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.1557 - mae: 6.8542 - val_loss: 63.9597 - val_mae: 6.2283 - lr: 0.0010\nEpoch 204/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.3718 - mae: 6.8846 - val_loss: 63.6063 - val_mae: 6.2087 - lr: 0.0010\nEpoch 205/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.4737 - mae: 6.8096 - val_loss: 63.3328 - val_mae: 6.2181 - lr: 0.0010\nEpoch 206/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.6072 - mae: 6.9091 - val_loss: 63.7706 - val_mae: 6.2421 - lr: 0.0010\nEpoch 207/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.4273 - mae: 6.8433 - val_loss: 63.6525 - val_mae: 6.2305 - lr: 0.0010\nEpoch 208/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8092 - mae: 6.9084 - val_loss: 63.8683 - val_mae: 6.2145 - lr: 0.0010\nEpoch 209/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.9453 - mae: 6.8924 - val_loss: 64.7427 - val_mae: 6.2832 - lr: 0.0010\nEpoch 210/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.3359 - mae: 6.8249 - val_loss: 63.9515 - val_mae: 6.2272 - lr: 0.0010\nEpoch 211/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.3373 - mae: 6.9205 - val_loss: 64.5304 - val_mae: 6.2450 - lr: 0.0010\nEpoch 212/300\n92/92 [==============================] - 1s 9ms/step - loss: 73.7797 - mae: 6.7197 - val_loss: 62.2874 - val_mae: 6.1112 - lr: 0.0010\nEpoch 213/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7601 - mae: 6.7547 - val_loss: 63.4857 - val_mae: 6.2029 - lr: 0.0010\nEpoch 214/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.3374 - mae: 6.8350 - val_loss: 63.6978 - val_mae: 6.2145 - lr: 0.0010\nEpoch 215/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.0008 - mae: 6.7422 - val_loss: 63.3794 - val_mae: 6.1994 - lr: 0.0010\nEpoch 216/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.2029 - mae: 6.7712 - val_loss: 63.5927 - val_mae: 6.2100 - lr: 0.0010\nEpoch 217/300\n92/92 [==============================] - 0s 5ms/step - loss: 75.3915 - mae: 6.8600 - val_loss: 63.0556 - val_mae: 6.1793 - lr: 0.0010\nEpoch 218/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.2284 - mae: 6.9069 - val_loss: 63.0376 - val_mae: 6.1753 - lr: 0.0010\nEpoch 219/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.9815 - mae: 6.7791 - val_loss: 65.0214 - val_mae: 6.3073 - lr: 0.0010\nEpoch 220/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.0275 - mae: 6.7996 - val_loss: 63.0855 - val_mae: 6.1825 - lr: 0.0010\nEpoch 221/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2820 - mae: 6.8503 - val_loss: 64.7715 - val_mae: 6.2746 - lr: 0.0010\nEpoch 222/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.1738 - mae: 6.8485 - val_loss: 63.4082 - val_mae: 6.1835 - lr: 0.0010\nEpoch 223/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.6699 - mae: 6.7788 - val_loss: 63.9253 - val_mae: 6.1961 - lr: 0.0010\nEpoch 224/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.1374 - mae: 6.8888 - val_loss: 64.4089 - val_mae: 6.2131 - lr: 0.0010\nEpoch 225/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.5086 - mae: 6.8301 - val_loss: 63.3346 - val_mae: 6.1893 - lr: 0.0010\nEpoch 226/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7138 - mae: 6.7300 - val_loss: 63.6246 - val_mae: 6.2022 - lr: 0.0010\nEpoch 227/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.0519 - mae: 6.7055 - val_loss: 64.5397 - val_mae: 6.2714 - lr: 0.0010\nEpoch 228/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.6469 - mae: 6.8099 - val_loss: 64.7155 - val_mae: 6.2460 - lr: 0.0010\nEpoch 229/300\n92/92 [==============================] - 0s 5ms/step - loss: 75.0928 - mae: 6.8521 - val_loss: 64.5538 - val_mae: 6.2800 - lr: 0.0010\nEpoch 230/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.5725 - mae: 6.7789 - val_loss: 62.9049 - val_mae: 6.1491 - lr: 0.0010\nEpoch 231/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.6855 - mae: 6.8106 - val_loss: 62.7695 - val_mae: 6.1578 - lr: 0.0010\nEpoch 232/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.0885 - mae: 6.9184 - val_loss: 63.2097 - val_mae: 6.1926 - lr: 0.0010\nEpoch 233/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.3857 - mae: 6.8087 - val_loss: 62.9658 - val_mae: 6.1604 - lr: 0.0010\nEpoch 234/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.0786 - mae: 6.8339 - val_loss: 63.1795 - val_mae: 6.1897 - lr: 0.0010\nEpoch 235/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.6807 - mae: 6.8399 - val_loss: 64.2749 - val_mae: 6.2907 - lr: 0.0010\nEpoch 236/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.2996 - mae: 6.6894 - val_loss: 62.9849 - val_mae: 6.1464 - lr: 0.0010\nEpoch 237/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.3904 - mae: 6.7934 - val_loss: 65.8527 - val_mae: 6.3515 - lr: 0.0010\nEpoch 238/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7334 - mae: 6.7765 - val_loss: 62.9795 - val_mae: 6.1818 - lr: 0.0010\nEpoch 239/300\n92/92 [==============================] - 0s 3ms/step - loss: 73.0263 - mae: 6.7158 - val_loss: 63.2767 - val_mae: 6.1907 - lr: 0.0010\nEpoch 240/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.7100 - mae: 6.8082 - val_loss: 63.1191 - val_mae: 6.1904 - lr: 0.0010\nEpoch 241/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.4755 - mae: 6.7829 - val_loss: 63.1898 - val_mae: 6.1740 - lr: 0.0010\nEpoch 242/300\n82/92 [=========================>....] - ETA: 0s - loss: 75.4741 - mae: 6.8514\nEpoch 242: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 4ms/step - loss: 75.2879 - mae: 6.8385 - val_loss: 64.4708 - val_mae: 6.2426 - lr: 0.0010\nEpoch 243/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5550 - mae: 6.7633 - val_loss: 62.7116 - val_mae: 6.1655 - lr: 5.0000e-04\nEpoch 244/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.9670 - mae: 6.7143 - val_loss: 63.5585 - val_mae: 6.1987 - lr: 5.0000e-04\nEpoch 245/300\n92/92 [==============================] - 1s 10ms/step - loss: 72.6613 - mae: 6.6429 - val_loss: 62.2841 - val_mae: 6.1221 - lr: 5.0000e-04\nEpoch 246/300\n92/92 [==============================] - 0s 3ms/step - loss: 72.0501 - mae: 6.6614 - val_loss: 62.6656 - val_mae: 6.1508 - lr: 5.0000e-04\nEpoch 247/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.4769 - mae: 6.6809 - val_loss: 62.5581 - val_mae: 6.1433 - lr: 5.0000e-04\nEpoch 248/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.4033 - mae: 6.7709 - val_loss: 62.3166 - val_mae: 6.1319 - lr: 5.0000e-04\nEpoch 249/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.2496 - mae: 6.7603 - val_loss: 63.2366 - val_mae: 6.1887 - lr: 5.0000e-04\nEpoch 250/300\n92/92 [==============================] - 1s 9ms/step - loss: 74.4481 - mae: 6.7831 - val_loss: 62.1442 - val_mae: 6.1245 - lr: 5.0000e-04\nEpoch 251/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.1795 - mae: 6.6283 - val_loss: 62.7997 - val_mae: 6.1457 - lr: 5.0000e-04\nEpoch 252/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.0440 - mae: 6.7862 - val_loss: 62.5677 - val_mae: 6.1463 - lr: 5.0000e-04\nEpoch 253/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7980 - mae: 6.7395 - val_loss: 62.5901 - val_mae: 6.1280 - lr: 5.0000e-04\nEpoch 254/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.3893 - mae: 6.6984 - val_loss: 62.3253 - val_mae: 6.1057 - lr: 5.0000e-04\nEpoch 255/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.7655 - mae: 6.6760 - val_loss: 62.5451 - val_mae: 6.1158 - lr: 5.0000e-04\nEpoch 256/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.8171 - mae: 6.6213 - val_loss: 62.3214 - val_mae: 6.1206 - lr: 5.0000e-04\nEpoch 257/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.8117 - mae: 6.7111 - val_loss: 62.5033 - val_mae: 6.1408 - lr: 5.0000e-04\nEpoch 258/300\n92/92 [==============================] - 1s 7ms/step - loss: 71.7410 - mae: 6.6260 - val_loss: 62.1108 - val_mae: 6.1008 - lr: 5.0000e-04\nEpoch 259/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.1305 - mae: 6.6080 - val_loss: 62.8544 - val_mae: 6.1538 - lr: 5.0000e-04\nEpoch 260/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.9505 - mae: 6.7222 - val_loss: 63.6571 - val_mae: 6.1914 - lr: 5.0000e-04\nEpoch 261/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.8044 - mae: 6.7497 - val_loss: 62.7990 - val_mae: 6.1562 - lr: 5.0000e-04\nEpoch 262/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.0610 - mae: 6.6362 - val_loss: 62.6720 - val_mae: 6.1386 - lr: 5.0000e-04\nEpoch 263/300\n92/92 [==============================] - 0s 3ms/step - loss: 71.6988 - mae: 6.6154 - val_loss: 63.1608 - val_mae: 6.1716 - lr: 5.0000e-04\nEpoch 264/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.5721 - mae: 6.7876 - val_loss: 63.9102 - val_mae: 6.2143 - lr: 5.0000e-04\nEpoch 265/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.4580 - mae: 6.7389 - val_loss: 62.8629 - val_mae: 6.1444 - lr: 5.0000e-04\nEpoch 266/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.8305 - mae: 6.7512 - val_loss: 62.4587 - val_mae: 6.1249 - lr: 5.0000e-04\nEpoch 267/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.0520 - mae: 6.5749 - val_loss: 62.3781 - val_mae: 6.1129 - lr: 5.0000e-04\nEpoch 268/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5013 - mae: 6.6831 - val_loss: 62.9357 - val_mae: 6.1631 - lr: 5.0000e-04\nEpoch 269/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5819 - mae: 6.7361 - val_loss: 63.2816 - val_mae: 6.1806 - lr: 5.0000e-04\nEpoch 270/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.9641 - mae: 6.7919 - val_loss: 62.6954 - val_mae: 6.1521 - lr: 5.0000e-04\nEpoch 271/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.3913 - mae: 6.7072 - val_loss: 62.2903 - val_mae: 6.1226 - lr: 5.0000e-04\nEpoch 272/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.6151 - mae: 6.5276 - val_loss: 62.6125 - val_mae: 6.1330 - lr: 5.0000e-04\nEpoch 273/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.7299 - mae: 6.6677 - val_loss: 63.3368 - val_mae: 6.1874 - lr: 5.0000e-04\nEpoch 274/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.1422 - mae: 6.6261 - val_loss: 62.9714 - val_mae: 6.1363 - lr: 5.0000e-04\nEpoch 275/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.2404 - mae: 6.6662 - val_loss: 62.8667 - val_mae: 6.1474 - lr: 5.0000e-04\nEpoch 276/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.8166 - mae: 6.6874 - val_loss: 62.5360 - val_mae: 6.1414 - lr: 5.0000e-04\nEpoch 277/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.3494 - mae: 6.6386 - val_loss: 62.4735 - val_mae: 6.1194 - lr: 5.0000e-04\nEpoch 278/300\n92/92 [==============================] - 1s 9ms/step - loss: 74.4075 - mae: 6.7796 - val_loss: 61.7671 - val_mae: 6.0849 - lr: 5.0000e-04\nEpoch 279/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.6042 - mae: 6.7283 - val_loss: 62.9896 - val_mae: 6.1652 - lr: 5.0000e-04\nEpoch 280/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.8141 - mae: 6.7024 - val_loss: 62.7218 - val_mae: 6.1374 - lr: 5.0000e-04\nEpoch 281/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.1270 - mae: 6.6843 - val_loss: 63.1052 - val_mae: 6.1733 - lr: 5.0000e-04\nEpoch 282/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.6175 - mae: 6.6892 - val_loss: 62.8165 - val_mae: 6.1555 - lr: 5.0000e-04\nEpoch 283/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5812 - mae: 6.7176 - val_loss: 63.3731 - val_mae: 6.1882 - lr: 5.0000e-04\nEpoch 284/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.7051 - mae: 6.7359 - val_loss: 62.9430 - val_mae: 6.1805 - lr: 5.0000e-04\nEpoch 285/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.3035 - mae: 6.6526 - val_loss: 62.8976 - val_mae: 6.1672 - lr: 5.0000e-04\nEpoch 286/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.1507 - mae: 6.6780 - val_loss: 62.9528 - val_mae: 6.1520 - lr: 5.0000e-04\nEpoch 287/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.0622 - mae: 6.7381 - val_loss: 64.3237 - val_mae: 6.2294 - lr: 5.0000e-04\nEpoch 288/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.7142 - mae: 6.6221 - val_loss: 62.3482 - val_mae: 6.1297 - lr: 5.0000e-04\nEpoch 289/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.8933 - mae: 6.7253 - val_loss: 62.9018 - val_mae: 6.1530 - lr: 5.0000e-04\nEpoch 290/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.2368 - mae: 6.6006 - val_loss: 63.0014 - val_mae: 6.1544 - lr: 5.0000e-04\nEpoch 291/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.0403 - mae: 6.6218 - val_loss: 62.6509 - val_mae: 6.1470 - lr: 5.0000e-04\nEpoch 292/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.2659 - mae: 6.7451 - val_loss: 62.5031 - val_mae: 6.1196 - lr: 5.0000e-04\nEpoch 293/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.8443 - mae: 6.5940 - val_loss: 62.4055 - val_mae: 6.1079 - lr: 5.0000e-04\nEpoch 294/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.4484 - mae: 6.7351 - val_loss: 62.4332 - val_mae: 6.1162 - lr: 5.0000e-04\nEpoch 295/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.3362 - mae: 6.6585 - val_loss: 63.1419 - val_mae: 6.1673 - lr: 5.0000e-04\nEpoch 296/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.0999 - mae: 6.6642 - val_loss: 62.5210 - val_mae: 6.1334 - lr: 5.0000e-04\nEpoch 297/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.6697 - mae: 6.6020 - val_loss: 62.5414 - val_mae: 6.1265 - lr: 5.0000e-04\nEpoch 298/300\n92/92 [==============================] - 0s 3ms/step - loss: 71.4369 - mae: 6.5955 - val_loss: 62.6853 - val_mae: 6.1529 - lr: 5.0000e-04\nEpoch 299/300\n92/92 [==============================] - 0s 3ms/step - loss: 72.3685 - mae: 6.7020 - val_loss: 62.2591 - val_mae: 6.1172 - lr: 5.0000e-04\nEpoch 300/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.3090 - mae: 6.6835 - val_loss: 63.7625 - val_mae: 6.1979 - lr: 5.0000e-04\n46/46 [==============================] - 0s 2ms/step\nRMSE: 7.985\nR^2 Score: 0.419\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/dc63f775-a2b0-45ec-bafd-bdd167a50287",
   "content_dependencies": null
  },
  {
   "cellId": "5249f208888f4d06b2f323c67d285ad0",
   "cell_type": "code",
   "metadata": {
    "source_hash": "ee31998b",
    "execution_start": 1753835566608,
    "execution_millis": 99098,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "5249f208888f4d06b2f323c67d285ad0",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, input_shape=(X_train_scaled.shape[1],)),\n    LeakyReLU(),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(128),\n    LeakyReLU(),\n    Dropout(0.3),\n\n    Dense(64),\n    LeakyReLU(),\n    Dropout(0.2),\n\n    Dense(32),\n    LeakyReLU(),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=32,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "17ca61ad37f44c5f9e5e77e4a9fc8ac8",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n92/92 [==============================] - 2s 7ms/step - loss: 384.1128 - mae: 15.7390 - val_loss: 467.0279 - val_mae: 19.1650 - lr: 0.0010\nEpoch 2/300\n92/92 [==============================] - 0s 3ms/step - loss: 160.1084 - mae: 10.0367 - val_loss: 304.6672 - val_mae: 14.8385 - lr: 0.0010\nEpoch 3/300\n92/92 [==============================] - 0s 3ms/step - loss: 143.0413 - mae: 9.4051 - val_loss: 186.9587 - val_mae: 10.9774 - lr: 0.0010\nEpoch 4/300\n92/92 [==============================] - 0s 3ms/step - loss: 140.6411 - mae: 9.4630 - val_loss: 135.1771 - val_mae: 9.1494 - lr: 0.0010\nEpoch 5/300\n92/92 [==============================] - 0s 3ms/step - loss: 141.4737 - mae: 9.3943 - val_loss: 101.0348 - val_mae: 7.9873 - lr: 0.0010\nEpoch 6/300\n92/92 [==============================] - 0s 3ms/step - loss: 132.2740 - mae: 9.1397 - val_loss: 89.9310 - val_mae: 7.5065 - lr: 0.0010\nEpoch 7/300\n92/92 [==============================] - 0s 3ms/step - loss: 124.7969 - mae: 8.9492 - val_loss: 86.0740 - val_mae: 7.2933 - lr: 0.0010\nEpoch 8/300\n92/92 [==============================] - 0s 3ms/step - loss: 124.1982 - mae: 8.8608 - val_loss: 91.0708 - val_mae: 7.4738 - lr: 0.0010\nEpoch 9/300\n92/92 [==============================] - 0s 3ms/step - loss: 116.6571 - mae: 8.6364 - val_loss: 82.9190 - val_mae: 7.1802 - lr: 0.0010\nEpoch 10/300\n92/92 [==============================] - 0s 3ms/step - loss: 117.6663 - mae: 8.6351 - val_loss: 82.9222 - val_mae: 7.2286 - lr: 0.0010\nEpoch 11/300\n92/92 [==============================] - 0s 3ms/step - loss: 116.3681 - mae: 8.5680 - val_loss: 93.5253 - val_mae: 7.6472 - lr: 0.0010\nEpoch 12/300\n92/92 [==============================] - 0s 3ms/step - loss: 113.3407 - mae: 8.5099 - val_loss: 80.4979 - val_mae: 7.1383 - lr: 0.0010\nEpoch 13/300\n92/92 [==============================] - 0s 3ms/step - loss: 115.7587 - mae: 8.6667 - val_loss: 78.9006 - val_mae: 7.0508 - lr: 0.0010\nEpoch 14/300\n92/92 [==============================] - 0s 3ms/step - loss: 109.1437 - mae: 8.3560 - val_loss: 85.6978 - val_mae: 7.4437 - lr: 0.0010\nEpoch 15/300\n92/92 [==============================] - 0s 3ms/step - loss: 114.0601 - mae: 8.4920 - val_loss: 76.4591 - val_mae: 6.9295 - lr: 0.0010\nEpoch 16/300\n92/92 [==============================] - 0s 3ms/step - loss: 110.6792 - mae: 8.3956 - val_loss: 80.1414 - val_mae: 7.0802 - lr: 0.0010\nEpoch 17/300\n92/92 [==============================] - 0s 3ms/step - loss: 112.3053 - mae: 8.4403 - val_loss: 77.8506 - val_mae: 6.9952 - lr: 0.0010\nEpoch 18/300\n92/92 [==============================] - 0s 3ms/step - loss: 108.9818 - mae: 8.3062 - val_loss: 77.3195 - val_mae: 7.0162 - lr: 0.0010\nEpoch 19/300\n92/92 [==============================] - 0s 3ms/step - loss: 107.2067 - mae: 8.2877 - val_loss: 83.9504 - val_mae: 7.2870 - lr: 0.0010\nEpoch 20/300\n92/92 [==============================] - 0s 4ms/step - loss: 108.3658 - mae: 8.3014 - val_loss: 80.3836 - val_mae: 7.0782 - lr: 0.0010\nEpoch 21/300\n92/92 [==============================] - 0s 3ms/step - loss: 108.5138 - mae: 8.3243 - val_loss: 77.8320 - val_mae: 7.0131 - lr: 0.0010\nEpoch 22/300\n92/92 [==============================] - 0s 4ms/step - loss: 106.9734 - mae: 8.2459 - val_loss: 79.4912 - val_mae: 7.0573 - lr: 0.0010\nEpoch 23/300\n92/92 [==============================] - 0s 4ms/step - loss: 103.1346 - mae: 8.1038 - val_loss: 83.6241 - val_mae: 7.1405 - lr: 0.0010\nEpoch 24/300\n92/92 [==============================] - 0s 3ms/step - loss: 107.9905 - mae: 8.2880 - val_loss: 80.0824 - val_mae: 7.0498 - lr: 0.0010\nEpoch 25/300\n92/92 [==============================] - 0s 3ms/step - loss: 107.6281 - mae: 8.2649 - val_loss: 75.2728 - val_mae: 6.9200 - lr: 0.0010\nEpoch 26/300\n92/92 [==============================] - 0s 3ms/step - loss: 112.1922 - mae: 8.4105 - val_loss: 82.0674 - val_mae: 7.1713 - lr: 0.0010\nEpoch 27/300\n92/92 [==============================] - 0s 3ms/step - loss: 106.2902 - mae: 8.1590 - val_loss: 76.9306 - val_mae: 6.9538 - lr: 0.0010\nEpoch 28/300\n92/92 [==============================] - 0s 3ms/step - loss: 105.2960 - mae: 8.2411 - val_loss: 77.1862 - val_mae: 6.9446 - lr: 0.0010\nEpoch 29/300\n92/92 [==============================] - 0s 3ms/step - loss: 102.3972 - mae: 8.0636 - val_loss: 80.7864 - val_mae: 7.0836 - lr: 0.0010\nEpoch 30/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.6274 - mae: 7.9980 - val_loss: 74.0457 - val_mae: 6.8803 - lr: 0.0010\nEpoch 31/300\n92/92 [==============================] - 0s 3ms/step - loss: 101.4192 - mae: 7.9804 - val_loss: 75.0765 - val_mae: 6.8119 - lr: 0.0010\nEpoch 32/300\n92/92 [==============================] - 0s 3ms/step - loss: 99.2135 - mae: 7.8923 - val_loss: 77.7678 - val_mae: 6.9409 - lr: 0.0010\nEpoch 33/300\n92/92 [==============================] - 0s 4ms/step - loss: 105.5224 - mae: 8.1362 - val_loss: 71.8828 - val_mae: 6.6977 - lr: 0.0010\nEpoch 34/300\n92/92 [==============================] - 0s 4ms/step - loss: 98.4739 - mae: 7.9825 - val_loss: 76.4664 - val_mae: 6.9042 - lr: 0.0010\nEpoch 35/300\n92/92 [==============================] - 0s 3ms/step - loss: 102.9254 - mae: 8.0480 - val_loss: 78.9024 - val_mae: 7.0642 - lr: 0.0010\nEpoch 36/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.5138 - mae: 7.9244 - val_loss: 73.8707 - val_mae: 6.8756 - lr: 0.0010\nEpoch 37/300\n92/92 [==============================] - 0s 4ms/step - loss: 101.8511 - mae: 8.0222 - val_loss: 79.6967 - val_mae: 7.0355 - lr: 0.0010\nEpoch 38/300\n92/92 [==============================] - 0s 4ms/step - loss: 103.8014 - mae: 8.0372 - val_loss: 83.2645 - val_mae: 7.2012 - lr: 0.0010\nEpoch 39/300\n92/92 [==============================] - 0s 4ms/step - loss: 99.7535 - mae: 7.9932 - val_loss: 74.3874 - val_mae: 6.8101 - lr: 0.0010\nEpoch 40/300\n92/92 [==============================] - 0s 5ms/step - loss: 100.4471 - mae: 8.0130 - val_loss: 76.0326 - val_mae: 6.9049 - lr: 0.0010\nEpoch 41/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.5371 - mae: 7.9811 - val_loss: 83.8159 - val_mae: 7.3271 - lr: 0.0010\nEpoch 42/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.8510 - mae: 7.9724 - val_loss: 72.7105 - val_mae: 6.7863 - lr: 0.0010\nEpoch 43/300\n92/92 [==============================] - 0s 4ms/step - loss: 97.8418 - mae: 7.9286 - val_loss: 79.2137 - val_mae: 7.0662 - lr: 0.0010\nEpoch 44/300\n92/92 [==============================] - 0s 3ms/step - loss: 98.3111 - mae: 7.8706 - val_loss: 74.4363 - val_mae: 6.8218 - lr: 0.0010\nEpoch 45/300\n92/92 [==============================] - 0s 3ms/step - loss: 98.6777 - mae: 7.8242 - val_loss: 72.0326 - val_mae: 6.7226 - lr: 0.0010\nEpoch 46/300\n92/92 [==============================] - 0s 4ms/step - loss: 101.1139 - mae: 7.9921 - val_loss: 77.5386 - val_mae: 6.9698 - lr: 0.0010\nEpoch 47/300\n92/92 [==============================] - 0s 4ms/step - loss: 99.2508 - mae: 7.9041 - val_loss: 72.6017 - val_mae: 6.7313 - lr: 0.0010\nEpoch 48/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.3045 - mae: 7.7661 - val_loss: 72.7634 - val_mae: 6.7581 - lr: 0.0010\nEpoch 49/300\n92/92 [==============================] - 0s 4ms/step - loss: 98.4374 - mae: 7.9096 - val_loss: 72.9061 - val_mae: 6.7906 - lr: 0.0010\nEpoch 50/300\n92/92 [==============================] - 0s 4ms/step - loss: 100.2222 - mae: 7.9674 - val_loss: 74.7102 - val_mae: 6.8571 - lr: 0.0010\nEpoch 51/300\n92/92 [==============================] - 0s 4ms/step - loss: 102.2875 - mae: 8.0097 - val_loss: 72.9417 - val_mae: 6.6971 - lr: 0.0010\nEpoch 52/300\n92/92 [==============================] - 0s 3ms/step - loss: 96.5140 - mae: 7.8247 - val_loss: 77.4503 - val_mae: 6.9405 - lr: 0.0010\nEpoch 53/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.2831 - mae: 7.7977 - val_loss: 78.6327 - val_mae: 7.0689 - lr: 0.0010\nEpoch 54/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.1770 - mae: 7.7927 - val_loss: 71.7623 - val_mae: 6.6964 - lr: 0.0010\nEpoch 55/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.8355 - mae: 7.7671 - val_loss: 77.3495 - val_mae: 6.9852 - lr: 0.0010\nEpoch 56/300\n92/92 [==============================] - 0s 4ms/step - loss: 97.5690 - mae: 7.8281 - val_loss: 77.0965 - val_mae: 6.9853 - lr: 0.0010\nEpoch 57/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.3979 - mae: 7.7380 - val_loss: 73.0755 - val_mae: 6.7519 - lr: 0.0010\nEpoch 58/300\n92/92 [==============================] - 0s 4ms/step - loss: 97.4138 - mae: 7.8163 - val_loss: 75.0422 - val_mae: 6.8746 - lr: 0.0010\nEpoch 59/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.4921 - mae: 7.5611 - val_loss: 71.3942 - val_mae: 6.6920 - lr: 0.0010\nEpoch 60/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.8484 - mae: 7.7410 - val_loss: 73.1291 - val_mae: 6.7699 - lr: 0.0010\nEpoch 61/300\n92/92 [==============================] - 0s 3ms/step - loss: 98.4911 - mae: 7.8688 - val_loss: 76.7199 - val_mae: 6.9113 - lr: 0.0010\nEpoch 62/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.0172 - mae: 7.5796 - val_loss: 69.1031 - val_mae: 6.5422 - lr: 0.0010\nEpoch 63/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.0293 - mae: 7.6601 - val_loss: 74.7726 - val_mae: 6.8702 - lr: 0.0010\nEpoch 64/300\n92/92 [==============================] - 0s 5ms/step - loss: 100.1976 - mae: 7.9216 - val_loss: 77.1556 - val_mae: 6.9066 - lr: 0.0010\nEpoch 65/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.8858 - mae: 7.7148 - val_loss: 73.2029 - val_mae: 6.7552 - lr: 0.0010\nEpoch 66/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.7404 - mae: 7.8108 - val_loss: 75.3007 - val_mae: 6.8691 - lr: 0.0010\nEpoch 67/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.6406 - mae: 7.7801 - val_loss: 73.0759 - val_mae: 6.6998 - lr: 0.0010\nEpoch 68/300\n92/92 [==============================] - 0s 4ms/step - loss: 98.4750 - mae: 7.8965 - val_loss: 75.2031 - val_mae: 6.8793 - lr: 0.0010\nEpoch 69/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.4579 - mae: 7.6029 - val_loss: 71.3741 - val_mae: 6.6944 - lr: 0.0010\nEpoch 70/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.6786 - mae: 7.7301 - val_loss: 70.9584 - val_mae: 6.6154 - lr: 0.0010\nEpoch 71/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.1770 - mae: 7.7742 - val_loss: 72.6918 - val_mae: 6.7322 - lr: 0.0010\nEpoch 72/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.6645 - mae: 7.7055 - val_loss: 78.5142 - val_mae: 6.9050 - lr: 0.0010\nEpoch 73/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.6964 - mae: 7.6256 - val_loss: 77.2871 - val_mae: 6.9635 - lr: 0.0010\nEpoch 74/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.8448 - mae: 7.7196 - val_loss: 81.5089 - val_mae: 7.0583 - lr: 0.0010\nEpoch 75/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.7110 - mae: 7.5876 - val_loss: 69.8094 - val_mae: 6.6101 - lr: 0.0010\nEpoch 76/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.1627 - mae: 7.7428 - val_loss: 77.7395 - val_mae: 6.9517 - lr: 0.0010\nEpoch 77/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.5654 - mae: 7.6941 - val_loss: 71.5291 - val_mae: 6.6674 - lr: 0.0010\nEpoch 78/300\n92/92 [==============================] - 0s 3ms/step - loss: 93.3078 - mae: 7.6573 - val_loss: 74.4263 - val_mae: 6.7625 - lr: 0.0010\nEpoch 79/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.1301 - mae: 7.7099 - val_loss: 69.7177 - val_mae: 6.5768 - lr: 0.0010\nEpoch 80/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.6537 - mae: 7.6834 - val_loss: 68.9976 - val_mae: 6.5395 - lr: 0.0010\nEpoch 81/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.7181 - mae: 7.5832 - val_loss: 70.8119 - val_mae: 6.6552 - lr: 0.0010\nEpoch 82/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.3686 - mae: 7.4822 - val_loss: 72.7454 - val_mae: 6.6872 - lr: 0.0010\nEpoch 83/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.6115 - mae: 7.5835 - val_loss: 69.2922 - val_mae: 6.5892 - lr: 0.0010\nEpoch 84/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.9770 - mae: 7.6666 - val_loss: 79.3099 - val_mae: 7.0145 - lr: 0.0010\nEpoch 85/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.3225 - mae: 7.5913 - val_loss: 72.5399 - val_mae: 6.7337 - lr: 0.0010\nEpoch 86/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.3837 - mae: 7.5896 - val_loss: 67.8549 - val_mae: 6.4595 - lr: 0.0010\nEpoch 87/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.7209 - mae: 7.5892 - val_loss: 70.8164 - val_mae: 6.6545 - lr: 0.0010\nEpoch 88/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.5335 - mae: 7.6441 - val_loss: 80.9876 - val_mae: 7.0935 - lr: 0.0010\nEpoch 89/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.2324 - mae: 7.5331 - val_loss: 68.1899 - val_mae: 6.4911 - lr: 0.0010\nEpoch 90/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.0732 - mae: 7.5151 - val_loss: 68.5749 - val_mae: 6.5245 - lr: 0.0010\nEpoch 91/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.2794 - mae: 7.6712 - val_loss: 70.6249 - val_mae: 6.5701 - lr: 0.0010\nEpoch 92/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.4534 - mae: 7.4806 - val_loss: 78.4816 - val_mae: 6.9180 - lr: 0.0010\nEpoch 93/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.0507 - mae: 7.5173 - val_loss: 68.6952 - val_mae: 6.4823 - lr: 0.0010\nEpoch 94/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.3473 - mae: 7.5464 - val_loss: 68.2315 - val_mae: 6.5025 - lr: 0.0010\nEpoch 95/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.5417 - mae: 7.5036 - val_loss: 69.8840 - val_mae: 6.5641 - lr: 0.0010\nEpoch 96/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.0064 - mae: 7.3854 - val_loss: 70.5251 - val_mae: 6.5835 - lr: 0.0010\nEpoch 97/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.5521 - mae: 7.4457 - val_loss: 69.3924 - val_mae: 6.5941 - lr: 0.0010\nEpoch 98/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.7556 - mae: 7.4708 - val_loss: 75.9986 - val_mae: 6.8359 - lr: 0.0010\nEpoch 99/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.3396 - mae: 7.5009 - val_loss: 69.9766 - val_mae: 6.5820 - lr: 0.0010\nEpoch 100/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.8261 - mae: 7.4813 - val_loss: 70.6833 - val_mae: 6.5865 - lr: 0.0010\nEpoch 101/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.6003 - mae: 7.5618 - val_loss: 69.3125 - val_mae: 6.5647 - lr: 0.0010\nEpoch 102/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.9148 - mae: 7.5472 - val_loss: 70.2489 - val_mae: 6.5754 - lr: 0.0010\nEpoch 103/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.2211 - mae: 7.3891 - val_loss: 72.1844 - val_mae: 6.6515 - lr: 0.0010\nEpoch 104/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.6819 - mae: 7.3955 - val_loss: 67.2044 - val_mae: 6.4562 - lr: 0.0010\nEpoch 105/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.8731 - mae: 7.4500 - val_loss: 77.0049 - val_mae: 6.7953 - lr: 0.0010\nEpoch 106/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.5264 - mae: 7.4495 - val_loss: 70.9923 - val_mae: 6.5940 - lr: 0.0010\nEpoch 107/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.5995 - mae: 7.5676 - val_loss: 69.0312 - val_mae: 6.5439 - lr: 0.0010\nEpoch 108/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.9579 - mae: 7.5330 - val_loss: 77.4334 - val_mae: 6.8561 - lr: 0.0010\nEpoch 109/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.8879 - mae: 7.5052 - val_loss: 69.1045 - val_mae: 6.5772 - lr: 0.0010\nEpoch 110/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.7955 - mae: 7.3442 - val_loss: 74.0260 - val_mae: 6.8052 - lr: 0.0010\nEpoch 111/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.6698 - mae: 7.4006 - val_loss: 69.8137 - val_mae: 6.5525 - lr: 0.0010\nEpoch 112/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.2541 - mae: 7.4723 - val_loss: 68.3825 - val_mae: 6.4930 - lr: 0.0010\nEpoch 113/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.5586 - mae: 7.5048 - val_loss: 68.0280 - val_mae: 6.5096 - lr: 0.0010\nEpoch 114/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.8898 - mae: 7.4318 - val_loss: 71.9264 - val_mae: 6.6536 - lr: 0.0010\nEpoch 115/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.9348 - mae: 7.4145 - val_loss: 72.1527 - val_mae: 6.6619 - lr: 0.0010\nEpoch 116/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.5368 - mae: 7.4782 - val_loss: 69.6999 - val_mae: 6.5945 - lr: 0.0010\nEpoch 117/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.1337 - mae: 7.4673 - val_loss: 68.3585 - val_mae: 6.5064 - lr: 0.0010\nEpoch 118/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.8392 - mae: 7.3874 - val_loss: 74.7887 - val_mae: 6.7205 - lr: 0.0010\nEpoch 119/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.7512 - mae: 7.4192 - val_loss: 68.3545 - val_mae: 6.4823 - lr: 0.0010\nEpoch 120/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.3093 - mae: 7.4535 - val_loss: 71.0651 - val_mae: 6.6069 - lr: 0.0010\nEpoch 121/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.7885 - mae: 7.4165 - val_loss: 69.0726 - val_mae: 6.5323 - lr: 0.0010\nEpoch 122/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.2905 - mae: 7.4142 - val_loss: 69.7531 - val_mae: 6.5301 - lr: 0.0010\nEpoch 123/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.1917 - mae: 7.3185 - val_loss: 72.0169 - val_mae: 6.6590 - lr: 0.0010\nEpoch 124/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.3112 - mae: 7.3250 - val_loss: 69.3789 - val_mae: 6.5285 - lr: 0.0010\nEpoch 125/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.3584 - mae: 7.4422 - val_loss: 70.5699 - val_mae: 6.5552 - lr: 0.0010\nEpoch 126/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.6604 - mae: 7.4161 - val_loss: 70.6471 - val_mae: 6.5920 - lr: 0.0010\nEpoch 127/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.0085 - mae: 7.5244 - val_loss: 71.1872 - val_mae: 6.6068 - lr: 0.0010\nEpoch 128/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.4559 - mae: 7.4565 - val_loss: 70.8056 - val_mae: 6.6306 - lr: 0.0010\nEpoch 129/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.0468 - mae: 7.3915 - val_loss: 68.1785 - val_mae: 6.4971 - lr: 0.0010\nEpoch 130/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.8315 - mae: 7.3265 - val_loss: 67.0756 - val_mae: 6.4196 - lr: 0.0010\nEpoch 131/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.4937 - mae: 7.4305 - val_loss: 71.3180 - val_mae: 6.6107 - lr: 0.0010\nEpoch 132/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.5680 - mae: 7.4915 - val_loss: 70.9545 - val_mae: 6.5746 - lr: 0.0010\nEpoch 133/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.8200 - mae: 7.4485 - val_loss: 71.4187 - val_mae: 6.6320 - lr: 0.0010\nEpoch 134/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.3383 - mae: 7.2832 - val_loss: 70.1836 - val_mae: 6.5776 - lr: 0.0010\nEpoch 135/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.6741 - mae: 7.4205 - val_loss: 68.4308 - val_mae: 6.4760 - lr: 0.0010\nEpoch 136/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.8304 - mae: 7.2857 - val_loss: 68.6144 - val_mae: 6.4817 - lr: 0.0010\nEpoch 137/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.2501 - mae: 7.5045 - val_loss: 69.4831 - val_mae: 6.5597 - lr: 0.0010\nEpoch 138/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.4574 - mae: 7.3543 - val_loss: 71.0027 - val_mae: 6.5544 - lr: 0.0010\nEpoch 139/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.5542 - mae: 7.3902 - val_loss: 68.9291 - val_mae: 6.5174 - lr: 0.0010\nEpoch 140/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.9818 - mae: 7.3660 - val_loss: 66.1499 - val_mae: 6.3630 - lr: 0.0010\nEpoch 141/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.2193 - mae: 7.3095 - val_loss: 69.5685 - val_mae: 6.5605 - lr: 0.0010\nEpoch 142/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.8681 - mae: 7.2297 - val_loss: 69.6626 - val_mae: 6.5152 - lr: 0.0010\nEpoch 143/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.6391 - mae: 7.3032 - val_loss: 69.6183 - val_mae: 6.5159 - lr: 0.0010\nEpoch 144/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.4409 - mae: 7.2172 - val_loss: 69.2263 - val_mae: 6.5668 - lr: 0.0010\nEpoch 145/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.0307 - mae: 7.2334 - val_loss: 68.8198 - val_mae: 6.4357 - lr: 0.0010\nEpoch 146/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.6700 - mae: 7.3914 - val_loss: 68.3962 - val_mae: 6.5187 - lr: 0.0010\nEpoch 147/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.8259 - mae: 7.2655 - val_loss: 68.9042 - val_mae: 6.4899 - lr: 0.0010\nEpoch 148/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.3039 - mae: 7.3222 - val_loss: 69.4378 - val_mae: 6.5034 - lr: 0.0010\nEpoch 149/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.7832 - mae: 7.3024 - val_loss: 69.0902 - val_mae: 6.5234 - lr: 0.0010\nEpoch 150/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.1581 - mae: 7.3736 - val_loss: 70.4940 - val_mae: 6.5745 - lr: 0.0010\nEpoch 151/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.0164 - mae: 7.3054 - val_loss: 67.4751 - val_mae: 6.3839 - lr: 0.0010\nEpoch 152/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.5389 - mae: 7.2708 - val_loss: 66.8695 - val_mae: 6.4397 - lr: 0.0010\nEpoch 153/300\n92/92 [==============================] - 0s 5ms/step - loss: 86.4384 - mae: 7.3654 - val_loss: 71.8754 - val_mae: 6.6257 - lr: 0.0010\nEpoch 154/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.0646 - mae: 7.3112 - val_loss: 66.8382 - val_mae: 6.3864 - lr: 0.0010\nEpoch 155/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.8958 - mae: 7.1942 - val_loss: 66.6443 - val_mae: 6.3975 - lr: 0.0010\nEpoch 156/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.9048 - mae: 7.2234 - val_loss: 66.4142 - val_mae: 6.3152 - lr: 0.0010\nEpoch 157/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.3964 - mae: 7.3507 - val_loss: 68.8780 - val_mae: 6.4804 - lr: 0.0010\nEpoch 158/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.9272 - mae: 7.1935 - val_loss: 67.9833 - val_mae: 6.4240 - lr: 0.0010\nEpoch 159/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.2284 - mae: 7.2028 - val_loss: 69.0868 - val_mae: 6.5427 - lr: 0.0010\nEpoch 160/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.1241 - mae: 7.2579 - val_loss: 68.9789 - val_mae: 6.5521 - lr: 0.0010\nEpoch 161/300\n92/92 [==============================] - 0s 5ms/step - loss: 84.1524 - mae: 7.2749 - val_loss: 67.9989 - val_mae: 6.4155 - lr: 0.0010\nEpoch 162/300\n92/92 [==============================] - 0s 5ms/step - loss: 84.1439 - mae: 7.2166 - val_loss: 72.3531 - val_mae: 6.6669 - lr: 0.0010\nEpoch 163/300\n92/92 [==============================] - 1s 6ms/step - loss: 84.8820 - mae: 7.3007 - val_loss: 68.3685 - val_mae: 6.4726 - lr: 0.0010\nEpoch 164/300\n92/92 [==============================] - 0s 5ms/step - loss: 85.1438 - mae: 7.2523 - val_loss: 69.5900 - val_mae: 6.4942 - lr: 0.0010\nEpoch 165/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.4682 - mae: 7.2698 - val_loss: 65.8752 - val_mae: 6.3169 - lr: 0.0010\nEpoch 166/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.9669 - mae: 7.1181 - val_loss: 65.7354 - val_mae: 6.2922 - lr: 0.0010\nEpoch 167/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.0774 - mae: 7.2182 - val_loss: 71.2199 - val_mae: 6.5619 - lr: 0.0010\nEpoch 168/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.7689 - mae: 7.2003 - val_loss: 67.3630 - val_mae: 6.3832 - lr: 0.0010\nEpoch 169/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.8228 - mae: 7.2514 - val_loss: 66.5896 - val_mae: 6.3441 - lr: 0.0010\nEpoch 170/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.7663 - mae: 7.2182 - val_loss: 69.4327 - val_mae: 6.4967 - lr: 0.0010\nEpoch 171/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.7664 - mae: 7.1261 - val_loss: 69.8088 - val_mae: 6.5176 - lr: 0.0010\nEpoch 172/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.2634 - mae: 7.1530 - val_loss: 68.7864 - val_mae: 6.4614 - lr: 0.0010\nEpoch 173/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.0587 - mae: 7.2580 - val_loss: 67.5058 - val_mae: 6.4381 - lr: 0.0010\nEpoch 174/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.0235 - mae: 7.2111 - val_loss: 65.3924 - val_mae: 6.3201 - lr: 0.0010\nEpoch 175/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8977 - mae: 7.0692 - val_loss: 69.3596 - val_mae: 6.5465 - lr: 0.0010\nEpoch 176/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.3700 - mae: 7.2178 - val_loss: 65.4078 - val_mae: 6.3217 - lr: 0.0010\nEpoch 177/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.1147 - mae: 7.0976 - val_loss: 67.9256 - val_mae: 6.4487 - lr: 0.0010\nEpoch 178/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.5139 - mae: 7.2083 - val_loss: 67.2188 - val_mae: 6.4029 - lr: 0.0010\nEpoch 179/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.3279 - mae: 7.2942 - val_loss: 70.1390 - val_mae: 6.5453 - lr: 0.0010\nEpoch 180/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.7318 - mae: 7.1412 - val_loss: 66.8066 - val_mae: 6.3864 - lr: 0.0010\nEpoch 181/300\n92/92 [==============================] - 0s 5ms/step - loss: 84.0977 - mae: 7.1973 - val_loss: 70.2181 - val_mae: 6.5879 - lr: 0.0010\nEpoch 182/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.1608 - mae: 7.2354 - val_loss: 67.1274 - val_mae: 6.4175 - lr: 0.0010\nEpoch 183/300\n92/92 [==============================] - 0s 5ms/step - loss: 82.5619 - mae: 7.1547 - val_loss: 68.2127 - val_mae: 6.4240 - lr: 0.0010\nEpoch 184/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.8447 - mae: 7.1858 - val_loss: 66.1089 - val_mae: 6.3311 - lr: 0.0010\nEpoch 185/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.9382 - mae: 7.3159 - val_loss: 69.7226 - val_mae: 6.4791 - lr: 0.0010\nEpoch 186/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.1195 - mae: 7.1964 - val_loss: 67.7771 - val_mae: 6.4049 - lr: 0.0010\nEpoch 187/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.8610 - mae: 7.2276 - val_loss: 66.6040 - val_mae: 6.3894 - lr: 0.0010\nEpoch 188/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.7456 - mae: 7.1089 - val_loss: 65.3142 - val_mae: 6.3150 - lr: 0.0010\nEpoch 189/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.3389 - mae: 7.2199 - val_loss: 66.3622 - val_mae: 6.3486 - lr: 0.0010\nEpoch 190/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.2913 - mae: 7.1880 - val_loss: 68.8342 - val_mae: 6.4853 - lr: 0.0010\nEpoch 191/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.6938 - mae: 7.2391 - val_loss: 65.2257 - val_mae: 6.3058 - lr: 0.0010\nEpoch 192/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.3835 - mae: 7.1167 - val_loss: 71.1020 - val_mae: 6.5543 - lr: 0.0010\nEpoch 193/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.3158 - mae: 7.0313 - val_loss: 67.0694 - val_mae: 6.3976 - lr: 0.0010\nEpoch 194/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.9058 - mae: 7.0444 - val_loss: 65.8020 - val_mae: 6.3512 - lr: 0.0010\nEpoch 195/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.9745 - mae: 7.2773 - val_loss: 65.9970 - val_mae: 6.3077 - lr: 0.0010\nEpoch 196/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.3544 - mae: 7.1452 - val_loss: 66.6183 - val_mae: 6.3720 - lr: 0.0010\nEpoch 197/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.9254 - mae: 7.1551 - val_loss: 66.4037 - val_mae: 6.3559 - lr: 0.0010\nEpoch 198/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.1638 - mae: 7.1666 - val_loss: 68.7889 - val_mae: 6.4409 - lr: 0.0010\nEpoch 199/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.6349 - mae: 7.1795 - val_loss: 68.1713 - val_mae: 6.3674 - lr: 0.0010\nEpoch 200/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.0993 - mae: 7.1143 - val_loss: 68.2082 - val_mae: 6.4075 - lr: 0.0010\nEpoch 201/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.7272 - mae: 7.2157 - val_loss: 65.8870 - val_mae: 6.2974 - lr: 0.0010\nEpoch 202/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.9555 - mae: 7.0381 - val_loss: 68.7435 - val_mae: 6.4521 - lr: 0.0010\nEpoch 203/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.2332 - mae: 7.1285 - val_loss: 66.0489 - val_mae: 6.3365 - lr: 0.0010\nEpoch 204/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.8282 - mae: 7.0783 - val_loss: 66.9082 - val_mae: 6.4249 - lr: 0.0010\nEpoch 205/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.0843 - mae: 7.0614 - val_loss: 66.6672 - val_mae: 6.4027 - lr: 0.0010\nEpoch 206/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.9122 - mae: 7.1353 - val_loss: 66.1122 - val_mae: 6.3358 - lr: 0.0010\nEpoch 207/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.5909 - mae: 7.1456 - val_loss: 66.8820 - val_mae: 6.4113 - lr: 0.0010\nEpoch 208/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.1617 - mae: 7.1978 - val_loss: 68.5822 - val_mae: 6.4468 - lr: 0.0010\nEpoch 209/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.1378 - mae: 7.1045 - val_loss: 65.1971 - val_mae: 6.3111 - lr: 0.0010\nEpoch 210/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.8460 - mae: 7.1445 - val_loss: 68.2257 - val_mae: 6.4446 - lr: 0.0010\nEpoch 211/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.5031 - mae: 7.3072 - val_loss: 67.8621 - val_mae: 6.4860 - lr: 0.0010\nEpoch 212/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1798 - mae: 7.0339 - val_loss: 66.3811 - val_mae: 6.3297 - lr: 0.0010\nEpoch 213/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.6296 - mae: 7.0429 - val_loss: 66.2217 - val_mae: 6.3131 - lr: 0.0010\nEpoch 214/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.9407 - mae: 7.0520 - val_loss: 65.1635 - val_mae: 6.2877 - lr: 0.0010\nEpoch 215/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.3896 - mae: 7.2462 - val_loss: 65.5432 - val_mae: 6.2842 - lr: 0.0010\nEpoch 216/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.2899 - mae: 7.1317 - val_loss: 66.2910 - val_mae: 6.3494 - lr: 0.0010\nEpoch 217/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.6964 - mae: 7.0400 - val_loss: 65.1248 - val_mae: 6.2625 - lr: 0.0010\nEpoch 218/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.9276 - mae: 7.1350 - val_loss: 65.2192 - val_mae: 6.2916 - lr: 0.0010\nEpoch 219/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.5500 - mae: 7.0570 - val_loss: 72.5243 - val_mae: 6.7281 - lr: 0.0010\nEpoch 220/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.4680 - mae: 7.1284 - val_loss: 65.5986 - val_mae: 6.3156 - lr: 0.0010\nEpoch 221/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.9955 - mae: 7.0133 - val_loss: 65.5805 - val_mae: 6.2967 - lr: 0.0010\nEpoch 222/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.6519 - mae: 7.1562 - val_loss: 65.0268 - val_mae: 6.2558 - lr: 0.0010\nEpoch 223/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.2480 - mae: 7.0110 - val_loss: 66.6717 - val_mae: 6.3321 - lr: 0.0010\nEpoch 224/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.5147 - mae: 7.0960 - val_loss: 67.1357 - val_mae: 6.3032 - lr: 0.0010\nEpoch 225/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.2375 - mae: 7.1762 - val_loss: 64.9351 - val_mae: 6.2723 - lr: 0.0010\nEpoch 226/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.6704 - mae: 7.0914 - val_loss: 64.8287 - val_mae: 6.2087 - lr: 0.0010\nEpoch 227/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.2000 - mae: 7.1765 - val_loss: 68.2047 - val_mae: 6.4594 - lr: 0.0010\nEpoch 228/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.1837 - mae: 7.0236 - val_loss: 68.1247 - val_mae: 6.3885 - lr: 0.0010\nEpoch 229/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.2976 - mae: 6.9326 - val_loss: 66.5776 - val_mae: 6.3598 - lr: 0.0010\nEpoch 230/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.0544 - mae: 7.0855 - val_loss: 65.4344 - val_mae: 6.2962 - lr: 0.0010\nEpoch 231/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.5672 - mae: 7.0637 - val_loss: 64.6975 - val_mae: 6.2283 - lr: 0.0010\nEpoch 232/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.1062 - mae: 7.0892 - val_loss: 65.3307 - val_mae: 6.2938 - lr: 0.0010\nEpoch 233/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.5042 - mae: 7.1202 - val_loss: 66.3528 - val_mae: 6.3386 - lr: 0.0010\nEpoch 234/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.5732 - mae: 7.1461 - val_loss: 65.5940 - val_mae: 6.2804 - lr: 0.0010\nEpoch 235/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.5225 - mae: 7.0421 - val_loss: 67.5537 - val_mae: 6.4380 - lr: 0.0010\nEpoch 236/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.3587 - mae: 7.0182 - val_loss: 67.3717 - val_mae: 6.3606 - lr: 0.0010\nEpoch 237/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.0204 - mae: 7.0550 - val_loss: 67.4046 - val_mae: 6.3857 - lr: 0.0010\nEpoch 238/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.7265 - mae: 6.9977 - val_loss: 66.8190 - val_mae: 6.3720 - lr: 0.0010\nEpoch 239/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.0516 - mae: 7.0783 - val_loss: 64.0681 - val_mae: 6.2195 - lr: 0.0010\nEpoch 240/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.7769 - mae: 7.0937 - val_loss: 65.8215 - val_mae: 6.3175 - lr: 0.0010\nEpoch 241/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.7989 - mae: 7.0800 - val_loss: 66.6902 - val_mae: 6.3703 - lr: 0.0010\nEpoch 242/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.5838 - mae: 6.9764 - val_loss: 67.3474 - val_mae: 6.3233 - lr: 0.0010\nEpoch 243/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.8338 - mae: 7.1252 - val_loss: 65.6068 - val_mae: 6.3225 - lr: 0.0010\nEpoch 244/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.0483 - mae: 7.0869 - val_loss: 63.4542 - val_mae: 6.1509 - lr: 0.0010\nEpoch 245/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1729 - mae: 7.0192 - val_loss: 65.6124 - val_mae: 6.2959 - lr: 0.0010\nEpoch 246/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.9802 - mae: 7.0543 - val_loss: 67.2418 - val_mae: 6.4237 - lr: 0.0010\nEpoch 247/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.6414 - mae: 6.8934 - val_loss: 65.0736 - val_mae: 6.2536 - lr: 0.0010\nEpoch 248/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.6499 - mae: 7.0190 - val_loss: 65.6104 - val_mae: 6.3189 - lr: 0.0010\nEpoch 249/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.5322 - mae: 7.1712 - val_loss: 64.9713 - val_mae: 6.3100 - lr: 0.0010\nEpoch 250/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.5586 - mae: 7.0250 - val_loss: 64.9599 - val_mae: 6.2658 - lr: 0.0010\nEpoch 251/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.0624 - mae: 7.0187 - val_loss: 68.0182 - val_mae: 6.4096 - lr: 0.0010\nEpoch 252/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.4298 - mae: 7.0686 - val_loss: 66.1603 - val_mae: 6.3413 - lr: 0.0010\nEpoch 253/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.1168 - mae: 7.0443 - val_loss: 66.6744 - val_mae: 6.3240 - lr: 0.0010\nEpoch 254/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.2638 - mae: 7.0231 - val_loss: 65.7582 - val_mae: 6.2960 - lr: 0.0010\nEpoch 255/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.4157 - mae: 7.0333 - val_loss: 64.7312 - val_mae: 6.2266 - lr: 0.0010\nEpoch 256/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.8676 - mae: 6.9974 - val_loss: 66.3138 - val_mae: 6.3113 - lr: 0.0010\nEpoch 257/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.7935 - mae: 7.0312 - val_loss: 64.6648 - val_mae: 6.2531 - lr: 0.0010\nEpoch 258/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.2747 - mae: 6.9440 - val_loss: 66.8916 - val_mae: 6.3777 - lr: 0.0010\nEpoch 259/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.4968 - mae: 7.1628 - val_loss: 65.8158 - val_mae: 6.3378 - lr: 0.0010\nEpoch 260/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.4765 - mae: 7.0327 - val_loss: 65.8998 - val_mae: 6.2783 - lr: 0.0010\nEpoch 261/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.7892 - mae: 7.0767 - val_loss: 69.1712 - val_mae: 6.4078 - lr: 0.0010\nEpoch 262/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.5875 - mae: 6.8860 - val_loss: 67.3760 - val_mae: 6.3600 - lr: 0.0010\nEpoch 263/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.8924 - mae: 6.9827 - val_loss: 67.1279 - val_mae: 6.3273 - lr: 0.0010\nEpoch 264/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.7915 - mae: 7.0248 - val_loss: 67.5854 - val_mae: 6.3630 - lr: 0.0010\nEpoch 265/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.3253 - mae: 7.0616 - val_loss: 66.3620 - val_mae: 6.3462 - lr: 0.0010\nEpoch 266/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.0402 - mae: 7.0393 - val_loss: 66.0735 - val_mae: 6.2927 - lr: 0.0010\nEpoch 267/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.6336 - mae: 7.0265 - val_loss: 64.8912 - val_mae: 6.2240 - lr: 0.0010\nEpoch 268/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8628 - mae: 6.9949 - val_loss: 65.8323 - val_mae: 6.2887 - lr: 0.0010\nEpoch 269/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.6502 - mae: 6.9918 - val_loss: 65.1556 - val_mae: 6.2818 - lr: 0.0010\nEpoch 270/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.0908 - mae: 7.0594 - val_loss: 65.5461 - val_mae: 6.2832 - lr: 0.0010\nEpoch 271/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.8699 - mae: 7.0661 - val_loss: 66.2850 - val_mae: 6.3015 - lr: 0.0010\nEpoch 272/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1561 - mae: 7.0017 - val_loss: 65.3895 - val_mae: 6.2861 - lr: 0.0010\nEpoch 273/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.3925 - mae: 7.0076 - val_loss: 66.4925 - val_mae: 6.3326 - lr: 0.0010\nEpoch 274/300\n74/92 [=======================>......] - ETA: 0s - loss: 82.0646 - mae: 7.1440\nEpoch 274: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 4ms/step - loss: 80.1781 - mae: 7.0469 - val_loss: 65.7395 - val_mae: 6.2784 - lr: 0.0010\nEpoch 275/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8470 - mae: 7.0152 - val_loss: 66.0216 - val_mae: 6.2900 - lr: 5.0000e-04\nEpoch 276/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.7036 - mae: 6.9749 - val_loss: 63.9127 - val_mae: 6.1833 - lr: 5.0000e-04\nEpoch 277/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8032 - mae: 6.8914 - val_loss: 63.9866 - val_mae: 6.1870 - lr: 5.0000e-04\nEpoch 278/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.1583 - mae: 7.0275 - val_loss: 64.2802 - val_mae: 6.1927 - lr: 5.0000e-04\nEpoch 279/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.3858 - mae: 7.0642 - val_loss: 66.0118 - val_mae: 6.3059 - lr: 5.0000e-04\nEpoch 280/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.8973 - mae: 6.9202 - val_loss: 65.0792 - val_mae: 6.2341 - lr: 5.0000e-04\nEpoch 281/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.5649 - mae: 6.9490 - val_loss: 65.7158 - val_mae: 6.2957 - lr: 5.0000e-04\nEpoch 282/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.2256 - mae: 6.9788 - val_loss: 64.6207 - val_mae: 6.2225 - lr: 5.0000e-04\nEpoch 283/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.3509 - mae: 6.9723 - val_loss: 66.2568 - val_mae: 6.2996 - lr: 5.0000e-04\nEpoch 284/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.1715 - mae: 6.9021 - val_loss: 65.8724 - val_mae: 6.2998 - lr: 5.0000e-04\nEpoch 285/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.6082 - mae: 6.8898 - val_loss: 64.8803 - val_mae: 6.2662 - lr: 5.0000e-04\nEpoch 286/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.3271 - mae: 6.9365 - val_loss: 64.5854 - val_mae: 6.2274 - lr: 5.0000e-04\nEpoch 287/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.9712 - mae: 6.9580 - val_loss: 65.7875 - val_mae: 6.2589 - lr: 5.0000e-04\nEpoch 288/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.0005 - mae: 6.8392 - val_loss: 63.5274 - val_mae: 6.1568 - lr: 5.0000e-04\nEpoch 289/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.6817 - mae: 6.9645 - val_loss: 64.5675 - val_mae: 6.2116 - lr: 5.0000e-04\nEpoch 290/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2282 - mae: 6.8007 - val_loss: 65.8965 - val_mae: 6.2880 - lr: 5.0000e-04\nEpoch 291/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.4888 - mae: 6.8338 - val_loss: 65.0896 - val_mae: 6.2754 - lr: 5.0000e-04\nEpoch 292/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.5802 - mae: 6.9300 - val_loss: 63.6927 - val_mae: 6.1588 - lr: 5.0000e-04\nEpoch 293/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.0581 - mae: 6.9562 - val_loss: 64.1304 - val_mae: 6.1942 - lr: 5.0000e-04\nEpoch 294/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.4399 - mae: 6.8961 - val_loss: 63.9578 - val_mae: 6.1844 - lr: 5.0000e-04\n46/46 [==============================] - 0s 2ms/step\nRMSE: 7.966\nR^2 Score: 0.422\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/04942626-0b2d-4725-85cf-f246633a5add",
   "content_dependencies": null
  },
  {
   "cellId": "385311c0c9724e9cb17905e1f4fc6d7c",
   "cell_type": "code",
   "metadata": {
    "source_hash": "5cdc402b",
    "execution_start": 1753835665768,
    "execution_millis": 72080,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "385311c0c9724e9cb17905e1f4fc6d7c",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(128, activation='sigmoid'),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(64, activation='relu'),\n    Dropout(0.2),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=32,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "17ca61ad37f44c5f9e5e77e4a9fc8ac8",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n92/92 [==============================] - 2s 5ms/step - loss: 375.7271 - mae: 15.7430 - val_loss: 609.1146 - val_mae: 22.4393 - lr: 0.0010\nEpoch 2/300\n92/92 [==============================] - 0s 3ms/step - loss: 146.0475 - mae: 9.6307 - val_loss: 432.0366 - val_mae: 18.3520 - lr: 0.0010\nEpoch 3/300\n92/92 [==============================] - 0s 3ms/step - loss: 136.1338 - mae: 9.2368 - val_loss: 234.1007 - val_mae: 12.5762 - lr: 0.0010\nEpoch 4/300\n92/92 [==============================] - 0s 3ms/step - loss: 130.1717 - mae: 9.0484 - val_loss: 128.0299 - val_mae: 8.8867 - lr: 0.0010\nEpoch 5/300\n92/92 [==============================] - 0s 3ms/step - loss: 123.6506 - mae: 8.8375 - val_loss: 84.2566 - val_mae: 7.1754 - lr: 0.0010\nEpoch 6/300\n92/92 [==============================] - 0s 3ms/step - loss: 114.3619 - mae: 8.4398 - val_loss: 80.4205 - val_mae: 7.0383 - lr: 0.0010\nEpoch 7/300\n92/92 [==============================] - 0s 3ms/step - loss: 110.5712 - mae: 8.2907 - val_loss: 73.0186 - val_mae: 6.7181 - lr: 0.0010\nEpoch 8/300\n92/92 [==============================] - 0s 3ms/step - loss: 106.4088 - mae: 8.1271 - val_loss: 74.6150 - val_mae: 6.7833 - lr: 0.0010\nEpoch 9/300\n92/92 [==============================] - 0s 3ms/step - loss: 107.0042 - mae: 8.1894 - val_loss: 71.9739 - val_mae: 6.6652 - lr: 0.0010\nEpoch 10/300\n92/92 [==============================] - 0s 3ms/step - loss: 102.2078 - mae: 7.9690 - val_loss: 70.2505 - val_mae: 6.5661 - lr: 0.0010\nEpoch 11/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.4209 - mae: 7.8586 - val_loss: 72.4681 - val_mae: 6.6447 - lr: 0.0010\nEpoch 12/300\n92/92 [==============================] - 0s 4ms/step - loss: 103.4199 - mae: 7.9450 - val_loss: 70.6875 - val_mae: 6.5531 - lr: 0.0010\nEpoch 13/300\n92/92 [==============================] - 0s 4ms/step - loss: 101.6784 - mae: 7.9548 - val_loss: 70.8001 - val_mae: 6.5722 - lr: 0.0010\nEpoch 14/300\n92/92 [==============================] - 0s 3ms/step - loss: 96.9804 - mae: 7.8415 - val_loss: 72.0679 - val_mae: 6.7232 - lr: 0.0010\nEpoch 15/300\n92/92 [==============================] - 0s 3ms/step - loss: 97.1508 - mae: 7.7745 - val_loss: 69.5536 - val_mae: 6.5278 - lr: 0.0010\nEpoch 16/300\n92/92 [==============================] - 0s 3ms/step - loss: 96.0499 - mae: 7.7262 - val_loss: 69.3379 - val_mae: 6.5256 - lr: 0.0010\nEpoch 17/300\n92/92 [==============================] - 0s 3ms/step - loss: 98.7089 - mae: 7.8105 - val_loss: 67.2794 - val_mae: 6.3823 - lr: 0.0010\nEpoch 18/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.2651 - mae: 7.6119 - val_loss: 70.0967 - val_mae: 6.5525 - lr: 0.0010\nEpoch 19/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.4716 - mae: 7.5505 - val_loss: 68.6772 - val_mae: 6.5174 - lr: 0.0010\nEpoch 20/300\n92/92 [==============================] - 0s 3ms/step - loss: 97.1796 - mae: 7.8021 - val_loss: 67.0357 - val_mae: 6.3695 - lr: 0.0010\nEpoch 21/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.0567 - mae: 7.6234 - val_loss: 71.4949 - val_mae: 6.6379 - lr: 0.0010\nEpoch 22/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.1686 - mae: 7.6551 - val_loss: 73.8716 - val_mae: 6.7268 - lr: 0.0010\nEpoch 23/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.4702 - mae: 7.5757 - val_loss: 71.6470 - val_mae: 6.5962 - lr: 0.0010\nEpoch 24/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.5190 - mae: 7.5221 - val_loss: 68.2103 - val_mae: 6.4779 - lr: 0.0010\nEpoch 25/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.8528 - mae: 7.5134 - val_loss: 67.5603 - val_mae: 6.3981 - lr: 0.0010\nEpoch 26/300\n92/92 [==============================] - 0s 3ms/step - loss: 93.6638 - mae: 7.5736 - val_loss: 67.6603 - val_mae: 6.4950 - lr: 0.0010\nEpoch 27/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.0284 - mae: 7.4526 - val_loss: 67.6704 - val_mae: 6.4815 - lr: 0.0010\nEpoch 28/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.4417 - mae: 7.4618 - val_loss: 68.5026 - val_mae: 6.4327 - lr: 0.0010\nEpoch 29/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.6152 - mae: 7.3380 - val_loss: 69.8182 - val_mae: 6.5715 - lr: 0.0010\nEpoch 30/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.3082 - mae: 7.4435 - val_loss: 65.0222 - val_mae: 6.2675 - lr: 0.0010\nEpoch 31/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.5715 - mae: 7.3248 - val_loss: 67.4517 - val_mae: 6.3827 - lr: 0.0010\nEpoch 32/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.8019 - mae: 7.4016 - val_loss: 68.9906 - val_mae: 6.4896 - lr: 0.0010\nEpoch 33/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.8961 - mae: 7.4936 - val_loss: 65.6192 - val_mae: 6.3404 - lr: 0.0010\nEpoch 34/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.2687 - mae: 7.2853 - val_loss: 69.0249 - val_mae: 6.5057 - lr: 0.0010\nEpoch 35/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.5987 - mae: 7.5175 - val_loss: 71.3544 - val_mae: 6.6949 - lr: 0.0010\nEpoch 36/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.5631 - mae: 7.2375 - val_loss: 66.0387 - val_mae: 6.3488 - lr: 0.0010\nEpoch 37/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.2378 - mae: 7.2721 - val_loss: 68.6701 - val_mae: 6.4341 - lr: 0.0010\nEpoch 38/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.3913 - mae: 7.3321 - val_loss: 69.8973 - val_mae: 6.5670 - lr: 0.0010\nEpoch 39/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.6240 - mae: 7.2569 - val_loss: 64.4327 - val_mae: 6.2503 - lr: 0.0010\nEpoch 40/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.5622 - mae: 7.2242 - val_loss: 64.0955 - val_mae: 6.2297 - lr: 0.0010\nEpoch 41/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.5557 - mae: 7.2261 - val_loss: 64.8216 - val_mae: 6.3011 - lr: 0.0010\nEpoch 42/300\n92/92 [==============================] - 0s 3ms/step - loss: 85.1992 - mae: 7.1286 - val_loss: 66.9629 - val_mae: 6.4431 - lr: 0.0010\nEpoch 43/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.4902 - mae: 7.2088 - val_loss: 66.4733 - val_mae: 6.3607 - lr: 0.0010\nEpoch 44/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.4904 - mae: 7.1350 - val_loss: 64.7610 - val_mae: 6.2982 - lr: 0.0010\nEpoch 45/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.4176 - mae: 7.0184 - val_loss: 64.4558 - val_mae: 6.2674 - lr: 0.0010\nEpoch 46/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.8874 - mae: 7.1296 - val_loss: 65.5935 - val_mae: 6.2977 - lr: 0.0010\nEpoch 47/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.9105 - mae: 7.0479 - val_loss: 64.6280 - val_mae: 6.2450 - lr: 0.0010\nEpoch 48/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.6487 - mae: 7.1124 - val_loss: 68.6923 - val_mae: 6.5409 - lr: 0.0010\nEpoch 49/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.0698 - mae: 7.1227 - val_loss: 65.6915 - val_mae: 6.3027 - lr: 0.0010\nEpoch 50/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.5560 - mae: 7.2193 - val_loss: 65.7415 - val_mae: 6.3623 - lr: 0.0010\nEpoch 51/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.8484 - mae: 7.1601 - val_loss: 65.7438 - val_mae: 6.3385 - lr: 0.0010\nEpoch 52/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.5660 - mae: 7.1047 - val_loss: 68.3658 - val_mae: 6.5085 - lr: 0.0010\nEpoch 53/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.2337 - mae: 6.9460 - val_loss: 68.3749 - val_mae: 6.4565 - lr: 0.0010\nEpoch 54/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.3743 - mae: 7.0868 - val_loss: 63.9624 - val_mae: 6.1977 - lr: 0.0010\nEpoch 55/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.3279 - mae: 7.0843 - val_loss: 65.2346 - val_mae: 6.2659 - lr: 0.0010\nEpoch 56/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.2548 - mae: 7.0405 - val_loss: 65.6697 - val_mae: 6.3268 - lr: 0.0010\nEpoch 57/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.1117 - mae: 6.9664 - val_loss: 65.1905 - val_mae: 6.2198 - lr: 0.0010\nEpoch 58/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.3528 - mae: 7.0789 - val_loss: 67.8173 - val_mae: 6.4539 - lr: 0.0010\nEpoch 59/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.6739 - mae: 6.8734 - val_loss: 64.5366 - val_mae: 6.2013 - lr: 0.0010\nEpoch 60/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.9338 - mae: 7.0093 - val_loss: 64.1986 - val_mae: 6.2010 - lr: 0.0010\nEpoch 61/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.3359 - mae: 7.0297 - val_loss: 64.2519 - val_mae: 6.2730 - lr: 0.0010\nEpoch 62/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.6151 - mae: 6.8620 - val_loss: 62.9147 - val_mae: 6.1612 - lr: 0.0010\nEpoch 63/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.5717 - mae: 6.9540 - val_loss: 62.8713 - val_mae: 6.1890 - lr: 0.0010\nEpoch 64/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.3702 - mae: 7.0365 - val_loss: 64.7300 - val_mae: 6.2494 - lr: 0.0010\nEpoch 65/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.1037 - mae: 6.9309 - val_loss: 63.6705 - val_mae: 6.1759 - lr: 0.0010\nEpoch 66/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.6349 - mae: 6.8153 - val_loss: 65.6722 - val_mae: 6.3333 - lr: 0.0010\nEpoch 67/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8947 - mae: 6.8247 - val_loss: 65.1797 - val_mae: 6.2525 - lr: 0.0010\nEpoch 68/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.4464 - mae: 6.9257 - val_loss: 65.0583 - val_mae: 6.2341 - lr: 0.0010\nEpoch 69/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.0922 - mae: 6.8242 - val_loss: 64.5416 - val_mae: 6.2307 - lr: 0.0010\nEpoch 70/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.4594 - mae: 6.9103 - val_loss: 63.5536 - val_mae: 6.1250 - lr: 0.0010\nEpoch 71/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.0901 - mae: 6.8137 - val_loss: 64.1749 - val_mae: 6.2002 - lr: 0.0010\nEpoch 72/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.1862 - mae: 6.9180 - val_loss: 65.1601 - val_mae: 6.2833 - lr: 0.0010\nEpoch 73/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.2225 - mae: 6.8709 - val_loss: 65.0773 - val_mae: 6.2779 - lr: 0.0010\nEpoch 74/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.7423 - mae: 6.8257 - val_loss: 69.0294 - val_mae: 6.4824 - lr: 0.0010\nEpoch 75/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.6604 - mae: 6.8145 - val_loss: 65.7456 - val_mae: 6.3381 - lr: 0.0010\nEpoch 76/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.4324 - mae: 6.7624 - val_loss: 64.8813 - val_mae: 6.2797 - lr: 0.0010\nEpoch 77/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.2534 - mae: 6.8462 - val_loss: 62.9438 - val_mae: 6.1083 - lr: 0.0010\nEpoch 78/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.8897 - mae: 6.8764 - val_loss: 65.5780 - val_mae: 6.3010 - lr: 0.0010\nEpoch 79/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.3255 - mae: 6.8012 - val_loss: 63.6668 - val_mae: 6.1812 - lr: 0.0010\nEpoch 80/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.3812 - mae: 6.6862 - val_loss: 65.1627 - val_mae: 6.2230 - lr: 0.0010\nEpoch 81/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.3242 - mae: 6.7004 - val_loss: 63.1928 - val_mae: 6.1634 - lr: 0.0010\nEpoch 82/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.1224 - mae: 6.7410 - val_loss: 65.8691 - val_mae: 6.3212 - lr: 0.0010\nEpoch 83/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.4171 - mae: 6.7038 - val_loss: 62.7595 - val_mae: 6.1595 - lr: 0.0010\nEpoch 84/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2657 - mae: 6.7348 - val_loss: 64.4504 - val_mae: 6.2891 - lr: 0.0010\nEpoch 85/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.6116 - mae: 6.7511 - val_loss: 64.5001 - val_mae: 6.2399 - lr: 0.0010\nEpoch 86/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.2505 - mae: 6.7417 - val_loss: 62.8396 - val_mae: 6.1160 - lr: 0.0010\nEpoch 87/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2358 - mae: 6.7219 - val_loss: 63.9448 - val_mae: 6.1632 - lr: 0.0010\nEpoch 88/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.1718 - mae: 6.6715 - val_loss: 67.9927 - val_mae: 6.4543 - lr: 0.0010\nEpoch 89/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.2813 - mae: 6.6278 - val_loss: 63.0687 - val_mae: 6.1259 - lr: 0.0010\nEpoch 90/300\n92/92 [==============================] - 1s 6ms/step - loss: 74.1893 - mae: 6.6330 - val_loss: 62.6594 - val_mae: 6.1393 - lr: 0.0010\nEpoch 91/300\n92/92 [==============================] - 1s 7ms/step - loss: 75.4479 - mae: 6.7295 - val_loss: 62.6365 - val_mae: 6.1464 - lr: 0.0010\nEpoch 92/300\n92/92 [==============================] - 1s 6ms/step - loss: 74.8111 - mae: 6.6664 - val_loss: 66.1810 - val_mae: 6.3480 - lr: 0.0010\nEpoch 93/300\n92/92 [==============================] - 1s 6ms/step - loss: 72.7573 - mae: 6.6100 - val_loss: 63.3639 - val_mae: 6.2056 - lr: 0.0010\nEpoch 94/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5656 - mae: 6.6663 - val_loss: 62.9995 - val_mae: 6.1364 - lr: 0.0010\nEpoch 95/300\n92/92 [==============================] - 0s 3ms/step - loss: 72.9102 - mae: 6.6153 - val_loss: 63.0724 - val_mae: 6.1519 - lr: 0.0010\nEpoch 96/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.2339 - mae: 6.5960 - val_loss: 63.5752 - val_mae: 6.2108 - lr: 0.0010\nEpoch 97/300\n92/92 [==============================] - 0s 3ms/step - loss: 72.6303 - mae: 6.5762 - val_loss: 63.5390 - val_mae: 6.1683 - lr: 0.0010\nEpoch 98/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.6200 - mae: 6.5343 - val_loss: 66.1188 - val_mae: 6.3731 - lr: 0.0010\nEpoch 99/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.0546 - mae: 6.6340 - val_loss: 61.5800 - val_mae: 6.0742 - lr: 0.0010\nEpoch 100/300\n92/92 [==============================] - 0s 3ms/step - loss: 73.5803 - mae: 6.6670 - val_loss: 62.6093 - val_mae: 6.1447 - lr: 0.0010\nEpoch 101/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.1389 - mae: 6.6454 - val_loss: 63.6396 - val_mae: 6.1784 - lr: 0.0010\nEpoch 102/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7235 - mae: 6.6370 - val_loss: 62.2584 - val_mae: 6.0820 - lr: 0.0010\nEpoch 103/300\n92/92 [==============================] - 0s 3ms/step - loss: 71.6068 - mae: 6.4755 - val_loss: 63.5022 - val_mae: 6.1697 - lr: 0.0010\nEpoch 104/300\n92/92 [==============================] - 0s 3ms/step - loss: 71.8867 - mae: 6.5868 - val_loss: 62.0495 - val_mae: 6.0737 - lr: 0.0010\nEpoch 105/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.9911 - mae: 6.6660 - val_loss: 64.3988 - val_mae: 6.2175 - lr: 0.0010\nEpoch 106/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.8204 - mae: 6.4876 - val_loss: 62.1530 - val_mae: 6.1036 - lr: 0.0010\nEpoch 107/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.4314 - mae: 6.6489 - val_loss: 62.0050 - val_mae: 6.0430 - lr: 0.0010\nEpoch 108/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.2042 - mae: 6.5543 - val_loss: 64.1783 - val_mae: 6.1844 - lr: 0.0010\nEpoch 109/300\n92/92 [==============================] - 0s 3ms/step - loss: 73.7274 - mae: 6.6793 - val_loss: 62.8694 - val_mae: 6.0745 - lr: 0.0010\nEpoch 110/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.4568 - mae: 6.4975 - val_loss: 63.0972 - val_mae: 6.1428 - lr: 0.0010\nEpoch 111/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.9524 - mae: 6.5777 - val_loss: 61.4314 - val_mae: 6.0128 - lr: 0.0010\nEpoch 112/300\n92/92 [==============================] - 0s 3ms/step - loss: 72.9677 - mae: 6.5469 - val_loss: 62.7437 - val_mae: 6.1040 - lr: 0.0010\nEpoch 113/300\n92/92 [==============================] - 0s 3ms/step - loss: 68.7695 - mae: 6.4373 - val_loss: 62.6273 - val_mae: 6.0652 - lr: 0.0010\nEpoch 114/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.5441 - mae: 6.5652 - val_loss: 61.8680 - val_mae: 6.0624 - lr: 0.0010\nEpoch 115/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.3473 - mae: 6.5742 - val_loss: 61.6301 - val_mae: 6.0374 - lr: 0.0010\nEpoch 116/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.2281 - mae: 6.5882 - val_loss: 63.1575 - val_mae: 6.1279 - lr: 0.0010\nEpoch 117/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.2485 - mae: 6.6455 - val_loss: 61.1788 - val_mae: 5.9906 - lr: 0.0010\nEpoch 118/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.4988 - mae: 6.4468 - val_loss: 62.2946 - val_mae: 6.0898 - lr: 0.0010\nEpoch 119/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.0793 - mae: 6.4560 - val_loss: 62.8295 - val_mae: 6.1324 - lr: 0.0010\nEpoch 120/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.4539 - mae: 6.6135 - val_loss: 60.4928 - val_mae: 5.9800 - lr: 0.0010\nEpoch 121/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.5028 - mae: 6.4901 - val_loss: 61.3048 - val_mae: 5.9829 - lr: 0.0010\nEpoch 122/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7408 - mae: 6.5962 - val_loss: 62.7131 - val_mae: 6.0980 - lr: 0.0010\nEpoch 123/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.2100 - mae: 6.5272 - val_loss: 62.7870 - val_mae: 6.1613 - lr: 0.0010\nEpoch 124/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.9418 - mae: 6.4737 - val_loss: 60.2296 - val_mae: 5.9515 - lr: 0.0010\nEpoch 125/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.3713 - mae: 6.5236 - val_loss: 61.6761 - val_mae: 6.0412 - lr: 0.0010\nEpoch 126/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.4572 - mae: 6.4306 - val_loss: 62.6589 - val_mae: 6.0846 - lr: 0.0010\nEpoch 127/300\n92/92 [==============================] - 0s 3ms/step - loss: 68.0660 - mae: 6.3993 - val_loss: 61.8625 - val_mae: 6.0605 - lr: 0.0010\nEpoch 128/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.1048 - mae: 6.4516 - val_loss: 61.4941 - val_mae: 6.0304 - lr: 0.0010\nEpoch 129/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.5855 - mae: 6.3943 - val_loss: 61.4461 - val_mae: 6.0097 - lr: 0.0010\nEpoch 130/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.0046 - mae: 6.4028 - val_loss: 61.5862 - val_mae: 6.0234 - lr: 0.0010\nEpoch 131/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.5844 - mae: 6.4508 - val_loss: 61.9920 - val_mae: 6.0602 - lr: 0.0010\nEpoch 132/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.4964 - mae: 6.5222 - val_loss: 62.0098 - val_mae: 6.0410 - lr: 0.0010\nEpoch 133/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.7524 - mae: 6.5135 - val_loss: 61.9665 - val_mae: 6.0627 - lr: 0.0010\nEpoch 134/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.8902 - mae: 6.3128 - val_loss: 62.2758 - val_mae: 6.0803 - lr: 0.0010\nEpoch 135/300\n92/92 [==============================] - 0s 3ms/step - loss: 67.9914 - mae: 6.3199 - val_loss: 60.8499 - val_mae: 5.9859 - lr: 0.0010\nEpoch 136/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.5369 - mae: 6.4344 - val_loss: 61.3074 - val_mae: 5.9952 - lr: 0.0010\nEpoch 137/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.8094 - mae: 6.4212 - val_loss: 61.6100 - val_mae: 6.0093 - lr: 0.0010\nEpoch 138/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.8510 - mae: 6.3411 - val_loss: 61.8265 - val_mae: 6.0175 - lr: 0.0010\nEpoch 139/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.2111 - mae: 6.3856 - val_loss: 61.6347 - val_mae: 5.9998 - lr: 0.0010\nEpoch 140/300\n92/92 [==============================] - 0s 3ms/step - loss: 67.7208 - mae: 6.3731 - val_loss: 60.2101 - val_mae: 5.8897 - lr: 0.0010\nEpoch 141/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.7426 - mae: 6.4093 - val_loss: 61.9304 - val_mae: 5.9999 - lr: 0.0010\nEpoch 142/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.3975 - mae: 6.4423 - val_loss: 63.5207 - val_mae: 6.0939 - lr: 0.0010\nEpoch 143/300\n92/92 [==============================] - 0s 3ms/step - loss: 68.5719 - mae: 6.3942 - val_loss: 61.4959 - val_mae: 6.0218 - lr: 0.0010\nEpoch 144/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.6480 - mae: 6.4021 - val_loss: 61.7517 - val_mae: 6.0514 - lr: 0.0010\nEpoch 145/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.1318 - mae: 6.3692 - val_loss: 60.8931 - val_mae: 5.9560 - lr: 0.0010\nEpoch 146/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.4165 - mae: 6.3906 - val_loss: 60.8186 - val_mae: 5.9473 - lr: 0.0010\nEpoch 147/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.6247 - mae: 6.3062 - val_loss: 61.1157 - val_mae: 5.9702 - lr: 0.0010\nEpoch 148/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.7540 - mae: 6.5468 - val_loss: 61.3816 - val_mae: 5.9963 - lr: 0.0010\nEpoch 149/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.5994 - mae: 6.3293 - val_loss: 60.6652 - val_mae: 5.9277 - lr: 0.0010\nEpoch 150/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.5714 - mae: 6.4577 - val_loss: 61.1798 - val_mae: 5.9377 - lr: 0.0010\nEpoch 151/300\n92/92 [==============================] - 0s 3ms/step - loss: 67.5211 - mae: 6.2963 - val_loss: 61.5064 - val_mae: 6.0012 - lr: 0.0010\nEpoch 152/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.5548 - mae: 6.3282 - val_loss: 60.1647 - val_mae: 5.8814 - lr: 0.0010\nEpoch 153/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.6196 - mae: 6.4220 - val_loss: 61.4791 - val_mae: 5.9991 - lr: 0.0010\nEpoch 154/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.6424 - mae: 6.4372 - val_loss: 60.0868 - val_mae: 5.8819 - lr: 0.0010\nEpoch 155/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.3507 - mae: 6.3227 - val_loss: 59.2390 - val_mae: 5.8315 - lr: 0.0010\nEpoch 156/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.8121 - mae: 6.3277 - val_loss: 59.9060 - val_mae: 5.8914 - lr: 0.0010\nEpoch 157/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.0431 - mae: 6.3324 - val_loss: 60.7878 - val_mae: 5.9711 - lr: 0.0010\nEpoch 158/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.6885 - mae: 6.2360 - val_loss: 60.2756 - val_mae: 5.8974 - lr: 0.0010\nEpoch 159/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.2145 - mae: 6.2516 - val_loss: 60.3167 - val_mae: 5.9382 - lr: 0.0010\nEpoch 160/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.8980 - mae: 6.3294 - val_loss: 61.5147 - val_mae: 6.0018 - lr: 0.0010\nEpoch 161/300\n92/92 [==============================] - 0s 4ms/step - loss: 65.9246 - mae: 6.2310 - val_loss: 61.1198 - val_mae: 5.9200 - lr: 0.0010\nEpoch 162/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.1246 - mae: 6.3549 - val_loss: 62.0235 - val_mae: 6.0561 - lr: 0.0010\nEpoch 163/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.7742 - mae: 6.3317 - val_loss: 61.2054 - val_mae: 5.9846 - lr: 0.0010\nEpoch 164/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.0898 - mae: 6.2836 - val_loss: 60.0164 - val_mae: 5.8923 - lr: 0.0010\nEpoch 165/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.5188 - mae: 6.3864 - val_loss: 59.2329 - val_mae: 5.8973 - lr: 0.0010\nEpoch 166/300\n92/92 [==============================] - 0s 4ms/step - loss: 64.4005 - mae: 6.2645 - val_loss: 59.3456 - val_mae: 5.8605 - lr: 0.0010\nEpoch 167/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.1822 - mae: 6.2751 - val_loss: 62.8726 - val_mae: 6.0962 - lr: 0.0010\nEpoch 168/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.8984 - mae: 6.3284 - val_loss: 60.9512 - val_mae: 5.9327 - lr: 0.0010\nEpoch 169/300\n92/92 [==============================] - 0s 3ms/step - loss: 67.3311 - mae: 6.3236 - val_loss: 61.4312 - val_mae: 5.9624 - lr: 0.0010\nEpoch 170/300\n92/92 [==============================] - 0s 3ms/step - loss: 67.5009 - mae: 6.3059 - val_loss: 62.7290 - val_mae: 6.1003 - lr: 0.0010\nEpoch 171/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.8994 - mae: 6.3174 - val_loss: 62.5186 - val_mae: 6.0433 - lr: 0.0010\nEpoch 172/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.9064 - mae: 6.3041 - val_loss: 60.1459 - val_mae: 5.8449 - lr: 0.0010\nEpoch 173/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.9709 - mae: 6.2980 - val_loss: 62.4689 - val_mae: 6.0435 - lr: 0.0010\nEpoch 174/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.2695 - mae: 6.2605 - val_loss: 61.6665 - val_mae: 6.0074 - lr: 0.0010\nEpoch 175/300\n92/92 [==============================] - 0s 4ms/step - loss: 64.9942 - mae: 6.1805 - val_loss: 61.1069 - val_mae: 5.9396 - lr: 0.0010\nEpoch 176/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.4746 - mae: 6.2636 - val_loss: 61.1731 - val_mae: 5.9043 - lr: 0.0010\nEpoch 177/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.9191 - mae: 6.1293 - val_loss: 60.8433 - val_mae: 5.9800 - lr: 0.0010\nEpoch 178/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.4739 - mae: 6.1054 - val_loss: 62.4562 - val_mae: 5.9476 - lr: 0.0010\nEpoch 179/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.5019 - mae: 6.2502 - val_loss: 63.2862 - val_mae: 6.1370 - lr: 0.0010\nEpoch 180/300\n92/92 [==============================] - 0s 3ms/step - loss: 64.9345 - mae: 6.2074 - val_loss: 59.8387 - val_mae: 5.8876 - lr: 0.0010\nEpoch 181/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.3307 - mae: 6.2299 - val_loss: 61.2581 - val_mae: 5.9513 - lr: 0.0010\nEpoch 182/300\n92/92 [==============================] - 0s 4ms/step - loss: 64.2912 - mae: 6.1736 - val_loss: 60.4333 - val_mae: 5.9241 - lr: 0.0010\nEpoch 183/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.0356 - mae: 6.2247 - val_loss: 61.9827 - val_mae: 5.9490 - lr: 0.0010\nEpoch 184/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.3935 - mae: 6.2402 - val_loss: 60.3455 - val_mae: 5.8918 - lr: 0.0010\nEpoch 185/300\n92/92 [==============================] - 0s 4ms/step - loss: 64.4893 - mae: 6.2071 - val_loss: 62.2238 - val_mae: 5.9796 - lr: 0.0010\nEpoch 186/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.9478 - mae: 6.2367 - val_loss: 62.1107 - val_mae: 5.9880 - lr: 0.0010\nEpoch 187/300\n92/92 [==============================] - 0s 3ms/step - loss: 65.5958 - mae: 6.2707 - val_loss: 61.4748 - val_mae: 5.9493 - lr: 0.0010\nEpoch 188/300\n92/92 [==============================] - 0s 3ms/step - loss: 60.4727 - mae: 6.0296 - val_loss: 60.2022 - val_mae: 5.8574 - lr: 0.0010\nEpoch 189/300\n92/92 [==============================] - 0s 4ms/step - loss: 65.8303 - mae: 6.1788 - val_loss: 61.3858 - val_mae: 5.9084 - lr: 0.0010\nEpoch 190/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.9452 - mae: 6.1277 - val_loss: 60.3327 - val_mae: 5.9078 - lr: 0.0010\nEpoch 191/300\n92/92 [==============================] - 0s 3ms/step - loss: 66.3448 - mae: 6.2465 - val_loss: 60.9304 - val_mae: 5.8975 - lr: 0.0010\nEpoch 192/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.2018 - mae: 6.1376 - val_loss: 62.3396 - val_mae: 6.0512 - lr: 0.0010\nEpoch 193/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.9851 - mae: 6.0815 - val_loss: 61.0543 - val_mae: 5.8987 - lr: 0.0010\nEpoch 194/300\n92/92 [==============================] - 0s 4ms/step - loss: 62.0178 - mae: 6.0199 - val_loss: 60.3900 - val_mae: 5.9402 - lr: 0.0010\nEpoch 195/300\n77/92 [========================>.....] - ETA: 0s - loss: 65.3946 - mae: 6.2603\nEpoch 195: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 3ms/step - loss: 65.6824 - mae: 6.2853 - val_loss: 61.5671 - val_mae: 5.9755 - lr: 0.0010\nEpoch 196/300\n92/92 [==============================] - 0s 3ms/step - loss: 64.3139 - mae: 6.1205 - val_loss: 60.5995 - val_mae: 5.8999 - lr: 5.0000e-04\nEpoch 197/300\n92/92 [==============================] - 0s 3ms/step - loss: 62.8551 - mae: 6.0710 - val_loss: 60.6610 - val_mae: 5.9163 - lr: 5.0000e-04\nEpoch 198/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.5444 - mae: 6.1638 - val_loss: 60.6773 - val_mae: 5.9228 - lr: 5.0000e-04\nEpoch 199/300\n92/92 [==============================] - 0s 2ms/step - loss: 63.6986 - mae: 6.0671 - val_loss: 61.5802 - val_mae: 5.9645 - lr: 5.0000e-04\nEpoch 200/300\n92/92 [==============================] - 0s 3ms/step - loss: 62.4135 - mae: 6.0109 - val_loss: 60.7748 - val_mae: 5.9086 - lr: 5.0000e-04\nEpoch 201/300\n92/92 [==============================] - 0s 5ms/step - loss: 62.6062 - mae: 6.1001 - val_loss: 60.5688 - val_mae: 5.9004 - lr: 5.0000e-04\nEpoch 202/300\n92/92 [==============================] - 0s 3ms/step - loss: 60.5445 - mae: 6.0013 - val_loss: 59.8797 - val_mae: 5.8569 - lr: 5.0000e-04\nEpoch 203/300\n92/92 [==============================] - 0s 3ms/step - loss: 59.6509 - mae: 5.9256 - val_loss: 60.2092 - val_mae: 5.8866 - lr: 5.0000e-04\nEpoch 204/300\n92/92 [==============================] - 0s 4ms/step - loss: 59.7809 - mae: 5.9310 - val_loss: 59.9827 - val_mae: 5.8620 - lr: 5.0000e-04\nEpoch 205/300\n92/92 [==============================] - 0s 4ms/step - loss: 59.7638 - mae: 5.9486 - val_loss: 60.3155 - val_mae: 5.8679 - lr: 5.0000e-04\nEpoch 206/300\n92/92 [==============================] - 0s 3ms/step - loss: 61.2576 - mae: 6.0306 - val_loss: 60.8463 - val_mae: 5.9232 - lr: 5.0000e-04\nEpoch 207/300\n92/92 [==============================] - 0s 3ms/step - loss: 64.0160 - mae: 6.1440 - val_loss: 60.1573 - val_mae: 5.8868 - lr: 5.0000e-04\nEpoch 208/300\n92/92 [==============================] - 0s 3ms/step - loss: 63.2584 - mae: 6.1172 - val_loss: 60.1452 - val_mae: 5.8686 - lr: 5.0000e-04\nEpoch 209/300\n92/92 [==============================] - 0s 4ms/step - loss: 59.7705 - mae: 5.9805 - val_loss: 60.3084 - val_mae: 5.8670 - lr: 5.0000e-04\nEpoch 210/300\n92/92 [==============================] - 0s 3ms/step - loss: 61.6077 - mae: 6.0439 - val_loss: 60.1215 - val_mae: 5.8618 - lr: 5.0000e-04\nEpoch 211/300\n92/92 [==============================] - 0s 4ms/step - loss: 64.1531 - mae: 6.1907 - val_loss: 60.4064 - val_mae: 5.8709 - lr: 5.0000e-04\nEpoch 212/300\n92/92 [==============================] - 0s 5ms/step - loss: 60.3432 - mae: 6.0125 - val_loss: 59.3115 - val_mae: 5.8190 - lr: 5.0000e-04\nEpoch 213/300\n92/92 [==============================] - 0s 3ms/step - loss: 60.6352 - mae: 5.9279 - val_loss: 59.9926 - val_mae: 5.8558 - lr: 5.0000e-04\nEpoch 214/300\n92/92 [==============================] - 0s 3ms/step - loss: 61.1036 - mae: 6.0281 - val_loss: 59.7388 - val_mae: 5.8499 - lr: 5.0000e-04\nEpoch 215/300\n92/92 [==============================] - 0s 3ms/step - loss: 62.4923 - mae: 6.0696 - val_loss: 60.2911 - val_mae: 5.8721 - lr: 5.0000e-04\n46/46 [==============================] - 0s 1ms/step\nRMSE: 7.696\nR^2 Score: 0.460\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/01214b28-4282-43ff-b118-91ca85e6edba",
   "content_dependencies": null
  },
  {
   "cellId": "44f1a695d8f74aa7b05458ba05886836",
   "cell_type": "code",
   "metadata": {
    "source_hash": "8960f43a",
    "execution_start": 1753835737898,
    "execution_millis": 69772,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "44f1a695d8f74aa7b05458ba05886836",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, activation='relu'),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(128, activation='relu'),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(128, activation='sigmoid'),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(64, activation='relu'),\n    Dropout(0.2),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=20, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=32,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "17ca61ad37f44c5f9e5e77e4a9fc8ac8",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n92/92 [==============================] - 2s 7ms/step - loss: 466.2141 - mae: 17.9260 - val_loss: 486.7916 - val_mae: 19.6911 - lr: 0.0010\nEpoch 2/300\n92/92 [==============================] - 0s 4ms/step - loss: 154.5234 - mae: 9.8692 - val_loss: 275.0973 - val_mae: 13.9610 - lr: 0.0010\nEpoch 3/300\n92/92 [==============================] - 0s 4ms/step - loss: 138.4125 - mae: 9.3466 - val_loss: 133.2607 - val_mae: 9.1931 - lr: 0.0010\nEpoch 4/300\n92/92 [==============================] - 0s 4ms/step - loss: 134.0977 - mae: 9.1606 - val_loss: 95.1738 - val_mae: 7.6711 - lr: 0.0010\nEpoch 5/300\n92/92 [==============================] - 0s 3ms/step - loss: 122.9230 - mae: 8.8087 - val_loss: 79.5237 - val_mae: 7.0003 - lr: 0.0010\nEpoch 6/300\n92/92 [==============================] - 0s 4ms/step - loss: 117.2660 - mae: 8.5729 - val_loss: 76.1551 - val_mae: 6.8563 - lr: 0.0010\nEpoch 7/300\n92/92 [==============================] - 0s 4ms/step - loss: 118.3391 - mae: 8.6315 - val_loss: 77.8129 - val_mae: 6.9751 - lr: 0.0010\nEpoch 8/300\n92/92 [==============================] - 0s 4ms/step - loss: 114.3142 - mae: 8.4556 - val_loss: 73.4324 - val_mae: 6.6898 - lr: 0.0010\nEpoch 9/300\n92/92 [==============================] - 0s 4ms/step - loss: 112.5823 - mae: 8.3243 - val_loss: 74.5533 - val_mae: 6.7096 - lr: 0.0010\nEpoch 10/300\n92/92 [==============================] - 0s 4ms/step - loss: 110.6237 - mae: 8.2625 - val_loss: 73.5267 - val_mae: 6.6522 - lr: 0.0010\nEpoch 11/300\n92/92 [==============================] - 0s 4ms/step - loss: 111.0977 - mae: 8.3265 - val_loss: 72.7291 - val_mae: 6.6215 - lr: 0.0010\nEpoch 12/300\n92/92 [==============================] - 0s 4ms/step - loss: 104.8734 - mae: 8.0477 - val_loss: 74.3425 - val_mae: 6.7084 - lr: 0.0010\nEpoch 13/300\n92/92 [==============================] - 0s 4ms/step - loss: 108.3786 - mae: 8.2362 - val_loss: 69.6090 - val_mae: 6.4752 - lr: 0.0010\nEpoch 14/300\n92/92 [==============================] - 0s 4ms/step - loss: 102.4387 - mae: 7.9263 - val_loss: 70.0314 - val_mae: 6.5363 - lr: 0.0010\nEpoch 15/300\n92/92 [==============================] - 0s 4ms/step - loss: 103.8232 - mae: 7.9573 - val_loss: 68.3249 - val_mae: 6.4785 - lr: 0.0010\nEpoch 16/300\n92/92 [==============================] - 0s 4ms/step - loss: 102.7403 - mae: 7.9832 - val_loss: 68.3773 - val_mae: 6.3699 - lr: 0.0010\nEpoch 17/300\n92/92 [==============================] - 0s 4ms/step - loss: 104.6190 - mae: 8.0877 - val_loss: 69.3771 - val_mae: 6.4304 - lr: 0.0010\nEpoch 18/300\n92/92 [==============================] - 0s 4ms/step - loss: 101.9364 - mae: 7.9172 - val_loss: 66.8956 - val_mae: 6.3530 - lr: 0.0010\nEpoch 19/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.4046 - mae: 7.7019 - val_loss: 67.7999 - val_mae: 6.3651 - lr: 0.0010\nEpoch 20/300\n92/92 [==============================] - 0s 5ms/step - loss: 101.2491 - mae: 7.9061 - val_loss: 65.7034 - val_mae: 6.2421 - lr: 0.0010\nEpoch 21/300\n92/92 [==============================] - 0s 5ms/step - loss: 101.6445 - mae: 7.8852 - val_loss: 67.3647 - val_mae: 6.3212 - lr: 0.0010\nEpoch 22/300\n92/92 [==============================] - 0s 4ms/step - loss: 97.0835 - mae: 7.7146 - val_loss: 67.9369 - val_mae: 6.3611 - lr: 0.0010\nEpoch 23/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.9223 - mae: 7.6731 - val_loss: 71.1246 - val_mae: 6.4871 - lr: 0.0010\nEpoch 24/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.6472 - mae: 7.7307 - val_loss: 69.4949 - val_mae: 6.4360 - lr: 0.0010\nEpoch 25/300\n92/92 [==============================] - 0s 4ms/step - loss: 102.4363 - mae: 7.9649 - val_loss: 68.1638 - val_mae: 6.3782 - lr: 0.0010\nEpoch 26/300\n92/92 [==============================] - 0s 4ms/step - loss: 96.0755 - mae: 7.6760 - val_loss: 67.9217 - val_mae: 6.4010 - lr: 0.0010\nEpoch 27/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.7446 - mae: 7.6971 - val_loss: 67.2164 - val_mae: 6.3422 - lr: 0.0010\nEpoch 28/300\n92/92 [==============================] - 0s 4ms/step - loss: 97.1487 - mae: 7.6896 - val_loss: 68.9358 - val_mae: 6.4801 - lr: 0.0010\nEpoch 29/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.5482 - mae: 7.5656 - val_loss: 68.2970 - val_mae: 6.4045 - lr: 0.0010\nEpoch 30/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.2581 - mae: 7.5053 - val_loss: 65.0993 - val_mae: 6.1762 - lr: 0.0010\nEpoch 31/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.5781 - mae: 7.4286 - val_loss: 65.5136 - val_mae: 6.2345 - lr: 0.0010\nEpoch 32/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.2089 - mae: 7.6581 - val_loss: 66.1557 - val_mae: 6.3313 - lr: 0.0010\nEpoch 33/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.8664 - mae: 7.4609 - val_loss: 65.1036 - val_mae: 6.2415 - lr: 0.0010\nEpoch 34/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.8733 - mae: 7.5203 - val_loss: 68.7397 - val_mae: 6.3965 - lr: 0.0010\nEpoch 35/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.0000 - mae: 7.5556 - val_loss: 70.5353 - val_mae: 6.5291 - lr: 0.0010\nEpoch 36/300\n92/92 [==============================] - 0s 3ms/step - loss: 91.6144 - mae: 7.5292 - val_loss: 64.9819 - val_mae: 6.2358 - lr: 0.0010\nEpoch 37/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.6758 - mae: 7.3888 - val_loss: 67.0582 - val_mae: 6.3665 - lr: 0.0010\nEpoch 38/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.1043 - mae: 7.3781 - val_loss: 66.7899 - val_mae: 6.3470 - lr: 0.0010\nEpoch 39/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.1283 - mae: 7.4647 - val_loss: 68.9134 - val_mae: 6.4623 - lr: 0.0010\nEpoch 40/300\n92/92 [==============================] - 0s 4ms/step - loss: 91.0402 - mae: 7.4539 - val_loss: 64.1791 - val_mae: 6.2235 - lr: 0.0010\nEpoch 41/300\n92/92 [==============================] - 0s 5ms/step - loss: 88.0369 - mae: 7.3033 - val_loss: 64.2878 - val_mae: 6.2269 - lr: 0.0010\nEpoch 42/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.8908 - mae: 7.2941 - val_loss: 66.0471 - val_mae: 6.3164 - lr: 0.0010\nEpoch 43/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.5381 - mae: 7.3200 - val_loss: 65.0597 - val_mae: 6.2553 - lr: 0.0010\nEpoch 44/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.2967 - mae: 7.3743 - val_loss: 65.9667 - val_mae: 6.2698 - lr: 0.0010\nEpoch 45/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.3591 - mae: 7.2889 - val_loss: 65.3624 - val_mae: 6.1795 - lr: 0.0010\nEpoch 46/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.6296 - mae: 7.3025 - val_loss: 64.6096 - val_mae: 6.1996 - lr: 0.0010\nEpoch 47/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.3383 - mae: 7.1757 - val_loss: 65.5432 - val_mae: 6.2137 - lr: 0.0010\nEpoch 48/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.4518 - mae: 7.2622 - val_loss: 67.7937 - val_mae: 6.3871 - lr: 0.0010\nEpoch 49/300\n92/92 [==============================] - 0s 5ms/step - loss: 86.4380 - mae: 7.2806 - val_loss: 64.8090 - val_mae: 6.2953 - lr: 0.0010\nEpoch 50/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.5547 - mae: 7.1130 - val_loss: 64.0072 - val_mae: 6.2276 - lr: 0.0010\nEpoch 51/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.4689 - mae: 7.2451 - val_loss: 67.9907 - val_mae: 6.5075 - lr: 0.0010\nEpoch 52/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.8137 - mae: 7.0976 - val_loss: 65.9858 - val_mae: 6.3448 - lr: 0.0010\nEpoch 53/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.7317 - mae: 7.1820 - val_loss: 63.1716 - val_mae: 6.1416 - lr: 0.0010\nEpoch 54/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.9836 - mae: 7.2184 - val_loss: 63.4274 - val_mae: 6.0672 - lr: 0.0010\nEpoch 55/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.9590 - mae: 7.1924 - val_loss: 64.9052 - val_mae: 6.2261 - lr: 0.0010\nEpoch 56/300\n92/92 [==============================] - 0s 5ms/step - loss: 86.3452 - mae: 7.2814 - val_loss: 65.6225 - val_mae: 6.3177 - lr: 0.0010\nEpoch 57/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.3270 - mae: 7.1467 - val_loss: 63.2711 - val_mae: 6.1269 - lr: 0.0010\nEpoch 58/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.3206 - mae: 7.1431 - val_loss: 67.1247 - val_mae: 6.3796 - lr: 0.0010\nEpoch 59/300\n92/92 [==============================] - 0s 5ms/step - loss: 86.2038 - mae: 7.1680 - val_loss: 63.7083 - val_mae: 6.1068 - lr: 0.0010\nEpoch 60/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.7823 - mae: 7.1334 - val_loss: 63.6885 - val_mae: 6.1793 - lr: 0.0010\nEpoch 61/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.2384 - mae: 7.1785 - val_loss: 62.8753 - val_mae: 6.1082 - lr: 0.0010\nEpoch 62/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.5097 - mae: 7.0393 - val_loss: 63.7330 - val_mae: 6.1364 - lr: 0.0010\nEpoch 63/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.7103 - mae: 7.1758 - val_loss: 64.3729 - val_mae: 6.2152 - lr: 0.0010\nEpoch 64/300\n92/92 [==============================] - 0s 5ms/step - loss: 84.3609 - mae: 7.1492 - val_loss: 63.2350 - val_mae: 6.1243 - lr: 0.0010\nEpoch 65/300\n92/92 [==============================] - 0s 5ms/step - loss: 82.7190 - mae: 7.0489 - val_loss: 64.4913 - val_mae: 6.2460 - lr: 0.0010\nEpoch 66/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.6846 - mae: 7.1134 - val_loss: 63.4742 - val_mae: 6.1092 - lr: 0.0010\nEpoch 67/300\n92/92 [==============================] - 0s 5ms/step - loss: 84.1790 - mae: 7.0814 - val_loss: 63.4591 - val_mae: 6.0965 - lr: 0.0010\nEpoch 68/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.4300 - mae: 7.0503 - val_loss: 62.8470 - val_mae: 6.1541 - lr: 0.0010\nEpoch 69/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.7421 - mae: 6.8527 - val_loss: 63.1013 - val_mae: 6.1186 - lr: 0.0010\nEpoch 70/300\n92/92 [==============================] - 0s 5ms/step - loss: 81.5581 - mae: 7.0121 - val_loss: 64.1019 - val_mae: 6.0976 - lr: 0.0010\nEpoch 71/300\n92/92 [==============================] - 0s 5ms/step - loss: 79.5019 - mae: 6.9652 - val_loss: 61.6529 - val_mae: 6.0066 - lr: 0.0010\nEpoch 72/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.9091 - mae: 6.9365 - val_loss: 63.6882 - val_mae: 6.1305 - lr: 0.0010\nEpoch 73/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.2626 - mae: 7.0545 - val_loss: 62.8212 - val_mae: 6.0886 - lr: 0.0010\nEpoch 74/300\n92/92 [==============================] - 0s 5ms/step - loss: 79.7211 - mae: 6.9095 - val_loss: 69.7034 - val_mae: 6.5082 - lr: 0.0010\nEpoch 75/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.5035 - mae: 6.9239 - val_loss: 64.6798 - val_mae: 6.1767 - lr: 0.0010\nEpoch 76/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.7886 - mae: 6.9368 - val_loss: 63.8347 - val_mae: 6.1273 - lr: 0.0010\nEpoch 77/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.7636 - mae: 7.0999 - val_loss: 61.9538 - val_mae: 5.9935 - lr: 0.0010\nEpoch 78/300\n92/92 [==============================] - 0s 5ms/step - loss: 79.8764 - mae: 6.9483 - val_loss: 65.6032 - val_mae: 6.2236 - lr: 0.0010\nEpoch 79/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.2127 - mae: 6.9856 - val_loss: 62.6887 - val_mae: 6.0405 - lr: 0.0010\nEpoch 80/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.7101 - mae: 6.8284 - val_loss: 63.5986 - val_mae: 6.0731 - lr: 0.0010\nEpoch 81/300\n92/92 [==============================] - 0s 5ms/step - loss: 75.6756 - mae: 6.7858 - val_loss: 62.8185 - val_mae: 6.0788 - lr: 0.0010\nEpoch 82/300\n92/92 [==============================] - 0s 5ms/step - loss: 78.0135 - mae: 6.8122 - val_loss: 67.3079 - val_mae: 6.3462 - lr: 0.0010\nEpoch 83/300\n92/92 [==============================] - 0s 5ms/step - loss: 75.3988 - mae: 6.7381 - val_loss: 62.6659 - val_mae: 6.0543 - lr: 0.0010\nEpoch 84/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.7033 - mae: 6.8771 - val_loss: 62.3577 - val_mae: 6.0042 - lr: 0.0010\nEpoch 85/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.3084 - mae: 6.8051 - val_loss: 64.1693 - val_mae: 6.1290 - lr: 0.0010\nEpoch 86/300\n92/92 [==============================] - 0s 5ms/step - loss: 78.3945 - mae: 6.8008 - val_loss: 63.5069 - val_mae: 6.0568 - lr: 0.0010\nEpoch 87/300\n92/92 [==============================] - 0s 5ms/step - loss: 76.7317 - mae: 6.7493 - val_loss: 64.6923 - val_mae: 6.1939 - lr: 0.0010\nEpoch 88/300\n92/92 [==============================] - 0s 5ms/step - loss: 76.5455 - mae: 6.7736 - val_loss: 66.6580 - val_mae: 6.3425 - lr: 0.0010\nEpoch 89/300\n92/92 [==============================] - 1s 6ms/step - loss: 78.0463 - mae: 6.8192 - val_loss: 64.1116 - val_mae: 6.1238 - lr: 0.0010\nEpoch 90/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.2818 - mae: 6.8476 - val_loss: 63.9365 - val_mae: 6.0812 - lr: 0.0010\nEpoch 91/300\n84/92 [==========================>...] - ETA: 0s - loss: 79.1868 - mae: 6.8493\nEpoch 91: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 5ms/step - loss: 78.2210 - mae: 6.8240 - val_loss: 62.1812 - val_mae: 6.0491 - lr: 0.0010\nEpoch 92/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.9684 - mae: 6.6315 - val_loss: 62.5338 - val_mae: 6.0895 - lr: 5.0000e-04\nEpoch 93/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.1654 - mae: 6.7021 - val_loss: 63.0663 - val_mae: 6.1071 - lr: 5.0000e-04\nEpoch 94/300\n92/92 [==============================] - 1s 6ms/step - loss: 73.0964 - mae: 6.6394 - val_loss: 61.6823 - val_mae: 6.0495 - lr: 5.0000e-04\nEpoch 95/300\n92/92 [==============================] - 0s 5ms/step - loss: 72.1953 - mae: 6.6089 - val_loss: 62.6202 - val_mae: 6.1174 - lr: 5.0000e-04\nEpoch 96/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.1208 - mae: 6.6555 - val_loss: 61.7280 - val_mae: 6.0027 - lr: 5.0000e-04\nEpoch 97/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.5541 - mae: 6.6401 - val_loss: 62.1782 - val_mae: 6.0028 - lr: 5.0000e-04\nEpoch 98/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.3286 - mae: 6.6229 - val_loss: 63.0912 - val_mae: 6.1198 - lr: 5.0000e-04\nEpoch 99/300\n92/92 [==============================] - 0s 5ms/step - loss: 75.1264 - mae: 6.7242 - val_loss: 61.3614 - val_mae: 5.9917 - lr: 5.0000e-04\nEpoch 100/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.9930 - mae: 6.8406 - val_loss: 61.9586 - val_mae: 6.0139 - lr: 5.0000e-04\nEpoch 101/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.8885 - mae: 6.5389 - val_loss: 61.8571 - val_mae: 5.9954 - lr: 5.0000e-04\nEpoch 102/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.2759 - mae: 6.6628 - val_loss: 61.7915 - val_mae: 5.9829 - lr: 5.0000e-04\nEpoch 103/300\n92/92 [==============================] - 0s 5ms/step - loss: 70.7612 - mae: 6.4716 - val_loss: 64.9204 - val_mae: 6.2047 - lr: 5.0000e-04\nEpoch 104/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.6861 - mae: 6.6252 - val_loss: 61.6273 - val_mae: 5.9905 - lr: 5.0000e-04\nEpoch 105/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.4529 - mae: 6.6420 - val_loss: 62.0048 - val_mae: 6.0084 - lr: 5.0000e-04\nEpoch 106/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.3864 - mae: 6.5957 - val_loss: 61.9003 - val_mae: 5.9315 - lr: 5.0000e-04\nEpoch 107/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.6785 - mae: 6.6455 - val_loss: 61.4049 - val_mae: 5.9411 - lr: 5.0000e-04\nEpoch 108/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.3082 - mae: 6.6630 - val_loss: 61.4712 - val_mae: 5.9344 - lr: 5.0000e-04\nEpoch 109/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.5693 - mae: 6.6611 - val_loss: 60.5975 - val_mae: 5.8955 - lr: 5.0000e-04\nEpoch 110/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.7444 - mae: 6.5362 - val_loss: 61.8343 - val_mae: 5.9687 - lr: 5.0000e-04\nEpoch 111/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.9396 - mae: 6.5625 - val_loss: 62.0413 - val_mae: 6.0097 - lr: 5.0000e-04\nEpoch 112/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.1156 - mae: 6.6549 - val_loss: 62.6615 - val_mae: 6.0361 - lr: 5.0000e-04\nEpoch 113/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.1913 - mae: 6.5829 - val_loss: 62.1443 - val_mae: 5.9847 - lr: 5.0000e-04\nEpoch 114/300\n92/92 [==============================] - 0s 5ms/step - loss: 74.0515 - mae: 6.6550 - val_loss: 60.3878 - val_mae: 5.9036 - lr: 5.0000e-04\nEpoch 115/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.0415 - mae: 6.5420 - val_loss: 61.9104 - val_mae: 6.0003 - lr: 5.0000e-04\nEpoch 116/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.7894 - mae: 6.5323 - val_loss: 61.4222 - val_mae: 5.9726 - lr: 5.0000e-04\nEpoch 117/300\n92/92 [==============================] - 0s 5ms/step - loss: 73.5597 - mae: 6.6399 - val_loss: 61.3943 - val_mae: 5.9777 - lr: 5.0000e-04\nEpoch 118/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.1289 - mae: 6.4567 - val_loss: 62.0224 - val_mae: 6.0048 - lr: 5.0000e-04\nEpoch 119/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.1614 - mae: 6.4215 - val_loss: 62.3060 - val_mae: 6.0341 - lr: 5.0000e-04\nEpoch 120/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.6102 - mae: 6.5348 - val_loss: 62.5754 - val_mae: 6.0086 - lr: 5.0000e-04\nEpoch 121/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.4627 - mae: 6.5977 - val_loss: 61.7072 - val_mae: 5.9773 - lr: 5.0000e-04\nEpoch 122/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.8922 - mae: 6.5339 - val_loss: 62.1168 - val_mae: 6.0371 - lr: 5.0000e-04\nEpoch 123/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.0844 - mae: 6.3999 - val_loss: 63.2658 - val_mae: 6.0432 - lr: 5.0000e-04\nEpoch 124/300\n92/92 [==============================] - 0s 5ms/step - loss: 69.6963 - mae: 6.4546 - val_loss: 62.0048 - val_mae: 6.0013 - lr: 5.0000e-04\nEpoch 125/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.3305 - mae: 6.4782 - val_loss: 62.8052 - val_mae: 6.0508 - lr: 5.0000e-04\nEpoch 126/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.4384 - mae: 6.5369 - val_loss: 63.0785 - val_mae: 6.0800 - lr: 5.0000e-04\nEpoch 127/300\n92/92 [==============================] - 0s 5ms/step - loss: 68.3598 - mae: 6.3306 - val_loss: 62.7494 - val_mae: 6.0379 - lr: 5.0000e-04\nEpoch 128/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.1110 - mae: 6.5144 - val_loss: 62.6127 - val_mae: 6.0516 - lr: 5.0000e-04\nEpoch 129/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.3081 - mae: 6.5082 - val_loss: 62.7488 - val_mae: 6.0111 - lr: 5.0000e-04\nEpoch 130/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.3211 - mae: 6.4555 - val_loss: 62.6571 - val_mae: 5.9566 - lr: 5.0000e-04\nEpoch 131/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.3224 - mae: 6.6185 - val_loss: 61.3223 - val_mae: 5.9394 - lr: 5.0000e-04\nEpoch 132/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.2086 - mae: 6.4564 - val_loss: 61.8293 - val_mae: 5.9705 - lr: 5.0000e-04\nEpoch 133/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.5599 - mae: 6.4644 - val_loss: 61.8667 - val_mae: 5.9156 - lr: 5.0000e-04\nEpoch 134/300\n74/92 [=======================>......] - ETA: 0s - loss: 68.8024 - mae: 6.3474\nEpoch 134: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n92/92 [==============================] - 0s 4ms/step - loss: 69.2318 - mae: 6.3666 - val_loss: 62.4567 - val_mae: 6.0159 - lr: 5.0000e-04\nEpoch 135/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.5721 - mae: 6.5124 - val_loss: 61.1927 - val_mae: 5.9153 - lr: 2.5000e-04\nEpoch 136/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.4510 - mae: 6.5070 - val_loss: 61.7980 - val_mae: 5.9755 - lr: 2.5000e-04\nEpoch 137/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.9105 - mae: 6.3812 - val_loss: 61.4109 - val_mae: 5.9286 - lr: 2.5000e-04\nEpoch 138/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.5427 - mae: 6.3070 - val_loss: 61.6152 - val_mae: 5.9444 - lr: 2.5000e-04\nEpoch 139/300\n92/92 [==============================] - 1s 6ms/step - loss: 71.1292 - mae: 6.4747 - val_loss: 60.6113 - val_mae: 5.8767 - lr: 2.5000e-04\nEpoch 140/300\n92/92 [==============================] - 0s 5ms/step - loss: 67.7072 - mae: 6.3289 - val_loss: 60.7373 - val_mae: 5.8768 - lr: 2.5000e-04\nEpoch 141/300\n92/92 [==============================] - 0s 5ms/step - loss: 67.2400 - mae: 6.3502 - val_loss: 60.4357 - val_mae: 5.8761 - lr: 2.5000e-04\nEpoch 142/300\n92/92 [==============================] - 1s 6ms/step - loss: 71.1076 - mae: 6.5113 - val_loss: 62.2701 - val_mae: 6.0016 - lr: 2.5000e-04\nEpoch 143/300\n92/92 [==============================] - 1s 5ms/step - loss: 68.1583 - mae: 6.3451 - val_loss: 61.5578 - val_mae: 5.9603 - lr: 2.5000e-04\nEpoch 144/300\n92/92 [==============================] - 0s 5ms/step - loss: 68.5811 - mae: 6.4123 - val_loss: 60.8702 - val_mae: 5.8963 - lr: 2.5000e-04\nEpoch 145/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.8071 - mae: 6.3866 - val_loss: 61.0894 - val_mae: 5.9042 - lr: 2.5000e-04\nEpoch 146/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.0685 - mae: 6.3694 - val_loss: 61.6075 - val_mae: 5.9572 - lr: 2.5000e-04\nEpoch 147/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.6825 - mae: 6.3343 - val_loss: 60.9162 - val_mae: 5.9023 - lr: 2.5000e-04\nEpoch 148/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.7746 - mae: 6.3296 - val_loss: 61.8017 - val_mae: 5.9573 - lr: 2.5000e-04\nEpoch 149/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.2920 - mae: 6.4153 - val_loss: 61.4650 - val_mae: 5.9518 - lr: 2.5000e-04\nEpoch 150/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.3168 - mae: 6.3954 - val_loss: 60.7871 - val_mae: 5.8975 - lr: 2.5000e-04\nEpoch 151/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.7785 - mae: 6.3372 - val_loss: 61.1548 - val_mae: 5.9314 - lr: 2.5000e-04\nEpoch 152/300\n92/92 [==============================] - 0s 4ms/step - loss: 68.0199 - mae: 6.3669 - val_loss: 60.7484 - val_mae: 5.8798 - lr: 2.5000e-04\nEpoch 153/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.8111 - mae: 6.4609 - val_loss: 61.2816 - val_mae: 5.9024 - lr: 2.5000e-04\nEpoch 154/300\n79/92 [========================>.....] - ETA: 0s - loss: 69.6126 - mae: 6.4370\nEpoch 154: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n92/92 [==============================] - 0s 4ms/step - loss: 69.0135 - mae: 6.4120 - val_loss: 61.1709 - val_mae: 5.9031 - lr: 2.5000e-04\nEpoch 155/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.6723 - mae: 6.2985 - val_loss: 61.1059 - val_mae: 5.9048 - lr: 1.2500e-04\nEpoch 156/300\n92/92 [==============================] - 0s 5ms/step - loss: 68.1358 - mae: 6.3025 - val_loss: 61.1869 - val_mae: 5.9086 - lr: 1.2500e-04\nEpoch 157/300\n92/92 [==============================] - 0s 5ms/step - loss: 66.8640 - mae: 6.2511 - val_loss: 61.0294 - val_mae: 5.9170 - lr: 1.2500e-04\nEpoch 158/300\n92/92 [==============================] - 0s 5ms/step - loss: 64.7334 - mae: 6.1711 - val_loss: 61.0995 - val_mae: 5.9215 - lr: 1.2500e-04\nEpoch 159/300\n92/92 [==============================] - 0s 4ms/step - loss: 67.4377 - mae: 6.3326 - val_loss: 61.0019 - val_mae: 5.8924 - lr: 1.2500e-04\nEpoch 160/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.7852 - mae: 6.2997 - val_loss: 60.7916 - val_mae: 5.8822 - lr: 1.2500e-04\nEpoch 161/300\n92/92 [==============================] - 0s 4ms/step - loss: 69.4010 - mae: 6.3931 - val_loss: 60.7056 - val_mae: 5.9028 - lr: 1.2500e-04\nEpoch 162/300\n92/92 [==============================] - 0s 4ms/step - loss: 65.5363 - mae: 6.2001 - val_loss: 60.6013 - val_mae: 5.8712 - lr: 1.2500e-04\nEpoch 163/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.2320 - mae: 6.3004 - val_loss: 60.9893 - val_mae: 5.9002 - lr: 1.2500e-04\nEpoch 164/300\n92/92 [==============================] - 0s 4ms/step - loss: 66.9808 - mae: 6.3182 - val_loss: 61.0618 - val_mae: 5.9066 - lr: 1.2500e-04\n46/46 [==============================] - 0s 1ms/step\nRMSE: 7.771\nR^2 Score: 0.450\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/781f11ee-b15b-4405-bf89-77d400959993",
   "content_dependencies": null
  },
  {
   "cellId": "befff588bea54ce895a599db76c29b5a",
   "cell_type": "code",
   "metadata": {
    "source_hash": "764d99b4",
    "execution_start": 1753835807734,
    "execution_millis": 245159,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "befff588bea54ce895a599db76c29b5a",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(128, activation='relu'),\n    Dropout(0.3),\n    Dense(64, activation='relu'),\n    Dropout(0.2),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=10,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "a5e3d54cb3324473ac76871231ca3961",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n294/294 [==============================] - 2s 5ms/step - loss: 228.4837 - mae: 11.8314 - val_loss: 233.2654 - val_mae: 12.5094 - lr: 0.0010\nEpoch 2/300\n294/294 [==============================] - 1s 3ms/step - loss: 147.0714 - mae: 9.5899 - val_loss: 98.9638 - val_mae: 7.8211 - lr: 0.0010\nEpoch 3/300\n294/294 [==============================] - 1s 3ms/step - loss: 129.6915 - mae: 9.0638 - val_loss: 93.5051 - val_mae: 7.6255 - lr: 0.0010\nEpoch 4/300\n294/294 [==============================] - 1s 3ms/step - loss: 127.7317 - mae: 8.8916 - val_loss: 86.2433 - val_mae: 7.3508 - lr: 0.0010\nEpoch 5/300\n294/294 [==============================] - 1s 3ms/step - loss: 121.9761 - mae: 8.7968 - val_loss: 80.1916 - val_mae: 7.1189 - lr: 0.0010\nEpoch 6/300\n294/294 [==============================] - 1s 3ms/step - loss: 117.4542 - mae: 8.5670 - val_loss: 79.4481 - val_mae: 7.0958 - lr: 0.0010\nEpoch 7/300\n294/294 [==============================] - 1s 3ms/step - loss: 117.4068 - mae: 8.5114 - val_loss: 90.3288 - val_mae: 7.3759 - lr: 0.0010\nEpoch 8/300\n294/294 [==============================] - 1s 3ms/step - loss: 114.1644 - mae: 8.4313 - val_loss: 88.1951 - val_mae: 7.2193 - lr: 0.0010\nEpoch 9/300\n294/294 [==============================] - 1s 3ms/step - loss: 111.3593 - mae: 8.3495 - val_loss: 89.5359 - val_mae: 7.4401 - lr: 0.0010\nEpoch 10/300\n294/294 [==============================] - 1s 3ms/step - loss: 108.4239 - mae: 8.1761 - val_loss: 80.5660 - val_mae: 7.1983 - lr: 0.0010\nEpoch 11/300\n294/294 [==============================] - 1s 3ms/step - loss: 112.9405 - mae: 8.3002 - val_loss: 91.3895 - val_mae: 7.4938 - lr: 0.0010\nEpoch 12/300\n294/294 [==============================] - 1s 3ms/step - loss: 109.0575 - mae: 8.2750 - val_loss: 74.6329 - val_mae: 6.7620 - lr: 0.0010\nEpoch 13/300\n294/294 [==============================] - 1s 3ms/step - loss: 105.6618 - mae: 8.0321 - val_loss: 76.4580 - val_mae: 6.8936 - lr: 0.0010\nEpoch 14/300\n294/294 [==============================] - 1s 3ms/step - loss: 104.3782 - mae: 8.0377 - val_loss: 73.1684 - val_mae: 6.7277 - lr: 0.0010\nEpoch 15/300\n294/294 [==============================] - 1s 3ms/step - loss: 108.1571 - mae: 8.2396 - val_loss: 76.3639 - val_mae: 6.8885 - lr: 0.0010\nEpoch 16/300\n294/294 [==============================] - 1s 3ms/step - loss: 103.9547 - mae: 8.0741 - val_loss: 74.2968 - val_mae: 6.7630 - lr: 0.0010\nEpoch 17/300\n294/294 [==============================] - 1s 3ms/step - loss: 107.7629 - mae: 8.2335 - val_loss: 71.9581 - val_mae: 6.7041 - lr: 0.0010\nEpoch 18/300\n294/294 [==============================] - 1s 3ms/step - loss: 104.5091 - mae: 8.0246 - val_loss: 73.1012 - val_mae: 6.8230 - lr: 0.0010\nEpoch 19/300\n294/294 [==============================] - 1s 3ms/step - loss: 99.8518 - mae: 7.9025 - val_loss: 73.5993 - val_mae: 6.7288 - lr: 0.0010\nEpoch 20/300\n294/294 [==============================] - 1s 3ms/step - loss: 100.0096 - mae: 7.8022 - val_loss: 74.6468 - val_mae: 6.7362 - lr: 0.0010\nEpoch 21/300\n294/294 [==============================] - 1s 3ms/step - loss: 104.7844 - mae: 7.9963 - val_loss: 75.8437 - val_mae: 6.8048 - lr: 0.0010\nEpoch 22/300\n294/294 [==============================] - 1s 3ms/step - loss: 101.4489 - mae: 7.9446 - val_loss: 71.8939 - val_mae: 6.7218 - lr: 0.0010\nEpoch 23/300\n294/294 [==============================] - 1s 3ms/step - loss: 102.3550 - mae: 7.9347 - val_loss: 77.5153 - val_mae: 6.8784 - lr: 0.0010\nEpoch 24/300\n294/294 [==============================] - 1s 3ms/step - loss: 100.2975 - mae: 7.8754 - val_loss: 71.7095 - val_mae: 6.6820 - lr: 0.0010\nEpoch 25/300\n294/294 [==============================] - 1s 3ms/step - loss: 98.9089 - mae: 7.8274 - val_loss: 72.9202 - val_mae: 6.6310 - lr: 0.0010\nEpoch 26/300\n294/294 [==============================] - 1s 3ms/step - loss: 99.5994 - mae: 7.8670 - val_loss: 87.5312 - val_mae: 7.4004 - lr: 0.0010\nEpoch 27/300\n294/294 [==============================] - 1s 3ms/step - loss: 98.2060 - mae: 7.7499 - val_loss: 74.6288 - val_mae: 6.8785 - lr: 0.0010\nEpoch 28/300\n294/294 [==============================] - 1s 3ms/step - loss: 96.4957 - mae: 7.7362 - val_loss: 72.5131 - val_mae: 6.6285 - lr: 0.0010\nEpoch 29/300\n294/294 [==============================] - 1s 3ms/step - loss: 96.5161 - mae: 7.6792 - val_loss: 75.7738 - val_mae: 6.8339 - lr: 0.0010\nEpoch 30/300\n294/294 [==============================] - 1s 3ms/step - loss: 94.9112 - mae: 7.5458 - val_loss: 70.3122 - val_mae: 6.5735 - lr: 0.0010\nEpoch 31/300\n294/294 [==============================] - 1s 3ms/step - loss: 97.2636 - mae: 7.8253 - val_loss: 75.3279 - val_mae: 6.6836 - lr: 0.0010\nEpoch 32/300\n294/294 [==============================] - 1s 3ms/step - loss: 91.3783 - mae: 7.4369 - val_loss: 74.1579 - val_mae: 6.8622 - lr: 0.0010\nEpoch 33/300\n294/294 [==============================] - 1s 3ms/step - loss: 94.9439 - mae: 7.6106 - val_loss: 68.7425 - val_mae: 6.4905 - lr: 0.0010\nEpoch 34/300\n294/294 [==============================] - 1s 3ms/step - loss: 94.1250 - mae: 7.5930 - val_loss: 74.4037 - val_mae: 6.7788 - lr: 0.0010\nEpoch 35/300\n294/294 [==============================] - 1s 3ms/step - loss: 93.1309 - mae: 7.6239 - val_loss: 74.1152 - val_mae: 6.7716 - lr: 0.0010\nEpoch 36/300\n294/294 [==============================] - 1s 3ms/step - loss: 93.4843 - mae: 7.6199 - val_loss: 73.7187 - val_mae: 6.6841 - lr: 0.0010\nEpoch 37/300\n294/294 [==============================] - 1s 3ms/step - loss: 93.8157 - mae: 7.6359 - val_loss: 71.3402 - val_mae: 6.4957 - lr: 0.0010\nEpoch 38/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.8947 - mae: 7.4128 - val_loss: 69.0815 - val_mae: 6.5215 - lr: 0.0010\nEpoch 39/300\n294/294 [==============================] - 1s 3ms/step - loss: 93.0723 - mae: 7.5385 - val_loss: 68.6436 - val_mae: 6.4730 - lr: 0.0010\nEpoch 40/300\n294/294 [==============================] - 1s 3ms/step - loss: 88.9181 - mae: 7.3736 - val_loss: 69.1154 - val_mae: 6.5330 - lr: 0.0010\nEpoch 41/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.6035 - mae: 7.4648 - val_loss: 67.5197 - val_mae: 6.5053 - lr: 0.0010\nEpoch 42/300\n294/294 [==============================] - 1s 3ms/step - loss: 94.6057 - mae: 7.6312 - val_loss: 71.7236 - val_mae: 6.6036 - lr: 0.0010\nEpoch 43/300\n294/294 [==============================] - 1s 3ms/step - loss: 91.8837 - mae: 7.4822 - val_loss: 70.2357 - val_mae: 6.5635 - lr: 0.0010\nEpoch 44/300\n294/294 [==============================] - 1s 3ms/step - loss: 91.8360 - mae: 7.5310 - val_loss: 74.7084 - val_mae: 6.7519 - lr: 0.0010\nEpoch 45/300\n294/294 [==============================] - 1s 3ms/step - loss: 90.5986 - mae: 7.5123 - val_loss: 71.7762 - val_mae: 6.5600 - lr: 0.0010\nEpoch 46/300\n294/294 [==============================] - 1s 3ms/step - loss: 92.6849 - mae: 7.4776 - val_loss: 67.9109 - val_mae: 6.4519 - lr: 0.0010\nEpoch 47/300\n294/294 [==============================] - 1s 3ms/step - loss: 92.3853 - mae: 7.5820 - val_loss: 69.2240 - val_mae: 6.5303 - lr: 0.0010\nEpoch 48/300\n294/294 [==============================] - 1s 4ms/step - loss: 88.4168 - mae: 7.3951 - val_loss: 68.7333 - val_mae: 6.5015 - lr: 0.0010\nEpoch 49/300\n294/294 [==============================] - 1s 3ms/step - loss: 87.9495 - mae: 7.3075 - val_loss: 69.2037 - val_mae: 6.5180 - lr: 0.0010\nEpoch 50/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.7021 - mae: 7.3909 - val_loss: 67.4700 - val_mae: 6.4197 - lr: 0.0010\nEpoch 51/300\n294/294 [==============================] - 1s 3ms/step - loss: 85.4007 - mae: 7.2332 - val_loss: 69.1054 - val_mae: 6.4335 - lr: 0.0010\nEpoch 52/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.3621 - mae: 7.3676 - val_loss: 68.3539 - val_mae: 6.5213 - lr: 0.0010\nEpoch 53/300\n294/294 [==============================] - 1s 3ms/step - loss: 87.5347 - mae: 7.2865 - val_loss: 69.1949 - val_mae: 6.5059 - lr: 0.0010\nEpoch 54/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.9303 - mae: 7.4152 - val_loss: 69.2808 - val_mae: 6.4913 - lr: 0.0010\nEpoch 55/300\n294/294 [==============================] - 1s 3ms/step - loss: 84.7659 - mae: 7.2119 - val_loss: 73.1039 - val_mae: 6.7075 - lr: 0.0010\nEpoch 56/300\n294/294 [==============================] - 1s 3ms/step - loss: 85.3263 - mae: 7.1598 - val_loss: 66.1747 - val_mae: 6.3284 - lr: 0.0010\nEpoch 57/300\n294/294 [==============================] - 1s 3ms/step - loss: 89.2154 - mae: 7.3555 - val_loss: 71.4523 - val_mae: 6.5749 - lr: 0.0010\nEpoch 58/300\n294/294 [==============================] - 1s 3ms/step - loss: 87.4852 - mae: 7.2948 - val_loss: 70.0555 - val_mae: 6.5420 - lr: 0.0010\nEpoch 59/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.4121 - mae: 7.0272 - val_loss: 68.9116 - val_mae: 6.4597 - lr: 0.0010\nEpoch 60/300\n294/294 [==============================] - 1s 3ms/step - loss: 85.7367 - mae: 7.2756 - val_loss: 66.5808 - val_mae: 6.3402 - lr: 0.0010\nEpoch 61/300\n294/294 [==============================] - 1s 3ms/step - loss: 85.7949 - mae: 7.2899 - val_loss: 66.5734 - val_mae: 6.3905 - lr: 0.0010\nEpoch 62/300\n294/294 [==============================] - 1s 3ms/step - loss: 83.9261 - mae: 7.0779 - val_loss: 64.8473 - val_mae: 6.2830 - lr: 0.0010\nEpoch 63/300\n294/294 [==============================] - 1s 3ms/step - loss: 86.3136 - mae: 7.1976 - val_loss: 64.0595 - val_mae: 6.2316 - lr: 0.0010\nEpoch 64/300\n294/294 [==============================] - 1s 3ms/step - loss: 87.2597 - mae: 7.3002 - val_loss: 68.7754 - val_mae: 6.4197 - lr: 0.0010\nEpoch 65/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.6618 - mae: 7.0447 - val_loss: 64.5890 - val_mae: 6.2771 - lr: 0.0010\nEpoch 66/300\n294/294 [==============================] - 1s 3ms/step - loss: 85.1376 - mae: 7.1401 - val_loss: 67.9488 - val_mae: 6.4794 - lr: 0.0010\nEpoch 67/300\n294/294 [==============================] - 1s 3ms/step - loss: 83.9057 - mae: 7.1948 - val_loss: 64.9330 - val_mae: 6.3143 - lr: 0.0010\nEpoch 68/300\n294/294 [==============================] - 1s 3ms/step - loss: 82.9785 - mae: 7.1076 - val_loss: 70.4023 - val_mae: 6.5634 - lr: 0.0010\nEpoch 69/300\n294/294 [==============================] - 1s 2ms/step - loss: 82.9768 - mae: 7.0873 - val_loss: 68.8788 - val_mae: 6.4631 - lr: 0.0010\nEpoch 70/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.2956 - mae: 6.9926 - val_loss: 65.4102 - val_mae: 6.2901 - lr: 0.0010\nEpoch 71/300\n294/294 [==============================] - 1s 3ms/step - loss: 82.2583 - mae: 7.0113 - val_loss: 63.6115 - val_mae: 6.1843 - lr: 0.0010\nEpoch 72/300\n294/294 [==============================] - 1s 3ms/step - loss: 83.2026 - mae: 7.0855 - val_loss: 70.4964 - val_mae: 6.5149 - lr: 0.0010\nEpoch 73/300\n294/294 [==============================] - 1s 2ms/step - loss: 83.1945 - mae: 7.0893 - val_loss: 68.6513 - val_mae: 6.3817 - lr: 0.0010\nEpoch 74/300\n294/294 [==============================] - 1s 2ms/step - loss: 84.8220 - mae: 7.1765 - val_loss: 70.8406 - val_mae: 6.5450 - lr: 0.0010\nEpoch 75/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.9620 - mae: 6.9060 - val_loss: 64.4957 - val_mae: 6.2138 - lr: 0.0010\nEpoch 76/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.9110 - mae: 7.0882 - val_loss: 64.3885 - val_mae: 6.2392 - lr: 0.0010\nEpoch 77/300\n294/294 [==============================] - 1s 3ms/step - loss: 79.9621 - mae: 6.9915 - val_loss: 65.5053 - val_mae: 6.2421 - lr: 0.0010\nEpoch 78/300\n294/294 [==============================] - 1s 2ms/step - loss: 81.1370 - mae: 7.0455 - val_loss: 67.9607 - val_mae: 6.3768 - lr: 0.0010\nEpoch 79/300\n294/294 [==============================] - 1s 3ms/step - loss: 79.2197 - mae: 6.9324 - val_loss: 65.5273 - val_mae: 6.3287 - lr: 0.0010\nEpoch 80/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.8122 - mae: 6.9712 - val_loss: 63.7802 - val_mae: 6.1899 - lr: 0.0010\nEpoch 81/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.7074 - mae: 7.0429 - val_loss: 66.2615 - val_mae: 6.3266 - lr: 0.0010\nEpoch 82/300\n294/294 [==============================] - 1s 2ms/step - loss: 79.4732 - mae: 6.9045 - val_loss: 65.4941 - val_mae: 6.2308 - lr: 0.0010\nEpoch 83/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.5195 - mae: 6.9475 - val_loss: 65.5636 - val_mae: 6.2431 - lr: 0.0010\nEpoch 84/300\n294/294 [==============================] - 1s 3ms/step - loss: 82.3303 - mae: 7.1241 - val_loss: 65.0322 - val_mae: 6.2737 - lr: 0.0010\nEpoch 85/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.5256 - mae: 6.9060 - val_loss: 68.9200 - val_mae: 6.3745 - lr: 0.0010\nEpoch 86/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.4347 - mae: 6.7841 - val_loss: 65.6853 - val_mae: 6.2819 - lr: 0.0010\nEpoch 87/300\n294/294 [==============================] - 1s 3ms/step - loss: 80.2266 - mae: 6.9291 - val_loss: 65.9398 - val_mae: 6.2372 - lr: 0.0010\nEpoch 88/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.0436 - mae: 7.0102 - val_loss: 65.7996 - val_mae: 6.2619 - lr: 0.0010\nEpoch 89/300\n294/294 [==============================] - 1s 3ms/step - loss: 79.0585 - mae: 6.8785 - val_loss: 66.9689 - val_mae: 6.3190 - lr: 0.0010\nEpoch 90/300\n294/294 [==============================] - 1s 3ms/step - loss: 80.4591 - mae: 6.9420 - val_loss: 64.4501 - val_mae: 6.1917 - lr: 0.0010\nEpoch 91/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.7038 - mae: 6.9070 - val_loss: 63.7719 - val_mae: 6.1588 - lr: 0.0010\nEpoch 92/300\n294/294 [==============================] - 1s 3ms/step - loss: 80.2794 - mae: 6.9672 - val_loss: 67.3555 - val_mae: 6.3297 - lr: 0.0010\nEpoch 93/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.2146 - mae: 6.8942 - val_loss: 66.0530 - val_mae: 6.2729 - lr: 0.0010\nEpoch 94/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.7316 - mae: 6.7817 - val_loss: 64.2738 - val_mae: 6.1547 - lr: 0.0010\nEpoch 95/300\n294/294 [==============================] - 1s 3ms/step - loss: 77.0158 - mae: 6.8172 - val_loss: 64.7296 - val_mae: 6.1956 - lr: 0.0010\nEpoch 96/300\n294/294 [==============================] - 1s 3ms/step - loss: 81.4143 - mae: 6.9115 - val_loss: 65.9416 - val_mae: 6.2592 - lr: 0.0010\nEpoch 97/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.4914 - mae: 6.8729 - val_loss: 64.2919 - val_mae: 6.1407 - lr: 0.0010\nEpoch 98/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.4517 - mae: 6.7856 - val_loss: 63.5388 - val_mae: 6.1632 - lr: 0.0010\nEpoch 99/300\n294/294 [==============================] - 1s 3ms/step - loss: 79.9969 - mae: 6.9325 - val_loss: 70.5169 - val_mae: 6.4083 - lr: 0.0010\nEpoch 100/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.8160 - mae: 6.7956 - val_loss: 65.0093 - val_mae: 6.1743 - lr: 0.0010\nEpoch 101/300\n294/294 [==============================] - 1s 3ms/step - loss: 75.9124 - mae: 6.7410 - val_loss: 62.6176 - val_mae: 6.0839 - lr: 0.0010\nEpoch 102/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.8179 - mae: 6.8544 - val_loss: 65.1848 - val_mae: 6.2334 - lr: 0.0010\nEpoch 103/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.4951 - mae: 6.7738 - val_loss: 67.0880 - val_mae: 6.2683 - lr: 0.0010\nEpoch 104/300\n294/294 [==============================] - 1s 3ms/step - loss: 78.7999 - mae: 6.8771 - val_loss: 63.1917 - val_mae: 6.1215 - lr: 0.0010\nEpoch 105/300\n294/294 [==============================] - 1s 3ms/step - loss: 75.4882 - mae: 6.7331 - val_loss: 62.6589 - val_mae: 6.0929 - lr: 0.0010\nEpoch 106/300\n294/294 [==============================] - 1s 3ms/step - loss: 75.9314 - mae: 6.7257 - val_loss: 62.7136 - val_mae: 6.1564 - lr: 0.0010\nEpoch 107/300\n294/294 [==============================] - 1s 3ms/step - loss: 79.9956 - mae: 6.8453 - val_loss: 63.4408 - val_mae: 6.1627 - lr: 0.0010\nEpoch 108/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.4476 - mae: 6.7769 - val_loss: 63.5945 - val_mae: 6.1251 - lr: 0.0010\nEpoch 109/300\n294/294 [==============================] - 1s 3ms/step - loss: 77.4926 - mae: 6.8769 - val_loss: 63.3140 - val_mae: 6.1398 - lr: 0.0010\nEpoch 110/300\n294/294 [==============================] - 1s 3ms/step - loss: 77.3102 - mae: 6.7793 - val_loss: 63.9269 - val_mae: 6.1898 - lr: 0.0010\nEpoch 111/300\n294/294 [==============================] - 1s 3ms/step - loss: 75.5117 - mae: 6.7272 - val_loss: 62.2563 - val_mae: 6.0741 - lr: 0.0010\nEpoch 112/300\n294/294 [==============================] - 1s 4ms/step - loss: 78.3585 - mae: 6.7998 - val_loss: 62.4031 - val_mae: 6.1388 - lr: 0.0010\nEpoch 113/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.4757 - mae: 6.7373 - val_loss: 62.8035 - val_mae: 6.1129 - lr: 0.0010\nEpoch 114/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.2751 - mae: 6.7188 - val_loss: 62.6235 - val_mae: 6.0758 - lr: 0.0010\nEpoch 115/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.2015 - mae: 6.5686 - val_loss: 64.0264 - val_mae: 6.1916 - lr: 0.0010\nEpoch 116/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.9207 - mae: 6.7645 - val_loss: 64.6237 - val_mae: 6.2231 - lr: 0.0010\nEpoch 117/300\n294/294 [==============================] - 1s 3ms/step - loss: 77.1059 - mae: 6.7152 - val_loss: 62.9068 - val_mae: 6.1132 - lr: 0.0010\nEpoch 118/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.2470 - mae: 6.6883 - val_loss: 64.7777 - val_mae: 6.2064 - lr: 0.0010\nEpoch 119/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.1150 - mae: 6.7245 - val_loss: 63.2307 - val_mae: 6.0915 - lr: 0.0010\nEpoch 120/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.5654 - mae: 6.6511 - val_loss: 62.5843 - val_mae: 6.0797 - lr: 0.0010\nEpoch 121/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.0154 - mae: 6.6695 - val_loss: 62.2996 - val_mae: 6.0405 - lr: 0.0010\nEpoch 122/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.8436 - mae: 6.5204 - val_loss: 63.1298 - val_mae: 6.0879 - lr: 0.0010\nEpoch 123/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.0231 - mae: 6.5290 - val_loss: 60.9413 - val_mae: 5.9620 - lr: 0.0010\nEpoch 124/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.9827 - mae: 6.5468 - val_loss: 64.1649 - val_mae: 6.1578 - lr: 0.0010\nEpoch 125/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.6692 - mae: 6.6610 - val_loss: 61.5768 - val_mae: 6.0248 - lr: 0.0010\nEpoch 126/300\n294/294 [==============================] - 1s 3ms/step - loss: 77.4778 - mae: 6.7578 - val_loss: 64.5933 - val_mae: 6.1788 - lr: 0.0010\nEpoch 127/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.7826 - mae: 6.6277 - val_loss: 61.4984 - val_mae: 6.0030 - lr: 0.0010\nEpoch 128/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.6142 - mae: 6.5706 - val_loss: 67.5722 - val_mae: 6.3009 - lr: 0.0010\nEpoch 129/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.5523 - mae: 6.6484 - val_loss: 63.2804 - val_mae: 6.0842 - lr: 0.0010\nEpoch 130/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.6484 - mae: 6.6176 - val_loss: 62.9885 - val_mae: 6.0535 - lr: 0.0010\nEpoch 131/300\n294/294 [==============================] - 1s 3ms/step - loss: 75.5710 - mae: 6.7193 - val_loss: 63.1912 - val_mae: 6.0727 - lr: 0.0010\nEpoch 132/300\n294/294 [==============================] - 1s 3ms/step - loss: 76.6369 - mae: 6.7750 - val_loss: 62.8362 - val_mae: 6.0647 - lr: 0.0010\nEpoch 133/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.4494 - mae: 6.6786 - val_loss: 64.1540 - val_mae: 6.0971 - lr: 0.0010\nEpoch 134/300\n294/294 [==============================] - 1s 3ms/step - loss: 74.6963 - mae: 6.6480 - val_loss: 62.4781 - val_mae: 6.0429 - lr: 0.0010\nEpoch 135/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.0684 - mae: 6.5141 - val_loss: 63.2037 - val_mae: 6.0448 - lr: 0.0010\nEpoch 136/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.8443 - mae: 6.5609 - val_loss: 65.2557 - val_mae: 6.1915 - lr: 0.0010\nEpoch 137/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.5320 - mae: 6.5689 - val_loss: 61.9872 - val_mae: 6.0514 - lr: 0.0010\nEpoch 138/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.0741 - mae: 6.6426 - val_loss: 61.1413 - val_mae: 5.9942 - lr: 0.0010\nEpoch 139/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.7924 - mae: 6.5900 - val_loss: 63.1147 - val_mae: 6.1167 - lr: 0.0010\nEpoch 140/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.0224 - mae: 6.5421 - val_loss: 62.5503 - val_mae: 6.0752 - lr: 0.0010\nEpoch 141/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.3930 - mae: 6.5840 - val_loss: 61.6393 - val_mae: 6.0060 - lr: 0.0010\nEpoch 142/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.1695 - mae: 6.4358 - val_loss: 62.4462 - val_mae: 6.0656 - lr: 0.0010\nEpoch 143/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.7393 - mae: 6.6379 - val_loss: 62.0719 - val_mae: 6.0355 - lr: 0.0010\nEpoch 144/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.1961 - mae: 6.5894 - val_loss: 60.5540 - val_mae: 5.9475 - lr: 0.0010\nEpoch 145/300\n294/294 [==============================] - 1s 2ms/step - loss: 73.5004 - mae: 6.6320 - val_loss: 60.9279 - val_mae: 6.0079 - lr: 0.0010\nEpoch 146/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.8512 - mae: 6.4547 - val_loss: 62.2515 - val_mae: 6.0969 - lr: 0.0010\nEpoch 147/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.4452 - mae: 6.5523 - val_loss: 62.5767 - val_mae: 6.0252 - lr: 0.0010\nEpoch 148/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.1659 - mae: 6.4279 - val_loss: 61.7046 - val_mae: 5.9973 - lr: 0.0010\nEpoch 149/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.0458 - mae: 6.5052 - val_loss: 62.5021 - val_mae: 6.0173 - lr: 0.0010\nEpoch 150/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.2093 - mae: 6.5897 - val_loss: 62.2820 - val_mae: 6.0664 - lr: 0.0010\nEpoch 151/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.4302 - mae: 6.5131 - val_loss: 61.7219 - val_mae: 6.0083 - lr: 0.0010\nEpoch 152/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.6789 - mae: 6.4855 - val_loss: 60.8498 - val_mae: 5.9677 - lr: 0.0010\nEpoch 153/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.0457 - mae: 6.5730 - val_loss: 60.9875 - val_mae: 6.0365 - lr: 0.0010\nEpoch 154/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.5669 - mae: 6.4999 - val_loss: 60.5980 - val_mae: 5.9554 - lr: 0.0010\nEpoch 155/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.7872 - mae: 6.5665 - val_loss: 61.3990 - val_mae: 6.0208 - lr: 0.0010\nEpoch 156/300\n294/294 [==============================] - 1s 4ms/step - loss: 70.1327 - mae: 6.3876 - val_loss: 60.0997 - val_mae: 5.9411 - lr: 0.0010\nEpoch 157/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.5742 - mae: 6.5468 - val_loss: 61.8245 - val_mae: 6.0319 - lr: 0.0010\nEpoch 158/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.5741 - mae: 6.4203 - val_loss: 61.0345 - val_mae: 5.9384 - lr: 0.0010\nEpoch 159/300\n294/294 [==============================] - 1s 4ms/step - loss: 72.2237 - mae: 6.5616 - val_loss: 61.8283 - val_mae: 5.9396 - lr: 0.0010\nEpoch 160/300\n294/294 [==============================] - 1s 3ms/step - loss: 72.1498 - mae: 6.5563 - val_loss: 61.2431 - val_mae: 5.9399 - lr: 0.0010\nEpoch 161/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.3543 - mae: 6.5314 - val_loss: 61.7948 - val_mae: 5.9660 - lr: 0.0010\nEpoch 162/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.2812 - mae: 6.4374 - val_loss: 63.2488 - val_mae: 6.0664 - lr: 0.0010\nEpoch 163/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.1775 - mae: 6.4758 - val_loss: 61.3184 - val_mae: 5.9889 - lr: 0.0010\nEpoch 164/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.1982 - mae: 6.4621 - val_loss: 64.9952 - val_mae: 6.2140 - lr: 0.0010\nEpoch 165/300\n294/294 [==============================] - 1s 4ms/step - loss: 71.9307 - mae: 6.5436 - val_loss: 61.8615 - val_mae: 6.0403 - lr: 0.0010\nEpoch 166/300\n294/294 [==============================] - 1s 4ms/step - loss: 69.2909 - mae: 6.3905 - val_loss: 60.5889 - val_mae: 5.9125 - lr: 0.0010\nEpoch 167/300\n294/294 [==============================] - 1s 4ms/step - loss: 72.7793 - mae: 6.5736 - val_loss: 61.6091 - val_mae: 5.9752 - lr: 0.0010\nEpoch 168/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.2524 - mae: 6.3516 - val_loss: 60.7535 - val_mae: 5.9211 - lr: 0.0010\nEpoch 169/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.8989 - mae: 6.5498 - val_loss: 60.5174 - val_mae: 5.8674 - lr: 0.0010\nEpoch 170/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.3353 - mae: 6.4003 - val_loss: 60.8304 - val_mae: 5.9013 - lr: 0.0010\nEpoch 171/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.6973 - mae: 6.4728 - val_loss: 61.0725 - val_mae: 5.9704 - lr: 0.0010\nEpoch 172/300\n294/294 [==============================] - 1s 4ms/step - loss: 71.1814 - mae: 6.4645 - val_loss: 60.9378 - val_mae: 5.9029 - lr: 0.0010\nEpoch 173/300\n294/294 [==============================] - 1s 4ms/step - loss: 68.9377 - mae: 6.4355 - val_loss: 62.1481 - val_mae: 5.9990 - lr: 0.0010\nEpoch 174/300\n294/294 [==============================] - 1s 4ms/step - loss: 66.9423 - mae: 6.2826 - val_loss: 61.8276 - val_mae: 5.9927 - lr: 0.0010\nEpoch 175/300\n294/294 [==============================] - 1s 4ms/step - loss: 69.2346 - mae: 6.3396 - val_loss: 61.1241 - val_mae: 5.9276 - lr: 0.0010\nEpoch 176/300\n294/294 [==============================] - 1s 4ms/step - loss: 71.8820 - mae: 6.4951 - val_loss: 60.4189 - val_mae: 5.8963 - lr: 0.0010\nEpoch 177/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.0648 - mae: 6.4090 - val_loss: 60.2908 - val_mae: 5.8404 - lr: 0.0010\nEpoch 178/300\n294/294 [==============================] - 1s 4ms/step - loss: 68.4026 - mae: 6.3373 - val_loss: 60.3593 - val_mae: 5.8838 - lr: 0.0010\nEpoch 179/300\n294/294 [==============================] - 1s 3ms/step - loss: 73.8559 - mae: 6.5655 - val_loss: 60.9680 - val_mae: 5.9309 - lr: 0.0010\nEpoch 180/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.3354 - mae: 6.3844 - val_loss: 60.2403 - val_mae: 5.9254 - lr: 0.0010\nEpoch 181/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.9565 - mae: 6.4168 - val_loss: 59.7420 - val_mae: 5.8648 - lr: 0.0010\nEpoch 182/300\n294/294 [==============================] - 1s 3ms/step - loss: 71.2581 - mae: 6.4557 - val_loss: 60.7116 - val_mae: 5.9171 - lr: 0.0010\nEpoch 183/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.4301 - mae: 6.2861 - val_loss: 60.8888 - val_mae: 5.8928 - lr: 0.0010\nEpoch 184/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.7864 - mae: 6.3095 - val_loss: 63.6694 - val_mae: 6.1008 - lr: 0.0010\nEpoch 185/300\n294/294 [==============================] - 1s 3ms/step - loss: 70.2178 - mae: 6.4188 - val_loss: 61.1779 - val_mae: 5.9113 - lr: 0.0010\nEpoch 186/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.7719 - mae: 6.3204 - val_loss: 59.6751 - val_mae: 5.8823 - lr: 0.0010\nEpoch 187/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.4681 - mae: 6.4153 - val_loss: 60.4952 - val_mae: 5.9435 - lr: 0.0010\nEpoch 188/300\n294/294 [==============================] - 1s 2ms/step - loss: 69.6499 - mae: 6.4145 - val_loss: 60.7895 - val_mae: 5.9155 - lr: 0.0010\nEpoch 189/300\n294/294 [==============================] - 1s 2ms/step - loss: 67.0245 - mae: 6.3114 - val_loss: 61.6649 - val_mae: 5.9901 - lr: 0.0010\nEpoch 190/300\n294/294 [==============================] - 1s 2ms/step - loss: 69.2504 - mae: 6.4371 - val_loss: 61.8070 - val_mae: 6.0312 - lr: 0.0010\nEpoch 191/300\n294/294 [==============================] - 1s 2ms/step - loss: 71.6006 - mae: 6.5020 - val_loss: 60.4981 - val_mae: 5.9206 - lr: 0.0010\nEpoch 192/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.5449 - mae: 6.2035 - val_loss: 61.3904 - val_mae: 5.9767 - lr: 0.0010\nEpoch 193/300\n294/294 [==============================] - 1s 2ms/step - loss: 68.2953 - mae: 6.3063 - val_loss: 61.0000 - val_mae: 5.9465 - lr: 0.0010\nEpoch 194/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.0928 - mae: 6.2217 - val_loss: 60.5285 - val_mae: 5.8937 - lr: 0.0010\nEpoch 195/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.4657 - mae: 6.3715 - val_loss: 61.1978 - val_mae: 5.9543 - lr: 0.0010\nEpoch 196/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.3576 - mae: 6.3674 - val_loss: 61.1394 - val_mae: 5.9068 - lr: 0.0010\nEpoch 197/300\n294/294 [==============================] - 1s 2ms/step - loss: 67.4243 - mae: 6.2909 - val_loss: 62.3198 - val_mae: 5.9730 - lr: 0.0010\nEpoch 198/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.5276 - mae: 6.3322 - val_loss: 60.4825 - val_mae: 5.9178 - lr: 0.0010\nEpoch 199/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.1773 - mae: 6.3446 - val_loss: 60.4277 - val_mae: 5.9151 - lr: 0.0010\nEpoch 200/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.7191 - mae: 6.3547 - val_loss: 59.3745 - val_mae: 5.8607 - lr: 0.0010\nEpoch 201/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.9709 - mae: 6.4034 - val_loss: 60.7743 - val_mae: 5.9034 - lr: 0.0010\nEpoch 202/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.5944 - mae: 6.3421 - val_loss: 60.9310 - val_mae: 5.9220 - lr: 0.0010\nEpoch 203/300\n294/294 [==============================] - 1s 4ms/step - loss: 65.2488 - mae: 6.2252 - val_loss: 59.9950 - val_mae: 5.8572 - lr: 0.0010\nEpoch 204/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.6946 - mae: 6.3012 - val_loss: 60.6108 - val_mae: 5.8751 - lr: 0.0010\nEpoch 205/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.8027 - mae: 6.2114 - val_loss: 62.0820 - val_mae: 6.0244 - lr: 0.0010\nEpoch 206/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.1742 - mae: 6.1687 - val_loss: 60.1431 - val_mae: 5.9333 - lr: 0.0010\nEpoch 207/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.3307 - mae: 6.1666 - val_loss: 62.1660 - val_mae: 6.0579 - lr: 0.0010\nEpoch 208/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.2160 - mae: 6.3108 - val_loss: 59.3178 - val_mae: 5.8481 - lr: 0.0010\nEpoch 209/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.8112 - mae: 6.2683 - val_loss: 60.8766 - val_mae: 5.9487 - lr: 0.0010\nEpoch 210/300\n294/294 [==============================] - 1s 3ms/step - loss: 69.6598 - mae: 6.3797 - val_loss: 60.7125 - val_mae: 5.8656 - lr: 0.0010\nEpoch 211/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.8393 - mae: 6.3813 - val_loss: 60.6873 - val_mae: 5.9223 - lr: 0.0010\nEpoch 212/300\n294/294 [==============================] - 1s 2ms/step - loss: 68.6712 - mae: 6.3523 - val_loss: 60.4228 - val_mae: 5.9223 - lr: 0.0010\nEpoch 213/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.2496 - mae: 6.2750 - val_loss: 59.4861 - val_mae: 5.8330 - lr: 0.0010\nEpoch 214/300\n294/294 [==============================] - 1s 4ms/step - loss: 68.2532 - mae: 6.3314 - val_loss: 62.0485 - val_mae: 5.9571 - lr: 0.0010\nEpoch 215/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.5662 - mae: 6.2201 - val_loss: 61.8605 - val_mae: 5.9477 - lr: 0.0010\nEpoch 216/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.4699 - mae: 6.2731 - val_loss: 61.4966 - val_mae: 5.9636 - lr: 0.0010\nEpoch 217/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.8503 - mae: 6.2382 - val_loss: 63.2308 - val_mae: 6.0525 - lr: 0.0010\nEpoch 218/300\n294/294 [==============================] - 1s 2ms/step - loss: 68.6608 - mae: 6.3019 - val_loss: 60.4814 - val_mae: 5.9023 - lr: 0.0010\nEpoch 219/300\n294/294 [==============================] - 1s 2ms/step - loss: 66.3236 - mae: 6.2451 - val_loss: 61.9394 - val_mae: 6.0060 - lr: 0.0010\nEpoch 220/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.8437 - mae: 6.1507 - val_loss: 61.1062 - val_mae: 5.9142 - lr: 0.0010\nEpoch 221/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.8245 - mae: 6.1682 - val_loss: 61.6957 - val_mae: 5.9153 - lr: 0.0010\nEpoch 222/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.4001 - mae: 6.2486 - val_loss: 61.5823 - val_mae: 5.8946 - lr: 0.0010\nEpoch 223/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.1848 - mae: 6.2213 - val_loss: 62.6831 - val_mae: 5.9182 - lr: 0.0010\nEpoch 224/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.4817 - mae: 6.3188 - val_loss: 62.5588 - val_mae: 6.0142 - lr: 0.0010\nEpoch 225/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.0146 - mae: 6.2542 - val_loss: 60.7290 - val_mae: 5.8733 - lr: 0.0010\nEpoch 226/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.9373 - mae: 6.2212 - val_loss: 59.2639 - val_mae: 5.7454 - lr: 0.0010\nEpoch 227/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.1861 - mae: 6.1502 - val_loss: 60.0299 - val_mae: 5.8686 - lr: 0.0010\nEpoch 228/300\n294/294 [==============================] - 1s 3ms/step - loss: 68.6814 - mae: 6.3097 - val_loss: 60.4969 - val_mae: 5.8952 - lr: 0.0010\nEpoch 229/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.3406 - mae: 6.2782 - val_loss: 60.0019 - val_mae: 5.8520 - lr: 0.0010\nEpoch 230/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.6401 - mae: 6.2589 - val_loss: 61.7093 - val_mae: 5.9173 - lr: 0.0010\nEpoch 231/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.6832 - mae: 6.2440 - val_loss: 59.6972 - val_mae: 5.8447 - lr: 0.0010\nEpoch 232/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.0399 - mae: 6.1497 - val_loss: 62.7080 - val_mae: 5.8931 - lr: 0.0010\nEpoch 233/300\n294/294 [==============================] - 1s 3ms/step - loss: 67.6776 - mae: 6.2705 - val_loss: 61.7751 - val_mae: 5.9367 - lr: 0.0010\nEpoch 234/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.8428 - mae: 6.1506 - val_loss: 61.6310 - val_mae: 5.9232 - lr: 0.0010\nEpoch 235/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.4791 - mae: 6.1416 - val_loss: 62.4811 - val_mae: 5.9624 - lr: 0.0010\nEpoch 236/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.4260 - mae: 6.1969 - val_loss: 62.0141 - val_mae: 5.9614 - lr: 0.0010\nEpoch 237/300\n294/294 [==============================] - 1s 2ms/step - loss: 66.0921 - mae: 6.1441 - val_loss: 61.1019 - val_mae: 5.9082 - lr: 0.0010\nEpoch 238/300\n294/294 [==============================] - 1s 3ms/step - loss: 63.6320 - mae: 6.1147 - val_loss: 61.5060 - val_mae: 5.9734 - lr: 0.0010\nEpoch 239/300\n294/294 [==============================] - 1s 2ms/step - loss: 69.5857 - mae: 6.3748 - val_loss: 61.6006 - val_mae: 5.9058 - lr: 0.0010\nEpoch 240/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.7857 - mae: 6.1227 - val_loss: 60.4231 - val_mae: 5.8070 - lr: 0.0010\nEpoch 241/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.0563 - mae: 6.1063 - val_loss: 62.7069 - val_mae: 5.9823 - lr: 0.0010\nEpoch 242/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.2240 - mae: 6.2194 - val_loss: 60.9275 - val_mae: 5.8925 - lr: 0.0010\nEpoch 243/300\n294/294 [==============================] - 1s 3ms/step - loss: 66.4623 - mae: 6.2304 - val_loss: 60.7996 - val_mae: 5.9113 - lr: 0.0010\nEpoch 244/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.3085 - mae: 6.1580 - val_loss: 60.2220 - val_mae: 5.8472 - lr: 0.0010\nEpoch 245/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.7296 - mae: 6.1839 - val_loss: 61.8043 - val_mae: 5.8830 - lr: 0.0010\nEpoch 246/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.1034 - mae: 6.1359 - val_loss: 60.3102 - val_mae: 5.8976 - lr: 0.0010\nEpoch 247/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.2072 - mae: 6.2027 - val_loss: 61.0222 - val_mae: 5.8945 - lr: 0.0010\nEpoch 248/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.3131 - mae: 6.1584 - val_loss: 62.4830 - val_mae: 5.9632 - lr: 0.0010\nEpoch 249/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.6083 - mae: 6.1715 - val_loss: 62.8347 - val_mae: 5.9671 - lr: 0.0010\nEpoch 250/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.5017 - mae: 6.1084 - val_loss: 62.8930 - val_mae: 5.9639 - lr: 0.0010\nEpoch 251/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.5787 - mae: 6.1744 - val_loss: 62.0302 - val_mae: 5.9330 - lr: 0.0010\nEpoch 252/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.4643 - mae: 6.2030 - val_loss: 63.1191 - val_mae: 5.9337 - lr: 0.0010\nEpoch 253/300\n294/294 [==============================] - 1s 3ms/step - loss: 65.0184 - mae: 6.1409 - val_loss: 60.1631 - val_mae: 5.8020 - lr: 0.0010\nEpoch 254/300\n294/294 [==============================] - 1s 2ms/step - loss: 66.4680 - mae: 6.2333 - val_loss: 61.0870 - val_mae: 5.8442 - lr: 0.0010\nEpoch 255/300\n294/294 [==============================] - 1s 3ms/step - loss: 64.3977 - mae: 6.1884 - val_loss: 64.0263 - val_mae: 5.9457 - lr: 0.0010\nEpoch 256/300\n272/294 [==========================>...] - ETA: 0s - loss: 67.0719 - mae: 6.2517\nEpoch 256: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n294/294 [==============================] - 1s 3ms/step - loss: 66.4365 - mae: 6.2281 - val_loss: 61.7264 - val_mae: 5.8919 - lr: 0.0010\nEpoch 257/300\n294/294 [==============================] - 1s 3ms/step - loss: 61.6098 - mae: 5.9255 - val_loss: 60.6014 - val_mae: 5.8065 - lr: 5.0000e-04\nEpoch 258/300\n294/294 [==============================] - 1s 3ms/step - loss: 62.7677 - mae: 6.0613 - val_loss: 60.8210 - val_mae: 5.8319 - lr: 5.0000e-04\nEpoch 259/300\n294/294 [==============================] - 1s 2ms/step - loss: 61.8333 - mae: 5.9315 - val_loss: 61.1052 - val_mae: 5.8434 - lr: 5.0000e-04\nEpoch 260/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.8976 - mae: 5.9722 - val_loss: 60.3820 - val_mae: 5.8157 - lr: 5.0000e-04\nEpoch 261/300\n294/294 [==============================] - 1s 3ms/step - loss: 61.8441 - mae: 6.0110 - val_loss: 60.2701 - val_mae: 5.8484 - lr: 5.0000e-04\nEpoch 262/300\n294/294 [==============================] - 1s 3ms/step - loss: 59.3749 - mae: 5.8784 - val_loss: 60.5539 - val_mae: 5.8052 - lr: 5.0000e-04\nEpoch 263/300\n294/294 [==============================] - 1s 3ms/step - loss: 63.3161 - mae: 6.0617 - val_loss: 61.3358 - val_mae: 5.8709 - lr: 5.0000e-04\nEpoch 264/300\n294/294 [==============================] - 1s 3ms/step - loss: 62.5728 - mae: 6.0253 - val_loss: 60.3602 - val_mae: 5.7803 - lr: 5.0000e-04\nEpoch 265/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.3354 - mae: 5.9478 - val_loss: 60.2420 - val_mae: 5.7892 - lr: 5.0000e-04\nEpoch 266/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.5206 - mae: 5.8934 - val_loss: 60.5727 - val_mae: 5.8734 - lr: 5.0000e-04\nEpoch 267/300\n294/294 [==============================] - 1s 3ms/step - loss: 62.4087 - mae: 6.0117 - val_loss: 59.9451 - val_mae: 5.8531 - lr: 5.0000e-04\nEpoch 268/300\n294/294 [==============================] - 1s 3ms/step - loss: 59.8722 - mae: 5.8981 - val_loss: 59.9627 - val_mae: 5.8373 - lr: 5.0000e-04\nEpoch 269/300\n294/294 [==============================] - 1s 3ms/step - loss: 59.7136 - mae: 5.8898 - val_loss: 60.3357 - val_mae: 5.8560 - lr: 5.0000e-04\nEpoch 270/300\n294/294 [==============================] - 1s 3ms/step - loss: 61.1846 - mae: 5.9817 - val_loss: 60.0458 - val_mae: 5.7821 - lr: 5.0000e-04\nEpoch 271/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.7142 - mae: 5.9098 - val_loss: 60.7190 - val_mae: 5.8540 - lr: 5.0000e-04\nEpoch 272/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.5698 - mae: 5.9264 - val_loss: 60.2534 - val_mae: 5.8281 - lr: 5.0000e-04\nEpoch 273/300\n294/294 [==============================] - 1s 3ms/step - loss: 59.2712 - mae: 5.8223 - val_loss: 60.0273 - val_mae: 5.7908 - lr: 5.0000e-04\nEpoch 274/300\n294/294 [==============================] - 1s 3ms/step - loss: 60.9707 - mae: 5.9707 - val_loss: 61.0059 - val_mae: 5.8699 - lr: 5.0000e-04\nEpoch 275/300\n294/294 [==============================] - 1s 3ms/step - loss: 59.2086 - mae: 5.8322 - val_loss: 61.3873 - val_mae: 5.8845 - lr: 5.0000e-04\nEpoch 276/300\n294/294 [==============================] - 1s 3ms/step - loss: 58.1452 - mae: 5.8464 - val_loss: 60.0276 - val_mae: 5.7954 - lr: 5.0000e-04\n46/46 [==============================] - 0s 1ms/step\nRMSE: 7.698\nR^2 Score: 0.460\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/fc3cab42-8128-4b58-98f1-ed7f29d6c0e4",
   "content_dependencies": null
  },
  {
   "cellId": "6eb739352c824eec9e05dbb4df6afd75",
   "cell_type": "code",
   "metadata": {
    "source_hash": "2250390f",
    "execution_start": 1753836052959,
    "execution_millis": 57300,
    "execution_context_id": "7911dbe2-c8c2-4ea2-bf83-ed60e6ed2c28",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "6eb739352c824eec9e05dbb4df6afd75",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n    BatchNormalization(),\n    Dropout(0.3),\n    Dense(128, activation='relu'),\n    Dropout(0.3),\n    Dense(64, activation='relu'),\n    Dropout(0.2),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=32,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "a5e3d54cb3324473ac76871231ca3961",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n92/92 [==============================] - 2s 6ms/step - loss: 397.1013 - mae: 16.0452 - val_loss: 562.2929 - val_mae: 21.4278 - lr: 0.0010\nEpoch 2/300\n92/92 [==============================] - 0s 4ms/step - loss: 142.4626 - mae: 9.5119 - val_loss: 397.9624 - val_mae: 17.5868 - lr: 0.0010\nEpoch 3/300\n92/92 [==============================] - 0s 4ms/step - loss: 125.6553 - mae: 8.9057 - val_loss: 236.6893 - val_mae: 12.8360 - lr: 0.0010\nEpoch 4/300\n92/92 [==============================] - 0s 4ms/step - loss: 127.0565 - mae: 8.9111 - val_loss: 157.0170 - val_mae: 10.0778 - lr: 0.0010\nEpoch 5/300\n92/92 [==============================] - 0s 3ms/step - loss: 126.8338 - mae: 8.8161 - val_loss: 108.7742 - val_mae: 8.2267 - lr: 0.0010\nEpoch 6/300\n92/92 [==============================] - 0s 4ms/step - loss: 117.9635 - mae: 8.5812 - val_loss: 95.9345 - val_mae: 7.7709 - lr: 0.0010\nEpoch 7/300\n92/92 [==============================] - 0s 3ms/step - loss: 115.2151 - mae: 8.5498 - val_loss: 83.2808 - val_mae: 7.2272 - lr: 0.0010\nEpoch 8/300\n92/92 [==============================] - 0s 3ms/step - loss: 114.7484 - mae: 8.4548 - val_loss: 94.6622 - val_mae: 7.7339 - lr: 0.0010\nEpoch 9/300\n92/92 [==============================] - 0s 4ms/step - loss: 109.2851 - mae: 8.2458 - val_loss: 82.1371 - val_mae: 7.1998 - lr: 0.0010\nEpoch 10/300\n92/92 [==============================] - 0s 4ms/step - loss: 111.3360 - mae: 8.3690 - val_loss: 77.0561 - val_mae: 7.0032 - lr: 0.0010\nEpoch 11/300\n92/92 [==============================] - 1s 5ms/step - loss: 106.6861 - mae: 8.1103 - val_loss: 86.6260 - val_mae: 7.3289 - lr: 0.0010\nEpoch 12/300\n92/92 [==============================] - 0s 3ms/step - loss: 106.2308 - mae: 8.1711 - val_loss: 76.0544 - val_mae: 6.9337 - lr: 0.0010\nEpoch 13/300\n92/92 [==============================] - 0s 4ms/step - loss: 106.6165 - mae: 8.1521 - val_loss: 83.4280 - val_mae: 7.1068 - lr: 0.0010\nEpoch 14/300\n92/92 [==============================] - 0s 3ms/step - loss: 103.5234 - mae: 8.0020 - val_loss: 87.0791 - val_mae: 7.4517 - lr: 0.0010\nEpoch 15/300\n92/92 [==============================] - 0s 3ms/step - loss: 101.7917 - mae: 7.9213 - val_loss: 75.4741 - val_mae: 6.8787 - lr: 0.0010\nEpoch 16/300\n92/92 [==============================] - 0s 3ms/step - loss: 103.8179 - mae: 8.1038 - val_loss: 76.7318 - val_mae: 6.9754 - lr: 0.0010\nEpoch 17/300\n92/92 [==============================] - 0s 3ms/step - loss: 104.8302 - mae: 8.0107 - val_loss: 76.6937 - val_mae: 6.9590 - lr: 0.0010\nEpoch 18/300\n92/92 [==============================] - 0s 4ms/step - loss: 103.3243 - mae: 7.9337 - val_loss: 74.7702 - val_mae: 6.8786 - lr: 0.0010\nEpoch 19/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.9768 - mae: 7.6110 - val_loss: 75.5372 - val_mae: 6.9322 - lr: 0.0010\nEpoch 20/300\n92/92 [==============================] - 0s 4ms/step - loss: 99.7106 - mae: 7.9183 - val_loss: 77.2383 - val_mae: 7.0013 - lr: 0.0010\nEpoch 21/300\n92/92 [==============================] - 0s 4ms/step - loss: 100.7598 - mae: 7.9412 - val_loss: 79.3302 - val_mae: 7.0497 - lr: 0.0010\nEpoch 22/300\n92/92 [==============================] - 0s 3ms/step - loss: 100.2587 - mae: 7.9125 - val_loss: 74.4196 - val_mae: 6.8776 - lr: 0.0010\nEpoch 23/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.4039 - mae: 7.6528 - val_loss: 83.5257 - val_mae: 7.2341 - lr: 0.0010\nEpoch 24/300\n92/92 [==============================] - 0s 4ms/step - loss: 98.4942 - mae: 7.7847 - val_loss: 79.7510 - val_mae: 7.0726 - lr: 0.0010\nEpoch 25/300\n92/92 [==============================] - 0s 4ms/step - loss: 100.0936 - mae: 7.8436 - val_loss: 68.7878 - val_mae: 6.5063 - lr: 0.0010\nEpoch 26/300\n92/92 [==============================] - 0s 3ms/step - loss: 101.3727 - mae: 7.8653 - val_loss: 80.1230 - val_mae: 7.1259 - lr: 0.0010\nEpoch 27/300\n92/92 [==============================] - 0s 3ms/step - loss: 94.8514 - mae: 7.6487 - val_loss: 70.2083 - val_mae: 6.6732 - lr: 0.0010\nEpoch 28/300\n92/92 [==============================] - 0s 3ms/step - loss: 97.0766 - mae: 7.7561 - val_loss: 74.5838 - val_mae: 6.7122 - lr: 0.0010\nEpoch 29/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.7843 - mae: 7.6649 - val_loss: 73.4728 - val_mae: 6.8096 - lr: 0.0010\nEpoch 30/300\n92/92 [==============================] - 0s 3ms/step - loss: 93.9130 - mae: 7.5886 - val_loss: 68.1186 - val_mae: 6.4739 - lr: 0.0010\nEpoch 31/300\n92/92 [==============================] - 0s 3ms/step - loss: 92.6827 - mae: 7.5655 - val_loss: 76.8751 - val_mae: 6.9279 - lr: 0.0010\nEpoch 32/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.7034 - mae: 7.5976 - val_loss: 72.9909 - val_mae: 6.7255 - lr: 0.0010\nEpoch 33/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.4211 - mae: 7.6696 - val_loss: 67.7464 - val_mae: 6.4908 - lr: 0.0010\nEpoch 34/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.8914 - mae: 7.4894 - val_loss: 76.1758 - val_mae: 6.9293 - lr: 0.0010\nEpoch 35/300\n92/92 [==============================] - 0s 4ms/step - loss: 95.0091 - mae: 7.6227 - val_loss: 74.1601 - val_mae: 6.8445 - lr: 0.0010\nEpoch 36/300\n92/92 [==============================] - 0s 4ms/step - loss: 94.3240 - mae: 7.5580 - val_loss: 66.6111 - val_mae: 6.4295 - lr: 0.0010\nEpoch 37/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.8326 - mae: 7.4612 - val_loss: 78.7525 - val_mae: 6.9884 - lr: 0.0010\nEpoch 38/300\n92/92 [==============================] - 0s 4ms/step - loss: 93.5354 - mae: 7.5305 - val_loss: 84.0763 - val_mae: 7.3045 - lr: 0.0010\nEpoch 39/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.9954 - mae: 7.4484 - val_loss: 69.5918 - val_mae: 6.5413 - lr: 0.0010\nEpoch 40/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.9761 - mae: 7.5143 - val_loss: 76.6783 - val_mae: 6.9405 - lr: 0.0010\nEpoch 41/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.1057 - mae: 7.5620 - val_loss: 78.0503 - val_mae: 7.0891 - lr: 0.0010\nEpoch 42/300\n92/92 [==============================] - 0s 4ms/step - loss: 92.8692 - mae: 7.4990 - val_loss: 68.4694 - val_mae: 6.5148 - lr: 0.0010\nEpoch 43/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.6722 - mae: 7.4117 - val_loss: 73.0122 - val_mae: 6.7935 - lr: 0.0010\nEpoch 44/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.6939 - mae: 7.3766 - val_loss: 69.3078 - val_mae: 6.5940 - lr: 0.0010\nEpoch 45/300\n92/92 [==============================] - 0s 3ms/step - loss: 89.7332 - mae: 7.3579 - val_loss: 67.0356 - val_mae: 6.4555 - lr: 0.0010\nEpoch 46/300\n92/92 [==============================] - 0s 3ms/step - loss: 88.8301 - mae: 7.3501 - val_loss: 74.6738 - val_mae: 6.7959 - lr: 0.0010\nEpoch 47/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.4794 - mae: 7.3729 - val_loss: 66.4539 - val_mae: 6.4210 - lr: 0.0010\nEpoch 48/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.5785 - mae: 7.3320 - val_loss: 67.4764 - val_mae: 6.4484 - lr: 0.0010\nEpoch 49/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.7433 - mae: 7.3784 - val_loss: 68.8042 - val_mae: 6.5247 - lr: 0.0010\nEpoch 50/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.3561 - mae: 7.4042 - val_loss: 69.4351 - val_mae: 6.6272 - lr: 0.0010\nEpoch 51/300\n92/92 [==============================] - 0s 3ms/step - loss: 90.8619 - mae: 7.3939 - val_loss: 72.8448 - val_mae: 6.6622 - lr: 0.0010\nEpoch 52/300\n92/92 [==============================] - 0s 4ms/step - loss: 89.4180 - mae: 7.3685 - val_loss: 74.4271 - val_mae: 6.8901 - lr: 0.0010\nEpoch 53/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.6612 - mae: 7.2209 - val_loss: 68.2220 - val_mae: 6.4941 - lr: 0.0010\nEpoch 54/300\n92/92 [==============================] - 0s 3ms/step - loss: 86.8915 - mae: 7.3171 - val_loss: 67.4261 - val_mae: 6.5060 - lr: 0.0010\nEpoch 55/300\n92/92 [==============================] - 0s 5ms/step - loss: 85.4510 - mae: 7.2755 - val_loss: 73.6133 - val_mae: 6.8285 - lr: 0.0010\nEpoch 56/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.7497 - mae: 7.3072 - val_loss: 66.2118 - val_mae: 6.4219 - lr: 0.0010\nEpoch 57/300\n92/92 [==============================] - 0s 3ms/step - loss: 87.5965 - mae: 7.2917 - val_loss: 72.2074 - val_mae: 6.6422 - lr: 0.0010\nEpoch 58/300\n92/92 [==============================] - 0s 4ms/step - loss: 85.7083 - mae: 7.2354 - val_loss: 74.3863 - val_mae: 6.8050 - lr: 0.0010\nEpoch 59/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.6325 - mae: 7.1379 - val_loss: 66.5499 - val_mae: 6.3261 - lr: 0.0010\nEpoch 60/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.3716 - mae: 7.0911 - val_loss: 73.5538 - val_mae: 6.7694 - lr: 0.0010\nEpoch 61/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.8203 - mae: 7.3062 - val_loss: 78.6250 - val_mae: 7.0378 - lr: 0.0010\nEpoch 62/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.7941 - mae: 7.1364 - val_loss: 64.8635 - val_mae: 6.3475 - lr: 0.0010\nEpoch 63/300\n92/92 [==============================] - 0s 3ms/step - loss: 83.9937 - mae: 7.1791 - val_loss: 68.0478 - val_mae: 6.5118 - lr: 0.0010\nEpoch 64/300\n92/92 [==============================] - 0s 4ms/step - loss: 90.5994 - mae: 7.4568 - val_loss: 76.9230 - val_mae: 6.9039 - lr: 0.0010\nEpoch 65/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.5893 - mae: 7.1453 - val_loss: 67.8094 - val_mae: 6.5076 - lr: 0.0010\nEpoch 66/300\n92/92 [==============================] - 0s 4ms/step - loss: 84.7345 - mae: 7.1530 - val_loss: 73.2814 - val_mae: 6.7686 - lr: 0.0010\nEpoch 67/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.7764 - mae: 7.1302 - val_loss: 68.2651 - val_mae: 6.5398 - lr: 0.0010\nEpoch 68/300\n92/92 [==============================] - 0s 4ms/step - loss: 87.3413 - mae: 7.2716 - val_loss: 71.5129 - val_mae: 6.7395 - lr: 0.0010\nEpoch 69/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.6405 - mae: 7.0850 - val_loss: 65.4361 - val_mae: 6.3539 - lr: 0.0010\nEpoch 70/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.8362 - mae: 7.0908 - val_loss: 68.1288 - val_mae: 6.5450 - lr: 0.0010\nEpoch 71/300\n92/92 [==============================] - 0s 4ms/step - loss: 88.8088 - mae: 7.3801 - val_loss: 66.1094 - val_mae: 6.3297 - lr: 0.0010\nEpoch 72/300\n92/92 [==============================] - 0s 3ms/step - loss: 84.4962 - mae: 7.1128 - val_loss: 75.4820 - val_mae: 6.8536 - lr: 0.0010\nEpoch 73/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.0271 - mae: 7.0561 - val_loss: 71.5458 - val_mae: 6.6552 - lr: 0.0010\nEpoch 74/300\n92/92 [==============================] - 0s 4ms/step - loss: 82.0800 - mae: 7.0906 - val_loss: 76.6873 - val_mae: 6.8670 - lr: 0.0010\nEpoch 75/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.6551 - mae: 7.0454 - val_loss: 66.2769 - val_mae: 6.3868 - lr: 0.0010\nEpoch 76/300\n92/92 [==============================] - 0s 5ms/step - loss: 83.3961 - mae: 7.1049 - val_loss: 75.4150 - val_mae: 6.8326 - lr: 0.0010\nEpoch 77/300\n92/92 [==============================] - 0s 4ms/step - loss: 83.9303 - mae: 7.0374 - val_loss: 67.8211 - val_mae: 6.4594 - lr: 0.0010\nEpoch 78/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.8151 - mae: 7.0766 - val_loss: 70.6353 - val_mae: 6.5817 - lr: 0.0010\nEpoch 79/300\n92/92 [==============================] - 0s 4ms/step - loss: 86.2481 - mae: 7.2414 - val_loss: 65.3993 - val_mae: 6.3452 - lr: 0.0010\nEpoch 80/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.5236 - mae: 6.9467 - val_loss: 69.8518 - val_mae: 6.5877 - lr: 0.0010\nEpoch 81/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.0988 - mae: 7.0238 - val_loss: 66.9644 - val_mae: 6.4141 - lr: 0.0010\nEpoch 82/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.8276 - mae: 6.9999 - val_loss: 68.2428 - val_mae: 6.4017 - lr: 0.0010\nEpoch 83/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.4962 - mae: 6.9681 - val_loss: 64.6092 - val_mae: 6.3289 - lr: 0.0010\nEpoch 84/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.5773 - mae: 7.0126 - val_loss: 76.6774 - val_mae: 6.8958 - lr: 0.0010\nEpoch 85/300\n92/92 [==============================] - 0s 3ms/step - loss: 82.0279 - mae: 7.0442 - val_loss: 67.6910 - val_mae: 6.3944 - lr: 0.0010\nEpoch 86/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.3442 - mae: 6.9926 - val_loss: 63.7019 - val_mae: 6.1653 - lr: 0.0010\nEpoch 87/300\n92/92 [==============================] - 0s 3ms/step - loss: 81.8417 - mae: 7.0528 - val_loss: 65.5266 - val_mae: 6.3174 - lr: 0.0010\nEpoch 88/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.0267 - mae: 7.0403 - val_loss: 74.6282 - val_mae: 6.8118 - lr: 0.0010\nEpoch 89/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.4852 - mae: 6.9991 - val_loss: 65.1373 - val_mae: 6.2842 - lr: 0.0010\nEpoch 90/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.9954 - mae: 6.9576 - val_loss: 63.0895 - val_mae: 6.1743 - lr: 0.0010\nEpoch 91/300\n92/92 [==============================] - 0s 4ms/step - loss: 81.0749 - mae: 6.9819 - val_loss: 63.7722 - val_mae: 6.2422 - lr: 0.0010\nEpoch 92/300\n92/92 [==============================] - 0s 3ms/step - loss: 80.8805 - mae: 6.9642 - val_loss: 73.3837 - val_mae: 6.6886 - lr: 0.0010\nEpoch 93/300\n92/92 [==============================] - 0s 3ms/step - loss: 78.5412 - mae: 6.8736 - val_loss: 67.6834 - val_mae: 6.3981 - lr: 0.0010\nEpoch 94/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.0340 - mae: 6.8865 - val_loss: 65.5056 - val_mae: 6.3125 - lr: 0.0010\nEpoch 95/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.4571 - mae: 6.8560 - val_loss: 65.4162 - val_mae: 6.2547 - lr: 0.0010\nEpoch 96/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.2139 - mae: 6.8298 - val_loss: 66.6864 - val_mae: 6.3686 - lr: 0.0010\nEpoch 97/300\n92/92 [==============================] - 0s 3ms/step - loss: 79.0556 - mae: 6.8981 - val_loss: 64.8353 - val_mae: 6.3019 - lr: 0.0010\nEpoch 98/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.0700 - mae: 6.8534 - val_loss: 71.6250 - val_mae: 6.6561 - lr: 0.0010\nEpoch 99/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.4868 - mae: 6.9318 - val_loss: 62.9342 - val_mae: 6.1788 - lr: 0.0010\nEpoch 100/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.4088 - mae: 6.8638 - val_loss: 64.0665 - val_mae: 6.1896 - lr: 0.0010\nEpoch 101/300\n92/92 [==============================] - 0s 4ms/step - loss: 80.9890 - mae: 6.9634 - val_loss: 65.0889 - val_mae: 6.2981 - lr: 0.0010\nEpoch 102/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.2487 - mae: 6.9414 - val_loss: 66.5130 - val_mae: 6.3428 - lr: 0.0010\nEpoch 103/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.6523 - mae: 6.7525 - val_loss: 70.3092 - val_mae: 6.4798 - lr: 0.0010\nEpoch 104/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.8750 - mae: 6.8679 - val_loss: 62.6026 - val_mae: 6.1329 - lr: 0.0010\nEpoch 105/300\n92/92 [==============================] - 0s 4ms/step - loss: 79.8004 - mae: 6.8593 - val_loss: 66.8217 - val_mae: 6.3374 - lr: 0.0010\nEpoch 106/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.0492 - mae: 6.6613 - val_loss: 66.3583 - val_mae: 6.3513 - lr: 0.0010\nEpoch 107/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.1829 - mae: 6.8083 - val_loss: 63.5456 - val_mae: 6.1553 - lr: 0.0010\nEpoch 108/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.0472 - mae: 6.7529 - val_loss: 71.2081 - val_mae: 6.6178 - lr: 0.0010\nEpoch 109/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.0345 - mae: 6.8016 - val_loss: 66.5490 - val_mae: 6.3238 - lr: 0.0010\nEpoch 110/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.8962 - mae: 6.7583 - val_loss: 65.7573 - val_mae: 6.3237 - lr: 0.0010\nEpoch 111/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.8073 - mae: 6.7607 - val_loss: 61.5460 - val_mae: 6.0388 - lr: 0.0010\nEpoch 112/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.0723 - mae: 6.8343 - val_loss: 65.1259 - val_mae: 6.2211 - lr: 0.0010\nEpoch 113/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.1134 - mae: 6.8652 - val_loss: 61.7527 - val_mae: 6.0504 - lr: 0.0010\nEpoch 114/300\n92/92 [==============================] - 0s 5ms/step - loss: 77.3849 - mae: 6.8259 - val_loss: 67.2435 - val_mae: 6.3923 - lr: 0.0010\nEpoch 115/300\n92/92 [==============================] - 0s 5ms/step - loss: 80.6972 - mae: 6.9504 - val_loss: 70.9352 - val_mae: 6.5762 - lr: 0.0010\nEpoch 116/300\n92/92 [==============================] - 0s 5ms/step - loss: 81.2909 - mae: 6.9767 - val_loss: 64.7014 - val_mae: 6.2315 - lr: 0.0010\nEpoch 117/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.3413 - mae: 6.7609 - val_loss: 62.1831 - val_mae: 6.1021 - lr: 0.0010\nEpoch 118/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.4985 - mae: 6.7800 - val_loss: 72.1625 - val_mae: 6.6028 - lr: 0.0010\nEpoch 119/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.9395 - mae: 6.7691 - val_loss: 63.2834 - val_mae: 6.1312 - lr: 0.0010\nEpoch 120/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.3479 - mae: 6.7923 - val_loss: 68.7197 - val_mae: 6.4826 - lr: 0.0010\nEpoch 121/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.5256 - mae: 6.7223 - val_loss: 66.0139 - val_mae: 6.2928 - lr: 0.0010\nEpoch 122/300\n92/92 [==============================] - 0s 4ms/step - loss: 78.8309 - mae: 6.8769 - val_loss: 72.3557 - val_mae: 6.6549 - lr: 0.0010\nEpoch 123/300\n92/92 [==============================] - 0s 3ms/step - loss: 76.3159 - mae: 6.7904 - val_loss: 68.9751 - val_mae: 6.4580 - lr: 0.0010\nEpoch 124/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.8381 - mae: 6.7468 - val_loss: 63.4855 - val_mae: 6.2023 - lr: 0.0010\nEpoch 125/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.4821 - mae: 6.7657 - val_loss: 63.3270 - val_mae: 6.1653 - lr: 0.0010\nEpoch 126/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.2433 - mae: 6.7895 - val_loss: 68.8615 - val_mae: 6.4520 - lr: 0.0010\nEpoch 127/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.6572 - mae: 6.8470 - val_loss: 71.2534 - val_mae: 6.5682 - lr: 0.0010\nEpoch 128/300\n92/92 [==============================] - 0s 4ms/step - loss: 77.0202 - mae: 6.8033 - val_loss: 68.2711 - val_mae: 6.4659 - lr: 0.0010\nEpoch 129/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.4716 - mae: 6.7160 - val_loss: 66.2898 - val_mae: 6.3513 - lr: 0.0010\nEpoch 130/300\n92/92 [==============================] - 0s 3ms/step - loss: 75.3535 - mae: 6.7350 - val_loss: 62.5681 - val_mae: 6.1308 - lr: 0.0010\nEpoch 131/300\n92/92 [==============================] - 0s 4ms/step - loss: 75.6950 - mae: 6.7538 - val_loss: 65.3066 - val_mae: 6.3019 - lr: 0.0010\nEpoch 132/300\n92/92 [==============================] - 0s 3ms/step - loss: 77.6715 - mae: 6.8662 - val_loss: 64.1957 - val_mae: 6.1961 - lr: 0.0010\nEpoch 133/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7859 - mae: 6.6765 - val_loss: 64.8125 - val_mae: 6.2561 - lr: 0.0010\nEpoch 134/300\n92/92 [==============================] - 0s 4ms/step - loss: 74.7864 - mae: 6.6136 - val_loss: 63.2989 - val_mae: 6.1620 - lr: 0.0010\nEpoch 135/300\n92/92 [==============================] - 0s 3ms/step - loss: 74.9252 - mae: 6.6935 - val_loss: 61.8661 - val_mae: 6.0828 - lr: 0.0010\nEpoch 136/300\n92/92 [==============================] - 0s 3ms/step - loss: 73.4090 - mae: 6.6558 - val_loss: 65.8127 - val_mae: 6.3012 - lr: 0.0010\nEpoch 137/300\n92/92 [==============================] - 0s 4ms/step - loss: 76.9632 - mae: 6.7563 - val_loss: 65.9818 - val_mae: 6.2974 - lr: 0.0010\nEpoch 138/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.6454 - mae: 6.6755 - val_loss: 72.7036 - val_mae: 6.7197 - lr: 0.0010\nEpoch 139/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.1902 - mae: 6.5490 - val_loss: 67.7461 - val_mae: 6.4317 - lr: 0.0010\nEpoch 140/300\n92/92 [==============================] - 0s 4ms/step - loss: 73.7924 - mae: 6.6559 - val_loss: 61.9457 - val_mae: 6.0521 - lr: 0.0010\nEpoch 141/300\n72/92 [======================>.......] - ETA: 0s - loss: 72.4831 - mae: 6.6072\nEpoch 141: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 3ms/step - loss: 71.6732 - mae: 6.5797 - val_loss: 67.1282 - val_mae: 6.3511 - lr: 0.0010\nEpoch 142/300\n92/92 [==============================] - 0s 5ms/step - loss: 70.4387 - mae: 6.4956 - val_loss: 66.8580 - val_mae: 6.3092 - lr: 5.0000e-04\nEpoch 143/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.4698 - mae: 6.5543 - val_loss: 65.1251 - val_mae: 6.2302 - lr: 5.0000e-04\nEpoch 144/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.7406 - mae: 6.5905 - val_loss: 62.9184 - val_mae: 6.1354 - lr: 5.0000e-04\nEpoch 145/300\n92/92 [==============================] - 1s 6ms/step - loss: 71.5992 - mae: 6.5121 - val_loss: 65.0366 - val_mae: 6.2370 - lr: 5.0000e-04\nEpoch 146/300\n92/92 [==============================] - 0s 5ms/step - loss: 71.7140 - mae: 6.5527 - val_loss: 62.2094 - val_mae: 6.0906 - lr: 5.0000e-04\nEpoch 147/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.0747 - mae: 6.5951 - val_loss: 65.7997 - val_mae: 6.2562 - lr: 5.0000e-04\nEpoch 148/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.4355 - mae: 6.5465 - val_loss: 65.9319 - val_mae: 6.2988 - lr: 5.0000e-04\nEpoch 149/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.7536 - mae: 6.4628 - val_loss: 63.8452 - val_mae: 6.1814 - lr: 5.0000e-04\nEpoch 150/300\n92/92 [==============================] - 0s 3ms/step - loss: 73.3723 - mae: 6.6272 - val_loss: 65.8713 - val_mae: 6.2707 - lr: 5.0000e-04\nEpoch 151/300\n92/92 [==============================] - 0s 4ms/step - loss: 72.6561 - mae: 6.5802 - val_loss: 65.3319 - val_mae: 6.2492 - lr: 5.0000e-04\nEpoch 152/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.0811 - mae: 6.5056 - val_loss: 63.1491 - val_mae: 6.1469 - lr: 5.0000e-04\nEpoch 153/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.8123 - mae: 6.5023 - val_loss: 69.6962 - val_mae: 6.5197 - lr: 5.0000e-04\nEpoch 154/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.0187 - mae: 6.3821 - val_loss: 65.0446 - val_mae: 6.2215 - lr: 5.0000e-04\nEpoch 155/300\n92/92 [==============================] - 0s 4ms/step - loss: 70.7635 - mae: 6.4828 - val_loss: 65.7060 - val_mae: 6.2655 - lr: 5.0000e-04\nEpoch 156/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.8972 - mae: 6.4187 - val_loss: 63.4280 - val_mae: 6.1520 - lr: 5.0000e-04\nEpoch 157/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.7800 - mae: 6.5104 - val_loss: 64.2087 - val_mae: 6.1868 - lr: 5.0000e-04\nEpoch 158/300\n92/92 [==============================] - 0s 4ms/step - loss: 71.8691 - mae: 6.4982 - val_loss: 65.2130 - val_mae: 6.2380 - lr: 5.0000e-04\nEpoch 159/300\n92/92 [==============================] - 0s 3ms/step - loss: 70.0040 - mae: 6.4719 - val_loss: 63.5110 - val_mae: 6.1597 - lr: 5.0000e-04\nEpoch 160/300\n92/92 [==============================] - 0s 3ms/step - loss: 68.5984 - mae: 6.3525 - val_loss: 62.6880 - val_mae: 6.1306 - lr: 5.0000e-04\nEpoch 161/300\n92/92 [==============================] - 0s 3ms/step - loss: 69.4808 - mae: 6.4217 - val_loss: 65.5925 - val_mae: 6.2779 - lr: 5.0000e-04\n46/46 [==============================] - 0s 881us/step\nRMSE: 7.845\nR^2 Score: 0.439\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/24450bf4-dfea-4cf6-bbbc-bcde9d67e82a",
   "content_dependencies": null
  },
  {
   "cellId": "accd9937aa494de58b487a81810fb10e",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "accd9937aa494de58b487a81810fb10e",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Key Findings - not like 100% tho since there are a lot more combination of everything",
   "block_group": "0584e3eedbd14cc980f601aaa5217a91"
  },
  {
   "cellId": "4a1d04ebf1134058825a90ddaa6fba62",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "4a1d04ebf1134058825a90ddaa6fba62",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "128 - 128 -64-32 works the best ",
   "block_group": "6e0e2af8285a4fe8ad317e6812bb1d77"
  },
  {
   "cellId": "a29fb4f16c734bc6950d5ff7053850d5",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "a29fb4f16c734bc6950d5ff7053850d5",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Best activation function: relu",
   "block_group": "4d12798297484f6499c3ad8f4d0b6020"
  },
  {
   "cellId": "2522bd5f03ad4a4d902eda55e6f9aa66",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "2522bd5f03ad4a4d902eda55e6f9aa66",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Batch size:16",
   "block_group": "5ebb4ca6dfc94a578bf1d04548a191d4"
  },
  {
   "cellId": "095aad610f09418fb5c5e01175b78f29",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "095aad610f09418fb5c5e01175b78f29",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "But theres probably a lot of ways to optimize this further",
   "block_group": "a1a721cf204e4f0fb11821cee373111e"
  },
  {
   "cellId": "c52b2ab5c3ff499a87a0d4277473def8",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "c52b2ab5c3ff499a87a0d4277473def8",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Best Model So Far",
   "block_group": "2621962f0afb495085a6d101e1b7e79b"
  },
  {
   "cellId": "3ce54d78c18c403fb24b7b28fc20d5e5",
   "cell_type": "code",
   "metadata": {
    "source_hash": "663b6b0",
    "execution_start": 1754058393584,
    "execution_millis": 189488,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "3ce54d78c18c403fb24b7b28fc20d5e5",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping\n\nmodel = Sequential([\n    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n    BatchNormalization(),\n    Dropout(0.2),\n    Dense(128, activation='relu'),\n    Dropout(0.2),\n    Dense(64, activation='relu'),\n    Dropout(0.2),\n    Dense(32, activation='relu'),\n    Dropout(0.2),\n    Dense(1)  # Regression output\n])\n\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])\n\ncallbacks = [\n    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, verbose=1),\n    EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)\n]\n\nhistory = model.fit(\n    X_train_scaled, y_train,\n    validation_data=(X_val_scaled, y_val),\n    epochs=300,\n    batch_size=16,\n    callbacks=callbacks,\n    verbose=1\n)\n\ny_pred = model.predict(X_val_scaled).flatten()\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nr2 = r2_score(y_val, y_pred)\n\nprint(f\"RMSE: {rmse:.3f}\")\nprint(f\"R^2 Score: {r2:.3f}\")",
   "block_group": "5f0d38ef76184c76a4d15c0c2b4513f6",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n184/184 [==============================] - 3s 6ms/step - loss: 277.2464 - mae: 13.1391 - val_loss: 394.1004 - val_mae: 17.3896 - lr: 0.0010\nEpoch 2/300\n184/184 [==============================] - 1s 4ms/step - loss: 139.8604 - mae: 9.4062 - val_loss: 177.1799 - val_mae: 10.7350 - lr: 0.0010\nEpoch 3/300\n184/184 [==============================] - 1s 4ms/step - loss: 126.8023 - mae: 8.9479 - val_loss: 90.8483 - val_mae: 7.4885 - lr: 0.0010\nEpoch 4/300\n184/184 [==============================] - 1s 4ms/step - loss: 116.1606 - mae: 8.5220 - val_loss: 87.3694 - val_mae: 7.3734 - lr: 0.0010\nEpoch 5/300\n184/184 [==============================] - 1s 4ms/step - loss: 112.5897 - mae: 8.3828 - val_loss: 77.0939 - val_mae: 6.9103 - lr: 0.0010\nEpoch 6/300\n184/184 [==============================] - 1s 4ms/step - loss: 112.2560 - mae: 8.3869 - val_loss: 74.7397 - val_mae: 6.8723 - lr: 0.0010\nEpoch 7/300\n184/184 [==============================] - 1s 4ms/step - loss: 106.4367 - mae: 8.1189 - val_loss: 77.6408 - val_mae: 6.9103 - lr: 0.0010\nEpoch 8/300\n184/184 [==============================] - 1s 4ms/step - loss: 109.0179 - mae: 8.2494 - val_loss: 72.8423 - val_mae: 6.7391 - lr: 0.0010\nEpoch 9/300\n184/184 [==============================] - 1s 5ms/step - loss: 104.6643 - mae: 8.0558 - val_loss: 83.0902 - val_mae: 7.1675 - lr: 0.0010\nEpoch 10/300\n184/184 [==============================] - 1s 7ms/step - loss: 103.1133 - mae: 7.9766 - val_loss: 73.1687 - val_mae: 6.7578 - lr: 0.0010\nEpoch 11/300\n184/184 [==============================] - 1s 8ms/step - loss: 104.1293 - mae: 8.0418 - val_loss: 77.4839 - val_mae: 6.8521 - lr: 0.0010\nEpoch 12/300\n184/184 [==============================] - 1s 6ms/step - loss: 104.1923 - mae: 8.0578 - val_loss: 70.1429 - val_mae: 6.6625 - lr: 0.0010\nEpoch 13/300\n184/184 [==============================] - 1s 7ms/step - loss: 103.9447 - mae: 8.0123 - val_loss: 75.2520 - val_mae: 6.8415 - lr: 0.0010\nEpoch 14/300\n184/184 [==============================] - 1s 5ms/step - loss: 97.2755 - mae: 7.7318 - val_loss: 72.3563 - val_mae: 6.7186 - lr: 0.0010\nEpoch 15/300\n184/184 [==============================] - 1s 5ms/step - loss: 96.6005 - mae: 7.7443 - val_loss: 70.2386 - val_mae: 6.6604 - lr: 0.0010\nEpoch 16/300\n184/184 [==============================] - 1s 7ms/step - loss: 99.0170 - mae: 7.8589 - val_loss: 74.8297 - val_mae: 6.8034 - lr: 0.0010\nEpoch 17/300\n184/184 [==============================] - 1s 8ms/step - loss: 99.9593 - mae: 7.8569 - val_loss: 73.1348 - val_mae: 6.7496 - lr: 0.0010\nEpoch 18/300\n184/184 [==============================] - 1s 8ms/step - loss: 99.4562 - mae: 7.8219 - val_loss: 72.3900 - val_mae: 6.7791 - lr: 0.0010\nEpoch 19/300\n184/184 [==============================] - 2s 10ms/step - loss: 98.1293 - mae: 7.7576 - val_loss: 70.7579 - val_mae: 6.6269 - lr: 0.0010\nEpoch 20/300\n184/184 [==============================] - 1s 7ms/step - loss: 97.9583 - mae: 7.8055 - val_loss: 71.2357 - val_mae: 6.5909 - lr: 0.0010\nEpoch 21/300\n184/184 [==============================] - 2s 8ms/step - loss: 99.4280 - mae: 7.8616 - val_loss: 75.0045 - val_mae: 6.7809 - lr: 0.0010\nEpoch 22/300\n184/184 [==============================] - 2s 9ms/step - loss: 95.4526 - mae: 7.6891 - val_loss: 71.4276 - val_mae: 6.6220 - lr: 0.0010\nEpoch 23/300\n184/184 [==============================] - 1s 6ms/step - loss: 97.6467 - mae: 7.7663 - val_loss: 74.2434 - val_mae: 6.6872 - lr: 0.0010\nEpoch 24/300\n184/184 [==============================] - 1s 7ms/step - loss: 93.1341 - mae: 7.5593 - val_loss: 73.3770 - val_mae: 6.7199 - lr: 0.0010\nEpoch 25/300\n184/184 [==============================] - 1s 6ms/step - loss: 92.9692 - mae: 7.5925 - val_loss: 68.7190 - val_mae: 6.4562 - lr: 0.0010\nEpoch 26/300\n184/184 [==============================] - 1s 7ms/step - loss: 93.0909 - mae: 7.5180 - val_loss: 76.9487 - val_mae: 6.9310 - lr: 0.0010\nEpoch 27/300\n184/184 [==============================] - 1s 6ms/step - loss: 91.2219 - mae: 7.4616 - val_loss: 73.0743 - val_mae: 6.7957 - lr: 0.0010\nEpoch 28/300\n184/184 [==============================] - 2s 10ms/step - loss: 93.1689 - mae: 7.5991 - val_loss: 71.8431 - val_mae: 6.6231 - lr: 0.0010\nEpoch 29/300\n184/184 [==============================] - 1s 8ms/step - loss: 91.5145 - mae: 7.5033 - val_loss: 75.9148 - val_mae: 6.8808 - lr: 0.0010\nEpoch 30/300\n184/184 [==============================] - 2s 9ms/step - loss: 90.6172 - mae: 7.4202 - val_loss: 67.8693 - val_mae: 6.4347 - lr: 0.0010\nEpoch 31/300\n184/184 [==============================] - 1s 7ms/step - loss: 84.8159 - mae: 7.2069 - val_loss: 70.5119 - val_mae: 6.5723 - lr: 0.0010\nEpoch 32/300\n184/184 [==============================] - 1s 7ms/step - loss: 88.9823 - mae: 7.3920 - val_loss: 70.8612 - val_mae: 6.5888 - lr: 0.0010\nEpoch 33/300\n184/184 [==============================] - 1s 6ms/step - loss: 91.5555 - mae: 7.5343 - val_loss: 70.2069 - val_mae: 6.5907 - lr: 0.0010\nEpoch 34/300\n184/184 [==============================] - 1s 7ms/step - loss: 85.3529 - mae: 7.2074 - val_loss: 73.2571 - val_mae: 6.7622 - lr: 0.0010\nEpoch 35/300\n184/184 [==============================] - 1s 5ms/step - loss: 91.3827 - mae: 7.4721 - val_loss: 87.4952 - val_mae: 7.3580 - lr: 0.0010\nEpoch 36/300\n184/184 [==============================] - 1s 4ms/step - loss: 85.5903 - mae: 7.1646 - val_loss: 69.9048 - val_mae: 6.5204 - lr: 0.0010\nEpoch 37/300\n184/184 [==============================] - 1s 5ms/step - loss: 86.1939 - mae: 7.2520 - val_loss: 68.8203 - val_mae: 6.4763 - lr: 0.0010\nEpoch 38/300\n184/184 [==============================] - 1s 4ms/step - loss: 88.1069 - mae: 7.3334 - val_loss: 72.1554 - val_mae: 6.6178 - lr: 0.0010\nEpoch 39/300\n184/184 [==============================] - 1s 5ms/step - loss: 84.0808 - mae: 7.1371 - val_loss: 65.5898 - val_mae: 6.3355 - lr: 0.0010\nEpoch 40/300\n184/184 [==============================] - 1s 5ms/step - loss: 85.8869 - mae: 7.2128 - val_loss: 78.1487 - val_mae: 6.8287 - lr: 0.0010\nEpoch 41/300\n184/184 [==============================] - 1s 4ms/step - loss: 86.3784 - mae: 7.2666 - val_loss: 65.1384 - val_mae: 6.3313 - lr: 0.0010\nEpoch 42/300\n184/184 [==============================] - 1s 4ms/step - loss: 83.5002 - mae: 7.0740 - val_loss: 67.8728 - val_mae: 6.4983 - lr: 0.0010\nEpoch 43/300\n184/184 [==============================] - 1s 4ms/step - loss: 84.2159 - mae: 7.1890 - val_loss: 70.2572 - val_mae: 6.5610 - lr: 0.0010\nEpoch 44/300\n184/184 [==============================] - 1s 4ms/step - loss: 85.7190 - mae: 7.2220 - val_loss: 69.1315 - val_mae: 6.5019 - lr: 0.0010\nEpoch 45/300\n184/184 [==============================] - 1s 4ms/step - loss: 85.2631 - mae: 7.2003 - val_loss: 69.3916 - val_mae: 6.4983 - lr: 0.0010\nEpoch 46/300\n184/184 [==============================] - 1s 4ms/step - loss: 86.4907 - mae: 7.2121 - val_loss: 65.9521 - val_mae: 6.3628 - lr: 0.0010\nEpoch 47/300\n184/184 [==============================] - 1s 4ms/step - loss: 84.4461 - mae: 7.1419 - val_loss: 66.9265 - val_mae: 6.4126 - lr: 0.0010\nEpoch 48/300\n184/184 [==============================] - 1s 3ms/step - loss: 85.0966 - mae: 7.2007 - val_loss: 69.8772 - val_mae: 6.5843 - lr: 0.0010\nEpoch 49/300\n184/184 [==============================] - 1s 3ms/step - loss: 85.5149 - mae: 7.1642 - val_loss: 64.6704 - val_mae: 6.3184 - lr: 0.0010\nEpoch 50/300\n184/184 [==============================] - 1s 4ms/step - loss: 83.0843 - mae: 7.0963 - val_loss: 66.9738 - val_mae: 6.4289 - lr: 0.0010\nEpoch 51/300\n184/184 [==============================] - 1s 4ms/step - loss: 86.4917 - mae: 7.2486 - val_loss: 64.7856 - val_mae: 6.3109 - lr: 0.0010\nEpoch 52/300\n184/184 [==============================] - 1s 4ms/step - loss: 82.7589 - mae: 7.1214 - val_loss: 66.0527 - val_mae: 6.3838 - lr: 0.0010\nEpoch 53/300\n184/184 [==============================] - 1s 4ms/step - loss: 81.7749 - mae: 7.0745 - val_loss: 64.2728 - val_mae: 6.2616 - lr: 0.0010\nEpoch 54/300\n184/184 [==============================] - 1s 4ms/step - loss: 83.2995 - mae: 7.0984 - val_loss: 64.3064 - val_mae: 6.2585 - lr: 0.0010\nEpoch 55/300\n184/184 [==============================] - 1s 3ms/step - loss: 78.9924 - mae: 6.9503 - val_loss: 75.7328 - val_mae: 6.8211 - lr: 0.0010\nEpoch 56/300\n184/184 [==============================] - 1s 4ms/step - loss: 81.8124 - mae: 7.0363 - val_loss: 63.6359 - val_mae: 6.2096 - lr: 0.0010\nEpoch 57/300\n184/184 [==============================] - 1s 3ms/step - loss: 81.2086 - mae: 6.9796 - val_loss: 65.1997 - val_mae: 6.3201 - lr: 0.0010\nEpoch 58/300\n184/184 [==============================] - 1s 3ms/step - loss: 79.4521 - mae: 6.9444 - val_loss: 67.4562 - val_mae: 6.4328 - lr: 0.0010\nEpoch 59/300\n184/184 [==============================] - 1s 3ms/step - loss: 78.1861 - mae: 6.8530 - val_loss: 66.5621 - val_mae: 6.3839 - lr: 0.0010\nEpoch 60/300\n184/184 [==============================] - 1s 4ms/step - loss: 80.3425 - mae: 6.9555 - val_loss: 64.5969 - val_mae: 6.2479 - lr: 0.0010\nEpoch 61/300\n184/184 [==============================] - 1s 4ms/step - loss: 81.8659 - mae: 7.0248 - val_loss: 67.1759 - val_mae: 6.4429 - lr: 0.0010\nEpoch 62/300\n184/184 [==============================] - 1s 3ms/step - loss: 80.5535 - mae: 6.9519 - val_loss: 65.1261 - val_mae: 6.2774 - lr: 0.0010\nEpoch 63/300\n184/184 [==============================] - 1s 3ms/step - loss: 80.4255 - mae: 6.9534 - val_loss: 62.9058 - val_mae: 6.1716 - lr: 0.0010\nEpoch 64/300\n184/184 [==============================] - 1s 3ms/step - loss: 83.0484 - mae: 7.0678 - val_loss: 68.0261 - val_mae: 6.4681 - lr: 0.0010\nEpoch 65/300\n184/184 [==============================] - 1s 3ms/step - loss: 80.2380 - mae: 6.8977 - val_loss: 66.2530 - val_mae: 6.3520 - lr: 0.0010\nEpoch 66/300\n184/184 [==============================] - 1s 4ms/step - loss: 78.2413 - mae: 6.8658 - val_loss: 66.5086 - val_mae: 6.3762 - lr: 0.0010\nEpoch 67/300\n184/184 [==============================] - 1s 4ms/step - loss: 76.2563 - mae: 6.7197 - val_loss: 64.7112 - val_mae: 6.2968 - lr: 0.0010\nEpoch 68/300\n184/184 [==============================] - 1s 4ms/step - loss: 81.5800 - mae: 7.0630 - val_loss: 69.8645 - val_mae: 6.5660 - lr: 0.0010\nEpoch 69/300\n184/184 [==============================] - 1s 3ms/step - loss: 79.4895 - mae: 6.8995 - val_loss: 65.0065 - val_mae: 6.3127 - lr: 0.0010\nEpoch 70/300\n184/184 [==============================] - 1s 4ms/step - loss: 76.7698 - mae: 6.8023 - val_loss: 66.2965 - val_mae: 6.3375 - lr: 0.0010\nEpoch 71/300\n184/184 [==============================] - 1s 3ms/step - loss: 79.6233 - mae: 6.9224 - val_loss: 66.0413 - val_mae: 6.2811 - lr: 0.0010\nEpoch 72/300\n184/184 [==============================] - 1s 3ms/step - loss: 80.8839 - mae: 6.9569 - val_loss: 66.7303 - val_mae: 6.3044 - lr: 0.0010\nEpoch 73/300\n184/184 [==============================] - 1s 3ms/step - loss: 77.9097 - mae: 6.8869 - val_loss: 68.1646 - val_mae: 6.4111 - lr: 0.0010\nEpoch 74/300\n184/184 [==============================] - 1s 3ms/step - loss: 81.9081 - mae: 6.9769 - val_loss: 72.4864 - val_mae: 6.6467 - lr: 0.0010\nEpoch 75/300\n184/184 [==============================] - 1s 3ms/step - loss: 78.3913 - mae: 6.8167 - val_loss: 64.6654 - val_mae: 6.2188 - lr: 0.0010\nEpoch 76/300\n184/184 [==============================] - 1s 5ms/step - loss: 79.1810 - mae: 6.9588 - val_loss: 65.1245 - val_mae: 6.2544 - lr: 0.0010\nEpoch 77/300\n184/184 [==============================] - 1s 3ms/step - loss: 77.8182 - mae: 6.7914 - val_loss: 63.5763 - val_mae: 6.1647 - lr: 0.0010\nEpoch 78/300\n184/184 [==============================] - 1s 3ms/step - loss: 76.4549 - mae: 6.7809 - val_loss: 70.2838 - val_mae: 6.5265 - lr: 0.0010\nEpoch 79/300\n184/184 [==============================] - 1s 3ms/step - loss: 78.3015 - mae: 6.8208 - val_loss: 65.5931 - val_mae: 6.3122 - lr: 0.0010\nEpoch 80/300\n184/184 [==============================] - 1s 3ms/step - loss: 75.7515 - mae: 6.7880 - val_loss: 64.3635 - val_mae: 6.2373 - lr: 0.0010\nEpoch 81/300\n184/184 [==============================] - 1s 3ms/step - loss: 76.4823 - mae: 6.7461 - val_loss: 68.5984 - val_mae: 6.4367 - lr: 0.0010\nEpoch 82/300\n184/184 [==============================] - 1s 3ms/step - loss: 76.0568 - mae: 6.7371 - val_loss: 64.1373 - val_mae: 6.2348 - lr: 0.0010\nEpoch 83/300\n184/184 [==============================] - 1s 3ms/step - loss: 78.7279 - mae: 6.8654 - val_loss: 63.3249 - val_mae: 6.1994 - lr: 0.0010\nEpoch 84/300\n184/184 [==============================] - 1s 3ms/step - loss: 74.5403 - mae: 6.6938 - val_loss: 64.2006 - val_mae: 6.1976 - lr: 0.0010\nEpoch 85/300\n184/184 [==============================] - 1s 3ms/step - loss: 75.2471 - mae: 6.6510 - val_loss: 66.5087 - val_mae: 6.3127 - lr: 0.0010\nEpoch 86/300\n184/184 [==============================] - 1s 3ms/step - loss: 76.5119 - mae: 6.8110 - val_loss: 63.1338 - val_mae: 6.1440 - lr: 0.0010\nEpoch 87/300\n184/184 [==============================] - 1s 3ms/step - loss: 75.8805 - mae: 6.7457 - val_loss: 64.3374 - val_mae: 6.1964 - lr: 0.0010\nEpoch 88/300\n184/184 [==============================] - 1s 3ms/step - loss: 75.5662 - mae: 6.6951 - val_loss: 66.4627 - val_mae: 6.3166 - lr: 0.0010\nEpoch 89/300\n184/184 [==============================] - 1s 3ms/step - loss: 77.5860 - mae: 6.7809 - val_loss: 64.0747 - val_mae: 6.2192 - lr: 0.0010\nEpoch 90/300\n184/184 [==============================] - 1s 3ms/step - loss: 76.5989 - mae: 6.7406 - val_loss: 64.1991 - val_mae: 6.2363 - lr: 0.0010\nEpoch 91/300\n184/184 [==============================] - 1s 3ms/step - loss: 75.4124 - mae: 6.7364 - val_loss: 62.2266 - val_mae: 6.1265 - lr: 0.0010\nEpoch 92/300\n184/184 [==============================] - 1s 4ms/step - loss: 77.1407 - mae: 6.7631 - val_loss: 67.5093 - val_mae: 6.3526 - lr: 0.0010\nEpoch 93/300\n184/184 [==============================] - 1s 4ms/step - loss: 73.3088 - mae: 6.6155 - val_loss: 63.0273 - val_mae: 6.1395 - lr: 0.0010\nEpoch 94/300\n184/184 [==============================] - 1s 3ms/step - loss: 71.8585 - mae: 6.5329 - val_loss: 64.2655 - val_mae: 6.2284 - lr: 0.0010\nEpoch 95/300\n184/184 [==============================] - 1s 3ms/step - loss: 72.4758 - mae: 6.6575 - val_loss: 62.1095 - val_mae: 6.0908 - lr: 0.0010\nEpoch 96/300\n184/184 [==============================] - 1s 3ms/step - loss: 71.5284 - mae: 6.5805 - val_loss: 66.2956 - val_mae: 6.2892 - lr: 0.0010\nEpoch 97/300\n184/184 [==============================] - 1s 3ms/step - loss: 72.7001 - mae: 6.5605 - val_loss: 64.2757 - val_mae: 6.2211 - lr: 0.0010\nEpoch 98/300\n184/184 [==============================] - 1s 4ms/step - loss: 75.3524 - mae: 6.7237 - val_loss: 67.2641 - val_mae: 6.3645 - lr: 0.0010\nEpoch 99/300\n184/184 [==============================] - 1s 3ms/step - loss: 72.2183 - mae: 6.5873 - val_loss: 61.7928 - val_mae: 6.0629 - lr: 0.0010\nEpoch 100/300\n184/184 [==============================] - 1s 3ms/step - loss: 73.1268 - mae: 6.5826 - val_loss: 63.4838 - val_mae: 6.1762 - lr: 0.0010\nEpoch 101/300\n184/184 [==============================] - 1s 4ms/step - loss: 73.3177 - mae: 6.6545 - val_loss: 65.6661 - val_mae: 6.2049 - lr: 0.0010\nEpoch 102/300\n184/184 [==============================] - 1s 3ms/step - loss: 74.2699 - mae: 6.6881 - val_loss: 70.3216 - val_mae: 6.4764 - lr: 0.0010\nEpoch 103/300\n184/184 [==============================] - 1s 3ms/step - loss: 72.6689 - mae: 6.5653 - val_loss: 65.0228 - val_mae: 6.2076 - lr: 0.0010\nEpoch 104/300\n184/184 [==============================] - 1s 3ms/step - loss: 73.5516 - mae: 6.6357 - val_loss: 62.1367 - val_mae: 6.0734 - lr: 0.0010\nEpoch 105/300\n184/184 [==============================] - 1s 3ms/step - loss: 73.0695 - mae: 6.6619 - val_loss: 65.7689 - val_mae: 6.2071 - lr: 0.0010\nEpoch 106/300\n184/184 [==============================] - 1s 4ms/step - loss: 69.9041 - mae: 6.4342 - val_loss: 61.2967 - val_mae: 6.0543 - lr: 0.0010\nEpoch 107/300\n184/184 [==============================] - 1s 4ms/step - loss: 75.8682 - mae: 6.7240 - val_loss: 63.7134 - val_mae: 6.1277 - lr: 0.0010\nEpoch 108/300\n184/184 [==============================] - 1s 7ms/step - loss: 71.6845 - mae: 6.4919 - val_loss: 62.9947 - val_mae: 6.1304 - lr: 0.0010\nEpoch 109/300\n184/184 [==============================] - 1s 8ms/step - loss: 73.0234 - mae: 6.6197 - val_loss: 64.3044 - val_mae: 6.1406 - lr: 0.0010\nEpoch 110/300\n184/184 [==============================] - 1s 5ms/step - loss: 70.9760 - mae: 6.5072 - val_loss: 61.1966 - val_mae: 6.0173 - lr: 0.0010\nEpoch 111/300\n184/184 [==============================] - 1s 6ms/step - loss: 71.3748 - mae: 6.5338 - val_loss: 64.6922 - val_mae: 6.1432 - lr: 0.0010\nEpoch 112/300\n184/184 [==============================] - 1s 6ms/step - loss: 70.2961 - mae: 6.4792 - val_loss: 63.0569 - val_mae: 6.0797 - lr: 0.0010\nEpoch 113/300\n184/184 [==============================] - 1s 5ms/step - loss: 71.7621 - mae: 6.5563 - val_loss: 62.4714 - val_mae: 6.0780 - lr: 0.0010\nEpoch 114/300\n184/184 [==============================] - 1s 7ms/step - loss: 70.3733 - mae: 6.5081 - val_loss: 63.2316 - val_mae: 6.0900 - lr: 0.0010\nEpoch 115/300\n184/184 [==============================] - 1s 6ms/step - loss: 72.2932 - mae: 6.5317 - val_loss: 62.4659 - val_mae: 6.0571 - lr: 0.0010\nEpoch 116/300\n184/184 [==============================] - 1s 6ms/step - loss: 71.0706 - mae: 6.5424 - val_loss: 65.0750 - val_mae: 6.1996 - lr: 0.0010\nEpoch 117/300\n184/184 [==============================] - 1s 8ms/step - loss: 70.4334 - mae: 6.4805 - val_loss: 63.9590 - val_mae: 6.1312 - lr: 0.0010\nEpoch 118/300\n184/184 [==============================] - 1s 8ms/step - loss: 71.9017 - mae: 6.5545 - val_loss: 70.1472 - val_mae: 6.3417 - lr: 0.0010\nEpoch 119/300\n184/184 [==============================] - 1s 5ms/step - loss: 71.5170 - mae: 6.4684 - val_loss: 61.3100 - val_mae: 6.0230 - lr: 0.0010\nEpoch 120/300\n184/184 [==============================] - 1s 6ms/step - loss: 69.1330 - mae: 6.4127 - val_loss: 62.9352 - val_mae: 6.0747 - lr: 0.0010\nEpoch 121/300\n184/184 [==============================] - 1s 5ms/step - loss: 70.5211 - mae: 6.4694 - val_loss: 60.0794 - val_mae: 5.9208 - lr: 0.0010\nEpoch 122/300\n184/184 [==============================] - 1s 5ms/step - loss: 71.5414 - mae: 6.4807 - val_loss: 63.8356 - val_mae: 6.1115 - lr: 0.0010\nEpoch 123/300\n184/184 [==============================] - 1s 5ms/step - loss: 70.1353 - mae: 6.4186 - val_loss: 62.0353 - val_mae: 6.0208 - lr: 0.0010\nEpoch 124/300\n184/184 [==============================] - 1s 4ms/step - loss: 69.1798 - mae: 6.4458 - val_loss: 62.1252 - val_mae: 6.0008 - lr: 0.0010\nEpoch 125/300\n184/184 [==============================] - 1s 4ms/step - loss: 68.6593 - mae: 6.3400 - val_loss: 61.5885 - val_mae: 6.0340 - lr: 0.0010\nEpoch 126/300\n184/184 [==============================] - 1s 4ms/step - loss: 69.8709 - mae: 6.4564 - val_loss: 65.0032 - val_mae: 6.1940 - lr: 0.0010\nEpoch 127/300\n184/184 [==============================] - 1s 4ms/step - loss: 71.5676 - mae: 6.4962 - val_loss: 62.5014 - val_mae: 6.0597 - lr: 0.0010\nEpoch 128/300\n184/184 [==============================] - 1s 3ms/step - loss: 68.8038 - mae: 6.3842 - val_loss: 69.8107 - val_mae: 6.4465 - lr: 0.0010\nEpoch 129/300\n184/184 [==============================] - 1s 3ms/step - loss: 69.3364 - mae: 6.3797 - val_loss: 64.8995 - val_mae: 6.1821 - lr: 0.0010\nEpoch 130/300\n184/184 [==============================] - 1s 3ms/step - loss: 70.9124 - mae: 6.4650 - val_loss: 61.3221 - val_mae: 6.0204 - lr: 0.0010\nEpoch 131/300\n184/184 [==============================] - 1s 3ms/step - loss: 67.1263 - mae: 6.2911 - val_loss: 61.3204 - val_mae: 5.9956 - lr: 0.0010\nEpoch 132/300\n184/184 [==============================] - 1s 4ms/step - loss: 70.3114 - mae: 6.4788 - val_loss: 61.4308 - val_mae: 6.0380 - lr: 0.0010\nEpoch 133/300\n184/184 [==============================] - 1s 3ms/step - loss: 69.7153 - mae: 6.4186 - val_loss: 61.1200 - val_mae: 5.9690 - lr: 0.0010\nEpoch 134/300\n184/184 [==============================] - 1s 4ms/step - loss: 71.4191 - mae: 6.4745 - val_loss: 61.8313 - val_mae: 6.0076 - lr: 0.0010\nEpoch 135/300\n184/184 [==============================] - 1s 3ms/step - loss: 66.5426 - mae: 6.2431 - val_loss: 60.4281 - val_mae: 5.9768 - lr: 0.0010\nEpoch 136/300\n184/184 [==============================] - 1s 3ms/step - loss: 66.6552 - mae: 6.2668 - val_loss: 61.7481 - val_mae: 6.0449 - lr: 0.0010\nEpoch 137/300\n184/184 [==============================] - 1s 4ms/step - loss: 68.3102 - mae: 6.3811 - val_loss: 60.2546 - val_mae: 5.9351 - lr: 0.0010\nEpoch 138/300\n184/184 [==============================] - 1s 3ms/step - loss: 63.3949 - mae: 6.1162 - val_loss: 61.6337 - val_mae: 5.9769 - lr: 0.0010\nEpoch 139/300\n184/184 [==============================] - 1s 3ms/step - loss: 70.2065 - mae: 6.4438 - val_loss: 64.3152 - val_mae: 6.1603 - lr: 0.0010\nEpoch 140/300\n184/184 [==============================] - 1s 4ms/step - loss: 67.2418 - mae: 6.3585 - val_loss: 61.3357 - val_mae: 6.0308 - lr: 0.0010\nEpoch 141/300\n184/184 [==============================] - 1s 4ms/step - loss: 64.3056 - mae: 6.1233 - val_loss: 61.7797 - val_mae: 6.0001 - lr: 0.0010\nEpoch 142/300\n184/184 [==============================] - 1s 4ms/step - loss: 65.9682 - mae: 6.1604 - val_loss: 62.6751 - val_mae: 6.0783 - lr: 0.0010\nEpoch 143/300\n184/184 [==============================] - 1s 4ms/step - loss: 67.0562 - mae: 6.2584 - val_loss: 63.3552 - val_mae: 6.0765 - lr: 0.0010\nEpoch 144/300\n184/184 [==============================] - 1s 3ms/step - loss: 66.7355 - mae: 6.2810 - val_loss: 60.3164 - val_mae: 5.9599 - lr: 0.0010\nEpoch 145/300\n184/184 [==============================] - 1s 3ms/step - loss: 67.0705 - mae: 6.2758 - val_loss: 60.7048 - val_mae: 5.9569 - lr: 0.0010\nEpoch 146/300\n184/184 [==============================] - 1s 3ms/step - loss: 68.1454 - mae: 6.2775 - val_loss: 63.3010 - val_mae: 6.0960 - lr: 0.0010\nEpoch 147/300\n184/184 [==============================] - 1s 4ms/step - loss: 66.2448 - mae: 6.2608 - val_loss: 61.4883 - val_mae: 5.9962 - lr: 0.0010\nEpoch 148/300\n184/184 [==============================] - 1s 3ms/step - loss: 69.3705 - mae: 6.3839 - val_loss: 63.5486 - val_mae: 6.1135 - lr: 0.0010\nEpoch 149/300\n184/184 [==============================] - 1s 3ms/step - loss: 66.0719 - mae: 6.2174 - val_loss: 62.0792 - val_mae: 6.0336 - lr: 0.0010\nEpoch 150/300\n184/184 [==============================] - 1s 3ms/step - loss: 65.5751 - mae: 6.2021 - val_loss: 61.1794 - val_mae: 5.9933 - lr: 0.0010\nEpoch 151/300\n179/184 [============================>.] - ETA: 0s - loss: 68.0600 - mae: 6.3193\nEpoch 151: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n184/184 [==============================] - 1s 3ms/step - loss: 68.2663 - mae: 6.3291 - val_loss: 64.3617 - val_mae: 6.1287 - lr: 0.0010\nEpoch 152/300\n184/184 [==============================] - 1s 3ms/step - loss: 66.0870 - mae: 6.3155 - val_loss: 60.0883 - val_mae: 5.9469 - lr: 5.0000e-04\nEpoch 153/300\n184/184 [==============================] - 1s 4ms/step - loss: 64.4790 - mae: 6.1769 - val_loss: 62.4081 - val_mae: 6.0640 - lr: 5.0000e-04\nEpoch 154/300\n184/184 [==============================] - 1s 3ms/step - loss: 64.4524 - mae: 6.1658 - val_loss: 59.6914 - val_mae: 5.9316 - lr: 5.0000e-04\nEpoch 155/300\n184/184 [==============================] - 1s 3ms/step - loss: 65.8220 - mae: 6.1730 - val_loss: 61.5262 - val_mae: 5.9945 - lr: 5.0000e-04\nEpoch 156/300\n184/184 [==============================] - 1s 4ms/step - loss: 61.3353 - mae: 6.0211 - val_loss: 59.1336 - val_mae: 5.8757 - lr: 5.0000e-04\nEpoch 157/300\n184/184 [==============================] - 1s 4ms/step - loss: 62.0881 - mae: 6.0613 - val_loss: 60.9177 - val_mae: 5.9824 - lr: 5.0000e-04\nEpoch 158/300\n184/184 [==============================] - 1s 3ms/step - loss: 65.2769 - mae: 6.1567 - val_loss: 61.3210 - val_mae: 5.9671 - lr: 5.0000e-04\nEpoch 159/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.6198 - mae: 5.9935 - val_loss: 61.2507 - val_mae: 5.9375 - lr: 5.0000e-04\nEpoch 160/300\n184/184 [==============================] - 1s 3ms/step - loss: 62.0071 - mae: 6.0052 - val_loss: 60.5526 - val_mae: 5.9362 - lr: 5.0000e-04\nEpoch 161/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.2030 - mae: 6.0064 - val_loss: 62.3827 - val_mae: 6.0086 - lr: 5.0000e-04\nEpoch 162/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.9095 - mae: 6.0008 - val_loss: 63.2472 - val_mae: 6.0602 - lr: 5.0000e-04\nEpoch 163/300\n184/184 [==============================] - 1s 3ms/step - loss: 62.0230 - mae: 6.0620 - val_loss: 59.6708 - val_mae: 5.9214 - lr: 5.0000e-04\nEpoch 164/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.5424 - mae: 6.0161 - val_loss: 60.2520 - val_mae: 5.9387 - lr: 5.0000e-04\nEpoch 165/300\n184/184 [==============================] - 1s 3ms/step - loss: 62.6850 - mae: 5.9926 - val_loss: 61.6546 - val_mae: 6.0213 - lr: 5.0000e-04\nEpoch 166/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.2154 - mae: 5.9772 - val_loss: 59.6074 - val_mae: 5.9077 - lr: 5.0000e-04\nEpoch 167/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.7926 - mae: 5.9322 - val_loss: 62.6379 - val_mae: 6.0846 - lr: 5.0000e-04\nEpoch 168/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.0598 - mae: 5.9294 - val_loss: 59.6395 - val_mae: 5.8683 - lr: 5.0000e-04\nEpoch 169/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.8356 - mae: 5.9650 - val_loss: 59.7673 - val_mae: 5.8949 - lr: 5.0000e-04\nEpoch 170/300\n184/184 [==============================] - 1s 3ms/step - loss: 62.1420 - mae: 6.0261 - val_loss: 59.7930 - val_mae: 5.9057 - lr: 5.0000e-04\nEpoch 171/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.7316 - mae: 5.9175 - val_loss: 60.3160 - val_mae: 5.9187 - lr: 5.0000e-04\nEpoch 172/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.0109 - mae: 5.9100 - val_loss: 60.4734 - val_mae: 5.9096 - lr: 5.0000e-04\nEpoch 173/300\n184/184 [==============================] - 1s 5ms/step - loss: 61.3145 - mae: 5.9771 - val_loss: 61.6414 - val_mae: 5.9934 - lr: 5.0000e-04\nEpoch 174/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.7428 - mae: 5.9439 - val_loss: 59.5769 - val_mae: 5.8942 - lr: 5.0000e-04\nEpoch 175/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.5538 - mae: 5.8956 - val_loss: 58.9353 - val_mae: 5.8363 - lr: 5.0000e-04\nEpoch 176/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.7744 - mae: 6.0055 - val_loss: 59.1475 - val_mae: 5.8635 - lr: 5.0000e-04\nEpoch 177/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.5063 - mae: 5.9091 - val_loss: 59.5899 - val_mae: 5.8923 - lr: 5.0000e-04\nEpoch 178/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.7850 - mae: 5.8895 - val_loss: 60.0222 - val_mae: 5.8981 - lr: 5.0000e-04\nEpoch 179/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.8131 - mae: 5.9690 - val_loss: 60.5683 - val_mae: 5.9310 - lr: 5.0000e-04\nEpoch 180/300\n184/184 [==============================] - 1s 3ms/step - loss: 58.5131 - mae: 5.8392 - val_loss: 60.5996 - val_mae: 5.9319 - lr: 5.0000e-04\nEpoch 181/300\n184/184 [==============================] - 1s 3ms/step - loss: 60.1599 - mae: 5.9484 - val_loss: 60.9271 - val_mae: 5.9552 - lr: 5.0000e-04\nEpoch 182/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.6038 - mae: 5.8497 - val_loss: 60.9599 - val_mae: 5.9468 - lr: 5.0000e-04\nEpoch 183/300\n184/184 [==============================] - 1s 3ms/step - loss: 61.2583 - mae: 5.9824 - val_loss: 59.6386 - val_mae: 5.8602 - lr: 5.0000e-04\nEpoch 184/300\n184/184 [==============================] - 1s 4ms/step - loss: 57.7727 - mae: 5.7953 - val_loss: 61.1100 - val_mae: 5.9717 - lr: 5.0000e-04\nEpoch 185/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.7989 - mae: 5.9533 - val_loss: 61.2346 - val_mae: 5.9676 - lr: 5.0000e-04\nEpoch 186/300\n184/184 [==============================] - 1s 3ms/step - loss: 59.0254 - mae: 5.8645 - val_loss: 60.5443 - val_mae: 5.9253 - lr: 5.0000e-04\nEpoch 187/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.2490 - mae: 5.9419 - val_loss: 59.5692 - val_mae: 5.8998 - lr: 5.0000e-04\nEpoch 188/300\n184/184 [==============================] - 1s 6ms/step - loss: 59.5188 - mae: 5.8771 - val_loss: 59.0800 - val_mae: 5.8483 - lr: 5.0000e-04\nEpoch 189/300\n184/184 [==============================] - 1s 6ms/step - loss: 62.1080 - mae: 6.0046 - val_loss: 59.7192 - val_mae: 5.9180 - lr: 5.0000e-04\nEpoch 190/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.7687 - mae: 5.9552 - val_loss: 62.2640 - val_mae: 6.0705 - lr: 5.0000e-04\nEpoch 191/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.6386 - mae: 5.9967 - val_loss: 60.3056 - val_mae: 5.9388 - lr: 5.0000e-04\nEpoch 192/300\n184/184 [==============================] - 1s 3ms/step - loss: 57.6408 - mae: 5.8190 - val_loss: 60.6863 - val_mae: 5.9889 - lr: 5.0000e-04\nEpoch 193/300\n184/184 [==============================] - 1s 4ms/step - loss: 59.9655 - mae: 5.9160 - val_loss: 59.4444 - val_mae: 5.8813 - lr: 5.0000e-04\nEpoch 194/300\n184/184 [==============================] - 1s 5ms/step - loss: 58.6144 - mae: 5.8535 - val_loss: 60.1140 - val_mae: 5.9276 - lr: 5.0000e-04\nEpoch 195/300\n184/184 [==============================] - 1s 5ms/step - loss: 60.0200 - mae: 5.9344 - val_loss: 59.4252 - val_mae: 5.8688 - lr: 5.0000e-04\nEpoch 196/300\n184/184 [==============================] - 1s 4ms/step - loss: 60.5275 - mae: 5.9707 - val_loss: 60.3340 - val_mae: 5.8981 - lr: 5.0000e-04\nEpoch 197/300\n184/184 [==============================] - 1s 4ms/step - loss: 59.2745 - mae: 5.8805 - val_loss: 61.2597 - val_mae: 5.9508 - lr: 5.0000e-04\nEpoch 198/300\n184/184 [==============================] - 1s 8ms/step - loss: 58.9390 - mae: 5.8828 - val_loss: 61.2692 - val_mae: 5.9193 - lr: 5.0000e-04\nEpoch 199/300\n184/184 [==============================] - 2s 9ms/step - loss: 58.7461 - mae: 5.8214 - val_loss: 61.3635 - val_mae: 5.9393 - lr: 5.0000e-04\nEpoch 200/300\n184/184 [==============================] - 2s 10ms/step - loss: 58.0711 - mae: 5.7707 - val_loss: 60.8445 - val_mae: 5.8866 - lr: 5.0000e-04\nEpoch 201/300\n184/184 [==============================] - 1s 6ms/step - loss: 59.5577 - mae: 5.8927 - val_loss: 60.6299 - val_mae: 5.8840 - lr: 5.0000e-04\nEpoch 202/300\n184/184 [==============================] - 2s 9ms/step - loss: 58.3317 - mae: 5.8403 - val_loss: 60.2687 - val_mae: 5.8754 - lr: 5.0000e-04\nEpoch 203/300\n184/184 [==============================] - 1s 7ms/step - loss: 58.4771 - mae: 5.8646 - val_loss: 61.4404 - val_mae: 5.9749 - lr: 5.0000e-04\nEpoch 204/300\n184/184 [==============================] - 1s 8ms/step - loss: 60.5575 - mae: 5.9067 - val_loss: 61.9513 - val_mae: 5.9547 - lr: 5.0000e-04\nEpoch 205/300\n177/184 [===========================>..] - ETA: 0s - loss: 59.1616 - mae: 5.8546\nEpoch 205: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n184/184 [==============================] - 1s 8ms/step - loss: 59.3716 - mae: 5.8716 - val_loss: 60.7470 - val_mae: 5.9169 - lr: 5.0000e-04\nEpoch 206/300\n184/184 [==============================] - 1s 8ms/step - loss: 56.2322 - mae: 5.7410 - val_loss: 60.6959 - val_mae: 5.8910 - lr: 2.5000e-04\nEpoch 207/300\n184/184 [==============================] - 1s 7ms/step - loss: 55.0730 - mae: 5.6556 - val_loss: 60.8077 - val_mae: 5.9020 - lr: 2.5000e-04\nEpoch 208/300\n184/184 [==============================] - 2s 9ms/step - loss: 58.0444 - mae: 5.7597 - val_loss: 60.0106 - val_mae: 5.8657 - lr: 2.5000e-04\nEpoch 209/300\n184/184 [==============================] - 1s 6ms/step - loss: 56.7502 - mae: 5.7477 - val_loss: 60.6445 - val_mae: 5.8952 - lr: 2.5000e-04\nEpoch 210/300\n184/184 [==============================] - 1s 5ms/step - loss: 57.5775 - mae: 5.8005 - val_loss: 60.4818 - val_mae: 5.8810 - lr: 2.5000e-04\nEpoch 211/300\n184/184 [==============================] - 1s 4ms/step - loss: 57.5950 - mae: 5.7892 - val_loss: 60.0572 - val_mae: 5.8525 - lr: 2.5000e-04\nEpoch 212/300\n184/184 [==============================] - 1s 6ms/step - loss: 54.5605 - mae: 5.6471 - val_loss: 59.3730 - val_mae: 5.8354 - lr: 2.5000e-04\nEpoch 213/300\n184/184 [==============================] - 1s 4ms/step - loss: 55.4220 - mae: 5.7236 - val_loss: 60.4031 - val_mae: 5.8887 - lr: 2.5000e-04\nEpoch 214/300\n184/184 [==============================] - 1s 4ms/step - loss: 57.1994 - mae: 5.7724 - val_loss: 59.2289 - val_mae: 5.8341 - lr: 2.5000e-04\nEpoch 215/300\n184/184 [==============================] - 1s 5ms/step - loss: 56.6797 - mae: 5.7435 - val_loss: 60.4277 - val_mae: 5.8697 - lr: 2.5000e-04\nEpoch 216/300\n184/184 [==============================] - 1s 4ms/step - loss: 55.4528 - mae: 5.6765 - val_loss: 60.6353 - val_mae: 5.9031 - lr: 2.5000e-04\nEpoch 217/300\n184/184 [==============================] - 1s 5ms/step - loss: 57.7170 - mae: 5.7719 - val_loss: 61.3657 - val_mae: 5.9259 - lr: 2.5000e-04\nEpoch 218/300\n184/184 [==============================] - 1s 4ms/step - loss: 58.1740 - mae: 5.7865 - val_loss: 60.5654 - val_mae: 5.8815 - lr: 2.5000e-04\nEpoch 219/300\n184/184 [==============================] - 1s 4ms/step - loss: 53.5062 - mae: 5.6195 - val_loss: 60.5344 - val_mae: 5.8913 - lr: 2.5000e-04\nEpoch 220/300\n184/184 [==============================] - 1s 4ms/step - loss: 55.2475 - mae: 5.6147 - val_loss: 59.7444 - val_mae: 5.8535 - lr: 2.5000e-04\nEpoch 221/300\n184/184 [==============================] - 1s 4ms/step - loss: 55.9687 - mae: 5.7495 - val_loss: 60.5355 - val_mae: 5.8854 - lr: 2.5000e-04\nEpoch 222/300\n184/184 [==============================] - 1s 5ms/step - loss: 54.7620 - mae: 5.6062 - val_loss: 59.4796 - val_mae: 5.8354 - lr: 2.5000e-04\nEpoch 223/300\n184/184 [==============================] - 1s 5ms/step - loss: 53.8558 - mae: 5.5926 - val_loss: 60.0954 - val_mae: 5.8560 - lr: 2.5000e-04\nEpoch 224/300\n184/184 [==============================] - 1s 4ms/step - loss: 58.0705 - mae: 5.7884 - val_loss: 61.0248 - val_mae: 5.9076 - lr: 2.5000e-04\nEpoch 225/300\n184/184 [==============================] - 1s 4ms/step - loss: 55.4050 - mae: 5.7503 - val_loss: 60.1205 - val_mae: 5.8584 - lr: 2.5000e-04\n46/46 [==============================] - 0s 2ms/step\nRMSE: 7.677\nR^2 Score: 0.463\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/06dae760-7a57-4d8f-ab89-470a612773c8",
   "content_dependencies": null
  },
  {
   "cellId": "c17a2de01ded4dc099b9826ddd978052",
   "cell_type": "code",
   "metadata": {
    "source_hash": "53ea11ff",
    "execution_start": 1754058583125,
    "execution_millis": 150,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "c17a2de01ded4dc099b9826ddd978052",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score\n\n# After your existing fit and predict:\ny_pred = model.predict(X_val_scaled).flatten()\n\n# Compute metrics\nrmse = mean_squared_error(y_val, y_pred, squared=False)\nmae  = mean_absolute_error(y_val, y_pred)\nr2   = r2_score(y_val, y_pred)\nev   = explained_variance_score(y_val, y_pred)\n\nprint(f\"RMSE:               {rmse:.3f}\")\nprint(f\"MAE:                {mae:.3f}\")\nprint(f\"R² Score:           {r2:.3f}\")\nprint(f\"Explained Variance: {ev:.3f}\")\n",
   "block_group": "c68e2e18895247fe80b1580dda225dcc",
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "text": "46/46 [==============================] - 0s 2ms/step\nRMSE:               7.677\nMAE:                5.836\nR² Score:           0.463\nExplained Variance: 0.466\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/62892739-8822-4a0a-be0a-08f241b0c61a",
   "content_dependencies": null
  },
  {
   "cellId": "2c63c7e8ee7e47c8aabec27c1b4b7b28",
   "cell_type": "code",
   "metadata": {
    "source_hash": "9db72b55",
    "execution_start": 1753981194776,
    "execution_millis": 1419,
    "execution_context_id": "40b0a145-acf6-4e52-91d7-b0b5015ddf65",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "2c63c7e8ee7e47c8aabec27c1b4b7b28",
    "deepnote_cell_type": "code"
   },
   "source": "X_test_scaled = scaler.transform(X_test)\n\ny_test_pred = model.predict(X_test_scaled).argmax(axis=1)\n\nfrom sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n\n# Predict (no argmax)\ny_test_pred = model.predict(X_test_scaled).flatten()\n\n# Evaluation\nprint(\"RMSE:\", mean_squared_error(y_test, y_test_pred, squared=False))\nprint(\"MAE:\", mean_absolute_error(y_test, y_test_pred))\nprint(\"R² Score:\", r2_score(y_test, y_test_pred))\n\nplt.figure(figsize=(6, 6))\nplt.scatter(y_test, y_test_pred, alpha=0.5)\nplt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--', color='gray')\nplt.xlabel(\"True Values\")\nplt.ylabel(\"Predicted Values\")\nplt.title(\"True vs. Predicted (Regression)\")\nplt.tight_layout()\nplt.show()\n\nerrors = y_test_pred - y_test\n\nplt.figure(figsize=(14, 3))\nplt.plot(errors, marker='o', linestyle='-', markersize=2)\nplt.axhline(0, color='gray', linestyle='--')\nplt.title(\"Prediction Error per Sample (Regression)\")\nplt.xlabel(\"Sample Index\")\nplt.ylabel(\"Prediction Error\")\nplt.tight_layout()\nplt.show()\n\n# Compute prediction error\nerrors = y_test_pred - y_test\nabs_errors = np.abs(errors)\n\n# Find the index of the largest error\noutlier_idx = np.argmax(abs_errors)\n\n# Remove the outlier from predictions and ground truth\ny_test_no_outlier = np.delete(y_test, outlier_idx)\ny_pred_no_outlier = np.delete(y_test_pred, outlier_idx)\nerrors_no_outlier = y_pred_no_outlier - y_test_no_outlier\n\nplt.figure(figsize=(6, 6))\nplt.scatter(y_test_no_outlier, y_pred_no_outlier, alpha=0.6)\nplt.plot([y_test_no_outlier.min(), y_test_no_outlier.max()],\n         [y_test_no_outlier.min(), y_test_no_outlier.max()],\n         'k--', lw=2)\nplt.xlabel(\"True Values\")\nplt.ylabel(\"Predicted Values\")\nplt.title(\"True vs. Predicted (Regression, Outlier Removed)\")\nplt.tight_layout()\nplt.grid(True)\nplt.show()\n\nplt.figure(figsize=(12, 3.5))\nplt.plot(errors_no_outlier, linestyle='-', marker='o', markersize=2, alpha=0.8)\nplt.axhline(0, color='gray', linestyle='--')\nplt.title(\"Prediction Error per Sample (Outlier Removed)\")\nplt.xlabel(\"Sample Index\")\nplt.ylabel(\"Prediction Error\")\nplt.tight_layout()\nplt.grid(True)\nplt.show()\n\n",
   "block_group": "6a944880260b4e6d96236101d738136c",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "text": "46/46 [==============================] - 0s 912us/step\n46/46 [==============================] - 0s 1ms/step\nRMSE: 8.213510985950949\nMAE: 6.076341098191772\nR² Score: 0.4063832122969444\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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h0uMkxDR85CMf4eMf/ziXXXYZ559/Pi+//DL33nvvmGGAv//7v+c3v/kNF198MR/60Ic48cQTSaVSvPrqq/zyl79k3759Bx06uPDCCwmFQnz+859H0zQuu+yyEb+/4YYbeOyxx7joootYtmwZ3d3dfO9732PJkiWcccYZxeetW7eOs846a9aXkSl1my+55BI2b97M9ddfz759+zj66KP51a9+RSwWO+h7rFq1in/+53/mK1/5CmeeeSbvec978Hg8PPvssyxatIivfe1rQCFw+f73v8+NN97IqlWrqK+v55xzzpmTNkIhV+yCCy7ggQceKLli9Tve8Q42bNjAN7/5TT71qU9x1lln8bGPfYyvfe1rvPTSS1xwwQW4XC527tzJHXfcwX/8x39w+eWX09DQwN/93d/xjW98g3e+8538xV/8BS+//DJ//OMfqa2tLSm3JxwO8/3vf58PfvCDnHDCCVx11VXU1dXR0tLC73//ezZv3sx3vvMdduzYwbnnnssVV1zB0Ucfja7r3HXXXXR1dXHVVVcB8NOf/pTvfe97vPvd76a5uZlEIsHNN99MOBzmwgsvnLANN954Y7Eu2Sc/+Ul0XeeHP/whuVyOm266qaR9OJ7777+fpUuXSikCcejKOaVPiEoxUTmC9evXj/t8y7Kcf/zHf3Rqa2sdv9/vbNmyxdm1a9eYcgSOU5ji/YUvfMFZtWqV43a7ndraWuf00093/r//7/9zDMMoqX3vf//7i1OvR3vwwQedd73rXc6iRYsct9vtLFq0yLn66qvHTLUHxp2aP9pQOYKDWbZsmXPRRReN+7tSt7mvr8/54Ac/6ITDYScSiTgf/OAHnRdffPGg5QiG/OQnP3GOP/54x+PxONXV1c5ZZ53l3H///cXfd3Z2OhdddJETCoXGbP9Mt3Eiv/rVrxxFUcaUnphs/916661jXv9HP/qRc+KJJzo+n88JhULOxo0bnX/4h39w2tvbi88xTdP5l3/5F2fhwoWOz+dzzjnnHOf11193FixY4Hz84x8vPm9oKv9EpQIefvhhZ8uWLU4kEnG8Xq/T3NzsfOhDH3Kee+45x3Ecp7e31/nUpz7lrF271gkEAk4kEnFOOeUU5/bbby++xgsvvOBcffXVztKlSx2Px+PU19c7F198cfE1hjBOaY8XXnjB2bJlixMMBh2/3++8/e1vd/785z+PeM5E2/Dwww87gPPwww8XH7Msy2lsbHT+z//5P+NurxBToTjOBNM9hBBCHDLLsjj66KO54oorxh1um23RaJTq6mpuvPHGYiHQI83dd9/N+973Pnbv3k1jY2O5myPmOclxEkKIWaRpGjfccAPf/e53SSaTs/pemUxmzGNDpSuGLzdzpPn3f/93/uZv/kaCJjEjpMdJCCEOE7feeiu33norF154IcFgkMcff5xf/OIXXHDBBeMmwgshpk6Sw4UQ4jBxzDHHoOs6N910E/F4vJgwfuONN5a7aUIcNqTHSQghhBCiRJLjJIQQQghRIgmchBBCCCFKdNjnONm2TXt7O6FQSBZ3FEIIIcQYjuOQSCRYtGjRQRdHP+wDp/b29mmvbSSEEEKII0dra+u4C3IPd9gHTqFQCCjsjOmucSSEEEKIw1c8HqepqakYM0zmsA+chobnwuGwBE5CCCGEmFApKT2SHC6EEEIIUSIJnIQQQgghSiSBkxBCCCFEiSRwEkIIIYQokQROQgghhBAlksBJCCGEEKJEEjgJIYQQQpRIAichhBBCiBJJ4CSEEEIIUSIJnIQQQgghSiSBkxBCCCFEiSRwEkIIIYQokQROQgghhBAlksBJCCGEEKJEEjgJIYQQQpRIL3cDhBBCCHH4sW2HtmiGlGEScOssrvKhqkq5m3XIJHASQgghxIza1Z3g3m1d7O5JkjUtvLpGc12QLRsaWFUfKnfzDokETkIIIYSYMbu6E9zyxD76UwaNES9+t4+0YbKtPUZ7LMO1m5fP6+BJcpyEEEIIMSNs2+HebV30pwxW1wcJeV1oqkLI62J1fZD+lMF927uwbafcTZ02CZyEEEIIMSPaohl29yRpjHhRlJH5TIqi0Bjxsqs7SVs0U6YWHjoJnIQQQggxI1KGSda08LvHzwTyuTVypkXKMOe4ZTNHcpyEEEIIMSMCbh2vrpE2TEJe15jfZwwLj64RmCCwGq5SZ+VJ4CSEEEKIGbG4ykdzXZBt7TGCHn3EcJ3jOHTEsmxcHGFxlW/S16nkWXkSOAkhhBBiRqiqwpYNDbTHMuzsLuQ6+dwaGcOiI5alJuDmgvUNk/YcVfqsPMlxEkIIIcSMWVUf4trNy9mwKEI0nWdfb4poOs/GxZGDBj3zYVae9DgJIYQQYkatqg+x8uzguDlKk+UuTWVWXlONvxybJoGTEEIIIWaeqipjgpuD5S69NStv/Bwon1ujK54t66w8CZyEEEIIMetKyV2ayVl5s0VynIQQQggxq0rNXWoMe2muC9IRy+I4I/OYhmblraoPHnRW3mySwEkIIYQQs6rU3KWOeJYtGxqoCbjZ2Z0kkc2Tt2zaoxleaBnArauct27yWXmzTYbqhBBCCDGrppK7tHZhmGs3L+febV282DpAS3+ajGHhd2t4XRr3v9aFqlK2kgQSOAkhhBBiVk01d2lVfQh7vcOO7gT1IQ+NES/1IS+ZvFX2ek4yVCeEEEKIWTVUUbzU3CXbdrh/ezeGaXPC0moWVfnRNbUi6jlJ4CSEEEKIWTVUUXx47pJp2ySyeXZ2J8dUFJ9KPac535Y5f0chhBBCHHGmUlH8rZyo8TOKfG6NnGmVpZ6T5DgJIYQQYk5MVlF8uEqu5ySBkxBCCCHmzHgVxUcbyona1h4j6NFHDNcN5URtXBwpSz0nGaoTQgghREWZak7UnLZtzt9RCCGEEOIgppITNZdkqE4IIYQQFanUnKi5JIGTEEIIISpWKTlRc0mG6oQQQgghSiSBkxBCCCFEiSRwEkIIIYQoUdkDp7a2Nj7wgQ+wYMECfD4fGzdu5Lnnniv+3nEc/vVf/5XGxkZ8Ph/nnXceO3fuLGOLhRBCCHGkKmvgNDAwwObNm3G5XPzxj3/ktdde4xvf+AbV1dXF59x0003853/+Jz/4wQ94+umnCQQCbNmyhWw2W8aWCyGEEOJIpDijlymeQ9dffz1PPPEEf/rTn8b9veM4LFq0iM997nN8/vOfByAWi9HQ0MCtt97KVVddddD3iMfjRCIRYrEY4XB4RtsvhBBCiPlvKrFCWXucfvOb37Bp0ybe+973Ul9fz/HHH8/NN99c/P3evXvp7OzkvPPOKz4WiUQ45ZRTePLJJ8d9zVwuRzweH/FPCCGEEGImlDVw2rNnD9///vdZvXo19957L5/4xCf427/9W376058C0NnZCUBDQ8OIv2toaCj+brSvfe1rRCKR4r+mpqbZ3QghhBBCHDHKGjjZts0JJ5zAV7/6VY4//nj++q//mo9+9KP84Ac/mPZrfuELXyAWixX/tba2zmCLhRBCCHEkK2vg1NjYyNFHHz3isXXr1tHS0gLAwoULAejq6hrxnK6uruLvRvN4PITD4RH/hBBCCCFmQlkDp82bN/Pmm2+OeGzHjh0sW7YMgBUrVrBw4UIefPDB4u/j8ThPP/00p5122py2VQghhBCirGvVfeYzn+H000/nq1/9KldccQXPPPMMP/rRj/jRj34EgKIofPrTn+bGG29k9erVrFixgn/5l39h0aJFXHrppeVsuhBCCCGOQGUNnE466STuuusuvvCFL3DDDTewYsUKvv3tb/P+97+/+Jx/+Id/IJVK8dd//ddEo1HOOOMM7rnnHrxebxlbLoQQQogjUVnrOM0FqeMkhBBCiMlMJVYoa4+TEEIIIQSAbTu0RTOkDJOAW2dxlQ9VVcrdrDEkcBJCCCFEWe3qTnDvti529yTJmhZeXaO5LsiWDQ2sqg+Vu3kjSOAkhBBCiLLZ1Z3glif20Z8yaIx48bt9pA2Tbe0x2mMZrt28vKKCp7KWIxBCCCHEkcu2He7d1kV/ymB1fZCQ14WmKoS8LlbXB+lPGdy3vQvbrpx0bAmchBBCCFEWbdEMu3uSNEa8KMrIfCZFUWiMeNnVnaQtmilTC8eSwEkIIYQQZZEyTLKmhd89fuaQz62RMy1ShjnHLZuYBE5CCCGEKIuAW8era6QnCIwyhoVH1whMEFiVgwROQgghhCiLxVU+muuCdMSyjC4r6TgOHbEsq+qDLK7ylamFY0ngJIQQQoiyUFWFLRsaqAm42dmdJJHNY9o2iWyend1JagJuLljfUFH1nCRwEkIIIUTZrKoPce3m5WxYFCGazrOvN0U0nWfj4kjFlSIAqeMkhBBCiDJbVR9i5dnBeVE5XHqchBBCCFF2qqqwuMpHwK2TMkzaopmKqt80RHqchBBCCFF282XZFQmchBBCCFFW82nZFRmqE0IIIUTZzLdlVyRwEkIIIUTZzLdlVyRwEkIIIUTZzLdlVyRwEkIIIUTZzLdlVyRwEkIIIUTZzLdlVyRwEkIIIUTZzLdlVyRwEkIIIURZzadlVypjwFAIIYQQR7T5suyKBE5CCCGEqAiqqtBU4y93MyYlQ3VCCCGEECWSwEkIIYQQokQyVCeEEEKIimHbTkXnOUngJIQQQoiKsKs7wb3butjdkyRrWnh1jea6IFs2NFTMzDoJnIQQQghRdru6E9zyxD76UwaNES9+t4+0YbKtPUZ7LFMxZQkkx0kIIYQQZWXbDvdu66I/ZbC6PkjI60JTFUJeF6vrg/SnDO7b3oVtOwd/sVkmgZMQQggh5oRtO7T2p3mjM05rf7oYCLVFM+zuSdIY8aIoI/OZFEWhMeJlV3eStmimHM0eQYbqhBBCCDHrJstfMm2HrGnhd4+/Hp3PrdEVz5KaYCHguSSBkxBCCCFm1cHyl96xYSFeXSNtmIS8rjF/nzEsPLpGwF3+sEWG6oQ4Qk3UZS6EEDPFth3296X4n6f2c2Agzaq6wLj5Sy+3RllZF6AjlsVxRp6LHMehI5ZlVX2QxVXj90jNpfKHbkKIOTcfpvyWQ6XXjxFiPhk6z7xyIMqr7TF8Lo286dBcH6Am4AHeyl/a3ZPi3ScspiOWZWd3IdfJ59bIGBZdfTHqkrs455yLK+L7KIGTEEeY+TLld65JMCnEzBl+nvG5NXwujYBHozuRJZHLc1xTVTF4Gspfqgt5uHbz8uL3sCuexWcmWdj5DHYuzc7nn2DtokvKvGUSOAlxRBk95Xdo9krI6yLo0dnZneS+7V2srA1WxJ3dXJFgUoiZM/o8k8iauDQVVVGpCWj0pwx296So9rtRFGVE/lJTjZ+VZwdpi2Z4443XeObhJzDNPDU1NZx22mnl3jRAcpyEOKLMpym/c2U+1Y8RYj4YfZ4JeXWq/W6S2TwAQa9Of8ogkTXHzV9SFNj18tP8+f7fY5p5mpub+chHPkJtbW05N6tIepyEOIKkDHPeTPmdK1MJJptq/GVqpRDzx+jzjKIorKoPksyZ9KcM/B6dvGUxkDbojGepCbi5YH0DqqpgGAZ33XUXb7zxBgCnnnoq559/PqpaOf08ldMSIcSsC7j14pTf8VTSlN+58tZJfvxt9rk1cqZ1RAWTQhyK8c4zNQE3xzVVURfyksqZZPM2mbzFxsWREUPhtm3T09ODpmm8853vZMuWLRUVNIH0OAlxRFlc5aO5Lsi29hhBjz6ih2Woy3zj4khFTPmdK8NP8pVeP0aI+WCi80xNwM2mZVW80hZjZW2QazcvZ0m1f0Q+pdfr5aqrriKTydDU1FSuTZiUnAmEOIKoqsKWDQ20xzJjpvx2xEZ2mR8pJJgUYmYd7DyzpNrP+09dytIFAQCee+45HMfhpJNOAijmMlVqeRAJnIQ4wqyqD42Z8uvRNTYujnDB+iNv6r0Ek0LMvFLOM5Zlcc899/Dcc8+hKApLly6loaEBqOzyIIozukTnYSYejxOJRIjFYoTD4XI3R4iKUal3c+Uy/ESdMwvDc6vqg0dkMCnETJnoPJNOp7njjjvYt28fAOeccw5nnHEGiqKMUx5EJ22YxRuZ2SgPMpVYQXqchDhCqaois8SGWVUfKtaPkWBSiJkx3nmmq6uLrVu3Eo1GcbvdvOc972HNmjXA/Kg1J4GTEEIMkmBSiNn1xhtv8Ktf/Yp8Pk91dTVXXXUV9fX1xd/Ph/IgEjgJIYQQYk4MDAyQz+dZsWIFl19+OV6vj9b+dLGXN5HNV3ytOQmchBBCCDEnTj31VAKBAOvXr2dvX5p7n9o9IgG8NujBMO2KLg9SWVWlhBBCCHHYiMVi3HnnneRyOaAw3HbMMcewty/NLU/sY1t7jCq/i5W1Qar8Llr6U/QkcuzsTjJ67tp4y7OUg/Q4CSGEEGLGtbS0cNttt5FOp9E0jUsvvRSYPAH8qAadVM4insmzoyvJoqrKKw8igZMQQgghZtQLL7zA73//e2zbZuHChZx99tnF3x0sAXx1Q5CW/jRLa3z0Jo2KqzUngZMQQgghZoRt29x7770888wzAKxbt45NZ11AZ1Yl0Z9mcZVv3MXGHcchkTUxLBtNUXBrKpeesJiQx1Vx5UEkcBJCCCHEIctkMtxxxx3s3bsXgGNOOp02z3L+38N7Gcjk0RRorguyeXXtiPUh+1M5dnen6E8bmLaN44BHV+lPGhzdGCnzVo0lgZMQQgghDplpmvT29uJyuTjtnL/ggQ4XLR39pHMmyZxJzrR4vSPB03v7aa4N0BHLYpgWLx+IkTEsgl4dXdXoSRhoqsMfXu1gYcRb9qG50SRwEkIIIcQhC4VCXH311YDCr15P0tLfx0AqRzZvE/TqhH0uDNOiM54lm7dprgvwzN4BcqZFbdBNOm/TlzXxu3U2LauiP50ve5Xw8Ug5AiGEEEJMmeM4PP7447z66qvFxxobGzE9YXZ1J0jnTLJ5m5qAG4+uoSoKXpfOwrCXRDYPCmiqgu3A3r40bQOF5Y4My2JPbxqfSy1WCa8k0uMkhBBCiCnJ5/P85je/Ydu2bei6TlNTE1VVVQCkDJOBTJ5kziTo1cfMnHPrKm5dpS+RI+DRsOxCvaaQR8fv0TBt6ElkiWfzLAi4y1olfDwSOAkhhBCiZPF4nK1bt9LR0YGqqlxwwQXFoAkg4NbRFMiZFmHf2OrfecvGraugQH8yj2U7I0oTaCq4A2664jkcB3wuba42rSQSOAkhhBCiJAcOHOC2224jmUzi8/m44oorWL58+YjnLK7y0VwX5PWOBIZp4XW9FWo4jkMyaxLxuwh7dPqTeWzsCd7NQcGhcrKbCiRwEkIIIcRBvfTSS/zud7/Dsizq6+u56qqrqK6uHvM8VVW4/MQmnt7bT2c8y8KwgltXyVs2yayJ16Xhd+ksqQmQtWz6Uwb9KYOgV8elvfW8oFdnQcBDOm+VYWsnJoGTEEIIIQ6qr68Py7JYu3Ytl156KR6PZ8LnHrUwxN+eu5r/fHAnPYlcMa8p4nfhd+ksXeDn3HX1xDJ56oIeOmM5+tMGqZyJpqrUh70sDHsApawL+o6nslojDlu27dAWzVRcBVghhBClOeecc6irq2Pjxo1jEr7Hc+66BppqfPzyuTZ29ySxHZsqn5vVDSEuWN/AytogL7fG2NYe48RlVSRzFoZl49ZUgh6NXT0pNi6OlHVB3/FI4CRm3a7uBPdu62J3T5KsaeHVNZrrgmzZUP41h4QQQoyvt7eXxx57jHe+853oemF23DHHHDOl1ziqIcz17whNeOO8ZUMD7bEMu3pSNEa8VPldZAyLXT2piljQdzwSOIlZtas7wS1P7KM/ZdAY8eJ3+0gbJtvaY7THMly7ebkET0IIUWF27tzJnXfeSS6XIxgMcsEFF0z7tVRVoanGP+7vVtWHuHbz8uLNdaUt6DseCZzErLFth3u3ddGfMlhdHyx27Ya8LoIenZ3dyYqsCiuEEEcqx3F48skneeCBB3Ach6amJk4//fRZfc9V9SFWnh2cN+kcEjiJWdMWzbC7JzmiPscQRVFojHiLVWEnuhsRQggxN0zT5Le//S2vvPIKAMcffzwXXXQRmjb7dZQm65WqNBI4iVmTMkyypoXfPX5in8+t0RXPVlxVWCGEONIkEgluu+022traUBSFLVu2cPLJJ5eUBH6kkcBJzJqAW8era6QNk5B3bPXYjGHh0bWKm2oqhBBHmnw+T19fH16vl/e+972sXLmy3E2qWHLFErNmqHrstvYYQc/I9Yocx6Ejlq3IqaZCCHGkqamp4aqrriIUClFTU1Pu5lQ0tdwNEIcvVVXYsqGBmoCbnd1JEtk8pm2TyObZ2Z2s2KmmQghxuLNtmwceeIBdu3YVH1u2bJkETSVQHMdxyt2I2RSPx4lEIsRiMcLhcLmbc0QaXscpZxaG51bVByt2qqkQQhzOstksv/rVr9i5cyder5frrrsOv3/2E7NHF0JuCHp4qS1KX8pgQcDNCU3V6Hp5+nOmEiuUdajuS1/6El/+8pdHPLZmzRreeOMNoPDhfu5zn2Pr1q3kcjm2bNnC9773PRoaGsrRXDFN822qqRBCHK76+vrYunUrvb296LrOhRdeOCdB0+hCyAMpg/ZolpxZWIfOpaksDHu5+NhGzlnbUNHXiLLnOK1fv54HHnig+LOuv9Wkz3zmM/z+97/njjvuIBKJ8Dd/8ze85z3v4YknnihHU8UhmE9TTYUQ4nC0e/dufvnLX5LNZgmFQlx11VUsWrRo1t93dCHk7rjFa+3xwVnXOjV+F4msyattMV7viHPPtk7OWFVXsatLlD1w0nWdhQsXjnk8FovxX//1X/z85z/nnHPOAeCWW25h3bp1PPXUU5x66qlz3VQhhBBi3nEch6effpr77rsPx3FYsmQJV1xxBaHQ7AclowshOw68ciCGaTvU+F2kDJvOeA6XphD26qQMi/19KcJeV8WuLlH25PCdO3eyaNEiVq5cyfvf/35aWloAeP7558nn85x33nnF565du5alS5fy5JNPlqu5QgghxLzT09OD4zgce+yxXHPNNXMSNMHYQsgd8QzRTB6fW0NRVCzbKSzsq6u4dBWfWyOZswh6dPpTBvdt78K2KysVu6w9Tqeccgq33nora9asoaOjgy9/+cuceeaZbNu2jc7OTtxuN1VVVSP+pqGhgc7OzglfM5fLkcvlij/H4/HZar4QZTM6ybKS8wGEEOWlKAoXXnghy5cvZ8OGDXNa1HJ0IeSMYWHZDi5NwbIdLNtm+KnLpSpkbIeMaVXs6hJlDZze8Y53FP/7mGOO4ZRTTmHZsmXcfvvt+HzTq+3zta99bUzCuagMcrGfGaOTLL26RnNdsGLzAYQQc6+jo4NnnnmGSy65BFVV0TSNjRs3znk7RhdC9rk1NFUhbzmoCjhOIQd26EqQtx00VcHv0ip2dYmy5zgNV1VVxVFHHcWuXbs4//zzMQyDaDQ6otepq6tr3JyoIV/4whf47Gc/W/w5Ho/T1NQ0m80WJZCL/cwYnWTpd/tIGybb2mMVmw8ghJhb27Zt49e//jWmabJgwQLOOOOMsrVldCHkxrCPKp+LvpSB361iA5oCDpDLW6QMi9qgm8aIl1SFri5R9hyn4ZLJJLt376axsZETTzwRl8vFgw8+WPz9m2++SUtLC6eddtqEr+HxeAiHwyP+ifIauthva49R5XexsjZIld/FtvYYtzyxj13diXI3cV4YnWQZ8rrQVIWQ18Xq+mDF5gMIIeaG4zg89NBD3HnnnZimyapVq9i0aVNZ2zS6EHLKMNm4JIKuKsQzJgpgmg7RdJ6BdB7Ldgh4dAbSeTpiWVbVBytudYmyhnGf//znueSSS1i2bBnt7e188YtfRNM0rr76aiKRCH/1V3/FZz/7WWpqagiHw1x33XWcdtppMqNuHhl9sR8aWw95XQQ9Oju7k9y3vYuVtUEZtjuI0UmWwymKUrH5AEKI2ZfL5bjrrrt48803ATj99NM599xzUdXy94+sqg9x7eblxVEHgKMXhdnfl6Y/ZWDYNooDXpdKXchDxrB5dEcPxzZVVeTqEmUNnA4cOMDVV19NX18fdXV1nHHGGTz11FPU1dUB8K1vfQtVVbnssstGFMAU84dc7GfO6CTL0So1H0AIMbsGBgb4xS9+QU9PD5qmcckll3DssceWu1kjjC6E7HNp3P1CG0/u6cOlKwykDPKWg+04qIqC40B9yMPymgCt/emKyo0ta+C0devWSX/v9Xr57ne/y3e/+905apGYaXKxnzmjkyxHy1RoPoAQYnblcjmi0SjBYJArr7ySJUuWlLtJ4xpeCLm1P83+/jRHLwoT8rpwHIdE1iyUJtBUwKGlP81N975JbzJXUbmxcoYVs0ou9jNndJLl8B48x3HoiGXZuDhScfkAQojZtXDhQq688krq6urmTV7v6JtqRVEI+966RvQksuzoSpDNWxzVEKqoiTDlH/wUh7Whi31HLMvo9aSHLvaVmPxXiUYnWSayeUzbJpHNs7M7SU3AXZH5AEKImWVZFn/4wx9obW0tPtbc3DxvgiYYeVM9muM4vNmZwLQcVtUFCXp0UjmTnGnTEPLQlyzvRBi5zRezauhi3x7LsLO7kOvkc2tkDIuOWFYu9lM0OsmyK57Fo2tsXBzhgvVS2kGIw10qleL222+npaWFN954g+uuuw6Xa2xvfqWbrAc9ninMqGus8mLaNs/tG6A/bWDaNrqqEnBrvNBily03VgInMevkYj+zRidZVkrCpBBidnV2drJ161ZisRgej4eLL754XgZNMPlN9a6eJLqmsDDs5eUDMTKGRdCr49J08pZNNG3Qk8zxekdcAidx+JKL/cwanmQphDj8vfbaa9x9993k83lqamq46qqrijPQ56uJbqqPXhTGo6t0xrJkDIuagLvYI+XRNRwv9CRyPL9/gPPWzf2IhQROYs7IxV4IIabGcRweffRRHn30UQBWrlzJ5ZdfPu1lySrNeDfVjWEvN937Br97pYO6kHvMRJhUzqSxykt3PFuW4ToJnIQQQogK1t3dDcApp5zCBRdcUBFFLWfSeDfVm1bUcM/2TpJZC0VRcGkqecsmmTXxuXWOaggRz+TLUspGAichhBCiQimKwqWXXsr69etZv359uZszZ9YtDHNUQ4iBlEHKsEjmTHRVpT7spbkuiEtTyOXtspSykcBJzBrbdiSnSQghpmj//v1s376dd7zjHSiKgtvtPqKCJijMuju+qZpX26JsDHvJ2w5uTSXkLYQtO7uTZatbJ4GTmBW7uhPFhL9KqvgqhBCV7Pnnn+cPf/gDtm2zcOFCTjjhhHI3qSyGz7rrSuSKs+6SObPspWwkcBIzbld3glue2Ed/yqAx4q2oiq9CCFGJLMvi3nvv5dlnnwVg/fr1bNy4scytKq9KLWUjgZOYUbbtcO+2LvpTBqvrg8XZECGvi6BHZ2d3kvu2d7GyNijDdkIIAaTTae644w727dsHwDnnnMMZZ5wxZmH0I1EllrKRwEnMqLZoht09hWJmo7/0iqLQGPGyqztZtoqvQghRSbq7u/nFL35BNBrF7Xbz7ne/m7Vr15a7WRWl0krZSOAkZtTohRtH87k1uuLZskwhFUKISpPJZIjH41RVVXH11VdTX19f7iaJg5DAScyo4Qs3hrxjlwLIGBYeXSvLFFIhhJhphzp7eNmyZVxxxRU0NTXh91dOr4qYmFy9xIyabOFGx3HoiGXLNoVUCDE9UlpkfNOZPZzP5/nDH/7AaaedVuxdWrNmzVw2WxwiCZzEjJps4cZyTyEVQkydlBYZ33RmD8diMbZu3UpnZycHDhzgE5/4xGFXBfxIIIGTmHGVOoVUCDE1UlpkfNOZPdza2sptt91GKpXC7/dz8cUXS9A0T0ngJGZFJU4hFUKUTkqLTGyqs4dffPFFfve732HbNg0NDVx11VVUVVWVp/FldLgM+UrgJGZNpU0hFUeWw+UkXS5SWmRipc4eTmQN7rnnMZ5++mkA1q1bx6WXXorb7Z7L5laEqQz5Vvp3VwInIcRhR/JyDt1kwYHjOJiWQ08yx+6eZMVd2GZbybOHXRpdXV0AnHXWWZx11llHZFHLqQz5zofvrgROQojDiuTlzIyJgoP+VI7d3Sm6ElkyeYtfPN3C9rZ4RV3YZlups4ebFgR573vfS2tr6xE7c24qQ757epPz4rsrmWlCiMPG6JN0yOtCUxVCXher64P0pwzu296FbTvlbuqcsm2H1v40b3TGae1Pl7T9Q8FBRyyL4xSe35/K8VJrlM5YmqxhURtwE/LqvNoW5ZYn9rGrOzHbm1IRhmYP1wTc7OxOksjmMW2bRDZPy97d1MV3FGcP+/3+IzZogtKHfFsH0vPmuys9TkKIw4bk5Yw13aGP0aVFFoY97OxK0pfMkcnbOA5omTzb2uNU+12kctYRlSw+ZvZwLEMovpfqntcAMPvboF6WTik1H2xvb2refHelx0kIcdh46yQ9/j2hz62RM60jZsmfoWHLbe0xqvwuVtYGqfK72NYeK6mHaCg42LAoQnssy57eJMmchaLAwoiXhrAXr0ujJ5GjO5HlhZYB2qKZOdq68ltVH+ITZzdz3dtXsEnZjW8waDrxxBNZvXp1mVtXGYYP+Y5nKB8MmDffXelxEkIcNmTJn7fMVDmBodIij+zo5o3OOF6XRkPIU6xB5NEV3AE3fUmD1v40iVx+TravUiSTCR749e20t7ejKArveMc7OOmkk8rdrIpRaj7YytrAvPnuSo+TEOKwMV5ezpChk/Sq+uARseTPVIYtD0ZVFYIeHduGoEcfU7hRURQ8LpW0YZHMlr9HYK4cOHCAm2++mfb2dnw+Hx/84AclaBplsnywnd3J4moSS6r98+a7K4GTEOKwUepJ+kjIwZnpYcugR8fn0sjlrXEvbLm8hd+tEfSUv0dgriSTSZLJJPX19Xz0ox9lxYoV5W5SRRo+5BtN59nXmyKazrNxcaQ4U24+fXePnCNcCHFEkCV/CmZ62DLkdbF0gZ8DA2n6UwZBr45LU8lbNsmsia6pNNX4x32vw9XatWt573vfS3NzMx6Pp9zNqWilrCYxX767EjgJIQ47suRP6bklpQ59LK7ycXxTNTnTxjRtBjJ5UjkTTVWpC3nQNZUTllZXxFDKbMlms/zhD3/gnHPOKS6ZcvTRR5e3UfNIKatJzIfvrgROQojD0pG+5M/ocgKNES8+t0bGsOiIZac89DH89fqSBktq/GiqgmU7JLImC4KVM5QyG3p7e9m6dSt9fX0MDAzw4Q9/+IisAj4XKv27K4GTEEIcpmZ66GP066UNE4+uccySyhpKmWm7du3il7/8JblcjnA4zIUXXihB0xFMcUZn+R1m4vE4kUiEWCxGOBwud3OEEGLOzfSiqZW+COtMcRyHJ598kgceeADHcWhqauKKK64gGAyWu2mHhUo6jqYSK0iPkxBCHOZmeuij0odSZoJpmvzud7/j5ZdfBuC4447joosuQtflsjkT5sNivhORI0AIIYQYxXEcurq6UBSFLVu2cPLJJ8vw3CEa6mF6vSPO71/tIJe3WFTlq9jFfCcigZMQQggxisvl4qqrrqKvr4+VK1eWuznz3lAP067uBNvb4yRzJssW+KkLeYuL+U6lon05SQFMIYQQAnj11Vd5/PHHiz9HIhEJmmbA8DUTXZqKokCV30VPIsdLrVH6UwYw9Yr25SI9TkIIIaalkpJ7D4Vt2zz00EM88cQTADQ1NbFs2bIyt+rwMHrNxL6UgeU4hD0ugh7oTxns7klS7a9GURR8bo2ueHZERftKO84kcBJCCDFl8zm5d7hcLsedd97Jzp07Adi8eTNNTU0H/btKu5hXqtFrJro1FV0tVJz36BpBr05/yiCRNQn7XGMq2lficSaBkxBCiCkZGnrpTxk0RrzzLrl3SF9fH1u3bqW3txdd13nnO9/Jxo0bD/p3lXgxr1RvrZlYqCgf8urU+N10J7K4AyouTSWZMzEse0xF+0o9ziRwEkIcNqQXYPaNHnoZmmk2n5J7Afbs2cMdd9xBNpslFApx5ZVXsnjx4oP+XaVezCuRbTvEM3lyeYvueJaFg71OzfUBErk8/SkDt66iKgqGaY1YzBeo2ONMAichxGFBegHmxuihl+FGJ/dWcq2nWCxGNptl8eLFXHnllYRCBz9GDpegcS4Mn0XXOpDh9Y4Ey2r8rGoIUhPwcFxTFbu6kuzvTxPy6piWM6KifWt/umKPMwmchBDznvQCzJ3RQy+jjZfcW4mOP/54dF1n3bp1JRe1PFyCxtk2/Pu4MOxl7cIwrxyIsrMnSV/aYNOyarwujYjfzUkRLxcd08i6heERPcSVfJxJOQIhxLw2uhcg5HUV68Ksrg/SnzK4b3sXtn1Yry41ZwJuHa+ukZ7ggjU6ubdSJJNJ7rzzTlKpVPGxjRs3TqkS+FsX8/H/xufWyJlWxQeNs2n493FBwMWbnQl2dCWwHQcc6Ihm+PPuPgZSBscsiXDdOau44OiFNNX4R/TSVfJxJoGTEGJem0ovgDh0i6t8NNcF6YhlGb3U6VBy76r6IIurxu8pKIeOjg5uvvlmtm3bxm9/+9tpvcZb+To2XfHMmG2Hyg0a59LQ99HnUnn5QIzuRBavS6Uh7GVxtZewVydv2rztqDo+flbzhD3BlXycHbmfrhDisFDJXfrTVclJ7qqqsGVDA+2xDDu7CwGrz62RMSw6Ytlicm+ltHf79u3cfffdmKbJggULOO+886b8GiPydfrTvN6RHwwE/NQFPYS8hUvp8BlhR6qUYZLJWwykDDKGRU3AXbyh8bp0GqtUDgxkeOVAjHcdN3EyfiUfZxI4CSHmteFd+iGva8zv51svwHxIcl9VH+LazcuL7eyKZ/Ho2ojk3lLMZoDoOA4PP/wwf/rTnwBobm7m8ssvx+v1Tul1RufPHdUQ5Kk9/bzekWBnd4oFQTe1ATd+t87SBf6KChrLIeDWsR2HnmSOkFcf0wts2g4Bj057NHPQXLCZOs5m2vw4kwghxASGuvS3tccIekaeqEfXhal08ynJfVV9iJVnB6cU+AwPlHoTOV5qibKnNzXjAWIul+Ouu+7izTffBOC0007jvPPOQ1VLy04Zamcil+fuF9roSxoc1RBkIG2wvz+N36Ohq5A0LGJpAyNvUR/2cs7a+or5fMplcZWPxoiXl1ujVPtH3sg4jkMya1Ib9KCplNQLPJ3jbLZJ4CSEmNcquUt/KubjVHdVVUqePTa8J603maO1P41LV9mwKMzK2uCMBoi2bdPT04OmaVxyySUce+yxJf/t8Hb2p3Ps7k5RH/JQG3KzpztFxrBYGC70WiWzhWGpE5ZWkzIs3uxM8PY19RXz+ZSDqiqcu66BR9/soSdhUB1w4dIKlcKTWROfW2dxlRdQSu4FnspxNhckcBLiCFfJ+TSlqtQu/ak4nKe6j56e3h4tJFdbtsPO7hQBj4uagHvGAkSfz8dVV11FLpdjyZIl02pnY8SLW1fZ15smmjZ4dm8/hmUT8bmKn0/Aq2PYNl63RsTvmrefz0zb3FzL29fU8/juXrKGRdIx0VWV+rCXlbUB+lLGvOkFHo8ETkIcweZDPk2pKrFLfyoqIcl9NoLo0T1piaxJNJOnKuDGraljFnmdToDoOA7PPfcciqKwadMmAOrq6g6pnYqi4DjgdWl4dZW+lEHaMKkNeIp/k7dsdFXFranzchLCbFFVhfedupSsadEWzVDtdxPy6uiqQmc8N296gScigZMQR6j5lE9Tqkrr0p+Kcie5z1YQPbonzbBsTNvGpRXy0UYv8jrVAMSyLP74xz/y/PPPoygKS5cupb6+/pDbCYV11ar9LtqjWXQVcnmb1ODnM5SvUx/2EvLqJHPmvJqEMNtW1Yf48Bkr3hr2TBnzrhd4IvIJC3EEmo/5NIdiPgxHTjXJfSa3aTaD6NE9aW5NRVcLOS8eXRuxyCtMLUBMpVLccccd7N+/H4Bzzz13yj1No9vpc3mJZ/IYlk06Z5HOWcQyefKWjWFaHOhP01jlw7IdfG6d5rogIKUIxjPfe4EnIoGTEEegwzmfZrT5Mhw5lST3mdym2Q6iR/ekhbw6NX433Yks7oA6YrhrKrMgOzs72bp1K7FYDI/Hw9suuJiaRUs5MJCZ1sU54NYxTJun9vSRMizShkk8Y6IqUB1wkTYsEhmHnOXQ2p9meW2ADYvDuDRlxOK0Mx0UzIegfzLzuRd4IhI4CXEEmsl8mko+sc+34chSktxneptmO4geryetuT5AIpenL5nDtGFhxAs4JQcgr7/+OnfddRf5fJ5QpApt1Rn8cqdJ9vWd0w4iM4ZFTyJHZzxLY9hLIuPgOA6OopDMmnh1lbWNYRaGPbzemQDHIZbOk3PZszb8NF+C/iONBE5CHIFmKp+mkk/s83U4crLhjdnYplKC6M5YIbiaTnA8Xk9a2FdYR3BbWxzbcfDoKrGMWXIA0t/fTz6fZ+GSZbSEN9IfhcaIa9pBpG073P9aF2GfC8u2GUgbpPMWXpeGg0MqZ6GqCkc3hlgQ9NIQ9tIezXLFSU001wVn5WahkoL+Sr45KgcJnIQ4As1E0chKOrGPZzo9KZVygZhoeGOme4dGr7/WGPGNed2OaIZ9fRl+8UwLmqpMKzieqCftXcct5pimCHUhz5T29+mnn04wGOJPvV76OxKHHEQO7dfV9UHylp9XDsToTxmAg6qohHw6PpeGS9MA8Ht0dE1hYcQ7K8NQlRT0V/LNUbkccuAUj8d56KGHWLNmDevWrZuJNgkhZtmhFo2spBP7RKY6HDkfLhAzOcQ6dv01k2UL/KyqD1ETcAPQl8zx7L4BfC6NRREfAY8+7eD4UBKFo9EoDz74IBdffDEejwdFUahpWsWeN3bMSBA5fL9qqsLxS6tI5kxcqoLXpaFrCtHBhHGY/RmOlZKDWOk3R+VSWv35Ya644gq+853vAJDJZNi0aRNXXHEFxxxzDHfeeeeMN1AIMTuGegE2LIoQTefZ15sims6zcXHkoCfEqZzYy2X4cOR4hl/8hi4Q29pjVPldrKwNUuV3sa09xi1P7GNXd2KOWz++qWzTZIZvb3XAzabl1YR9Ont6Uzyzt4/uRJZ4xuCZff0AnLyimrDPhaYqhLyFYbb+lMF927uwbWfS9xpuqCdt7cIwTTX+cYMm2y4kX7/RGae1P82+ffu4+eab2bZtG/fcc0/xeW8FO+Nvq8+tkTOtkoLI0fs17HXREPJi2g5uXcW0nTEJ7Kvqg7M2g24mt226Rt8chbyH/vkfLqYcLj/22GP88z//MwB33XUXjuMQjUb56U9/yo033shll102440UQsyO6fYCVEKxxoMpdTiyMezlh4/tqejesyEzMcQ6Xm9hyOvipOU17OpKsr8/zQv7B1hW48elKmxYXs2C4MiFcWer12N0r18o2Yq/6xVwHBobGzn77LOLz53JulezkcB+KMpd0wsqp9erEk25xykWi1FTUwPAPffcw2WXXYbf7+eiiy5i586dM95AIcTsKqUXYLSZ6vmYTUPDkTUBNzu7kySyeUzbJpHNj7j4dcSzFd97NqTUbZrsM5zoglgT8HDSiho2r6plSbWP89cvZOmCAIuqxr8oznSvx/BesIhPY3HiTfydL4PjYEWW8LaLLicSiRSfPxTsdMSyOM7IXg/bttndkyTiKxSqPFivyHj7dSiBXVNVVEUZkcA+20NUk23bXPR4QWX0elWqKZ/VmpqaePLJJ6mpqeGee+5h69atAAwMDOD1eg/y10KIw8FM9HzMhVKm97/RGa/43rPhDnVdvsl6CxVFoT7sIW2Y1IU8+Fxz0+sxvBdsVbWLzI7HMePdALibNtLiWspDO/o5qrG6GBSOl6fndans6kqwvT2O6UDaMPnW/TtYVR86aK7aTCewH4pKWLi6Enq9KtWUt/jTn/4073//+wkGgyxdurTYdfrYY4+xcePGmW6fEKICjDfbrNwn9lIdbDhyvAuE4zgksoVq1oZp4dbUirpAHEqidakXxJW1gQmD46EenZW1wWKPzqF81sN7wSCPnUmAquM76jRcNUtozObHHRYaHuy82DrAa+1xehI5bBzcmsrenkIpgd6kUUxmXlk78X6rpErX5V64ulw3R5Uys3UyUz4TfPKTn+Tkk0+mtbWV888/H1UtjPatXLmSG2+8ccYbKIQor8lmm5XzxD4Vk1UvHn2BGEgXLtIDaQPTskkbFs31QTKGNcetntx0KjLbtoPtOIR9Ort7khyzOFI8h8PIC+KSav+4wXFHNMO29jh508Zx4NsP7Dzk2YfDe8FU1YV/3ZmgaGiBKmDyXr9V9SHMdQ6P7+wmmjYKlb59blAKa8sdGMiQH5wN9/OnW6jxu9nTm5pw5uR4+7VcF/NyBnLl6PWaDzNbARRn9ABqiQzDYO/evTQ3N6PrlXMnNlo8HicSiRCLxQiHw+VujhDzytjpyIXp6EMnzoPdwc8XQ9vZ0pemO5HFtBw8LpVc3sKlq9SFvCyt8c/r6dfDL0q9yRyt/WlcmsqGxWEaq3wjLojDt3Oyv1tU5R9zPEy2f8YLQBQFfnffQzzekiWwcPm4vWCJbJ5oOs9nzj9qTFCzozPBDb/dzoutA2TzNrqq4NZVfG4dXVVIGyaqqrAw7CWdM1k22JM23rE8Xtvny8V8tgzf/pxZ6I1cVR+c8ZujUs41s7m/pxIrTDniSafTXHfddfz0pz8FYMeOHaxcuZLrrruOxYsXc/3110+v1UKIilJqraaPnxWc97NqVtWHuOb0ZXzlt6+TyJr43Sq2Aw0RH811Aar97oPOrqvkIYbRF6VFVT5qg262tcd5sSVKb9KgNugZt7dwqNejdSDNLU/sRVHgmMURFEUpDmc2hDx0xnOT7p9d3Qnu2dbJq20x0oaJ362zYWGAUPfL7N/1Jn5Vo8sVIbioruRhoV3dCb778C52dCdQUNBU0DWVvOVg5UyCHh2PSyOXLwSFmqqwuMpXDM4ONnNS6hjNTa+XbTvcs62TAwNpFlf5cBxQlcqc2QrTCJy+8IUv8PLLL/PII4/wF3/xF8XHzzvvPL70pS9J4CTEYeJIm47sc+nUBt00Rmpx6YWaPSHvW7kdk21vJfdKTBQAN9UEWFzl45W2GCtrg1y7eTlLqsefVamqCqqiEM+YNNcFiWby7O5O0Z82MO3CIr0Bt8YLLfaE++fbD+xkR2cCy3EAB4+dI7zvUYJ2ElVVOenMt/OngfCIYaF0zmRPb4qgR2fjksiI1xzarr5UDreuks3bqHah7bqmYFoOGcMi6NWwbYecaVEVcGOYNo7jFPfDZFXkK73I61yZzYV6bdvh1y+18asX2rAsm7aBDLqmUuN301wfoCbgqbhzzZQDp7vvvpvbbruNU089dcTJdP369ezevXtGGyeEKJ/5UKtpJqUMk5xls7jajzbOhXCi7a30XonJAmBVVQuBUDqPoiiTBgBDx0M2rxZ6jXImbpeGR9ewHYeBVI6eZI7XO+IjLm627fDzp1t4uTWKW1MI+VyErThLoy/jcgwMdNyrNnPB205nVW+yGIDu6i4MDYKC48BdL7TxSmusGIwObdeiiI/2aBZNsdBUFct20DUFTVUwbZuMAdm8Rd6GrGGxrS1GW7QwnX+oQvp4n+2RduNQDru6E/z86Rbu3dZJbyqHz6UR8OiENIWuRJa+VI7VDSFq/G6y+copfTDlwKmnp4f6+voxj6dSqTEHlxBi/jrSpiNPZ3sruVdiaOhwW3uM/rQxOGNtrFID4IBbx6OpvNmZIJbOYzsO/ak8luOgKQoeVyFoeX7/AOetayguSvzsvj4eebMb27apCfuoznawKP4aCg5ZPcgL2jr0bpUDA+nisNCfd/fy82daUBRYWRsg4HGNCUZN2yFrWqxYEKAh5CGaNtBUBdtxMC0HVQXLLsyOBHBrCg1hN163Tk8iSzJnclxTFTUB97if7ZF24zDXdnUn+Mnj+3huXz/gEHDrqKpC2rDIGBYuTSVlmPQmc1T5XHhcGr2JHCwsd8unUQBz06ZN/P73vy/+PHSi+PGPf8xpp502cy0TQpTVTBbhG72MRiUu0zCd7a3UpWd2dSf4/iO7+db9O7jt2RZ2dyd5ak8f/ancmOeWGgAvrvJRF/LQ0p8mls3TlzLI5E1yeYu0YdKXMjBth13dCdqimWIb/uPBXbRHs6QGk881I4mCQ9xTz96ak/EEQ0Qzefb0porv9XJrDMeBY5dUEfa5x13qw+/S8OoambzFqoYgC4KF3iNdLeQ65U0bw3JwgIjfxZJqH5m8g+M4VPtdZAyT3T1JbNse97OdD0Ve56uhG462aBpFgdqQB79bK/QWKpDMmcQyebx6YYmbnGlj2g5/eLWjIpY/mnLg9NWvfpV/+qd/4hOf+ASmafIf//EfXHDBBdxyyy3827/927Qb8n//7/9FURQ+/elPFx/LZrN86lOfYsGCBQSDQS677DK6urqm/R5CiNLNRJVqGHkR/88Hd/Kt+3fw/Ud2V8QJcLjpbG8lVlceve7e+sYI9SEPB6IZXmwZGBE8TSUAVlWFE5ZXk8lbJHMmlmWjqgq6poICOA6GabO7J8X2jlixDUGPXkjaVlVShsXz+cXsDaznQNWxOKpO4Y/fUmow6kAx0K32uzl15QJW1gbwuDQUFFAUXJpCU7WPpmo/NhDL5Nndk+TAQAZVVeiKZ3mlLTbuZ1sJ1bsPV0OfcY3fjeU4uHWNar8bBYjnTBzbwXFsHCBnOfjcOicvr2Egna+I9fGmHDidccYZvPTSS5imycaNG7nvvvuor6/nySef5MQTT5xWI5599ll++MMfcswxx4x4/DOf+Qy//e1vueOOO3j00Udpb2/nPe95z7TeQwgxdYeyEDCMvYhX6uK5Q0rd3qEetM5YFst2SOUqo1divIVZdU3l6EUR6oIeepIGr3XEyVtTD4ABqn0ucEADNE3FtsF2HNxa4cKnKtCfzPHIG93FNizyGpyu78WyTLy6St522GXW4FAIQGLpPBGfmxW1AaD0YDSTt0YEui5N5fRVtZx9VB1rG0Mc11TFmoVBAh6NVM4k4nOxtMZPyKOTyJl0RDMksvliYvzoY3myQHpHVxK3rrKqoTDbrNwX8vlm6DMOeV3oqkoimy/WTctbDpYDeavw/fG5NDYsCrMg6KmY5Y+m9W1ubm7m5ptvnpEGJJNJ3v/+93PzzTePKKAZi8X4r//6L37+859zzjnnAHDLLbewbt06nnrqKU499dQZeX8hxOSmOx35YPk/O7qS3P5cK5cev5iQx1Ux0/cPtr3DZ9Bl8iat/Rn29qQ4ecXIxXDLsfTMxOvQuTl+aTWvtcfojhcSuKv97ikXK00ZFoqi4HFp+N0aDgyWARicoZa3sBzY35dmXWMYc6AdZeefWeKYpNB53VyOpipk8iaJnIlh2tiOw2kra2iqLiRYTyXXrGmwttZbdYYKRVjPWdvAhsVh/u8f3yCds6gPF/aHRwe/WyNn2nQnsgQ9OtectozldcFxt3e86t050yaXtzFtlbtfbOMevbNiZlDOF0OfsaYqeHWVPb0pNFXBpavopoWqKJg2oBR6/oaS7yslr2zKgVNLS8ukv1+6dOmUXu9Tn/oUF110Eeedd96IwOn5558nn89z3nnnFR9bu3YtS5cu5cknn5TASYg5NJ3pyJMNuQykDXoSWba3x9jRlaDG76moi89E2zumHpLbh8+l8ey+AR7d0ctJy6vHFJM8lOrKU60NNVlCc03AzSkra3i9I84VJzWxYVFkysGqqij43Cqm5WBYDm5dRVMUrMFhOl1T0XEwLRu9dyeZlpcBcAK1DLACJeMUexUK9bI0jm2q4upTlhbbMdWlPiYKdFsH0oCCw9jhPo+u4tY0XJqGcpDtX1UfYvnbArzQOsC2tjhP7u7F51KI+F2Ds/hsXm2LztoMykquDzZdQ5/xq21RhvrqHMdBGVxQ2XZAUwrDu8NPHZWSVzbld1++fPmks+csq/RlCbZu3coLL7zAs88+O+Z3nZ2duN1uqqqqRjze0NBAZ2fnhK+Zy+XI5d4aw4/H4yW3Rwgxcya6iPencrzUGiWdM9FUhYVhH363VjHT9ycyWT0kv1vnmX39vNmZIJu38brGLj0z1QvgZLWhJqrWfrDemmzeptrvYcOiyLSm0K+oDVAb9BJNGyhQKE8wOEzl1VU0VSHoUlmZeo18dzsAroZVeFecwKaMya7uJO0DGdKmxcoFAU5ZWcOWDQtHfN7TWepjKNAd2sc7uhN0xrIsCLhQFOhPGQS9Oi5NJW/ZJLMmQa/OgoCbTH7ya9bQ57Cru7B48EDawOtS0bVC0KhrKtV+F6mcNeMzKCu5PtihGPqMd3Qn6EnkqAu5yeZt0jkTywHHgQUBN9X+wuOJrEnIq1fM4uFTDpxefPHFET/n83lefPFFvvnNb04pOby1tZW/+7u/4/7778frHX+a7HR87Wtf48tf/vKMvZ4QYnomWjx3d3dqsDChi5xp43NpFTF9/2Am60FbEPSwuXkB7dEsV5zURHNdcMLhvVIugJPVhnq9M059yEM0nR/xWuevr8era4S9roOuQzfdC09TtZ9TV9Rw/+uFSTqqooBjYzuFYbyAmucUdQ96NoqDgnfFCXgajwKgJqCxaZmLV3SVFbUBrt28gqYJCm5OZ4Hb0fvYsh36UnkWVXlI5WwG0gbJnImuqtSHvSwMewBl0t6L4Z9DyKNjWBb5wfULvS6NxogXXVPpSeTQVIUXWgZmrK7TTNQHq+TeqlX1IS7a2Mjr7XEUBXwuBa9Lo15VyJk2mqrgdimkchYDaYPOeOUsHj7lwOnYY48d89imTZtYtGgRX//610tO3n7++efp7u7mhBNOKD5mWRaPPfYY3/nOd7j33nsxDINoNDqi16mrq4uFCycu5PCFL3yBz372s8Wf4/E4TU1NJbVJCDFzxhtySWRN+tNGMWG3Puwl5C2chiq9qODB6vr4PTq6prAw4h3R9qleACfLDTNMm0d39OBzaWxeVcsiT+G1ntrTx32vdVIX8mCYNq39aTpiWTYsGrsO3aFceFRV4X2nLmV3b5KXWqLkbQfX4PR/gIAOqpFCc3noqTueqFnFyoyB36MX27Ck2s8HTl3GsgWBSd9rKrl14+3jVM5kb0+KNzuTvG11LW49hGHZuDWVoEdjV09q0iBy9OfQm8oRz5ooKIS9GlnTJprOs6jKS03ATV/SoLU/TSKXn9a+ney9p1MfbDZ7q2YqIFvXGGb9ojAuTR1RrX8gbbC7O0VXIks2X6jrdGxTVcUsHj5jA4Vr1qwZd8htIueeey6vvvrqiMeuvfZa1q5dyz/+4z/S1NSEy+XiwQcf5LLLLgPgzTffpKWlZdJ6UR6PB4/HM72NEEKUpJQT53hDLpm8RSZvkjdV/B6d5rrgiN6bSkj+nGjb5qpA5kQ9W47jsLsnhaooxbwPTVXIW04hZyyZQ9cUTl2xoLAOXdvB16GbjpW1QZrrgrQPZLAcB9N20FWVhrCH2qCb7QdsLN1HUAnTlzDoSeSoDXqm1YZScutG72OguIbe0YtCPL8/yrP7B9jcvIAqv4uMYbGrJ3XQIHL052CYNuZgXpc6uJBwJm9hmDYel4bHpZLImiSzh37sHmrV8tmsZj+TAdniKh+r6kNsa4+xOvLW96Mm4KFqmYtX2mIH7Z0shykHTqNzhhzHoaOjgy996UusXr265NcJhUJs2LBhxGOBQIAFCxYUH/+rv/orPvvZz1JTU0M4HOa6667jtNNOk8RwUdFd0Ie7qZw4Rw+5DKQNbBuqwi6ObowUl7wYUu7kz4PlFU0laRmmdwGcqGcrkTUZSBtU+QtVtA2rsObaru4k2bzFwrCHVM4ibVgj1qGb6QtPWzRDNJ3njNV14DhYba+ghRvIB4K8fCBKnBC6o7KxNsiqetjTmyLg0XnPCYs5vbl2xr+nQ8U2Qx6dPb0p2qMZ0jkT0ykEdCGPTs6yaY9m0TXloEN+Q0Z/Dm5NRVcVLMvG0VQ0BfKOg+UUimrm8hZ+t0bQc+jHbilVyztjhWNr9DlwNqvZz3RAdrB8tlJ7J+falD/hqqqqMScAx3Foampi69atM9YwgG9961uoqspll11GLpdjy5YtfO9735vR9xDzz+GaMDkfTOfEOXzIJZHNc/eL7bT0p6j2j+y1Kcf0/eFK2bapJi1PZ9mOiXq2DMvGtGzcuoKmFoY1hoKpoNeFS1NIGwaGZQMj16FTD7IO3VQMbVOjppHb9SREO6B/P/tqzyBjWNSFPEQzhaVYqn0ejl3iYmd3klcOxDi9uXZG2jDc6x1xtrfHMSyL/lRhKZiQR2dB0IOuKSQyeQzL4ew1dRy/rLrkG63Rn4NH14j4XcQzeTJ5C00t9PyZtkN/ykDXVJpq/OP2Rk7VwXo3O6IZ9vVl+MUzLYNT+t86B3p0bVbW2JutgGw6+WzlNuXA6eGHHx7xs6qq1NXVsWrVKnT90CLtRx55ZMTPXq+X7373u3z3u989pNcVh49KX1D1cHYoJ87hQy5uXeWWJ/aVFHzMVc/iZNsWcGu80hbjf57az7WbV3DN6cu4f3t3SSf56QzvTTQdf6jHI57O01jlw3EcupM5soZF0KORt+xiQDVkNoY+A24dv50h/eqjkEuAqkHT8fQOOAS9enHobqgds5m7tqs7we9f7SCZy2NahTXzfC6V7GCdpoVhL0Gvi55Ejn19aa48aWnJx8/ozyHk1WkM+7BsB8uyiWdN3LqKbTvUhTzomsoJS6tnJOifrCRDXzLHs/sG8Lk0FkV8BDz6iHPgWWvqZmWNvdlc9Hi6teLKZcqRzllnnTUb7RDioCp5QdUjwUydOEu9w5zLnsXR2+Y4hTpDPckc7dEMsbTBzu4kHdEsxyyp4vyjG3ine9FBT/JTrUkEEw9fgIMNZM3CrK6n9vaTMUwGBmfXuTWVJTX+YrI9zM7QZ7avjfD+x3CsPIrbj3/tmQwQwOzrQ1c1oun8iKR/mJ0Abuh8kMtbNIQLx57PrRXKBKiQyVv0pYzC7LcqL93x7KTH5nhB+ujPYXmtn75Ujmjepi7kZc3CEEGPTiJrsiA4czO+JjoG0jmTZ/b1A3DyimrCvkIwPvwc+Ny+fjyaOuOLc8/2osfTqRVXLiXtud/85jclv+A73/nOaTdGiMnM5h2POLiZPHGWUp17LnsWh29bfyrH7u4UHfEMfUkD23EIujVcuoZvVL2ptQvDk77uZBfAPb0pgh6djUsi4+6fcYPLRWGe3R+lM55lQcDNgqCbtFGYru1zadQG3cXvxkwPfTqOw5NPPskDDzyA4zjkvdX015/IQj2EZjk4DvQkCoFKTcBdrL2jKMqsBHBD54NFVT50TWV3TwrDclAUB00pJM7HM3mCHhdHNYSIZ/ITHpuTBekjK5NbNNX4qQ/ZeFwqqgKW7XDMkpkfVhrvGDCtwizGDctHVqmHt86BPfEcdSEvrQPpkoP1Ukyn9/RwVdIWXnrppSW9mKIoUyqAKcRUzPYdj5jcTJ84J7rDLEfP4tC2tUfT7OxOkjYssoZVHPrJ5G2ypo0CrK4PTqkNQxfAe7Z18mpbjN5kjng6j0tTcUJw1wttvNIaG9OTNjq49Lk0fvtyO91JA9O0GcjkSQwWciwsUVHoMVlS7Sebn5nyA8Pt6k7w2Ct7cByHTLiJ3uqjyZoQ60qgAHnLJpWzUBR4ozPOLlWlxu9mZZ2fvlR+xnPXhp8P6oKwIFgoZpk3HfKOg6IUhoVXNwTxuTRyeXvcY7OUIP0TZzePCPIbw146Bs81szmsNPoY6Ixlue3ZVhZVjX9jWDgH2mxaXk3KMEvOxSvFdHpPD1clneFs257tdghxUHLHU15zdeKczZ7FiXKmFlf5WFkX4DcvtWPZNkGvi2g6j9tVmD2lqgoK0BnP0lTjL7bhwEAaRVFKu4A6kMyatA1ksB2HJVU+llT78Lomrpo+PLhs7U+zpyfF6vpgcXhoqC5R3rJ5vSN+SOvQTWZXd4Jb/7yf/sBRLFxey4KG5RixDPvb4mTyFmGvTi5vkTctDFMl7PMADm3RNK0D6WINnpkMLkaeD3QWhn10xzMEgzo2haRt23aoDbgnPDZLDdI/flZwzLE2V73aw4+BgFvH5zr4OXBdY5iVdYEZTbieTkX3w5VcYcS8IXc85TVXJ87Z6lk8WM7UsU1V3Pn8AWwHsnkLy7bRVY2MaePSVGoCLgbShV4ev0djV3eSnzy+j3h2bAVvn0svBlOZvMlP/7yfvmSObN4i4NYJuFW64ll6UgbrFoY4qj7I7t70pL1Yw/eLoijF/JYhh7oO3Xja29t59tnnaA2uLQQXDREUpYr+VI6d3Uls20ZTIJEx8egabk3FsB0S2TyaquBzadhAQ8jDytrxF9KdruHng9X1QVbVB0nmzMJ+9+gYeZOqgJvOeG7C/KPZCNJLmdAw3UkPUzkHqqoy4wnX83EG3GyYVuCUSqV49NFHaWlpwTCMEb/727/92xlpmBCjzeUdj9SJGt9cnDhno2fxYMMx15y+DMt2qAm4sWyHeCaPaTsoVmF4pybgxuNSiaYL0/1j0UKVaEWB5rrgYKXqPA+/2cWvX26jLuQh7HXh1VV6k4VzZHNdgH19aWwc9vVlisuCdMWyvN4RZ83C0KQX6dleh260V199ld/85jeYpkmmLkdj09HFxPnd3SnSg8vmJLJ5+pK5wan4On3JHBG/m7ULQ3j0QlL7QDo/K7mHxzZF2N4R4+UDUVbWBtiwOMyOzgQdsULNpmq/e9L8o5kO0kuZ0HAokx6meg6cjYTr+TYDbjZMa626Cy+8kHQ6TSqVoqamht7eXvx+P/X19RI4iXHNVCAyFxduqRM1udk+cc50z+LBhmNebInyld+9js+l0RnL4nWpRPwudF0ll7eoD3lQVZWcaaGpKroK29riuHS1uB5cf8rglQNR9velyOQtehM5VjcEqfa72d2TJOTVifhdxDIGA6lCjSNdVdAGCxb2Jg2S+6M01wUmvEjPVY+r4zg89NBDPP744wA0Ll3By3oTtYOBaiJr0hHPkDUsouk8hmkVZrAlc7g0hZDPRXowuA37XJi2TXciN6O5h8O/o8msSW8yR0/CoDboZmmNn03LazhxWTXrGsOTHpszGaSXkisFHPKkh5k+B07n3DyfZsDNhikHTp/5zGe45JJL+MEPfkAkEuGpp57C5XLxgQ98gL/7u7+bjTaKeW6mA5HZvHAf6myuI6WnajZPnDPdszjZcMxAOk93Iksia7J51QKaavx0xjLEsyaqouDWNQbSeQIenWQ2T1XAzb6+NHnL4filbwVNL7YM0B7N4KAQ9uqYNnTGsvSlDDQVTMuhbSBNNG1g2uDRFVRFwaGwEnzArZEyLFr603h1ddztmGy/tEezeFwqqxoK34vpHne5XI5f/epX7NixA4DNmzez6thTeP6eN2kbSFPld9OdyNKXNNAUcLs0oFA3KWNYdMaz1Ie8WLZdLMSZzpmYlkNnLDsj34nR39FFVYV16fb0JgsVyk9cwuYSK5TPVDBaSq7Uvds6cWBGJj3M1DlQbhKnZ8qB00svvcQPf/hDVFVF0zRyuRwrV67kpptu4pprril5kV9xZJitaeWzceE+1NlcchKaOTN5Vz3RcMzQciWm5eB3q7h1jdUNQVKGSTpnYlhOoedJgc54rjj0s6Tah+PAoip/8TUS2XxhhXddRVUULNsiNDiMlcnbhDwu+pIGpuWgKqAo6mAbQBmcOq+rCtm8TWc8y/IJ8oHG2y850yaXtzFtlbtfbOMevXNax11/fz+/+MUv6O3tRdd13vnOd+JrWM7vXumktT9DfypHxKuTyFmYlo3P60JTwHAGt1strOfWl8oR8bpwayp9yRzP7OvHpSrc9mwrPtehfScm+o6GfS6OXVLFzu4krx6IsbnECuXDg9EdXQlCXh1NVbBsZ7A2k6ekIL2UXKlXDsRAKQRrM5FPdajnwEooJjxfbzSnHDi5XC5UtfClr6+vp6WlhXXr1hGJRGhtbZ3xBor5a74VrDyURNFKOAkdbmbqrnqi4Zih5Uo8LhXbKVTmDvtcHNdUVVyZvS+ZY31jmJNWLCgO/diOw388sJO0YeI4MJA28Lr0wV4qsBwHRSkEQtV+NykjQzJXWPZDVRVUCj1Qmqpg2ja6pmJYDh5dBRQG0vmS98vrHXF+/2oHumqxqMqH361P+7jL5XJEo1FCoRBXXnklGVe4eEyvXRjkzS6IpfNEM4XaVinDLKxqr6nUBQvLrGRNi1gmT0PYSzRt8Nz+AQA2LK9mUZX/kL8Ts5HMvao+xDlr67n1iX1sb4+TtwqTAZbXBnjv2vqS2lhKrlQ6Xxiq9E8w7DeX5VQq4dw8n280pxw4HX/88Tz77LOsXr2as846i3/913+lt7eXn/3sZ2MW7RVHtvlWsHK6iaKVcBI6XM1Ez+JEwzFDa785jkNDxFesdF0T8FC93E00nWdvX4r3nbqMt62uG7EMzNDrVftdmJaN360N9jQVXjfg0XHrKg7gd2soCuSNwvpmbk0lbzsYg3WhXKpCwKPj0RUM02HBqIWPJ9ovi6t8/OaldgzT5qiG0CEfd42NjVx11VXU19cTCAT5/iO7RxzTAY+LVw5EiWYMHAXyloNbg5qAi5DXhapCV9zGBFQF3uxK4nNpnLzirWKNh/qdmI0Zl7u6Ezz0RjcBj85pKxcUF8qNZ00eeqObpmo/Prc2afBeSq6U36WDQkWUUyn3uXm+32iW/AlZloWmaXz1q18lkUgA8G//9m/85V/+JZ/4xCdYvXo1P/nJT2atoWL+mW8FKw928psoV6PcJ6FSzWW3eCV1wU+UG2SYFmnDIuzTaa4LjPjsFEVB1xTqgh6a60Ze3Ie/3oGBNM7g4y5dIZEx8btVavwuFEXBMC18Lo2Iz0VTtcobHXESOZOASyMUcBfXwnNpCi0DGdY0hDihqbqk7TrU486yLO655x6OO+44Fi9eDEBzczNQqBk1+rVrAm6Ob6oikTUBB8OyqQ14yJo2A2kDXVVZUevH59K59PjFPPB6F4sivjFlEw7lOzHTMy6H3/Qc1RAcsR8XOg4vtkb5yu9eozboJmfZE/aKNIa91AbdvNYRZ1VdkLDPNW4F91g2z+sHec5clFOZ7NzsOA6m5dCTzLG7Jznj393D4Uaz5MBp8eLFfOhDH+LDH/4wmzZtAgpDdffcc8+sNU7Mb/OtYOXBFtacKFfDtJ2KDxDnslt8rrvgSwnSxssNcmsqzfWFXKJq/8henoNdyIrVwF/tpCfRSXcih9el4XjBM5jvY9k2AykDj66xuj7ENacv58XWAb778C7ShkWN343PM5j0HjcIe11cc/py9AmSw0c7lBuTVCrFbbfdRmtrKzt27OC6664bsUj7RK8d9rlYGPbSFc/g1TWOXhTGo2sYlo1LVeiIZVheG8Tv1siZFn63Nm7bvC6VgXSObe0xgFmpY1SKyScOGHTHCxMHFkYWsLg6OG6vyNDxvqc3RUt/mj3dSYI+F/VBDyGfTjpn4Siwvz9Ff8ooPKcnRWOksNad16XNeQHJic7NQ8sNdSWyZPIWv3i6he1t8UP+7g7/jsYzeXZ1Jyr+RnMyJV+xPvWpT/HTn/6Ur3/965x++un81V/9FVdccQV+f2VumCi/+VawcqKeiY5ohmf3TZyr8Y4NCys6QJzLbvG57oKfSpA2Xs5UxrD46ZP7pjV7b1V9iE++PcixS6v4xTMtpHImNX437bEMvUmDVK6wTMoZq2t53ylLWVUf4qiFIWqDHm59Yh/7+lL0pw1cmsqahkJgde66hpK3fbo3Jp2dnfziF78gHo/j8Xi45JJLikGTbTu0DqR59UCMaDpPVzxDY+StZGZFUWiuD9CXypHImuQtm+qAG8WAnV1J4tk8lgNvdiXY3Z0ikTE5elGEmmHDj/0pg9faY3Qnctz2bCv3+7tmrY7RwUw2cWB3d6o4DOvWNTRVGdMrYjsOP/3zfvpTBktr/BimzYstA3QmcuzsSqKq4NJUfC6NXS6NxoiXjYsjdMaydMSy9CRzHNUQ4oSl1YdcTmUqvbzjnZv7Uzleao0WetZtWFrtZ1GV95C/u6O/o7m8TWt/mk3Lq8c9bivhRvNgFMdxnIM/7S2PPPIIt9xyC3feeSeapnHFFVfwkY98hFNOOWW22nhI4vE4kUiEWCxGODz5gpxi5o2+kI4+yVXiWPaIL3reZF9fBtOyR+RqQOHkurM7yYZFYRxge3t8RNfz8OdsXBzh42c1z3nXs207fP+R3cXqyrPZNtO0ueneNyYcrpjp/TA2SCskRk/12Br+eefMQrCxqj44pQvZ6GPGsmFRlY9z19Vz+jhT403T5oXWAfpSBgsCbk5oqh63p2myi+F0Ptvt27dz9913Y5omCxYs4KqrrqK2tra4DT9/qoWn9vYzkM6Rypo4wMq6AMcsqaIm4Cm+9outUXCgNujGsGxypk1PIkfY52J1fWFtuKf29HEgmqEu6OH4pdXUBNzF0g09yRxLqn2cumIBmXzp54Oh/fF6R5zn9g3Qk8hiWPa4n1kpgURrf5pv3b+DKr9rxEU8nsnz5J4+VAVsB05buWDEkGMim2cgZRQX011dH2RfX5rHd/aSzZtoamFhYwCHoZw0L7qq4nNrHLskgq6q7OpJsn5RhL+/YE3JPY3jmU4v7/Dvz8Kwh9c7EnTEMuiait+tc1xTFTUB9yF9d8f7jnbFMzyxq4+wT+ek5TXF42r4vo2m83zm/KPmtMdpKrHClG+Bzz77bM4++2y++93vsnXrVm699VZOO+001q1bx1/91V/x2c9+dtoNF4ef+Viif3jPxO6eJL94pmXSXI3dPSnefcJiOmLZWalofij5QtPJg5nO++3qTnD7swf447ZONBV6kwY1fjfN9QFqAp4Z74IfL0/CcRwcB6r9Lg4MpLl3Wycrz56bmjhTfQ1dVzl5xYJJt+/Pu3t54LVuOmIZVBV8Ln3ExXAqvS+O4/DII4/w2GOPAYVcpssvvxyvt3AjsKs7wbcf2MnLrVFURaE+5CXjNWmPZtnRlSSWMTm9eUFxWGlpjZ9rTluOx6WypzfJ71/uwDBtjl0SKc66PnpRmJxp0ZPM8VpHjJOXV/Nae4yeZI66oJujG8PomkpIU6dV7sOjqdSFPGxaUcO6hSMLXZYaSEw2cSBvWeAoNES8xYkDQ3xujb29eeJZk2ULCmUpXmwZIGdaRHw6yZyFpimFchOAZTv0p/KsaQgykM6zpzfNpmXVHNUQoieRo2NwDcTpmG4v7/Bz8yttUVoH0nhdGg1hL811wWIv4aEsPTNeLlNjxMeyBX729KbY1Z3kpOXusuV6Tde0xw6CwSAf+chH+MhHPsLvf/97/vIv/5K///u/l8BJjDEfS/QPzeZKGYW7x4Bn8inEdSHPrASIpV4AJgp2ppoHcyh3rvt6kziOg8/tQgG6ElkSufzgnatnRrvgRweE/SmDXd1JBtJGYaYc0JPo5JimKs5cXXfQ1zvY7L1SgsmZqi22qzvBz59u4eE3uknnLYIenbqgB1/V2MWAp3Jj0tXVBcBpp53GeeedVwxwbNvhnlc72dGVwK2rLAgULmRel4ZH0zgQzdCXzPHc/gHWN4aLrw3w25fbeWZvP693xPG7NfKWzar6EDUBNzWBQk/Ta+2FxYdfbI3SnSj0NB3dGB7R0zDexXn4Pu9N5PjDqx0MpPMjgoPWgQwpo4eVtYHi57GjK853HtpFWzRDjd/NwrAXXVPGDSQmnjhgkzFsQj4XzXXBMTcdGcMaLD1h43frdMQyxDJ5fG4N21EwbQddLSy+7AC6ppDNWyRzFkGvTn/KKK55eCjfiUNNtB46Nz+2s4cfP76HlQuCVPldY7Z3Ot/doe/owrBnxILUIa/OqvoQ/SmD/X1pllT7qQ975tViwdMOnNLpNLfffju33HILjz/+OM3Nzfz93//9TLZNHEbma4n+qeSRNNX4ZzRALPVOcrJgZyrtn86d69CJe39fioF0noF0nlgmj1sv5HXkTZvdPSmq/e7B3ImZqSA9PCDsTxm81BolY5gEvS5cXh3DtOmKZ/nFMy00RryHtAxFbyLHSy1R9vSmivt3ZW2A45ZWURvyzHjl+p88vo/n9vXjOA5N1T5MG3qTOVKGxbFLIvSljBEXw8luTIZvx8ln/wUbNx7D+vVHj3jPAwNpntnXT8awCI26QfB7dJbW+BhI56kNurn6lKVsWlbDnt4k335gJzs6E2QMk1QuT860GMjk6YhlOWNVLQuCHmoCHk5ZuYDXO+JsXlXLn3b2sLTaj+0UhsNC3rd6eYZfnIcf05m8xf6+FKbtcPLymuJxPF5wsKMzwefveJk9fUkAVBQ8LpX6kJeVdYExPZG27eDRNc5aU8dze/vpSeToihcu8M11QxMHRn5vhnpFmuuD9MRzpA2TdL6w7qBLVbAGez4dxaGwaUohyLId8rZNUNNJ5QqBhGJwSPmPMzGjV1UVmuuC1AcLAebo14Hp5WmmjMJSOO3RDNFMHtMq1Cyr9rtZVR/khGXVvLB/gP5UYR9W+kjEcFP+tP785z/zk5/8hDvuuAPTNLn88sv5yle+wtve9rbZaJ8QZTXVBPeZChBLvZMcnpw67uK1py0vqf2NYS8/fGzPlO9c26IZHt/Vw87uJFmjUK7BZvAiYTm4tMJMq5Z+N6+1J9A1lduebRkz7DRVQwFhKpdnV3eSjGFSE3APS2KGiM9FKmdOaWqzbTs8sbuX+1/rYl9vimTOpDuew+fW2Lg4zMraIO3RNL95pZ07XzhAU42f2sGSBTMx8+jebV20RQuLB1cF3GiqiqaCezA/aE9vijUNwTEXw/GOuz+9sJ1Hnn6Jfd7VWDhU+9ysqg/hqUuMWHD2f55s4Y2OOCnDJJUz8Wd1agIufIMXSbeu4dJMNFUtDlf//OkWXm6N4taUwXpVharntu2QyVk88EYX561tYEHQQzZvUe13UxNw05MwODCQQYERF9GagLt4ce5J5LhnW2fxmA5aOju6EpiWw8sHYsXcm8Ln/FZwcPvzrfzo0T3s70sBoKsKjuqQyNrEMgla+tPFNhzTVEVjxDvO0J+XTcsLhU4zeZOf/nn/hMOgl5/QxP2vdbGtPYbfVUgez9sOmqKgKGBZDi5dRQFypg04uAZ7oTRVHZyFeGjDUjNV8mU2JvL0JHK09qexHagOuHF5dfKWQ08iSzJnsro+wNGNYa4+ZSlhn2tejEQMKTlwuummm7jlllvYsWMHmzZt4utf/zpXX301oVBlR4ZCHIqZnsVTqlLuJHd2JYim85MGOw+83sX56+sP2v6OeHZad67bO2K81h7HtB0CHh3HgWTOJG872I6F46j0JnI8uacft6Zy0uLwjFSQHjrRP7Ovj4FUjuBgL0Qub2HaDolsnsVVflbWBkrO4RpKjr73tU5imUL1bnNwvTW/W+fVthixTJ79/RnyZiHx17BsIj7XjMwaHPrMa/xu2qIZXNpbycKKohSHeEzbIWdak14M/+e3D7HrhcdRcEi5VQa8i0hkTHqTxpgFZw8MpPG6tWJl85RhYlgWDWEvmqKQyVvkTRvFKay/F0sbPLW7D00p9Eh1xrI4FHJ5vC4Vw7TpTxo8s7efk1fU0JcyWBTx8uzefkzLxrJsakMeTJviRXSoJ23DojAvt0ZHHNO9yRyKAnWhQlHS3T1Jqv3VI3qqdnYl+Mnje+mIZWCwHbYDhlkYKtOUwmdpmDb9qRw3P7aHkFcnmTOp9rupDXjQVGgdSJMyTFbWBTiqIXzQYVBVpTCLMpEl6NGIDdbxcpxCUrjfpWE7Dpm8PVhlXSGRKax52BnPsSB4aOePmSr5MtPnOdt2eLk1iktTsWwb92BPlkdXcAfc9KUMtrXHeecxjSwMe0nnrWltf7mUHDh9/etf5wMf+AB33HGHVAifQCUV/RMzpxwJ7qXcSQ5PTp0s2Lnk2EXjtn/DogjHNEUwbYcDg0Mii6Zw52rbDo++0V2olO3Wihf6kFKY5p81bWzTxnIc/B6dE5dWs6S60NZDLXY3dKLf3hFjIJOnCoeeXKGgpWEWLlLVfrOwjtuwIGOiYc21jSEefL2LJ3b3Ec/k0RVQVZWcaaMpCrm8RUtfmpaBDBoU8n90la54lnULw6yuDx5y4b6hz7w24Cnmx3j0t+oguTSVZM4kkTUnvBhalsXWX/2G3a+9ggL0uxuwq5bgs1Vimfxgzwfcu60Lx3HoTxkcszhCLm+TzCawbAevrpIyLPb3pXENBlK2A5m8zX89vgfLdmgdyLAo4mEglce0HcIenaRRCFrVwbXeYpk8z+zr58SlVThANJPn5BXVvDxY6iDo1anyu+hJGDyzr59Ny6o5tqmKX73QNiKAd2squqpi2s6I/KCh3q90zqQjliOaNnBpCo5TCFwse3DCuEOhC1KBTN5iQcDFnp4UmbxFbcBF20Cm2PvVXBegN5njjucO8K7jFhHyuvjY21bSMXjsjz6vDz83xLMmr7bFiGdMPC4NZ3BZGst28Ls16kJuepNGcc3DY5Yc+vljJnuKZvI81xbNsKcnxYbFYXZ2J+lPGQS9Oi6tcFyblk3WcNjfn+HbD+w8fJdcaW9vx+UaG9GKgvm87o44uLlOcC/lTnJ4cup4hgc7axeGR7R/KG/nrhfayJoWllWo3+NzqTTVBMZ9v9EX67Zohq54Do9euKi5BxesdWkquldBMywSGbPYG/FGV4LuRG7cmXatA2lURZnSvl1VH+J9Jy/lzc4EHbEslg1uXR2cWl7oTXh+/wBNNf5Jc7hebYty32uduHWFVNZEVRT8Hh3TtgenoztYduH/Hdsh6HehayrZvE3KKMwaC/tchzxrcOgz11SFGr+b7kQWd0AtXgzzlo2uKAykDU5ZsWDMxTCVSnHHHXewf/9+HKDd30wstLJwpz9suC+dN3n5wAAKCourfaiqyuqGEH0pg654oQfIMC1MG9y6guWASy2sSdefMoh43eQtm654DhTwuDR0VSGoFKbgG5aF7YBKIefn5BUL+NPOXhojXkJeF8c1KezuLtSwsmwbXVNwqQoXbmykNuQZc8MQ8urF/VHld2HaNsZgT6DjOOzpTeE4Nm5dwbQVXFphaMxyCgsqoyjYjoPiKOQtG0VRiGVyWA6omoew560hpN5kDpemsL09zptdicLM0MHz+NqF409RHzo3vPO4Rdz2bAt3vXCAgXQhoLSdwhqETTU+jqoPUR/2Ftc8nInzx0z3FM3UeW7oJmBlbZCARy9+3qlcYci3yu+mO5GjI5bhqIbQ4bvkigRNE5vv6+6IkSbqOZzLBPdS7iSHJ6eW0k0/1P5d3Qn+OCyHxO/2kcoV1mV7dt8Afrc2pl7VeHeuKcPExqHK7yaRzZPJW7h1tdBDY9qkDQuHwlIktUEPuqbQPc5Mu13dSW55Yi/xjDnlm45TVyygMewtLCwb8uDSVNx6IdiwbZuW/gxhn8FAOsfvXu6kb7Dg4PBhzYVhh1cOxAi4tcGLtjbYQVH4n+XY2A7oKuQtcGwH3aWArpAybNqjGVbWBiZdy3CyWkxDv/O7Cknn2zvirKzzk8jli3fquqowkMrjcaksrvKNuRh2dXWxdetWotEotqKxzb0GNbQYddQyMkGvTmIwOPS41GLQXRNwc+rKBbxyIMrO7gSmDY4DtqPgd6ssHVyvrT9lkNVMgh6NRM4CHHyD1cFdmoLm0UjmHHwulQ1LImTyNsmcOaI3c2gtwKGZVtrgcNxQov3oG4ahopuJXJ6eRA5dU1GVQr2fjliWgEdnQdBDbzJH2igEUIVeDXCUQjDvUOiB8gzWShr6b11VCvtCV7BcGvv702iqQsSrszDsxe8eO5NxPKqqFJLj03k2Lq5C1RQcu3Ankc6ZBLw67zlxCZvHqel1qKbaU3SwkZGZOM8N/xxHf94uVeGVtigKsKouOGmyf6WO2FTGWhfz2OGw7o54S6X0HJZyJzk8ObXUbvrRxysweEJzOLoxzHP7+nlm7wCbVy3A79EnvXMNuHWqfW4SAbPQK2M5ZE0bw7HJ5i1wnMLsOnehR8Kja7gDhV6LoZl2HdEMrf2FZOjmuuCUbzo64lm8bo0FATc508Y1uLCuYVr0pwwsx6G1P8O//eEN2gcy1Ic81IW8IypZ5+1CO9ODQ1IKhW3UVAVNVciZhR6zoVLBQ7OmDMsh5NVJDw6fKcrIGVJDieYPvt5FRyyLqigjluoBxhxrVX4XmqrQl8qzuj5Ie7RQXbpQhVznzFW1XD1YhXy4TCZDPB4nGK7ijcAGkikd36ihPij0BhpmHpemEHDpIwKUobXo4pk8pm2TNwv7pSbgxuMqvE7Qq5M2LOrCXtJ9KYy8Q9aw8blVLBsyZmG4zutS2N2TImva3L+9i95UbkRvpqIoxaG2RDaP16UVL+Lj3TDUBDwcuyTCM3sH0DWVvmQOr0tn4+IIxyyJcOfzBzBth2gmjzWYb2fa+ULwZzuDAbxKU42fbN4qDCsrCtqw+kEDaaMQZDkOmlqYFVrqeXzEmncLQ2O+izu7k7x6IMbm5toJj+VDUWpP0Vyd38b7HIc+71jaoDOWo7HKO6PrGM4lCZwO0XxZ4HU+m6vcsYP1HF5z+jJ8Ln3OcthKuZMcSk4ttZt++PE6kM6PqH2ka4VZU7m8TXssWwx2JrpzXVzlY1V9iN6kQdhrk8lbhH0usnmTbtPC0VTCHp3GKh/JnFnsCRrKU4mlDV5tiwGwKOLFcUBVpnbTkTIKr3vishr29qYYSBskc4W8kpxp49NVNE2l2ueiK5Yhms7zUmt0xMws92AvVd60cGsqWdMioGqFhX5VBYdCj4VjF/4/bRSGooYurOm8RV8qh2E6HLOkEKgOJZo//GY3mbxFoFiLqbCExeudcRzHGZOc3BHLoqkKjWEv0UyemoCbiL8wDHjuuoYJeyyWL1/OFVdcgRaq5cBjLQTNdKEsxLChPigkSxumzYZFVYS9Ots74gTcGsmchWEVeodUBdyaRk2gMG3eNayitUtTSTkma+pCGHmL1oFMYajXtjEsB8u2cRyI23myeYvF1X5W1ProTGRL6s2c7IahL5XnpOU1/MXGhdQNKwMB8HJrjN5koQp7VzyHbdp4dLXQpsHZnUurfaysC/J6ewxtcB+6tEL+WiZvkcpZeHSVVM4i5NWLRS8Pdh63bYfn9vfzQks/C0ZVwS7l72fKwXqK5nJkZLLPcVdPEl1VWNMQGnPNhPmx5IoETodopqaDivHN1R3SwXoOX2yJ8pXfvU5tYPJV0mc6yDvYneRUu+mHjtdsXuPVttiI2kd5yyGRyZO3bM46qo4TllVPug3DT44AdipHd7zQO1Lo/VGoDrhZtiDA3t4UfckcbpeGAiSyBs/u76cnYRDy6jy7b2DM9PRSLjZDQwJel8pJy6tJZE1ypsUbHQlUDIJeFznTJujR8bp0PLpCKmeOmJkV8uqEPDrJrEljxENbNEsmb6MOJoUDDG29Sy38lDMLU8q744UlP57a009jxMt7Ny1hT2+Snzy+l+f2D2A7sKTah2k7g7WYTI5ZEuHxnX0kcya1QfeY5OS+lMGCoIdrNi8vBF3jfAaGYfDHP/6R008/nbq6QoHPNWvWYNsOq+oH6E0Z5AZ73YaScg3TojOeY2HYy+WbCsN4b3QluHd7F5ZTCA9N06E/bVAb9LCyNsjrnXHyloNHfyvPSlNV6oIe3E1VOI5DPGeRyOZRlMLsNQuKyeRtAxle0gtr8b3UGuWZvQMc2xRBU1Us2yGRNcfMLJtOkvLw49ClqYXj0DCxbQdNVVha4+OkFTXkrcIMt4jPhe3A7t5UMZE8Y1goRqGu04q6wIiL+kTn8aHz0wstA2xvjxPxuTgw4Cnm8R3s7+dKOUZGJvoc1y+K4HVpeF3jLwBd7rU9S1FSy+LxeMkveKStBzdT00HFWHN5hzT5Kun5Qm5O1qRxVS2Lq8efTj9bQd7B7iSHgqsDA2n29BZq2KyoDdBUPfZvAm4dj6byZmd8TO0jj67geDV6Ehb7+lJcddLSkhK0r928nJ8/3ULr6ylQoMrvIpu3qfbrWLbD3t4UtUE3u9IGPcnCun+245A2Cgm+NQF3ocdnWI2X45qqCPv0g15shg8JrK4vrI8Xz1CouO0t9JjUh700Rry0R7N0Jwo5MaNnZvndOnUhD2Gvi0zeJjbYc2XaDiqgqoV9Vx/2krcsOqJZ0jkTVYWgx8WS6sI6XL9+qQ1VKdSuUijUr3mrFpM6uFZblIG0gTVY8ycccI2pb7O7J4mqKOMmJEejUbZu3UpXVxdtbW18/OMfL1YBHx3MpnMmiZyJYRoYpkNj2Mt1567mqIYwu7oThRdUKHSloaBrhbpNmbyF312Y/dWTyOIe7J1LZgv7M+jR6IzbvOeEJvb1p3h2b39xvToU8GhKoZK4XVgPzrIdllR72dWd5vFdvThOIcBZviDAezctKWlB5sluQoZfpHd1J6gPeQZne+qEfTrJbKF3zO/SaK4LkM5bxNL5we1mcAc42E5h+0evFDDeeXz4+akm4CLiKwyzdiWy9KVyrG4IURf0EPLqZb8OlGtkZLzPcahm3HxZ/H08JX2KVVVV43apjcey5lc9hkM1G4XDxNzfIU22Svqu7iSm5eB3q7h09aCrpJdjgsCe3mTJa3PVhbw8vbef+pBnzPGaylk0VnnpiedKPomurA1S43ezrDbA4iofbk3lzc4EPckc1X4XXfEc3YkswcGLXzxjYNpDlZQtcqY1OL1fKc782t2TZE1D8KAXm/GGBDJ5i0zeJG+q+D2FQpuqqhYTjJPZPJYDmXwhL6kjlmXpAj/nrK3njY4EnlaVXd2JweBBI+DWsB3wDv53T9LEpSvYDtSHvBzfVE3Ao7G7J8lTe/rIGBYRn5u0YbKoSi0mJCuKQsCjsb8vM1hqQEXT3kpOHtr2tmiWBQHXuAFjS0sLt912G+l0mkAgwMUXX1wMmoaMDiKimTyqAs31QS4/oYmjFoaK3y/LdthydENxqM492DP12M5ent0XZf2iMPFsvjCDDoegpzCE9WJrlAVBD8csqWJPb4rjl1bx+K6+wvIigzMRc4M5UuDQn8rRnQAFm42LqqkJerBsm0TW5KE3ulm2wD/m+zHVJOXxLtKpnMmdz7fSEY1j2A4+vVAR/Mk9fSRyJk01hXXmsnmLrriBg4PfpbKnJ0WN311cA3H0eXy8XMEDA1naomksyyaWLVTNrgt6qPa70XWV01aOnQk5V8o5MjLe51iO2ngzqaTA6eGHHy7+9759+7j++uv50Ic+xGmnnQbAk08+yU9/+lO+9rWvzU4rK1i5CiQe7ub6DmminsNE1mQgbeAZLKjnHlWUsNRClBMFeTMxtDeVnjlVVdi0vJp7tnWQyOZhsHxA3rJJZk18bo01DSFimXzJJ9G2aIY9vSmah82QWdUQJGmY9KeMwUDGosbvIm/ZBL2uQuFIr4uW/jTdiRxhb2F9rGIOVDLHHk0Zd9r9aKOHBAbSBrYNVWEXRzdGirlMNQEPxzVVFdZOS+Toimep9ntGDAG9fU09bdEMrxyI8rOn9rNsgR+/Sydv2ezpSdGVyBbqPKmFAHrD4ghBr/7Wki+eQrK4W1eJZmw6YlkWV/mKM88cB/KWhaYUjh9t2LE9tO29yRwRr048k+eNznjxuHjxxRf4wx/+gG3bLFy4kKuuuopIJDLhPpmsx2b490tVVcK+4cGXi5OWV/NGZ5J03mJBwI3jQM60SOZMXmmN4tELtZIefL2Llv4U8UyetGHiUhU0rTAbMW/ZmLY9WN/LJpO3WRj2sLjaX+zpG0qcnqmboOEX6Qdf7+I/H9xJTyKHWy/k6zm2w0AmTzJnsiDgJpu3yJoWCgqLqr2YllNcrieazqNryrjn8fHOT3UhD292JsiZhdmljuNgOQ57+lKEvS7WLAyV7TpQaSMj83Hx9+FK2ktnnXVW8b9vuOEGvvnNb3L11VcXH3vnO9/Jxo0b+dGPfsQ111wz862scPP9IKhEc32HNLrnEApBU1ciS8Yw0RRYWOU/6CrpUwnyZmJob7yeOWdw1le13zW4NldXcW0ugHWNYY5aGGIgZZAyrGJtlfqwl+a6AK7BGkWlnkQTuTz96dzgxWKw7s5gkLK9rRCkWLZDNm/TWOWjJuDijY4ELl2lPuyhM5YtBDEBNy5NxXYKs6NW1gdLvukYHigksnnufrGdlv7UmHXGqv1u6kJeTlxezaXHLybkcY0IKIZfeBdFugm49RGzzvb3p3nBsgm4dMChNuDmza63lnzJ5i16FAWXChGvTn/apDOWobHKh0cvFNRUUBic1DXYI/MWXVWIpfP0Jg1+8XRLIZ9OU2iIv0m2YwcA69ev513vetdBS8RM1mNzsO9XY5WPbN7iipOWsjDipTue5ZYn9rKnJzXY66rSGc/SFs3QlzTwulQ8uoap2OQtB10rLGybH5xlqaCA4xDyusZ8h4Ienef39/Pc/mo2LauZkeBiR1ec/3xwJ53xLAvDHtx6YQHiWCaPg0Mub6MHVDx6IWHeth1yeQtdK8wCjWcM9valqAt6xj2Pj95/juPQk8gR9Gj43SqZvEU272BaDitrA+hqoRf27WvqyxI8VeLIyHxc/H3IlMPLJ598kh/84AdjHt+0aRMf+chHZqRR89F8Pggq0VzfIQ3vOXyxJUraKOSGpHMmfSmDgEdnQ9A9JjCaaiHKIeP1EqVyJs/8/+z9d5wl13nfCX9P5Zvv7dw905ORE0EEAgSDxCCSkrVaSTZtkWvRlF7ZWntlrWlbXn02eLW0krW7lryvRNGmScneDxUp6bUicwQJEiQIEGFy7Okcbq5cdd4/Tt3q7pnumR6gBzOQ8Hw+wEz3dN97btWpc57zPL9wbpXn59v8yMP7dqT5cunJd60f5GJzccZwWurMMlV3uGOqmmMM7p9u8Oxsm3uqtqLjZ67lACeXevkierWK2KmlLn/y1Cynl/qcW3FxTH0DwNvm9skKS13F0HvgQIOpWoGuH3NK7xMlaiN1w4R60VKCkoGSBBguWbzn4ctp91e7h4NEwTI0PvqVszxzsUWjaFHJtJAGNhfvfnD6iq+91UYjhBKmLJoKA7SnoTaZpqtA6EJkbTdTp+1HxLEkTlJW+wl+pJhaUaqMXxslOxeU3KiovNTxCeIEXVPA+qJl0PdDOudXsID7Hn6MH3jnW3cMndgudvJ8OZmf4J56gd//xgynlvpYhkbFMTF1QZRImv0AP0pIpWo9WoZGIhPiJBOfhNxypGAZHBpZB10P5upKP6DjRXz4i2d4cl9zVzz//vCbsyx3AyarTq7ijVSK7003JIxTzq72qdoGIlMUjxJJkqpxN4oW77p7gjfeMrrlOn7p9RtUpofKNpYu6AYxXpjwwP4GUxmrdOPh6eV2mbhZOyOvVPP3a951pqen+Y//8T/yb//tv930/Y985CNMT0/v2sBeCbHV5H8lToKbMW7ECenIWIW33D62qbxfsnRSLIJItWpqBStv/bxYIcqtqkSDTWS1H9D2Is6tuHzv3RO8856JK24iG0++a/0gaxkl2WZs0PZCZpoev/Kp4xwZLeeGtLdPVphreyx2g3wR7QXxpkX0aripQfK32gsZq9i03BDH0DYBvC1dtTiHKzZTtULOYhuAjku2TtEyuH9fHYESEZxteTx8YJjXv0TNG8dUhrGnFnsgoFawePTQ0JY6SJfGxo3mxGKXimOga0KZGGeK0IdGSkSpSo5Mx8jlBaqOwVzbV/5uqtCCF6d4vRBLF+wfLjNcttg/VODMspsrKmtAEEuGSjaPHBrKsUvVok1y95u5ePEic84BZKbQ/lLiWp6vmabLE2fX0IRg+BIywcbroopnkpKp40ZK3VsIEFKiaRrDJYuqY9B2Q1b6AScXe8RJim3q1AomQ6Xd9fyzDIVJ9MIkbxkr9Xel+J5K8MOYRJJLYVi6IEFJTjx7sc3DB7eugF16/cLMRsTMDh9RnDJVLzBVV3N+4+HpRmnFvdoZ2b245sTp3/27f8cP//AP85d/+Ze87nWvA+Ab3/gGJ0+e5BOf+MSuD/BmjZtFKPGva9yIE1KaSo7Nd5msFbh/up5XYqIk5emZJsu9gBfm27zu4BB+lL5oIcqtqkQbEx7b1OkHMU+eX2O+419xExmcfPtBzOmlPl6Y5Ew5L0xY7UUIFHHqUkPaARh6q0UUuLKm1aMH+PQLmeDfeJnRisXTMy16QUzJ1un5MS/MtxkpWYxW7E3VOCEER8bKdP2IhU7A3kaBomXgR0p5eW+jyDvufvH3dmM173UHhzPae8SaG+JF6Y5fZ5BI/9bj53h+rkOUKLzOWMVmyjFY7YeUbV0Z4wYxYeYD1/bi3PA2SiQIiaEp8cuphsPD+xv4sWSlF7Kn4TBUtgjjlLYXEiaS+/fVSVrzhN0VrOl7mG/7ygS1NM6Jhc6u4Pqu5fk6u9Kn5YWMlu3LKl2GpmFnxr7DZYu1foSbGdqWHQOkpBeoCtpaP+TPn1sAqWQNEimpZTIYe4eKTNbU87Ebnn9JVgHr+gpnF2XAdwl4CSQZmy5KVcKkZerifiwxdNVKPbnU5fe/OcPPfM/tGJe0VC+9fpfOg4Kl2HuD6zU4PK10g8tU+19OEsmrnZHdiWtOnL73e7+XEydO8KEPfYhjx44B8P3f//385E/+5N+YitOrFisvT7zcJ6RBQjNVdy6rHN2/r6FAxZ2Ao/Ody0DFlwpROqbGcjdgvu0zXLZ5253r2IaNVSIp5WUJj6Lqq1PtWj+84iYyOPl+49wqq/2AsmPkOKe1rI1SL5qMlm1abgSQG9IeX+huaWAK8KEvnL4i2P0Pn5phuRPkyV+jaHFktMyZlT5dPyZJE5Y6KQ/uH+LdD+3jc8eWNm3QZmZ0qmvqz/Or/V25t9uxMRsli+mh4jVtyqeWunzu2BIl2+DRQ8NomiBNJR0/xtAFY2VbqU0DLTdi31CBjq983oqWjiYEfpRQtAzGqzb9bPNsuhGPHB7mz56Z5/m5dp6QjZZthksmI/1zeDPfAeDJJTgVVvMWkmXo3L9/kfe9/uA1X5fBZlnItLTiVPKuuyd4+kKLMyv9Kz5fQoJc5+7nYRkaBUMjiFLunqqiazqnV3r0/JgoTlh1lZXMkbEyxxY69PwIoSnguKOra+GYBiMbkrLd8PxrFCw6XsS5VZc0OwC5oUpqgmTz5xAb/hRCsT2bboSmKS9GKeFNt44yWrHza+dm+lrve/1+Pv38EqeWuvk8UAzBcq7jNDg83T1V4+kLrRvuMvFKbY/dTPGiACLT09P8wi/8wm6P5RURr1qsvLzxcp6QrgSYHSrZvO7QMEfnO7z7oWnunqptGsfGJO/bM01m1lzcUG2gjqnz6eeX0ITgyFhlEz5CSlhzwzzhgczMVVNg28mafsVNZHDyfX6+TduLsE2dNGsZtTxlZTFcsjENjX4YZ+2TdcD6fMe/7HVn1twrMhonqjbPzXaIkoSKY7LaCzi93M8VyEHimAq387oDQxweK5NIyTfPrrHcDVjsKCuQRw8P87Y7x3ZVjX232JibLDTGy5teayJjgg1EKo8vdvnzZ+ZpuiFrfZcwVtT+VKIMg7Pfq2Q+cbMtjy+eWKZk6zxyaCgTg0xZavUpLX6HKFkG4AzjnPBLOJaGqQn8WGHAPvb4OfY2irz1jvFN493uGdlYHV/pBaz0QkAyUraV0OVoiR987Z5Natwb78GhkRK1oknHjXCqOmFmoKsLgWVoaJrAMXVaXsyRMYc31IdZ6iil8HrB5LtuG+HcikfB0klTiRcpA+F+kijtMCQza332DynD4SsRP3aCDRoo2s80VdKUpJJ2GBGlMrfN2Ri6BoauiA1hnGYefZKKrSQhPvX8Ap95YZHhspVpj61fu8OjZd56+xgPHWzw3Fybr51aRRcCU9eI03RTBe/e6Rp//NTsqy4Tfw3iRSVOX/7yl/nwhz/MmTNn+IM/+AP27NnDf/kv/4WDBw/yhje8YbfHeFPFqxYrL3+8XCekqwFm/SihUbS4e6q25XiOjFVI74QTi11GM0zPaMXGi5JN1chDI+UcH9HI3N5NXT2KUspcZLDiGCRSXpU9eGSswo88vI9zKy79IMYNIU6Uhs6gwqMAx1oup3ClzelKCeRahk05u9InRbLQDgjjFNvUGCrZxJpgpRfS9QMSKfm5P32BkmMwUrYYLilG24MHds8d/lrGfrXPvTEGz/lE1c7NSQcA+sFzPhCp/J47Jzg0UuL/+7lTtLyIKAFDBycDS4dJykLHV6KMccJKT7Vy7tu7ro+XBi7V898iTZqkCI7qh3g+HKHqGGiayDf84ZKJFyb89lfP8eZbRjEM7YqwAVhvuRZMjdW+wlMJJKsCRsoWz891mG+rlvBW83pvo8gjh4b5i2fnObnYzQBWEjKmnG3qvOX2MQ6NlDiz0ieIFTi8bBvcNlHBNgzmOx5hLNF1jaIQdPwYicJ0BXHEc36bIEp54MAQpi62JH7sFB6x8TBxdrlPkNnBbBdJCpYhCJN08KnQNUGYKMZp1TEIEsmFNY+CqSEgv3ZPnF3lUy8sMFqxFasU6IcJF9ZcbEPbVMGLU7mp0nzpvLrR6uKvxs7jmhOnT3ziE/z9v//3ee9738tTTz1FEAQAtNttfuEXfoG/+Iu/2PVB3kzxqsXKX994qYD0NJV8+oVFwiTltfsa69VIXdtUjfzJN5dzfMTFpptr5GhCZFpKis0khMAL4h2xBx87PML33j3Bk+fX2FMvEMYpz822s5P05mQMrsxK3C6BHGCxOl5E2dYp2wbnVj3iNCWVGq6p03KVZQtIZCpp+UpUUGQbzUzTpR/GHBotbUqaXirLaPD7C22fJJVqwyu8eDZmP1QChnMtj5YX5V5+A8bgparmh0bKjJZtqo5BYikaesnWs7apYm2t9EJSKak61iZ2WdxdoX/0S4g4IMTgC+Fh5pIKppYQxJqyS8naecNlhzhNObvS56mZJkMla1vYwGxLsRzX+iFHRkt883yLIEoYr9rZ/QxZ6AQ8sK/OqeV+XikHLrsXjx0Z4TNHl+gFcaZBRa5TVDV0vu/eyVwDqxtEPD/b4f/39CxlW3kXtr2IVCrLmm6Y5k0/kf0vTuHcWp8oTRmvOtw1VaPrR8ysueypFziz0rsmeMSRsQrveXgfJxa6tFf6aELJI2hCECcpSQoDtFsiIY5TklTmgzI1QcuN0YRgrGJzsa1kSSarJezsmp5Z6RPFKSv9EEMXPHJwGC9KmGt52KbO990zuemAMLPm4hg6cy2XhXaQM18NTWOoaDFRs191mXiFxDXfoX/zb/4Nv/mbv8mP/uiP8ru/+7v59x977DH+zb/5N7s6uJsxtttUBieIphsSp5LiNj48r8bNGy8VkH4t1chBa++vnltguTvPUiegXjAzLSXl1XZpsnal5ELTBO+8Z4L5js9aP2SialMrmiy0AwyNTEG7tK0S8sbYKoEcYLHcIMbQNcarDmMVm/NrHoamvNsW2h4CkTG+BKahTHIbRZN+mGy5SWuaeMlEi42/70UxM2seZ5f7PHywcUVD2SvFcjdgZs0llQofNfDy22iLsnGTm215rPQCphtFlnsBXpjgRSmWoaELgSYU/mWy7jBZU2D5jhcRJinRaotSHNCmyOPprSwnBhIIU2X3YxsataLJeEXNxzhVQOvlbsCTZ5ub1KsHVYzxis2ZlR6rvZCHDw7TC5JNsglAbrbcC5J8bn719ArPzLQ33YtDoyXWeiH7h4pM1WyWu6Fq+aLsdSxD49h8h+++bYwgTvjc0SW+cXaNY/Mdzi73sLNnqGjq+FFKlKZoQvnZIdYxRkjJxaayNrJ0wS//VYcoTpmsFhAa9IKEW8cv/5wLnWBLeMTrD49w794aM2suRUvHMnQ0Ab0gxg0TkOtuM1Gq7FYEYGjqeU3SlKGKTZRIolh5Fw6SLVPXOLfSp2TpjFds+kGCGyqT61vH1SHp1FKPt92xvl7sqReoF00+/cIili6oFExMXYmrLnY8Zpoub79z/BXhMvFyyyncbHHNidPx48d505vedNn3a7UarVZrN8Z0U8dWm8qlVPKhks1/fXruqlTyV+Pmi5cCSL/WauSRsQr/+LvKvGa6zse/cYF+EHNopETRNuj60TVJA4Cqerzr7gk+88IScy0PS9cQSFKpTs0AHU9VGTYmgXGc8tRMU5nLlixeO924LIGME8li1ydOoVpQFbFUSqoFgzhJ6fhKt6Zg6ZQtA1DqyXEiSdl6k55teQRx8pKIFpcSNaasAgVT58lzTb54YoWHDjSYrBeuiY2ZppJnZlqYusIeWbrScBrYoqz2Q56b6/AD903lm1w/jAmSlNsmqoSzbUBpBYVxip+qyoxtavzAfVM8N9fha2dWaLtq8297OqPJEVZEnSAVCCQbR5dIJYEwCC9MMHUNicwT9aYbcWqpl+PMDD1rKfWU+Ggi5Sa6PKjNXxkyK9bb+dU+H/nKGWxdyQNUHZM0lTx5bo0Lqy7376uzt1FkZs3LAeD9IKYfCv782QWGKzZfObnCiYUuXhjTC2OarswA1yj8W5apCFRVBxRIfZBsp2mKFyXMrHk03QgvSvhm0kRK2DdUwDIEK91wU7WmZOk8dSG9DB6hVPKH+KvnF0nUy+eio36cIlOZgd4VAzJnXAqBQCVTSkE/IYhVSy2KU5rZPFYkCKUNoVp7afbrV4Bs5KW2S+Zf1v58JaQerzLKX0TiNDExwalTpzhw4MCm73/lK1/h0KFDuzWumzYurUoUTI3ji116fgwIRso2t46XeX6+c1Uq+atxc8aLBaS/GNFOTRO84ZZRJmpOvhgtdYNrkgZ4/2MHADZUXRIQMFy2KdgGF9dcjs13ObbQvUzL6LNHF/mtx89xbrWfs7sODJf4B48d2JRALvcCvChhX6PILeMVhkqKtaRrgjBeP7kLVCVBnd5V9WkAlu0FKlGoF00WOz7dIOLzR5dfNNFiO6LG9FCJomXwjXNrHF/o4kcpjnnl5HfjCbrjRZxe6nH3nionl3qXiVTGSUqaCu6brufjGtx7x9R4zXRdJTH9AD9WlZmSrZh19+9v8KlnZ5joHGNe7KOXmPTDlK6oowFCSAqWRphIokRtpFJKgjhlzQ2ZNGxW+yG3jVc4MFLCjxfwI51nZ9vK8sUx8+rYWj+gFybMtlz2DZUwdCWPYBvrJIQ4lRxb6NJ2Q5a6Sv29aOoYmSejoWlK0NGPmWv5FC2DU8tKKb1aUEKYYVZt/M0vnMaPEgQSN1JJDXpKGCs+Xpyu5w2GplpnUioDZUvXsE2dnh8RxilzHR8j8/aLEo2OF3FhzWO5F1IvmgyVrLxa03JDlnuK7XopRuuxIyNMVh1WeiqhjCS5kOkg4ZdIipaOoSlZhXrRpF4wWewGuayHH6lkdLWvOgpk7WfLUPpnAoEbxFBWB5StIBuzWdv3oQMN5tsBzcxI2tBUBXeiatN0o5saH/sqo1zFNSdOP/ETP8FP//RP89GPfhQhBHNzc3zta1/jX/yLf8H/+r/+r9djjDddbGyz/MWz86z1Q+oFk6GMZTFos7zKsHvlxosBpL8UjNR2yRpcXRrgd75+AS9KabrrVZe5lsuT55oAPLi/QdkxWOwErPUDlnsBaaq8vH7xL4/R9SOGS1beljyx1OUX//IYP/uu2/nvv+tw3oL8na9fYKruUC0oAdAoSXHDhJ4fY+qqLWfqGkGU4EcxUkpqBSsHSBsZOH2QQPb8+CURLa7UGh0u2zx2eJi5ls+7H5rOFbC3eg4vPUEHUcrMmsuDBxq8ZrqeK7EPrGkmMyPjkayKd+m9v2WszEMHGnk7ydQECx1177/w9Gnu7n6LkvApyZAvaXdm8wMSQAeKloGVyR7EqSRNIU1T2v0ws3axed/rD1BzLGxd4/hCJ7d82ShO2SiYNPsRp5f73DFRyUVHrUzAda0XEiQpGhF+nKJrgiSVrLkRlqEEKzUBLS8ijFUC5kcJHS9UBwMp8+S4ZOnMd3zSNEXTNOJUogvQNQ3HVHY7WyloKdyRRtHS8TKWaSIlBU1QylS9BRJdU5UpN4yp2LqqporMf85RrdVvnW9uao0BTDeKvPnWUT71wgJapqVlGxpCqHafqQtuGa/wtrvGeeLUKs/OthWo29SVKKyvgPSakHhhDKZBwdRws6qfoalqmqbBQsZQHSRblx6SBhXpQyNl9jaKl4HDEyk5t9LfMT725W6XvdyM8pu5HXjNidP/9D/9T6Rpylvf+lZc1+VNb3oTtm3zL/7Fv+CnfuqnrscYb8o4Mlbh++/TePZim1vHK7mtw2Ayvcqw+5sXLxUjtVWythNpgK+dWWO0YuUsLZWkqeoBUnJ2tU/BVJo5UZIw1/L5P/7sedwgputH7GsUcpXqiqNaHxeaXs7cmh4qsqde4PnZDs/NtfNq2unlPraukVo6bqhac6mUOKaGGwnCROZecV0vola08KOYubbPwweGKNvGSyJaXK01WrQNDF0wUXO2ff62OkEvdjyOzsc8daHJQweGeHBDEqRYiZK2F19WObz83mv0uhHnMy2vCdFi4ehnKZEQaDYn9SPIQOSVuo1dnIKlZ8KdMSnQD5UKd7lg8KOP7uetd4yTppLRisPXz64xVrEvS9T7YcK+oQL9MOHZuQ6TNZuOH2XaRCl+nFWFkBRMlSRIKRHIDAcU4xh6ZnAtWWgr41uRseI0ISiYGpoQGFnlSUowkJiZ0e9Af6pkqzliGkpl3DY0CqZOIlXLsGwbtFwFntc1QcEyctV1kOiaSr51oa6FYnIqK5V+EDNZV356W7Xr3vPIPpZ6AScWu8SpJA4TQIHCa0UL29T5/LFlTi/1qBZMLF3LE0khUKrnls5KLyRKEqRUjFVVRVWSI8MlVS3q+jFlW+f0co9DI2Vk1mbVNHFZRfpS8sJOiSCDebuTdtluJh8vJ6P8Zm8HXnPiJITgf/6f/2f+5b/8l5w6dYper8edd95JuVy+HuO7qaMfKFxDw7S2/PdXGXZ/82K3RTv7YYwXJZQTg5VesIkSD+oU3vZCbtugNTQgKVQcxWg6t9KnVjBplFRy3zdijs136YcxUzUnT5oGMbDHGDC3Hj44vKVS8mo/oGwbxGmKY6qNxA0T+mFCxTYI44S1foDsC+JUEiYBsy2PoqkWwdV++JL8CF+qn+F2J+jJWoH9w0XOrPQ5tdTjoQND+SY3qCRvVTncVsvL1Bh1z/Ps8WMYQNeoMdd4DVXNQvgxZ1b6pGlKkrU441QiSYmypMMQqOqWENwyWubYQpdTS12OjFV48ECDv3punq4fKTZY1k5U7Ew17y42PQ4Ol+lklUWZStwI4iShYArGqwXKjsF82ydOlSGvlglBelGilLizNlscJJi6aq0ZukbTTbANVTECpYkEZG1HELpKlIQE21AtKfU+qp1XcQxqBYN+BnXQNYGpqz+jJMULU+I0zZiaqt0XxAlRmkJMzkK9dbxCx4u2XGuPjFX4H992C3/17ALPzrZxo5g0VSDxasFk31ARN0w4t9IjiFRrdV+jgGFoqp3nKbyhEFnr0lBaU7qmxmkbGpYp6AcJF9b6zDQ9oiyJ/NXPnMw3/I0yJCVLpxckeTJetvUdExd22i7b7eTj5WKUvxLagdecOP3Yj/0Yv/Zrv0alUuHOO+/Mv9/v9/mpn/opPvrRj+7qAG/WOLXU5U++PcfppR7nVno4psFQ0eLwWClXjN1tI9pX45URuynaudTxObXY47mLLTRdUDB0hkp2Ps+6fgySTYnDwDfLsHW6fpyb6dqG2txKtoGhK3CtF6kT9KUnyIKlKNfLPcUuG3yOgVLyt86vsdwJSJFoQlCyNCzDoFYwFeA8SWn2I4RQis0DscQ9jQJTNbV5/uWzC9QLJvNtf1NrU0pJx4s4tdzjrqkak1WHreKlykdsd4IWmVDpWj/k/KrL3kaRsap91cphmkpsQ6lkPz3TZKRssadqU13+DsnqeQBOp6MsFe6gqimbFV1TwpgdL8poZpCkKX5M7nXXKNkULIPxqsO9e2ubWIl3TFa5daJCsx/SD5O8najYmSVMXWOkbPNjbziAEIKjCx2ePLPKc7Mdun6ELpQApakrxfo0VRpUUZyxzDSZJ03r11YQxJIoTag5JghBx4uUbYlcty8R2X+apoQlK47BA/sbzHd8iqbO2RUXN4zoBwm1okWjbDHX9EmlJIgT5S2XqmRMEwo0D+q6KF0qg4pjcGikjGNoBFdYa4+MVfjH313O5RL+5KlZLqx5ubiplOCYBlIqK6XFrmK5KgkKk/1DJYI45f59Dcarimln6RpRmnJmqc9i16frRxxf6Obei1P14mUb/jvuHufoQodPvrC4Ln+A0o26dbyyI+LCTtplqZT89lfP72ry8XIYr79SBKav+RP+9m//Nr/0S79EpbL5onuex3/+z//5b0TitG5uGmTmphG2IVjqKsDra6brNIrWdTGifTVu3tjtnvxnjy7ya585wULHI0nBMQWhoXBF3SDivr01mq4Cy+ob3mdQDXAzirRlaBgb/j1K0k2thkHbY2N4WTvjyydW+OwLS5tOrLdNVHh+ro0fJ0gJBVNHIrCyNoypC/aNlIgakrGqzXzbZ0+9gG3oebVsULmZrDk0imb+dy9KOLHQZb7tY2SK1B/+0pktT8kvtTV6ZaV4i9fub/DU+SZrfWXgfKXK4eB0f2qpy/NzHXpBzP7hIrahIf02IEj33Mu3ZxxoB3T8hCBRZsGpVJ9FZMrWbphkoGlBwTTyis7h0RKapm1qieypF7h/usGzs23uqdq5v+JAr2tQHdvbKHJmpccXjysw/t6hAmtuiK6JTM3dz64pmTJ4NlcuASaZGcOwYuv4sWr3SSlxw0QRA1A4rTgdmP6KLPGSjJYtekHCY4dH+IdvPMRMy+Wb55r4kZIZqBQMfvYTz3FquZe/nqFrJBleSmTJnamLvIXXD2KOzrdJgTceGbniWjtohc+suaz0QqbqzoaN2cAxNM6seCBAS1C2OZrGUleJvO5pFJASqhskHQDq+0yePL+WXXedhw8Moet69rqbN/y3DdTec8koqbh02+tzboqdtMtOLnZpudGuJx8vh/H6K0VgeseJU6fTQUqp9Iq6XRxn/RSYJAl/8Rd/wdjY2HUZ5M0Um60YKoxWHJ6eadEPYkq2Qc+PeH6uTdk2KTsG9+6t3eghvxovQ+x2WfzEQpd//9mTLHYD9tQLrPaVAawCpar2xTfONnlwf4PDo2Xm236elFQcg0bR4mLTJYgSGiUF0IZ1ZfI9tQJpkrKcGaBuTJzSNGWx42MZOq1+yFSjkJ9Ynzi7yh9/e1b9TvaalqFadFEiGa/YuGHM2RWX77ptjOWuz+HR8mUn1MEi2HIjfui1e3hmps23Z5oKh5JIJusOt41XcEz9iqfknbRGL01oJ6sO8x1fiWUmkn4Q5YD3jVEwde6crPIjr9tHtWBumwxvbC1UMkBzvWiy3A3oBQmv2fcoVS1Ar44x1p7j3IpLnEpKtoGja0SpJIxVVWWoZGJogjU3pGTp1IoWk7UCh0fXK9kbWyIbk8fFbpAnj70g3pQ8AptO8gAXmz7LXZ9G0WSho4SMNbFugLtVWIZi5vmxap+lqaSQtekGdj/Kdkdh3JRogsy0qCyGy2o859b6m56Xb19ocXi0zN9+YC8fe/ws51fdbC4mCA10oaFpYKAhM2B3vWhhGYKOG5FIWOwGnFnpXfV52zZhzm6rJiVSKJC6hhwArdg/VMIxNU4sdqk4BromaPZDziz38eKUMEoIE5NvXWhnXnVqTm1OaEKSVPKOu8Yva9Vdqm92TWPPomDpnF2J6Pgqcd/N5OPlMF5/pQhM7zhxqtcV8FQIwa233nrZvwsh+Lmf+7ldHdzNGJdmxEMla51+nLmvryz1GavaTODwX544z2eOLvLWO8Z57PDITcMKeDV2L3a7J5+mkj/81gzL3YCJqo1jGliGzlpfSQJ4kfJBqxcE77p3ksmaw8ceP8eJxV6+oFcdI6NNCxxTWUGEcZJjQo6MVyg7Bq0zqyx2AyTki+BKLwAEU3WHWycq64KJtkEcp7S8ECSMV22amRaRqSsdm+V+gKUpMPGBkSIXWy7FbUr3g0VwpGLzj950iH/7yWP4UcKR0TLVgrnpfa90Sr5Sa/TShDaMU4JIWcSYusZM0+Xsap+HDwwxXF5nyW08QT+4f2jb5/bS1sJqP2QoXKBuJKyWDrDWDzndNnhwvzpUliwFWDezSsqgXVMwdUxH8OZbR3nnXZP81tfOMVQyqRcHDEbVvqw4Bm6g2HYLbZ+SpVpVV0setyIZHBkr0wvizGhX6T5FWdakGHEQK53IvCAipUqCkoztp76n8Es1x2CobDHbUhVSwXqbb6Lm8OZbxy6zgbn0eRkqWfz91+/nNz9/Rmk1JRKZgEDpadm28rtL0xQ3jAGDqUaRQyNFVnoRv//NGf6b+6Zww4SyY1CxzcuS3YKpkySSi003J/V0/Rg/SpmqO7TdKG97OqbBeK3ARKa2ft90PTNn7uCGSkjT0jUOjpTo+CFl28hFUl8zXc+TJ5XQhHT8iP3DqnJYLWzGFu4kodlJu0wlv+lVn7sXk3xcb+P1l6MduBux43f//Oc/j5SSt7zlLXziE59gaGgo/zfLsti/fz9TU1PXZZA3U2yVEQ+VLB460GBmzeU7s23CJGV6qIgbJJxq9XhmpsUXjy/z3beN8Z5H9t1wYNtfp7jRlNXr0ZMfJOeWIbAyXFLB0pkylZWKF6mT6ljVYrRic2SswltuH+O3Hj/H83PtXI9pqmbTKJq03IgmIYau5crkjaLJSi/ge+6cYKnjc37NZa0f5jpOuia4Y7K66cTa9WOaXkTNMVjsBtiGw0RVCVt6UaJAx0HCxIhDtWAyVS/seBGc7/is9FQVd7vq1HabynZzYGNCO1G1ifspLyx2afvKf+y+6Qa3jZf55vkWXzyx/KLEMjcepEBiLTzH7f4J9fmsOqFTZa0fKiwaysdsrOIwXLLo+DFRmmJqGuNVWwmNpnB4vMzrDg7zxNlVFtsBTS/KxR5tQ1mBlGyD33vyAgXTyCubA+mIrZ6F7datwaFvtR/kFZdB0iSEACFRvDuyxCpF1zRMXWAZBkmaUnFMRss2cZriR0rp24sT9tQKtP2QkZLD+x87yBtuGQGuLq/xwlyKJiRmJm9haQJNU9imnh9RsHQaJZv79zUYyhKfphuy1PX41vk1/vyZOdLsNfcNFbk/E3QdAKb/6rkFZpouq/2QRsGkUbIZLitdp3rRJEokk/UCt09WsHU9lwp49mI7M2c2uHOyytMzTSSSOJWcWemjaep5HSpZKmFe7tEoNnJ5Ak1oJFK+pIRmJ+2yw2NlljvBjpOPa11Dr6fx+svRDtyN2HHi9OY3vxmAs2fPsm/fvstKgH9T4koZ8UInyMwtdWabHkkqs7aJyXI35CunV/DjhB97w8FXk6ddiOtJWd3pYnI9evL9MCaRYBt61hLT89ezTR3T0FjuBuiaWvxOLXX53LElSrbOI4eG0DWleN31Y3RNqEQrTpmsOYxWbPwo5eRSj6GSxfsfO8CBodIm5fCSafDrXzx12QIfJqpFWLAMICCIU+pFiynTIYyVlUY/SNg/XETX1Cn8aiyiu6dqpFLywnxHCTzWtgaCb7epbDcH3n7nOJ9+QSW0wyWLYwtdjs136YVKl6fnx7T9ZW4fr/Ca6RrHF3ocW+jhRwmOaez4BD1ISAqajnf0q9CaA+CCuQ/XqGEKkQt/DqjzB4ZLPHSgsel6bNTx8aKE2ycr/PHTs7nGVq1g0nJDzq/6aEJwy3iJw6OVHVc2t1u3Boe++baHY2rMtzyiRFXCLENpbkWJRNcBqZhumlLlJE0lhqH8++7ZWweULc9cW2GI3DChXjDRdcGzs22mGg62oV9VXuOJM2t4kSIsDEyOAXQtwY1S3CChYqdM1wvousZaP+CJM2sstj36UYIuFM7PDdcrjHNtj7fcPsbnji2x1g+5faLC8cUufT9mLrPLCeKUlW5ApWBy11Q1b40CuH7ESi9ACJhuFHjyXJN+mFC2TUxN4EYJQZQw2/Qwh7VcKb/rx1QyxuKRsTJLHf8lVVN20i7726+d5tMvLO4o+Xixa+j1Ml5/OdqBuxHXXO/63Oc+R7lc5u/8nb+z6ft/8Ad/gOu6vO9979u1wd2MsV1G3PVjmv0AkDmleHiDIF2jZOKHCbMt76ZgBVyveLkqQNeTsnoti8n16MmXLINGwaTrRbS9CKukbVr8BlYeR8bKTFYdPvylMznm7tJF8uRSj4PDJRolizPLfc6vuluW1h8+OJz/3sCM9NIF3tI1DE0jTSWmrtScnUxQ0DZ1iMExJC0v4nUHh5luFNdZRM8vkshB00egC8FUvcBqL+DXPnOSNVfZFnW9iDsmK5i6vimp2GpTudIcOLGo5BaqjsEzF9us9gL6YYSQoOvqM7ihoo/3w4S7pqq4UcK7H9p3RbHMre5VMfVwn/0iBF3QdJK9D7DUK+NlQpKaEIRxwlI3oGAaTNWdLVs1Ax2foqlzbL7LZNVhtGzRdCPabkTbiymYOiXbwA1VArPTyubGdWurJLYXJDxyaJjTSz3m2z5zLT/TeVKvpWlCJUq6wNI1On6MZQj2DRW5Z896S0qOwULHo2DqPLCvzsGRMl6U5M/lm24dYc0Nc80vIGeoVRyDJFXmw7apoWvgx4rIkKaSXpjk+KuWG/HVM6vcOVXludk2ix0/q3oCurJL8eMYP0xwDI2ipfFbXz1HyTJyJl3JNjZZZaUSao7BfXtrm5ImKVVFCQQHh0ucWOxlWEORCXGCY+oIIE7U+ren4RAlSpR2oaM2/B9+YA+ffn7pJVdTdtIu0zSumnxcq3HyyxXXux24G3HNidMv/uIv8uEPf/iy74+NjfEP/+E//GufOG2XETfdMG9jRFmlaeODYeoaPRnTKFo3BSvgesTLJVp2PSmr64zJkKpj5H5dz85uvZhcj578nnqBI2MVVvohQZxssvwIY2WWO1F1+OEH9jDf8a9a8Wq6ET/6+gNoQuwood3ucFBxDGxDcHq5j6GpSkp3uUfFNhgqKcaUbWrsqRfyU2Gagpv5oYFiSQEEScqZlT5JKrllvMxkzaHrxZxb7XOx6VK0DXQhcjq4oWk8eng431SuNgeeutBkqevTKFi4YUycpEgpsDMsTyogTBQGxw0i5to+wyUzF8tMU7lJhmG76+WvzlG98GVkHCKsAsXb34ReHuK+XsDzc20utjzKtkEUpzx8YJjDIwHzHf8yCYiNG6cETi/3uGW8TNlW+Ju1fsizs20qjp75Y6pqxgALdrXK5mDdOrrQ4a+eXySME1Ip0RCqejBc5LEjI1QdE3O2za1jFc6u9ull7UQ3SAjihNGSspRa7Sv5gbsmq8SZfETZ1jm92KMfJhwZK3NkTCXylUzg8tsXWnzky2dZaPucWOwSxgokVbR0ipaScylYCmBuaYJK0aQXKFkCP1TjNTXFrBMClrs+j58KWe2GhElCnCpwu6FpaJrA0CCIEs6uuoyWbc6tuTx6aDi/7kMlm8YBK9c9W+4FDJdsVvsRlqFvSjbKtoHMdLbW3FBhzcKEREoMIdCFAvePlE16YULbUz52Xphw33R9PaERYleqKVdrl10t+Tg0Ur5qy/RGHvCvZztwN+KaE6cLFy5w8ODBy76/f/9+Lly4sCuDutljq0kZJ5KRksVErcD5tT6mvvk0GWWWE5WshHujWQG7HS+naNn1oqwONuMLay5xnHJutZ9jSxoFk34YX7aYXI+e/MbkHFTi0Q1iwjgkjCWTVYefeust3Dpe5dhCZ0cVLy9KuH2ies3vv3GBn295meq0sgUpOwYdN6TtR7S8iKGSxVsPj/KWO8YJ45RPfGuG//dr55lte5QsDSF0yrbBwZESCy2Ps2sucZrm121PQ4lOttyIOFFSBnGacma5T6VgcttEJb/uO5kDJxa6dD2FBwoTpQQt5cBfVaAJSRinWGWLlZ7S7Rm0Pnd6AOh2O8g4JC0OsTL6WsaNCn7X5/hCl8VOoPBLFYexaoG33zWGJgQfe/zcFTdOL0ryeyqEoFowCZNUebplbdtB++/S+zxYV7aq/AJ0fUVTD+KEJFX4HAF0g5j/8rXz7GsUMXSNIEl57b46caZevtZXshd/674p7piocnyhy//zuZN8+ugSlqFEIS1dY7HjM1S286RpEE03YqnrqwTLMVjo+IjMN85DJU+LHY9OEBPGKW6Y0gsSDF2pjxtZZSxOJN0gIZXgRymdjk+QrCuvG5l4ppoHYBmCIE6Zabqqzahtfj4HivBVxySMU/7WvZOcXOxdlmzcs7fGHz+lWqdxmtIoWBTMmH4Yo2cK6IN7ZegataLJPXtqvP+xg0w3ijtOaK5ljbxau+xKycfVHAleLtr/lToU16sduBtxzYnT2NgY3/nOdy4z+X3mmWcYHh7e+pf+Gsalk7Jg6vzpM3M8eW4NXYhN2JQBBXys6mBo4qZgBexmvNyiZdeLsjrb8vj2TJPlrkqEVZVHGYku9wJ0TfDUheamxeR69eQ3LrCnlrq0MoHBw2Nl/vZrp7l1Qi2wV6p4SSlZ6vhqg/Gi3PrhWt//9HKPhbbPudU+1YLJQweGWOkFLGZqyhVbzeV9Q0UEgt/4wmnOrfRZy6QOHFPPNJlkVimJSKVqZQ9sKiqOwXI3wDF0wjih7Stm04AhWHMMji90+e7bxtA0ccU5sNYPuLDm0gtjoiSlH8aEsUTTlBq2yDzZTE0DoTbdfhAzlQHDf/trOz8AvPa1r8WyLMzhPXzm6OoWkgpVHFOx9377q+d5/yXmydsx4C69p4M26UBBe+D7N4iNlc2tEr9DoyXOLPc4s9yn5uggDJp9xVoDZSI81/IwNFXlG0hFBLF63UcODefjO7XU5fPHl1SSkFUegzih6Yb4ccpY2cbQRF5Vk1JyaqlHnCgzXSVmKdCzVlcQJ7S8CFNT2mOGpnwPhVD6RmG8nvQmUmIZ6u9emCjV++yaDFplmpCZx536riYUtszIWn6DOTLwIIzTNGcGFi19S5A9wHdm2nzj3Kpa39OUoZJJmCS4YYxEsT8H/o137anx3z2yn/3DpS2frZermrJd8nEz0P4HQP1nZ9u4YUzRUtjCd949cVO0464U17x7/8iP/Aj/9J/+UyqVCm9605sA+OIXv8hP//RP8/f+3t/b9QHezHHppHzn3RPMtXzmWz7NfshoxSZOZU4BPzRSYqET3BSsgN2Ml1u07HpRVrt+xIVVlyRNGS6ve3/Zho5V0ljNVLS7frTp965XT34nC+x2Fa+1fsippS7nV10qjsHvfP0CT55tXlPbdPD+F5su3zi7xh89NctUXckjXFh1aXoRXqgqFxLJSn+Nb19oIge2IVJVAcIkZaUXAioBSDJsy8ERZdcSJgrIvtDxVGVFiNz/TKDwg+dXXb58cpnvv2+K6aHitnNgrR/w9EyLjhdRc0x6QZzT/geU+iDOsDqGkmlouyEFU+e7bx/NAeVHRkv0ApUMWLrGkdESp5b7/OVTZ6mtHeW+R7+LoVqFPfUCd999NwCHR2v8208ev6qkwk+++TD//RXu61b3tOKoVtZix0MC9aJFECd0PDbZdVya+BVMh+VuwBeOL3FysUfJ0hgqOix0AlIJlYIBCPpBzFI34KH9DZZ6ClD/D15/ADdKNo0vjlN+/8kZzq/2OZLpc/WCmOVewNnlHmdW+jw722Kp6zOcKdwbmkbTVbilIEqJU8lU3aHnx3hRigRcP6ac2a/Yho7M5rCS1JCZtZDCjCkcmMZQycINE86u9JXKeaZB5UcZgD0LQxMkUrK35ihsVs/nmYttvEyywNB0lrshuib5i2fnmag5Wz4j77h7nNmWt2l9HypaLGWm2bqAthdzZKzMP/nuwzcEXL3TuNG0/1NLXX71Myc5sdDdhH08u9zn2EKX//Ftt9zUydM1X5UPfvCDnDt3jre+9a0YxoDOmPKjP/qj/MIv/MKuD/CVFEfGKvzYGw7gmBqfP7bExaZHyTYYKdvsqTtK4+UmYQXsZrzcp5frRVntBcoXbqO6tVJRlrllSC9I6AVb+2Fdj1Pk1RbYQcVrtuXyzMUWjaJFIiXH5zu0/ZhG0eT+adVy+dqZFY4vdvjbD+xlrOrsaIxnVnp88rlFnrqwxpmVHnNtj64fKU8vAQOVn4Fez0DledA2CROZawMBJKkC1UaJ5MKaS80xCaKENE1puxFRkmabJTiGjpVpBnX9iBfmOrww18lNhy+dA1IqVpcbxBi6xlRdVYtmmh5JqqxnDF2jYCrz1iBJKRgKX/PGW0c5MFziT749h2NoPH56lWY/zKw4NBolmzpd5r/xLdZkwNG5JmtjDzJZK/C2O8d4/eGRTFIh2LGkwgBLNdvyOLHU3XQ/tqpiTtRszqz0cqZb21MJ/MCu4213jvHp59crv0035Nh8lzU3pOWGdIIYN4RukCgTW6HwQgXLwDYVC63lxUzWHE4v9xFCbGrvnlrq8vtPXuQvn1tA12ClFzJUtBipWJxf7ePHKRVbYX80Qe6ksH+4mGHMZJ5oVRwzb49FqaTlhhiaoFY0ccOE2ycqXFzzOL/m4kcKHyeF0g7zI0kxu+dFS4Hle2E8cKxRMzJLpNIMl1QwDf7uQ/tU1ejsmsJrZQfblhtRLZjct7fGaj+8ol7YVuv7LWMV6lkrf7hs80++6wi3jt+8mz7cWNp/mko+/vULPDPTwtIFlYKZeyx2vYhnZlp8/OsX+F++786bdp+85sTJsix+7/d+jw9+8IM888wzFAoF7rnnHvbv3389xveKiyNjFf6X77uTt9w+xmdeWGK+7WXGl+KmYgXsZlzP08t2PfDr0R4rOwYFSyeIUnQR54KmqVRmq6mEWkEpwm8VN/IU6Zg6y92Ak4u9vCJ2aLTM/uEi51Y9mm6IG8Q8M9Pia2dWuX2iymjZviJ4fyNubbhkU8vweR0vBqFO2Emq2iKbxKYFyJRNXlygfh7U9xMJ/UBVq75zsYWZtWxSqdotjqlwM0KoBKxo6fTDhC+cWOTtd45vOQfiRLLY9YlTqBaM/DMlKSx1A3qBaleOVRzCVGZgXovbxqu853X7cCPFsruw5uGFMZpQeBk/1in05xlNTqAjiYwSFwtHmFvu8fTFFl88scR33z7GI4eGr+kAsVVL7eBwkb1DRRxL58H9DS6uuZxddVns+ARxSq2gPAdVNW6zXcdCK+A7sy2Kps5M0+XkopJXKDvKtkVKiCSIKEGIgZGuJAlinEwFHrH1QWcwF86v9tE1wXDZIknV9T693MM2NCZqDgVTV23SIFGEAT/i3GofN0yoFgwOjpY4Ot/NoQyKjalEJNOs6mBoGqNlh0MjZdpeyDfONmm6ASKrZK72Azp+hJF16cq2jhspI2INZfsySJ70bP5M1h3+9v172VMv8PRMi0Qq9qeR4ZKm6gVMXWOial+xOr7d+u6YxiYQ+M0eN5L2f7Hp8sSZVXTB5ZX9ssLJff3MKhebLvu2aHXeDPGi63C33nrrlgrir4aalG+4ZZTXHx65aVkBuxnX6/RyNZDubrfHKrYSzDu11OP8mqtoxoZi+fhhSiwllq68vZi85pe/LnFiocuvf/4Uq/2Au6aqxInkm+fWkAjcMOHofBcpZY5FSaVqfS20PUbLVo7ded/r91MwjU22JJdadJxcMji31s9acRBtM6Y0VfiSS607Bl/Gl/ifpRJabkgYK0V0UxcULJ3BNJJSEmcYlMW2n29ql86B5Z5SVt/XKHLLeCUTIgwwMiYWUlHfFzoBIxWbI2NlXruvkc+XL55Y4tRSnyBOqTg6RqaHdSg6y23MArBAnXPOXUS+QcUxlZBoN+ArJ1dY6YUZsPnqB4gTix1+/fOnWe0pm5SDwyWOL3b4ra8t40cJBVOxuvYPF/lb901x794af/LULIamcctY6TI5gacvtvnVz5xgoeNTMDXafkyaSvY0ChmOKB3oW2aVGVUpNHRBFKf005SKbdIomFsKJA7mwpHRMiu9kCRVG520YbHjo2nqZw1dJVX1goUXKfmAVj9iT8PBMQ321gsstgOWuj5WSSVrPT+mkSVZbTdiT6OQV33rRZsHDzT47NEllns+uieIU2XrMqhyDuY1qKQJFLtOE4rFWbR0UglfOb0CwL6hAmMVhzU3YK7p0w9iji10OKVp1AsmlqFdsTp+I9b36yHzcqNo/2dW+rTdiOGKtSW0o1Y0We2FnFnpv7ITpw984AN88IMfpFQq8YEPfOCKP/t//9//964M7K9D3Og+9ssV1+P0slOW3m62x/bUC7xmb52Tiz2MrN/kRUkOyCUzYP3Il84yWS3kAO2XGi92UTyx2OGDf3aU08s9CpbGSi/ENjSEJhgv25xZ7YOEyarNTNPHixN1IpeSpU7AGbvPG4+M8PRMmw/+2VFGShZBkuIYOiNlSy1cQ8Wc/t50I7boUl4WGy06Lv2HwcZm6gJbV0DkOGNNDahRA2NXJcqjtHyEUDihVLJpU7sch3WRPfUCjaKZ4528MGFP3SGIJT0/YqRsMVkv8N7X7eeRg8PMd3xemGvzl8/OEycJlq6qHgYx96cnGGMNgOfjCZ5nmuFEY6K6vujXSxZemNByAySCuZbPLWNbC37es6dGP4j5+T9X961o6Rn+q818yydOVXXTMhTo/uRSj498+Sw/8caDuTHtpRpQa/2ApY5P0w1xTB3T0ElSVV1b7PgMFa3M2FkokHyGBYriFCOrNCUpjFZtKo7BqeX+poPORgxj2VY+iMtdH7No5m00L0zwI2UqPVkr8MC+eiYlELPY8fm7D+/jyydWOLXcZ6Jm0/JCZpsuEkHZNtjXKPLCfIdUphwaKeWt164f5zpLCIFjaLhRQuZBjcj+04SqMKXZ146p5eQcP0p4Yb7Dv/2rY9QLFsvdIK9ODnBOg1bRfNtDE4KVbgATV57nL9f6fj1lXm4U7V8KEGz3Hjd/cWFHidO3v/1toijK/75d/E1VE381dvf0cq0svd1avDRN8Jp9dT7x1EUqtpGZrUZIqcDKlqHK+qeXe/z650/xU2898pIWrjSVPH56hc8eXWS+rRShC+bOFsVTS11+/fOnOb3co140KWWMnmY/pONFSu8mVUnSbDvAi5OMqQQiVRWYcyt9hsuWwqL4MZNHRtjTKOKGMS/Md7iw5jJWcYiSkCfOrGYedi8uBApzomUVpYFEQNk2eGBfA9vU+OqpFWaaHlGcEsQKEwOgaxqaAE8kLHUClrsBt2/Y1AY4rFNLXVb6IedWXfYNFfCjFC9McnHGfhCyb7jEA/vqnFru8/ljSzw90+LMsmJXHZvvKGB6prZe0lNKqUuC4KvhAc6lw5iGoGBurqoamkYQR+rPJCWIky0FP2+dqHDbRIUPfWHzfQujhONLfeJE0igYaLpGECvl7n2NAheaHr/35AxjFZupS6q2A1xXnCh8UdkxWeuHSCkp2jp+lNJ0I+IkxdQEGCCz8oxEJU+aENiGxlTN4dRy/7KDzkYMoxCCI2NlVnoBp1f6JInEz7KYc2su4xVl6TNI7oSARtHmrskaR0bL/NWzC3ztzCotN1ItXyRBlPCd2Ta3jJcxNI3Vfogbxsy1fJa6PgsdnyBSdigjZZvFjk/kKSabJtQmPGjTCcDQBoKUgm4QEcWKGHBmuUejoHSWLjT7NAom+4ZL+b20dA1D19A1wTMzLV5/g7xFNx6kVroBf/HsPE03um4yLy/3Af/gSIl6waLlRoxXtcs6FG03olawODhyc1abYIeJ0+c///kt//5q3Jxxo/zbtju9ADsSExzEy83S2xgjFZvpoSJBlHB6pU+YpNi6RtE2GCqa2KZOk4DVfvCSJBZOLXX5+BMX+PzxJbVJ2wajZZtC3bnqojhILFd7QQ6OVZufznjVoevHrPRCBDKvMIjMIkNK5XBfdgzabsTzsx2KlkHR0jANtWlUHJMjo2XOLPc5ttDG0jVW+0G+4b6YkCgQswKNaznbTReCk0tdRit2zoRKpVR2M2lKnChD14Jt4Fg6cSr5y2cXGK+oqubR+Q5//uw8fqjEII+Mljm20OHkUo8wShip2rS9CD9KqDhmvqkXTI3PHVti33BRvVZWeYlTZZBq6RprPnwuPYwmU5bTbBFPUlK53mv0wpjlbkA/TEil8morZC0upbqdfaisIvL4yRVW+wEFS8vvW5h9RgG4UUpJCFKpSAmW0Kg6BhfWXJIkVUD1op23srp+zJobYpuqHXVopIQfJbTckCiRmLqgl2kVCaGSg5gUUihlWD0pVZJmaNqWB53LMYxZQiiV6KOuC+JEXlYnuLRNf2ZFeeKdX3MJYkXCGKSWHT/i+HyH1+5vIBF8+0ILL0oyfShVOYqSlAtrbl4BHrSDxQAULsDM8KRdP1YsuwyDBwo7FyQKJzbf9llzI2pFBQyPkpSeryjxt4yVOL3cvyEixRurS16UcH61T5xKHj4wlLd/bxaRyhcb040ijxwc4tNHF1ntKzHRHBzux6RS8uihIaYbN2+35q+PmNCrAdx4/7ZLTy8vZjw3UmOkZCkWpCYUa2ikbOXsLiEEQZxg6DqTtcI1JW+XniL//DtzfOtCi1TC3kaBOJWs9AL6YXxVds/GxHKlpzZI2xiI/gnGqjazTUXtjzOg0Ua8URqn6GGiEphMQ0nX9E26QNWCyWTNYabpYmf6NwMZgWsNTSiBw7JjsNYLMtVqtelGScqJxS5H5zuAMpeVUtDPqhimBiLT8BkuWtw3XeP8mssH/+wFhksmL8x3sxZVVi0QIjek7UcpQdPHMjQcU6cxsAWR6l4om6SQxU5AxwtZ6wccZokEwYlwFACXzXMwTtUhQGRto/m2nwOfa45F1/dIg5jxisWt41WKtoGZaRo9P9/h+GKHW0bLzKZKO6psK1xZJqyet4Z1TSU8zX5Exw/pBwn9IOb0Sp+RssVUrcjhsRKphChJQKr7XiuYHBkr0/GVPpaGYg/qmnIvqNg6vQDQyZlkbS/i4EgpF2uEzQedycwYemDXcnpJ4dwOj5YI45SFbkAYpRRMgRvEHF/ocNdUlYVOsMne46NfOcc3z61hCJioOSy0g8wbUGe8atF2I755bo2CZTBSsbhlrEI/SHh6pslaP82lDAaVJiFyCSdsHSSqohrFap4amkpWAWxDIKVQ2LxE4hgaUSpZ6ga5wObAALtaMDi30t9yfbmeh9JL4QnlxMg1wZ652OY10+vWNi+nSOVW8VKug6YJ3vPIPpZ6AccXulmFVN1P29C5b7rOj7xu302dDO4ocfqhH/qhHb/gH/3RH73owbwaLy1uFv+2lzqeG6kxMgC6f+3MCkKo052WHVk3CpmOVmzOr269uF4aW50i3TBBAI2Sha4pXy6rpLHWDzmz4nLbeHnbRXGQWB4cLtEo+ix3fawNvogVx6ReiFnuhVsmO3EKHS+mZOskmV3GnkaRsr1+PYUQ3DZRYbbl0fFjkiS9jCW303AMpZivqkzZZiYAIUmk8kCLBhugoYxeB4bZKVA0VDXsSOYxttRRrcWqU8+MhyVrfeWbN1lziFPJchzk2JfRsoVjKuuSp2daHBktMdfyCZOEthdRsHTiOOFh/Ty36MukUrCYlmnLyxN3UyirlpnVPiXbxIsSipbOSMmm40fommBvvUAviFnsBhyylXDnYjeg1Q9o+zHLbQ80jairNvAgTvJkD1TFTUiYb/sYmiBMVFY1VDTpBkluoLvY9dhTL+KFKXZmyPvE2TXiVH0d6hperOQf9tYLLPcCVvoRIrvOK92Ax0+t8sihoVyscbvn/PbJCnNtj+/Mtlns+pRsnSiVNL0IJBRthWkMk4jufISpazx2ZGSTvcdsSxEu6kWLlV5IKiVVx6AfJlxc89A0QRinxGlArWDSyLBZHT/GDVWyLVA4Jo2s2iUUQ1PXNMXMTFIkGQsWtRmbupZ/bRkaYZJiGhoiSSlZOndN1RgqWRuqeNGW68v1PpReCk8YmAqPVlRb6/Ryj0axkT/nL4dI5VaxG9fhyFiFH7x/Dx/7yllOL/dyLOC+RpEfvH/PTc9M3NHOU6vV8r9LKfnjP/5jarUaDz74IADf+ta3aLVa15RgvRq7Gy+Hf9u1JEAvZTw3UmNkAHQ/sdTlzHIfy4hz/JASMtU5PKraITtJ3rY8RS50cMMEN4gzo1CV9Aghclf1JIUgTrZcFAeJpRcpT7BeEG/ys+tnbTDb1PAzCpvGOmB70B7pBUmGPVLSC9863+TwWCk3OHVMncNjZWbXPBa7PteSNwmgaAqSrDXY6ocEsUTXIJYZVRyhvt7wEZNUstxVGB1dU+NMU0kxY5kN8DxFS0ci6fgREqg6Bn6sTFUHCRMoscuVXohlKLPXKEk5vSxZ6wdYusZYxWal1eV16QuM6F1SCd+M99CWzpafS9cFQoIfS+JU6bI1inbWVlLCjaahURYG822PuZZHx1M2HV5mUtsLU3RNkkrlZbbxvgxujpIakCSJ2vBLls5UvUDLjZhpebTciJYbsdwNKds6XV8pyytNHDVfgzCh46cULYMgS3wNTWBmOJ40lYSx8oLbaq5e+py/5fYxvnxymZNLPUAleEGcYumCcsGkVjDxo5SWG2IaGm+7Y10J/fRyj6GixWzLI81IF5ahkUglkJpk9zhKFVtuuRfy2aNLii+QSYEMcHIDksGgdayJDBBu6QSu0pCqFUyFlUuSHAxvaQJDkElRKBVyTSgZkmpBHdC2W1+ut6XUVvCEgVp8nMp8XRj4E8L1F6ncKnbrOpxa6vK5Y0uUHYM33DKCnjFYu37M544tsX+4eFMnTzu64h/72Mfyv/+rf/WvePe7381v/uZvome95yRJ+Mf/+B9Tre7MC+vV2P243v5t15oAvZTx3EiNEVCnoX/y3Yf5YC9Up6GsPadK+SUaRYuTS72rJm9bXbvTyz2aXkSSpFn1yaXlKp+3aiYE1w9iOtucemFzYnnLWJnXTNc5tdSj6Yb0stP5SNUmafsEGZNqndq/7usFSudmqGhTdYxcsPA103UaRYv5ts/rD42QHEj5/W9eZKXnQyIvkxnYKgxNVWYGOBxTF+g6VDLT2lhCkqyDlAdjSiQkcYqpC7Ss1eJlCdFqN9yE55ES4jhFz9TIdaEUsNNUEqZpvsnKzBTWDROVbHkRqYShsoURdHg4fJoiIaHU+UJ4iNm0tu3nimPJWM1mzVWsNdvUQcBY1WG84nB0oaOENjXFRIwTmYl4ZuwvMlHGRGLqGbD5kuupa+uSDRIwNUGjZNP1lTWJpQnQNYZKyoA6SSVelFKwsuuZJTRppsA+WbOxTQMBjFVspd0kFSbJDRPCOOWTzy0ipbzic358ocs/eP0B5ls+jqlxYc0jjn0ksNQJlOQDoAtBz4/4zNFFjoyV8wrpSMnOgPSZwbBQEhky88vz44R0IMLEZi2mFDbCxTYxNwWqHSm9CBCYhqBRVEKbXj8hzoRPDV2jGyY5ID6VykNvpetTL5rbri8vh6XUVvCEgVr8Uja+gcr+4B5fzwPkVrFb12Hj69w6XrnsYPxKwG5dc6r60Y9+lK985St50gSg6zof+MAHeP3rX8+v/Mqv7OoAX42dxfX0b3sxCdBLHc+N0hjJ33+0wk+88RAfe/ws3SDiwHCJsaqDHyWcXOrtKHm79Nqt9QNOZq7whibQsrbPaj+k7cfUCyaNkmoNNt2Q1x0c3nJR3CqxvH9fjeVuwHzbZ7hsc+/eGh/6wmksQ9WaUql86gSSMJE5NqTiGJRsnX6gAOo9P+KF+Q6jZYfhssU77h4H4Phijy+dWCKIk02J11ahCYU3GQhkJllGJKVqEUqgYGokyWBMasGMsk1ToqoZcbz+dZhIZbqcKML5eM1BaBClEjeKciB2KjOrlyxDNDRB0TLUGIQkilNMXVMMrWiFW3rH0EnppDafCY9s2Z7bGDHQ7IeKwaUJJqsOd07VqGRA64WOap0aGvihqqqYmsDLPPJ0yJXR43Q9kR1ct43TaXCNTV3Q8ZVJrgTKmS5RyTbpBxGaENSyKkTLjej4yiRZCAXIX2gHVAopw2ULTdOwMyibwuspX7rvzLZAwp5GYZMUwKCFMlG1ObnYZWF/ncm6w9H5jhJVzQRibUNDZNphQldaYd86v8Y3zzdwM1seXYis6uSiCYU1ijP/xChJkan6/AkZ4DvDvYjMb25g4WNmxr+pBENsUArXBBNlm6Kp0/LUwUPXFD7P0AR+nBDGStm+bOtomiBJ1dz2opSRsr3l+vJykFW2gicIITg8VqIbRCx3A4ys5dj1o5flAHlp7NZ1uJHkn92Ka06c4jjm2LFj3HbbbZu+f+zYMdI03ea3Xo3rHdcLF/RiE6DdGM+N0hjZ2MNPpMQNE16Y77LUDbZdXLeKjdduYHQ6aFF0vTjfGFRlRbU4ekHESNlhb714xUXx0sRyYMb6+sMKV7LWVxYWASo58qOEOJWk6fopXhNQtA1uGa+w2gtpuiGJVNWDB/Y3ePeD0/ln/GdvvwU/ivnSiWWSDcf9rRIoKcHQ1ytIA12iMFZClgBxkuab2sAaY2NCJlMQGnl7UENyselimzqjFYeRssXxhW5GPxdZm0vZu+RjylpebhgzWnEoZ9eh5ytDX7s5h07KbFLlC+Ehwh0uh4mU1AoWfoZXm6w7eYJxeLRE14+42HSVnYmpcGRJKtGzKpqjC+IkzXFdWvbBi5noZ9kyaHoRUZwSS4XRMTWBJ9Vn64eqLZhKiUCQChipWHS9CCHUs1exDQqWxmzLZ7UfEqYD1fHBPdqM11voBEgkRcvIfA5VBTNOUgxdqbh3PJWURYlkZtVVLVBTo2IbpEAQp9iman+u9kK+2QlwP3WcWtFkZs3n7HKf2ybKdHyTbhAThImyYkElMKaeSdqkEilEpuWl5sHgphq6uk6JVHNivOrQDWLqRUv57HVDJms2q/2QtX7I9FCB4wsd2l5CIiWGgErBomjp1Aom9+6pcbHlbQLHX/rMvRxkle3gCUMlm/v21vjG2SaGrvwyHdO4IS4Uu3UdbgaD4Zca15w4vf/97+fHf/zHOX36NA8//DAAX//61/mlX/ol3v/+9+/6AF+NncX1wgW92ARot8ZzNY2R3Wa5XNrDn6oX6AcxZ1Z6lGyDH3ztHh7bob7LxmvXdENOLvVIErWRx1nCoAuFZRicvoUQjFYs3vf6q2MFrpRYxnHKdKPIs7NtCoYqpyepzLBaChfkmDplS2e0bHNopETXj3PBwv82A2gOrm+cSv4/bzzIWj9UTL5YbUT9QHnAaZpAA7xYokGm6qw+o6EJpcWkJWQ4Z8IE9OygpWmqqqBrMm9R6dkmGiMxNA3HVMKHNUNjtGSx3AmIk5ShkkXLiwiiJE9EBiFQbS8/Tplv+0zrBdLMjuXOySqfbU4xH8GpeIjLyfTbh4Za3IdLFnMtj88eXWK4ZGGZOhVb2ZvUi0o5248TdKFh6EoryY8UY25QHRNCVVdSobA+qlWl2oADEHScSrRMSkLPqmxmmhKEMUMlm34QI2VK248UCy47yUspKVo6HU8jjhMWOx77h0uXGY8vdwOSNMUQGrPNPqeWXbwwpuyYmI5qrZ5Z7pFKuH28wh2TFZCSpd4qQZSgCYWbKlqKfTrX9um4EYmE82su+ygxVbc5vtDj6Zk2+4eL9PyIi66XJdIywy+pZMnSNUxDV/M0qzIJAaah2JVJKvPEMYhVpeg103UaJRtD12i5Ee9+cJpnZtqcXu4RxSlHF7poQumHVR2DobLN4VGF57NNnZYb5RpeV3qOX+qhdLv1amMV+cRiL2O6isynMeahA0O8854JRiv2DXOh2K3rcKMNhncjrnlk/+f/+X8yMTHB//V//V/Mz88DMDk5yb/8l/+Sf/7P//muD/DV2FlcL1zQi02AXg6c0m6zXLbr4Svadp2TSz2evdjmscMjO3q9wbV74uwqcy0XL1TUayEEpq6ShEGlxdY1pA63jpfZWy9QsPSrvj5sn1gahsY/evMh/tUnnqXtR1RsBRwfVIEMXWk5DVecnE1ULZi5YGHFNjdd35VewEo3YLkXksqUqmPghimOaWDqKhkMN+ByogyAq5IXkWFx1pWe02wcAjCEYhX6kdpALV1QtHWSFCq6YKTsILPK0VjZJpaSMyt9RjImYdQLt8RdSRTextAEQZzSbLZ4xJknmHoNU/UCTTdiLh7e0XXeGKauVKnjVGIaOn6UKGZcVsUZrdj8wzcd5GNfOU8vjBkrW6z0Q7peRCoV4HugEK4J8uu2DmrXiNIYmVXrwlipcqcStHSAgVLvfcdUlTNLfc6v9ojjlEpF3UsvTFjrB7S8CFMXpGg03QhT96gWLMaqDiNlm9PLPc6vupRttVE/P9+haOmbkq+erxI5x9JpZyD0vUNFqnMd3DCmYKpEseVFLDe9vOqnCzANPZPZMDg8WuLZ2Y7yistadGbWq5Rp1sLMyAQFUyOIhALGmzoSwVBJiXuGcYoXxhRtlUAcHivnhIZBtWKkYvPff9dhZlsez821+b1vXFCmylIdVAZzfuPvbFfh2K1D4NXWqyNjFd5y+xi/9fg5np9rEyWqrXxguMTfeXAvb7xl9Jrn6m7Gbl2HG0n+2a245sRJ0zR+5md+hp/5mZ+h01HaK6+Cwm+OuB64oJeSAF1PnNL1YLls7L0DdLwob8FUHOOae++aJnj7neN86oUF2p4CSEupFLE1oTHwCpYbzAeaXkR/ocvRhc5L7u+/7c4J/se2z69//hRtL0KQoGvKMFfTNIZLFodH15WTNy5aXpjw219T17dgKgFMN1SfwYug7cW03IitmvP597JNMEnSPLExdWWx0fFjxZzLKlJGBoiO00woUwoqjs5wycYxNRbaPlLCfMenVrTwwpjFVCmMx0m6JeNPoNpqQsKE3uWN+mmcOMYOTvOt8wZxJjp5DWRBQN1XN4xZ7YdEWZUoTlLqJYu7pyqYukGzH/OGW4b5zNElekGMZWjEUhInkpB1NtigSmYOkiFdQ2jqmkQZTqlg6oSJVBUpCSIF3RCMVx1MTePgaJHTKz1SlFdcL4iZa3kZ81NjT6NAnKScX3UJYsn+oQKNksW3L7RoeRGNoslr9zVY64fZzySUbJ2KY9IPYlqZZMNo2abpKn0o21CtrjiVtP2IfpDk3nQb50HbjZhuFGj1A+bbHpYGoVRVv6GiTdeP6AZZBTZrtSqWn6Bom0gpCTPvQFCaZ0tdJfx6/74G043ipo13Y7Vi46Hi0yVb6VhtU+GwdNWKPLbQuayisxuHwJ2sVwCfO7ZEydZ55NDQTcc0263D8I0m/+xGvKhaWBzHfOELX+D06dO85z3vAWBubo5qtUq5XN7VAb4a1xbXAxf0UhKg6zGe68VyGfTe/Ujj2HyXNTckTlMMTWOoaHFgpLitRMB2UbB0Ris2uia4sOaqtgpqg7N11X5K4gTHNKgVDBxDo+3F/Pkz8xwaKb3khfK9r9vP9FCB3/3GDPNtH8fUKVkaq/0oZ/FFSZoByz2GyzZvuX2MT7+glMnHKzZPX2wrPExFOZnPrHms9oJNSZOpKQJ9mm6WPdA0VVmSWaXENhTA1dIFBctQwO00JUokY1UnV0oWSUqUCII4puWm9IIEx9QoWAZ3TFQIIqXB5IfJZUxBNry/AO40V7iHc2hIXKPCkr2fr55e2WQ2rMGWSeBWISXMtfxN7xckkuVuwNfPxjxyaIjTyz1+6LV7WO6FPHuxTdNVIn+apkyQYb3CNGDa6ZrGaFlt8DOhi66ptsZUvYCeJWsrvYB+kOBHKacWO5xd7mHoGgeGi6x0lYjnSl+BwzMxdNpuTNnRmag5CASnl/tE8x28WPnCKUNkdW+Hyya9QFnbqBagwNI1JmuqCtp0Q6VCjsJthXFKEEt8kvwzSdZB7so6xcOPE4JY4hgCP06xEx1T15iqF1jo+IRJipclUErkUtIomKq6JASJVN8frzo0surSpUnTdtWKq1U4Ti71QMLvfP1C7tV4aeX6payBO1mvPvncAhJueqbZbh2GbzT556XGNSdO58+f553vfCcXLlwgCALe/va3U6lU+OVf/mWCIOA3f/M3d/xaH/rQh/jQhz7EuXPnALjrrrv43/63/413vetdAPi+zz//5/+c3/3d3yUIAt7xjnfwG7/xG4yPj1/rsP9GxfXwHnopCdBuj2c7VsaACWQbGs9cbHGx6V6Tu3bJMgjjlKcuNIkTmekiGYRxwmzLZa7lsqdRpGDurI0GKhmzDI1HDw2zf0hhjpa7gWKRJSlpxhIzdcU48qKE/cNFgji9poVyK+zEwMPt9HIPIWCi6jBZK/C2O8cYqzh8+oVFnrqwxpkVJchZsnRsQ+OPnprl2dkWYaxUvRc7AZpQ4N/Rsq30hTLqucZAZFCgaVomYCjzrGW0bBOnksWOEvMTQmGORio2b7xlBFPXOLfS5/m5DqYuqBUsWl5IECt8UNtTbcaSbWDpqsqyp15kvuUrs9YcPH554iRIeVCf4XaWATifDnFGv53ORR8/K/UMfudaaC3dINn0XhpZgphCP0j49oUWDx8YYqRi80/feoT/409fwIuSXOwxzlowSkMpRdeF8hZE0A9jqo6BoQs0obzeDF0xAzUhaPZDQFXSml6GyxFq/hwYUfNLoAgBA2B6P1Cg7qm6Q9HSudj0iBO5oV2l5tfAq83QlFSGk2rZOARxqhJZQ9Nwg5hTy0rLydQFQbyeeA4kILQcu5fmIpmaoiICCs+10PGZqDoMlSz8MOGeySozTY/5jk/J0gjilJJtcOtEgSNjFR7Y3+COyWpeDd1pteJKFY6TSz3mWx6NooUERko2usaWleudroGXPouplFdlkX3nYhuESvJudqbZbh2GbxT5ZzfimhOnn/7pn+bBBx/kmWeeYXh4HR/wgz/4g/zET/zENb3W3r17+aVf+iVuueUWpJT89m//Nj/wAz/At7/9be666y7+2T/7Z/z5n/85f/AHf0CtVuN/+B/+B37oh36Ixx9//FqH/WrsQrzcZpDbxVasjLV+wOklZdYaJepE/tGvnOO/e3Tfjk8vk1WHIDNF3dcooGlahhUJ8cKEXhjjRgn/9ek53nXPxFVfN02VZlAQJSx3A6aHipQdg2dn25xe7uMGMVq2MdaLSoW6YOkcGStj6tqOF8oBduLUUpemF6ELpUi+2guIEslkzeHQSBkvUpvLXz63wPsfO8BtE2X+9Dtz9LJkcwCs/fKpJWZWPcqOofy94gRdCPw4zFp+KgZsMAAyPJOpqy10gOHpZLYi1YKpaPQoVtRDB4YQAo4vdDm20KUfxPhxqph+pmqjgZIhaPsxpgb1YoHDoyU0TTBRc3j6YnP9Wl9yTWwivts6w6TeRQLPM81RJikmgpqj0fUFQsocX3QtsTFpEqiEQBMil0douhG9IM7BraNlm6lagSBJee5im4pjKGZlEDO75oIQHBwu5m2qI2NlkhR6YaJA+IkkSVOWuj5+nGLo6g6MV5xMDFGy2AlZm2ll1R6VSK0PUpBkVcVawcTQNCxDKXgvdwN6QcJrpusKR5ZZu5i6RqNkY2qCnu8y2/SoFy32NgostH28MGG8auNFMf0wQR/MBTmQVVCA5zReZ0bKFPysDWdmEgRrbshE1caNEpZ6qvXpGDr7h0vsHy7xXbeNctdU7bIN9VqrFVtVOCxdJYFRIun6Ec/Nhhi6RqOoWthb2R5dbQ3cCsdULRis9ILLDJoHUbB03EhVsYvbAKJvNqbZbu0FN8uecq1xzYnTl7/8Zb761a9iWdam7x84cIDZ2dlreq3v//7v3/T1z//8z/OhD32IJ554gr179/Kf/tN/4uMf/zhvectbACXEeccdd/DEE0/wyCOPXOvQX42bNK6VGXcpK2OtH/D0TAsvTChnmxLEnF3t8bHHz+0Y7zTf8bFNjXrBpOlGGLpgtadaE0j1vrah8c3zayx0/Cu+7sZkZqbpcXS+y/6hIkfGy7zpllGGShZPnlnN6eYAoxWbIxnQNU7TfKG80vUZYCcurLm4QUwviDOT3wApoV40mW16NEoWd0xUGK/YnFru8eEvnuH4gvJ521MvYBqK2dfylCJ1ECfEbopt6BiahqaBLsk0oBSI29igp5OkEinI2nXKO2//cIm7Jmt0/Yhza316fpLhNFQ7/+mZFm03JMgkGoRQc2HgsTcoXAE0vZjbp0z0zIoiyQQUt0Mo6Uhqmk8oNZ4StzDPEKYOwyUr1xSSgKWrz/RiYwCATzNByVzgEsmeeoETS12CJGVPo6hUzRXbXslSZNi5lheTSGW6G6ZK6buQGRorzSalfu4GMbom0KVQ87RoKQFOoFGUnF3tM1wySaWq/EVCZsQDhRnzooTRso1S81LVoaGSlckPdEGqVmpk6hmeSgHQRys2F9aUUOveRoHFjmr5Nt0o05JSCb8fpUSxYgBoQuBfgnkSgKlppFLSDZRHohcmNPshHS/OiQMHR0vcPlFloePz5ZMrHBm7vOr6YqoVl/7OC3Md/v1nTwLgWAamrnBly12fXhBzy1jpmqo82+GYTi/3mFlzGSlbTA9dXgH3woSiaYDgFc002624USb11xLXfBfSNCUZcIo3xMWLF6lUXnxfMkkS/uAP/oB+v8+jjz7Kt771LaIo4m1ve1v+M7fffjv79u3ja1/72quJ01+TeDHMuI2YhYHpqBcmuQHmWj9kvOpw754ap5b7V215DR7U5+baxGnKa/c1OLvS51TmLWfrGkXHoF4wCeKEPfUCa1cw4b10AX1w/xDfOr/G6ZUei12f2ycqxLHSqtHJ9HvyOo76c7BQLncDPvvC0pbX59BImU8+t8iFNZdmP8CPVDXCj5I8oVnth7hhxELH5+h8R2n56IKnZ1pICeMVS7Xd4hTL0ChnJ1uAMJZUC1pmJKte25CKDTjAscQZvihKZO6oKgRYhs6bbh3lZ77nduY7PkcXOvz5M/MEmfjn0fkuHS9ColpBuqbYb/0oJYxljoOKMr86P5Z85eQqT9sdKgUjBw2z4appQg0hBVwsPhscIUFjemqMsViy1PFZ7PhKJJNM/uFFzNmNkUjY0JnM07gjoxU0TWxK8vuZNc5Sx0dkn1eJO0LHV8lqlEiiJMkwT4I33jKCZegsdn2eudDCDSNSqfBhVgZi8sIkq7RKmv0IxzSQCKq2QZik+FFKP0xyc+OpekHJFPgxwyWLsmOw1AmQQKNkKpabVM9F0w1JUknFVm3G44td3CChbBvsHSpwYKjEV06vECVK+qAvVeIrBJswZKDsasqOYpW23CirbAmWuymapuZAyTa4ZaxMtWBScbbHKw6e2W4Q0fMHhwuuuskOKhxpKvndb1zAjxL2NgromrqWtiGwsmRytuUzXDI3VXm229SvhGO6d0+N+ZbPc3Od7OfXzbQHuKx799ZUZXSuQ8nS6QVJTkwp2/orgmm2G3E9/QB3M645cfqe7/kefvVXf5X/8B/+A6D6r71ej3/9r/813/u933vNA3j22Wd59NFH8X2fcrnMH//xH3PnnXfy9NNPY1kW9Xp908+Pj4+zsLCw7esFQUAQBPnXA+bf9YgrPUQ3e8Z8I2NwfTZupgp/sTNm3EbMwkbT0TD3kzM4PKoWr7Kt863zTb55fo0H9w9tmeQMHtQ1N+T0Up+uFzM9VGS5GzBStnAMPTcHjVNFRZ+oKhzVl04uc3i0fMUFtOLAbRNVvn1hjfm2z0LbR2SWDxVbZ2+jQJySt03u21tjpRdQdQx+5+sX6Icxh0ZKTNmbr8+77p7g1FIXN4jxI6VpNNfyCeJ0HTAtIYgUJkkCQRRQsjWCSDHdLrZ8TD3E0jVKtk7BMtYBvhr4UYplKIHCKE43mQb78XqiZGbtqlzRWdN4+MAwhqExPVRkeqjIoZESn3xuke/Mtriw5uYMuzBOFaU/SojSNFMezxhxG7KRKFUn8oHE5caNWSK5W5+nnTqcS4cAWJUlRssm/+0De/jIF88SJxLH0nB0HV0TdPyY+CVUm9bfe/OfArh7j2IaD5L8zx1f5MJqn14Qk0qlZB0LkRvPBmHMclexHr9zMWW85jBVd1hzIyZrSjNK0wRenFI0DQqmqiolqWSxG+Bl7TJdE9RLJkudgPmOj2Mo81uZKbh7Ucpsy1OMuETh7OpFM5vbimVYK1jct7eGqWss93xOLvYQtk7R1jk8UuH4UheZyhwHuKde4OxKnzhVbEAvWldHH4Qh1F4RxJKCpZKBfpAQxCm6BiNFi4laIddWUvNqa2zPqaUuf/XsAl87s8rFpksYp5Rtg/0jJe6fbuxok51tecy3fUq2QZwqX8RBDDwjV3oB9YKZV3mutKnbhr4tjknTNO7eU+XbF1p8Z7bN4dHyZbisd9w9AcCxhS6ffH4xe85UOq4Lwa0TlZueafZS43r7Ae5mvCgdp3e+853ceeed+L7Pe97zHk6ePMnIyAi/8zu/c80DuO2223j66adpt9v84R/+Ie973/v44he/eM2vM4hf/MVf5Od+7ude9O/vNK7kIn5svsvprFqRSslE1ea+6Qa3T1SoOObf6ERqYwvr+fkOPT9m/1CR0YqFrokdM+MGmIX/94nzuemoqWuZn5xqBT15rslqP6DjRXz4i6d5cl9z06J66YM6WXPoehEXWx5tLyJKU4YcBy3Xs1FKy1GScmqpx0zT5SNfOcNY2bniArrWDzm70scydPbUHZa7ygRVz4DSi52A4bJFvWiy3A358qkVao7ByTChF8QZu0hmbTwrvz6fPbqk/OmCmLJjEMQpvSBSbTPWAbuxXAdPp0A3WM84okSSpIkyoY0T7EDJBEgUDb5o6USJqgjEmRZTDgSGvMwSJyCESnRqBZMDI0VOLHZ5y+1j+f0btEp+98kLHJvvoGuKNRcmKW6YbKpYpen6e2ys5KQS+kGcWXGo7xskvME8x0GjSSw1lvwyLgrsG8SSTzw5pzbX7BqhSxxTtcDaXoy8xtxJF5f7y22Msq3zzfNNirbBaNnm8HiRjz7u0gtiipZOECUZS0xVyfw4IYoFI2Wb/cNFqo5Bkuk0jZVtWm6EH6lWlkxV9W+lF7KK8sIjS5hsU8m1V2wD1x5UodJNCu5VRydFsdQsXShMXz/Mq3z1ksmdkzWGShZSSo4vqPlUKVgEccr0UAEvUmKaXpRwZqXPPXtquGHCSi/AjZJNc2+gUK8JoboVUqmmS6kSlOlGkWrB5O6pGvWieVnScSm259RSl1/9zEmevdhmrR/klTk/TokzQcydbLL9MEYTgtGyzUovwCopMLqfGR5bmazDVL3Annrhqpv6m28dvaIa9mS9wEov5OBwmZYbbYnLOrXUVT+cT3ix/vVf83g5/AB3M645cZqenuaZZ57h937v93jmmWfo9Xr8+I//OO9973spFK69jGhZFkeOHAHggQce4Mknn+TXfu3X+Lt/9+8ShiGtVmtT1WlxcZGJiYltX+9nf/Zn+cAHPpB/3el0mJ6evuZxXSm2e4ieOLPKHz89y2TNYbRs0eyHzLY8Hj8V8offmmW0bHNkvLzjU9Fft9h43cq2jkBhcJZ7Ab0w5jXT9ZwWvRMWiUqeDjLf8ilYOo2iRcUxaLpRhnlSjLZawWS4ZG86uQzaXJc+qHdOVQlidRKME5mDpsMooWgbjJQtnrnYouNFOKbOoeEyhi7WF9DbRjfZrHS8KPv5kLGKTduL8SJlNqtnWj0dT3mtGbpSk+75MbWCejTHqzYagrmWx3Iv4J6pKtNDRSZrDrNNjzADb5u6xnJXUdUHp/2NDfXtMNCD/V+Znkq8ULnJyxQcU9Hjl3uB2qAzN3nL0LB1DS9KcnzQYJPcP1Tk0SMjWBvA7XvqhbwCu9INeOLMKlEiKWVsOVNX5sSRXDfC3dT6EqCTmfoOhCNR49Nij7dYpxjWPBIpeCKaxsVSoPtMImGp64MQuT2LF6nKSt+/9qQJoFYwWHO3BukOxv+nT8/x2aNLFEydNFNsbxQtokSZEqeJRBeSOJaQgDCUFtR8y2PF0GkUTQwt5Y6JKu977ADHF7t40XmV0CcpBUtk5r5qHBXHpOLopCm0Mv2xqmPkOlOmpnBRjmmQSEkUS8brNt0gRiL47ttGsA2NmaZLo6gwNl0/Zs0NMy9DdWioFszcP63jqQTg8GiJe/ZU+drpFVrp5mth6hmVMsOAxXFCveIoFqct+MnvOsxXT61i6GJLjzyQObYnTSUff+ICz8y08sSnbOtZgpyy2gupOiarvfU2OrBl9b9kKdZhoe6w2g85vtAlGFRUpXoyHFPn9km1Rl9tU//W+aaSF7kCRmmkbPNjbziAEGLbVl+SSt5x5/hlrbqdQA52K25Ex+SV5l93TYlTFEXcfvvt/Nmf/Rnvfe97ee9737vrA0rTlCAIeOCBBzBNk89+9rP88A//MADHjx/nwoULPProo9v+vm3b2La96+NaH9/WmXHZVkygrh9RsjSafcVA6gXZSRFJ2wuZWXN3fCr66xSXXrfVvsJOVAsmZVtVZU4v92kUlRr0Tlkk040i9+6t89xcOzdaPbXUwwtjGkUF8h6rOkzUHCYgP7l8373alg/qUMnm0KgaX8cLOL+qRAQbRYsDIyVWugqka+ga41UnPyUPFtBvnlvD1jXmWi7z7UDharo+pqbRD5QG1EDoUJmQqlOuEHDreIXZlkeYpAwVLVpuHz8SuEGMG8b4kWSlG3BwpMhtE1V0jXxB6XiuUuYW61ifneYECqcjMTPn+ihFgcVjtYAODIKlUGyoJE1x0xQhBDXHQGQVmDiV6LqGQFUKFto+Xz65zInFLvNtHw1lwRElKaMVZRVStqFetNnTkFxYdXNFcVAJCFlCZ+qQSvX3kq0ShGnL5d7oKAUR40uDz4eHWUgrmLqyANEATSgVaokgTFIcQ8OLUzpuSJi+uMN8GKebfm+Q5A3+3gsTipbOWMUmyoQnUwn1ggFSI9ZUEpGkEKEwXDKRKhnQBGaGbdM1wbdnmnz/fZOcXOjhmAZvunWEb5xr0g8Gz4XI8EQpI7bNVN1hsR1kXoVkHnAao1U7F6k0swTdj1NVQRTw9rvGmaw5fOzxc5xY7FLJrFa6fkio65QcM29/D/zTnpvtMNtyeWamiUQlWhLVltM09R5xqvSkdE0gU1VtCuKESsHgjUdG+G/v28NiO+C5uTZhnHJ6ub/ukaepVuYbj4ywp15gpunyxNk1ZeAswbH0fEMvmBr9MGG25XHLWJlTSz0eP73CdzLbla3wgQNV/yBK8OKENM0IB0IipQLQf+nEMnvqhatu6ksdn9GKzUzTu6Ia9t4tvPBgc+KgaRrVgrbp31+uxOFGYYxeaf5115Q4maaJ7/u79uY/+7M/y7ve9S727dtHt9vl4x//OF/4whf45Cc/Sa1W48d//Mf5wAc+wNDQENVqlZ/6qZ/i0UcfvaHA8O0y464f03QjhosWsy3lju4GSqcFZHaiVyqwQ6V006noZig9Xu+49LoNNGOirKpTdpS5aNePqRbMHbNILtVoKds6q/0Ay1AWEwVL36SOPViAzq70t3xQVVvNpWAaDJWUOW0qVUJyfL6Ll3lzVQvrOCpYX0CXOwGGJnji7BqWoWEaqtIRJAm+r5SfNRQtW9fUdVAecmpxXe0pS4kzy31W+kGOwRECZKqAwycWFVPvlrEyf+fBab50YplukFC1jU2K09ciiR3EklioGpUmBO+4a4xvnG3SdCMcQ0MysAhR7TolP6Waf7au2kR6muKFCaeX+4xXbY4vdPjObJs4UXo8ajOO0IQGJAghVPXRUZW8Vj+g5SfrbcXs5C8lhLHyr9OF+nz75RIPRefRhGQ1LfCF8AhdaaMxUCIX2IZKQjRN+ah5UULdMbnQdBU4G6VcHqfrieZOLpkbKvCzYwgsQ1dSBAK6XkQ4MFHWNCxdUy00Q+CGKXNtn7Kl42TVk7a/rryuAZau44YKID5esXHDhJk1l1PLvfzZqTgmtYLJC3NdFrs+Lal0neJUSRlcbHpZoqmA/lIIdAEjZZuKnbLWV63dOBMdnag52IaWMTo3Wn508CMlTVAvwt0j9U3ki9PLbkY8SJlp+vQDBdAeXH8903BKMkFLXROULB0vVO3Ae/fU+ZHX7cMwNN5x9zhHFzp88cQymlDSHJYhcr+7xW7AmZUeF5seLS+knAHV9axCNWhNm5qGHypZi44X8TvfuICUbIuXGaj6r7khjq7hOHoutGnpGo6pc3Kpx2deWMSLEqauuKmnPHhwiH64/KLUsG+GxOFGYoxeaf511zyKf/JP/gm//Mu/zEc+8hEM46V9iKWlJX70R3+U+fl5arUa9957L5/85Cd5+9vfDsC/+3f/Dk3T+OEf/uFNApg3Mrab4IpCneKY6uYLFBCTbNNNpKLfdH3FcNrbKN5UpcfrHRuvm5QSKVXC1OyHjFUsTF2jH8QEcULblZxa7nHnVJXJqnPV196o0fLUhSYdL6JWMDO80zrYFNYXIOCyB1VKmVerKo4ChN8+UeHCmstaL2CpF5AkKXdOVrllvIKhKWr8gFauqiwpciDZnLVElHmrzAHbmqYYa6mMcmHDimOw0PZY60cULI1G0cwtPTaCjrUMz9L1I04u9vjTp+eIMrnulhehaSIHhV9LGBkg29AERdvgjbeM0Q8SFjs+vSAhShKiOM03ekOXxElWIUYQZcarFcdgruVydrlHmEjKts5Eo0CcSlZ7IW0vZrpRIMgwR46h0/IioiSh5JgZLpDc104JS2R4p0zyIImhYfloQnI+afDV6ACJ0BHZ9TU0QcHUKTtKSqFkGbS9EF0I9g0V6fgxtYKZ63MZuqqIXmoSvO210pTXX5RKZJxScUzSVG7Ckg2qQIkEU9cRpDmmSaYK0yU3trWEyFhyEi/TEqs4il212gs3rTnDZYc33GLT8SK+dmaV2Zan5DIym5QwVr+/0g85MFxEZqzIgqUzadgsdVVycv90HSGUfU7JMji11M0sPwwePTSMEIrltdwNOLPco1ZQz8nTMy1abkjTDbF0Nd/iRAGspQQ/kRSEYmmGsZq/ysRaWbW8/tAI735oOt+ED42UGavYFEwdIRQBQNc0phpFDo0UWe1HfOr5RW6frCDkADclVHU0TtV9kKqqLyWs9gLansLB3be3vi1e5vvuncxEbtXzOjDZrtgGQyULTSMzOO5TsK++qd8xUc0JENeqhn2jE4cbjTF6pfnXXfNdePLJJ/nsZz/Lpz71Ke655x5Kpc26FH/0R3+049f6T//pP13x3x3H4dd//df59V//9Wsd5nWL7Sb4oILihnFOk5bZQy40dTJKU8VSWuuHaBq44bXZd7ySY3DdBi2spqtaXh0/phsov6wklXznYlu1GXSBY+p8+EtndlQmHgCPv3l+jQ9/8TTDJVtZTFxSWh8sQIdGSpseVIC5lsdC28MxdXp+gm1qnFnpK5d2wDE0elkL4cxy/zJLlomaTZLhhB460GC+rRhJA7q6JhTmQ22sgjiR9II41xZa7YcglbBhkCVcG/OfQRXGzXRzOn7Ep48ukcgU24AwUe0RlQiQq2pfLVQyJzI1c0Xz96IY29R5861juGGCH8V84cQya1mLVeTMN0gSicgqUnEGXDZ1VeUpZYmhbWgMlSyabsiaGzJWcfDChMPDJQSw0E1IElWFSrMKwqVtuzRTPNCRHNcO0JFlTskGUlML/wBnFSXKENgNYxpFi1vHy/RDpW/lRgkIKFk6q72svZgxvnZcotPU3CROiZKUfhgryxm5fp+CKGW25WcmvetefZ0gQRNpboQ7iCij/hdMDV3TcMMITUiKlsFwxuzcuOYMTJnXzaAHulYqaStbOmGc4oYpw2WTthshHQWsrxZM7t1bp1owObnU4549NSarDh/+0pnM8mN947QMjW9faLLcC3h+roWhCTpeSC9QquVjVYe5lgciq0Jm4cUSRygJiyQDsJdtk7fcMcbPvON2DGO9FTXb8mi5EY8dUebZG/0hVUKpc2qpxwP769SKyj/P0KDpqeqkrqlJHMXqlHpquUfNMTk0Ur7s+d+Ilzm70kcT0CgalG0TiapoKk0x9SwBpKRM1krMt/2rbuqaJl6UGvaNThxeLozRdvipV5p/3TUnTvV6Pccc/U2M7SZ4xTFoFE2OLfikmS+XpmXYh4ylZOhqc/LChI4XUTCNm6b0uFux3YOxp16gXjD59NFFLEPLwKwGRTtmseNn7U1BGKdM1gvcNl7FMbVrKhNrmuDB/UM8ua/Jc3NtLqUQXIo1GDyo355p4QZKNHKpG5BRfvKNyDJUElB1DNpexFMzLUZKFsMVG1NXANzFjsdM0+WB/Q2COGGqrthCS90gA6yHijmGmhO2oeFLpbfU9ZWRapxICpbOUi/Iq3KXeqgNkqcgTkhSCJMY21BA1yJqbqUSSgWdtX644+RJ18S6wrOUfOnECqBO/yBwozTX9hlgejQxqKyo3/PClHNrLkkiMRyl1eOFCUXfYKhk4pg6ZVtdQ0vTWHND5lqeqr5saJVttTbWhcddxgKPR/tBaHhRyoI9hilT/Ez3SI0dkjQllcrMGODsSp+yYzBWcRCQM6fGazbeinpvM3s2d1J1sjQFzC5ZOm6kZBoGfPbBtSlYWsYaTPHjdYuWJIUEuWWK5kdppnMFUqrkbHqoyJHR8pZrTteP8cOEqmMqYkEqabkhuqYxPVzitkmNuVZAwTJY6YUsdwMm6w63jisc2MmlXr4hzXf8bTF/9+9r8MJch7kM7+ZkoqjjVZXwB9kE2Jh7SlRVVReqCmSbOvdN1/lHbzq0KWmC9Wr0lK08+S6NQZW44pg8cmiYTz2/kHkUZgm2zJJtISiYWmYRI/P7v93rgVLqFkJD0wS2sfnnoywTLFkmb71jnL96bmFHm/qLUcO+0YnDy9EqvBp+6pXkX3fNu/bHPvax6zGOV0xcaYIbmkbB1OmISBl6inVxPF0o3IXCUyhD0LfcXr9pSo+7EQN9lWdn2/SjmJJpcM+eGu+8Z0IxXHIE7WCLFDimRr2gko+KbfL2O8aoZQBx4JrLxNeyAA0wHf/+sydZ7gYZsDozFE1SNCGoFUw0TeCGSXaPBUGS0gliCraBY6jy/mDXsAwFjnbDOMcajVRsyo7B+VVXtXGFEj4ceJcVbeVnZesJY5UCs21fOcTLy9lwlk4GdFYVHlvXKWRKzwVTp+Iol/eWH2MaGmmUXhW7o2sZnkgqplrFMXCDmCADNusC+pn1DKhW1UAE89IXTjPF7yRJMXS1IfXDmDBJqBetTFcqZabl5cnOpbHx+4aAfXqbR43TWCIl1i1O6AewdA0vVPpV8pK+5HpiJzANwVzbZ7zq8MH/5m5KjsHHHj/L2ZU+I0WL2aaft3h2EgIYrzosZ/YgRVMjEIJGweRC6OXXx4+SrD0lL/s80YYq2gBYP3htKZW0g6Grytn+oeKmJH/jnG66IU0vYqRscf++BqambarWKMmBHu9+aB9hnPLNc02Wu35mA5Ru2pCOLXS23TiHSjavOzTMk+fWSNKUPfUiR+c7lG2Di00vlx8YtCgtXc3PwZMqBBwYLvIPHj2QM902xk7bVBXH5D2v28e51T5Pnl3DNlTVdsAidQyNw6Oq7ffCfDdLFC//PBurzndPVTmx0GWlGzBcUkrsG9l9uia4d2+Nxw6PMFlzdn1Tv/Sg+b7X7+fTzy+97InD9W4V7hQ/9Urxr9vxVUjTlF/5lV/hv/7X/0oYhrz1rW/lX//rf/2iJAhe6bFdZvzo4WEe2F/nP375jFI83nA6F4LcqkKJIpo3VenxpcZAX+XEYpdkw05xdrXPscUuf++haVpuxEMHGiy0A9bckH6gsAyNkjKCtTIg78YT74spE+/05JKmkmPzXSZrDvdP1wnihC+cWKblRvlJ2Q0TyraOYwiargLz2rqGF8RcXHPRsrbN3kaBQyMlojjN2TXjFRtDUyDZomWwb6jAuVUXXQjGqxYtL0IgsHVBwVIihAVbZ6JqcX7V3VQtWjfT1YmT9cqVrgkaRZM1N1pnTCFJkhTT1PIqZyq31x5SG3aa25D0goSlrk/XT7LWhcDKXgc2JzaCdV2jNPu3Ab5PTyTSVG2xfpgw2/SUSKNgh3giyZ36Aq81ZhECFpIyx5igXjbxgpgoVTg5U9PohwMj2fXNtOlGtD1lnbPc8fmTp+d490N7+e8e2c/HHj/HuZWeEoeUEkMXuAEKL3aFEMCtkxW0xT6r/UBh1dIUN4rRs4QySiEON5sID8I2dYgzKxK5mdU3wDjFqcQxFTh58Ptbzek4kYyULG4brzBcsjdR+bt+jKLUKxLD9FCRt90xzkzT5exKH4BDIyX2NtTztNXGufH1wlgxPUHpexm6YrFFWaIWkeYgbRBYOhhC4MYpRVNnqGzxJ0/P8uxs+7LW+1ZV/MF7B7Fiyj18YDjfQP/eQ/u4uOblAHchoFG0ODRaYrpRJE5Tzq26zLW9y9r1G6vOfpzQdJV0w1pftZAH4PsgUpYz903XecfdE/lBazc39e0qMG+/c5z/xpp6WROH69kqvFb81CvBv27HidPP//zP87//7/87b3vb2ygUCvzar/0aS0tLfPSjH72e47tpY7uHaKbp8snnF1nqBngZ3imR6iSuCQUqrRVM/sEuMhR2qrtxvfQ5NuqrDNpwA9+nrh/xzEwLMxOpOzxaZm+juEmrJUgS2p6qZoTJ5RvXiykT72SRG/T1p+oFKo6ZazPpmlI0RkqizM0eoXBDUSpJ0yQfV8UxkVKpKNumTseLcnbNF0LJHQAAzCJJREFUQiegZOm03BDpqFbMZE3ZXXT9iLarBBH3NkocGi1xZrnPUtenZJvYhk4arbewgHzsg0skJehZdcEy9JwxFWWtk0bRou3H2Sa6dQxA4Y6uU7B0wlT5zClAcsrDBxpECcy1XEDpIm1MwCSXY6kGBbiNbZwoVT5mliE2tfq2S+Z0Eh4zz3PYWAPgRDLKV8NpdKEh+yrpligdMJX4iQw/JvPKh0DZ2WgoodFvnlujH8a8/7EDvP+xA/z+kxc5vZypeceS9Ip1ufVwg4TXHRzi1FKXMyt9DE0nSVVSVBDQD5Jtq2n9UElPGIMEb0PyNDhwCASTtQJ3TVVpuVF+YLh0ThdMnT99Zo7n5zqs9nzOLLs57k4xzuANt4zkm92Zld62rZIBPX+wcTYzFf2BabYXphweLXFkrMx8O6BRMLmYVQ2LlkY/kEq2AtUqRQw0twR376ly397GtgytS6vEBVNjruWz3AvoBzEF0+DwiGLWHRmrcMdklbumqpi6hmlom/BQoJ6z6aEiJcvYtup820SF3/7qedb6obqXyz1m1pTwbdePmaw6fNdtY/zI6zabhO/Wpr6TCsztE9WX/D47jevZKnylaTTtJHacOP3n//yf+Y3f+A3+0T/6RwB85jOf4fu+7/v4yEc+ssl7529SbPUQTTeK3DGpmFhCCCoFEw2FnRgYYD52eIg3HhndlTHsVHfjeupzDPRVNCEYLq232Qa+T4udgGdnOxwaLeUn2mphvRzc8WCwfVj65XPpxZaJr7bIXdrXD5OUJFFg4Y2RZMDjPIHJXjtOJW6YMF6x8SIlEzCdYbneeMsInz+2xEI7zQDwMXsbBW6bqOIYGt+ZbeOGagPeP6zkEoRAeW8FMbahrCsKloYfpflGHCXrzLYUCKLBwqY0fFpuyIUooaArdlyUpPT87Vt1cVb5MHTFjDM1QckycQOfRKYsdgNed3CYiZpN11+m7UdXvOaDdtNA9VsXaiON4+wzSNU+S8J02/ZYkZC32qcY0VxSKfh6NM2JZAwJWKZGnKR5ZavnK1kHTcswUlLkkgmmIXK3+X5m1bLaC/jU84v85JsP8zPvuA2QfPP8GkGUcmalz5WbmupznVjoZtY+Oo8cGkYTgoW2z2LHo+XFNAqqGuNfYtY2qDwrlpvMvyfzPxUNvlowefTwMCXb4NxKf9OBYYAXHCRP903XOTrf4YsnVtAE1IomFgZtNyKVkqWMyg/w0a+cZbbl0ShajJRsdI1NG/Vg43zqQpO5lkcYK5awTKFSMAHBck8RNwxDw9DU/Et0hSsyNJFLM+hZ9jxUNLljsnZVV4BBRe3jX7/A548t4UUJJdvgwHCJqbrDfMfPDbsPjZQ5Mlbhubk2t9TKW1ZHXruvwdvuGOfTL2yuOt89VePuvVU+f2yJi02Xe/fU0DSNvY0iHT9ira9wd/fva/CvLgGx71bcaAbbdnG9MEY3g9TCbseOd6ILFy5s8qJ729vehhCCubk59u7de10G90qNekEpWA9O2ykCXQPDUhT3jfT4lxI77Rtfb32Osyt9Wl6onNe3OFHUiiYrXZ+KYyh/qEtMLEuWni20yq5iY1xPRsml7QlTE7iZHYZSGlq3KVFjURtcIlW7rmjq+Bn1e6hkcmHVRROC//Cl05xc7NPsByRStdNMTYGlZ5seI2WbB/cP0Sj2GN5wzYZKNq+ZrnN6qY8bKJxclKa5sGcQp4QZ9srUyJlZ3SAmiFOGyxZ+nGkMmTpJKmkULZZ7YW5dAqqdZhuCKJa5uriyJFEGr0tdJUEggNNLPZr9CMsQdIP4MuPWS2NT2iHI1NGVZUvR1Ll3T40Ti13cMNz2NQyRUhEBvjT4XHiYxbSiAPW6suhYaPvomYikahUpbaROEKNvwBUamkDKFD9WoPt+EFNxjE2n23c/NE0/TDi30qNkG4RRmF8TTay3IQeVv0GiM99Wgofff98UXz+zxh2TVbwoYaETIIAUuckqZnBtdMAwFAZogIPUhBIcLZhKa2qgkL7VgeHSA5Ctayy0fbQMGO1mWMs9Wet4tR/yyecWWOkFfPN8Ew2YbXoYuhJ1PTyqfmaQTH73bWN88M9eYKnrZ+bLMfWCyd3jFQ4MFzm51GMqc0aYbbrEqcSPFcutaJlM1pV10bmVPkkqmWoU8/kLV64wHBopM1S02DdcZE+9kOGa1lt3g4TiJ99c3lQdmajaJKkySm66IXvrxXyjPzK2XqFb6QY8faHFx5+4wLNzbQqmThRLDo8pyZJawaJWsBgp26z2QuY7/nWpgNzMFZjrgTG60VIL1yN2PNI4jnGczZo6pmkSRVc+gf5Ni9mWR8uLeP3hYeZaPktdHz9SxqWjFZsDwyWaG8rvLzZ2emo5MFR6WU43QrINVwhAAagf2N/gq6dXtzSxnKoXKDsGp5b7W5aJ33bn2DUbKl+tNXlpXx+y9lKkbEdyPzY26yLpmrKHSFEVsn4YkySJkpfI2HlNN8w3b10Dy9TxM2DPD9w/xf5Gkd/66nlOL/fyUy+o5Km+3yRMVJLihkkugilRCdGemoMfp/SDJMMvqcpX3AmoOsoQ1otSSpaG//9n70+DJMvu607wd9/uu4fHnkvknrUvKFShqgCQIAGQoMhurSNpSLGblGSmDWSbRH0ZaazHTKYPGn2ZbqlbUo/JJJI2bSQlkSJEkS2QQBEAib1QC2rLrNy32CN897e/d+fDfe6xZ0ZERmZGZsXfrFDIygiPF8+f33vu+Z//OdFKr6/v5qy0OBJNWzVCLhTjFkSpEqZnPy9OYakboG1bl6QqkaoNOFm26WRhridGCkzV8vzwZvO239uWDq+Fp+lJi65UhwxNoAJZExWA2xfqK5dpiWPpdMJY2SNkADdOJQ0vHrB5S1krUxNicLrtn7L/4+s3ubHs0tYhSVaYs2jdI20ZgrGSzcnRAn6kNHJelHComuPESIHzcx2SNMXUNKJNPg+xBF2u6MT6U2SOoalpSMvANjQuL3Sp5CyePbJyYNjsALTQ9rMxeZ0nJivkbX3dKL/Gty8vc72utHVDBWvQRl/s+HSDmDNjhYHT9n/94QxRknJkKIdlaMgUgiTl6lKPSs5ksuJwve6St9TE3/GRAlcXu/ixRBcCDeXr1AsTRorWGvPZfm3FMEw3Pa4s9Tg1Wtywwa4HFKsZqu9eWablqbtdzVmcWiVA77POlxY6/Lf35qj3QnKWGqgo2LrS8gXRIO7pdte3V7XfGZi91hg9aKuFe1HbBk5SSn7xF39xTZyJ7/v8nb/zd9Z4Oe3Ex+lRrP6H4uRIkXzmpVKXIRK1uc22fCxDu+sPxWanltVizqJtcHG+w5s3G/f8dHNypEAlb9J2I5yyvuGD0XIjqjmTI0PZB2NwFBeDPxcdg7/wscODgOTVNPFjE6XBpMlWgcrr24/AHVuT6/v6lr7iIQSCPku/2nNHCOVEnKQos8ZUGfAZmTu1pQnm3Ag9M2EEgRep5HrL0Lix7PJv/+QKeUtnuRcy3wqYafo8faisdFZ+xK2GS9NNmBrO0/VjvOxZWe6pMX4/SqnkTZ4/WmWxE7DQDugEavTf1C0Ktkbd7XGzoUayNU3ps1b7L6WSNS3JIJL4cURfG61rINMVwfeaya9tPhdxCjMtH8fUGS3ZhEnKty4t4cfJmq8TSF40bnErrTCbKl3HfLqWAZVSMW5RKnnyUInlbshCx+fwUI65po8QAiObiBwwa6nSnTmGRiLBDxPevdVkqlZgruVTsAwmyw62ofPnnjvMrYZH5/ISaQZINdYCJw04VMnhZu7oZ8aKAw8jN4yp5k3ylo6b6X3W/o4r962f76cBtqEjUf5ZBUdnpGCTSsn1ZZeXTjgDXcn6gxIoO4JuGGPoyoByvuPzYm1ozefPMTVuNV28MObESAE9A+j9Nnq9FzLd9KnlDV47N89yLyBnaVTz1sCOoyhl5hbe5bkjZW7WXUZLNi9MqZ91uJrj8oISyy/3AsV+ZqL1zdj1rRiG3QAKP0oYLdmcHS9Rdkx0jTVtvdNjpQ33ruPHKpJHaNQK+oa4p3vNgDyKDMzt6kFbLdyL2vY78wu/8Asb/tvP//zP7+nFPApVsAxsXePSQodLi12iWIV7moaarppteWhCsNQJ2GA0tINav8jUe8FAzBmn6WDRe29m6zFjWFmMOn7Ezbq7K3r2yFCeV04O85UP5lnuBpRyJmYWudDxIlIJnzgxzDs3W7cNsfxwrsPf/tGTzGaLY8Ey8MKEX//OJoHKV7NA5bLDmfHimvbjubk2oIDCnVqTq/v637u6TJikmLqmDBUlgw1QF8qXS0rl/VJyjMwoUtkU+HHCeMmhnYmWbWNlOtAylO+QbWgsdpXoteoYCF2QSFjs+Lx23sfUBCL7GUXb4IkJtTFdXugx2/IynyGlk3vuSIXhosPx4QI3Gy7v3GqSSnjqUIVKzqTjR3T8hLYfD7Q1fXuMPjiyDA1NyMwKAfxYbe+2rr4h2EThvLpdtZ3qZtNlvSDm8mIwGB3vl0XMj1lXOKy3OS2X+B3/GcJVy5JAeSxNVhwKtmp/X1vucWJkJWi2mleGjufnOiy0fdwoJpVQdkxA4kepamEiM/PVmH/3p1ewTZ0gSrFNDVMXXJjvkqJCfIOM0Vt9HaWcwWhJAYG+FsY2dCp5MxsycCg5BromMjuCle9f/Vvr/Qw5Q/DyiSqOZSo/q0B9pvtO8j/z7OTgOV19UGq4EZcWujTcED9MaLoxtqEhWx4dv7RGP7jYCQijNMvSlKyWEAohKDoGS90AW9eYbfkcquRYyqwW+r5G/a+r98LMUiPhUCW3psU8dNzKYqdC3DDm2HBBWRVIuW2GYSeAog+GGm60xh0cFJu+mklff8hUnnsWix21aa+Oe+rLCZ4+VCGVkvNz7T2fbHsUGZg71cPk0bSd2jZw+qj7N223vChmsRPww1stklTFHCQpAwNAI8uv+uHNJp88NbLrD+PqRSZKUt6+2cQLkyxCwKAXxDTdiO9cWkLCbRejIE750lszLHWDXQnHNU3wcy9PsdAJuDDXGYxCg0DXNJ47VOKzT4zxu29O3zHEcrWuIE0l/+brlzcPVI5VoPJo0RosPiXHpGDp/OH78yDgC0+OD1pgdxKmnvyxIk8cKnFxQV2/paucM6TEjVS0g0zV79oNIuxs1N93labINjSO1PK8N9NS92TV26oLQSgTGl6EF6q2bUcT6JoKzO35ibI5MAQFU6cXJYRRyjcvLfPp08O8eHyImZbDG9caGLpqbZr6ihZsvhUMRtNrBaWvOzpUYK7tkaSSnq7hRTFIZRPQBz/9OBdNCLx4xW9I4YWVrX67IGmr6oUpbuhjGmKNRqosfD5vXaSiBURS49vhsQFo0oUC9aNFi46fACpIuR8IvdQNefZwhdevNTB0FU58OGM059o+UqqWa5jIjGmTmXBcxR/1hze8KKGaM3lsogwScoZOlGZhwKGyDdBQvkphnNL2Yip5ZTj54XwXx9Q4MpRjqRNyY9lFSkmtYFLvygHw6keR9J+LkmPQ8mKSFC4uurxwtMqnTg0PDhNhrNqzT6yaquoflPxI593pFl4YU3RMirbS2XX8iCBOWewGA+DU34RLjslQwWS5G2IV1tp9GJqgF8QMFSzCLHy5lrdY6PhrvtbUNbp+zGzLJ2/pAwDZr76Ted7WubbU46UTNdwd5rbtBFDsRCe0/pAphOD0WJFuEFPvhdkQRULDDZlr++iaYLkb8C++evGeBN0+igzMduph8WjaTj0aXOADqvUaGi+K+fVvX8ePs5HjjHXoBhFeFFOwDSo5izNjBS4v9u6qPdZfZN6dbtFyVeZWLZtok1LFZhwbzqMJZd440/Q4O75xMbq40KXtRRia0hntVjh+eqzE3//8Gb78njLAdLOE+GcPV/nC0+OZiHRnNPxtA5W9iOGCRcONBsHAoDyIkkzJ2w2SNQDtdq1JTROcGi2SNw06foKWAZu+t1GcKksJS9cYLtp0/HgwKv3iiSEavYiSbWBmQG11KyyRSgjcDeJBLIieGWb2gmQgRvZjSRArxiogwa27vHY+4XOPj3OokmOmoia3pFixbej4Mcu9ABDUivZA23JqbIWRyVk6qZTU3RU9okRpljSRudyLFd2NZAUsxMlGE86dVl/NZggIsv92WGvxY9YVLJHQTS1eC09Tl+r9sHSoFSzGyw6WoZNIH9PQBoHAeVs9K6YueOl4jZ96ZoLRkk3BMnh/psX/548uEMQx850wi1SRIMQgaDZJpfLq0gSnRpXm8MO5Ng03pJ8V12fG7KxfqwllObHQ8UmR1LsBbhgzWS7yiePDzLY83p1uD55HWGGW1gvquxmgSlLJXMvj617E2bEiZyfKDBesQQzKasahz2R/ONfGC+PBZx1grGQRxjFumHJtqcdULYcfpcy2fIaLNo6pXO/dzMS0aBsD9/muH5EzdX7ssVH+9OISXpQMnp3+/e7nSLphwomRAhLVpi5qYo2tSMkxdp3b1l9Lz4wXubDQ4cJ8h0PV3JaAYidtvf4hsxeo96V/vc8dqXA5swDxM23f4WqOhU7AbNu/p0G3jxoDs916GDyatlMHwGmXtdl0y1JXTQqdHS+x0A5IpGoTCKGiFwq2arFU8uaGMeOdVv/UcmG+w/W6SzVvZjEHSTYqbXB6TEUr3Ki72FnS9+rTzUxTuQiXHZOz46W7Fo6fHivx97Y4Udysuzvu6982UDlNqeRMWl60xvtJ/X/FqezUE0oAtqlRdlTivRelRFmrYSinNh5NCKo5k/Gymh763BPjvHpimP/vn1zh3ekWY0WLphsSxCk5U4VohXGaTZet6G+6QbTivr2q5Kp/JxKWOiHfv1bn5RPDnBorsNwL1GYVJ8RpSsMNaXkRI0WbU6PFNe2T549WuTjf5cJ8h5a3doij325Lpcr42uw6UrkRNKlct41gYDvlRSrT7iljnheNWwNTy6+Fp/BZeSaG8hZF28QydMJYTYlNDeXU7+pGRKkyZDw+MsL/8MqxQTumE0TEacrhan+IpYdpqFH5xXagDCaFCt/t+z1FicTQBNNNj0RK8qaBJiRSKiZR+bQL0oxtjGJ1CElSiWPqPDZRwtA1jtYKHK7m+NqFBW7WvUy/pBFG6QaB+WrNXBRLOmnEubkO852Ao0N5pobzGxiH8aIyib2y2KWWmV3232vH1Ck6Jo6Z0vIizs22GcrbPHO4wuefHOMr7y/w3kyL545UeG+mza2GRxAlyndJaJwdz/HEoRIzTV+N+I8VB9OddTek60e4YcrpsSL/8Atnee2DRb57dZk4Tml40SCvcShnYhgar55cMau8HcPQB0vnZtsDV/MgM9sMolStW4a2KaDYSVvvcDVHNW/yzYtLmVO7HORLnhzJYRmCkyNFfuHVY/z+O7PMtvz7YhPwKDEwH7U6AE67qK2mWy4vdik5BiNFi7ytpmPU+HoWvZBKTF3bM/Hf6bESP/PcJOfm2iRZSKihaYyVHU6NFqkVLOJUaWt+5plJLs5315xupoZzxGnKVC2/Z8LxrU4Uu+nr90/ZC21/jdGdpWsYmjaIuVnt/WT1Ez/ZuSeUGyWMFG2EAC+Isc2VBVmmaty/aJv81U9M8dRkeXDynml5PH2kzPuzLRKU500j00yoVhiE6YqT9E6m0+JU0vEiLi92+fhUlbGyw1hJiYGvLfWIU0mtYHN2XL3fq2sob3G4kuP9mRYCqNoakVTfK6Ukkx9tyij1WbbN/kLXxQat0naq/3JV4SEEfBiP8N1oipS171M7E++2vZDFToimCS4tdOkEsXJE1zUsQ8PSBNeXXf7wvXneutngRt3FC9V0Y5qJxCcrDnEqM9YvJYoltqFnQdzqc9nxo4G7exAnxEmSjeILojjF0FdMPSVKkJwz9QEj0vaiAdNn6zq2oXOoYnNhvjto9fW/P10HlvtxN1GcstAJGCla/A+vHMM29IG+5sO5Dr/+7Wucm23T8lUw9nxHZ6LsUHQMun5MJWfy9KEKc22fv/LSUZ4+tBI6qwnBTMvjel3pk/KmRtlRmqecqZO3DP5/37nBZx8fW9M+en5KDR/MtjyGizZf/LHTnJ0oMd3w+d23p+n4ivWt5Ey8MOHKco+yY/LYROmOuW39g+dbNxtcmOsQZ3rExyZURuVM01fr1nOTPDFRvuNE7GbrydOHykgp+eq5ea4t93BDlVRQySvt23TT5WbD5bmjVf7aK1Pougr1Xs9w94dubEPjhzeb3Gy4HBteG26/23pUGJiPWh0Apx3WVjYApqGRszTiRDLX9qnmTJa6wYBST6XMmAhl879X4r8nJso8NVnG0FWK+HoX3QF1Plnm80+MrznddIKI//2PLw1MAtfXXo7F7qav70UxS101zZOzNExdV6fEsQJDOZMryz1OjhTW+MQU7d17QhUsg5GijWUILsx1afYCklSia4plmqzkMHSNRi/k//zudRY7Acu9kKWuapXlLQ03TDGEwDZUZpya8hIbpqy2W31Lg/m2zzvTLaZqeX7h1ePKlyiMyZs6v/f2DO/PttewEPVeyKWFDhfnuypcWECCui5JQrCeBmF7ou8UiLebHLxpCb4XH2M6rXA1GWJt6AhZwK2a4lrqKfBbtnSWukGmyRIQJQwVLN680eRrF5aoOAbdICZJZPYsSJpehB8muFGiXOujBDdUU2zVgp6xkaqNvdQLs9ZVSpSkREnmkS6VS3ocSxxDY6xkI4GZpjcQgH//ah1DU2aj1bxJww3JmxpTwwWWexFNN8SPkgHjuBqLakL9+roQjJUtklTZK/zOG7dY7oUqEqQXcmFeGVjWCiZhkqihhDDhRjbddmy4wKnRAmbmzfT0ocqazfj0WIlfePU4//T3P6DrK7d6Q9eYKFicGi0ylFdi6g/nOvzCq8cHppFBrA5Ynzw1MmB74jjle1fqlG2Dkq3jhomKtdE0To4UMDSND+c6/PhjY1syJ/2D53I3pNELMXVBrWDR8iLenW7x/NEqZ8eLXFzocmm+y+cf36j3udN6omuC5V7I//KVC7w/06YbxANdVhArxjpn6iRSmcyGScqthqesJVYx3OpzpIT4UZLiRwm/+q2r/Pwrxx7ZdtpB3bkOgNMOayvdjaWrjV0T0HAjnpgoD4JRi44KcU2l+v4jQxup+N3W4WpulYtu7rZMzvrTzW7aZ3dTO+nrX1ro8Ovfvg4oMW2cRdbMtz2WewFFx1Cp8JpGN4jXLJpnJ9TrbOUJtdW9P5y5fr9+rY6laxyp5tSUW6pMJn9wrUHZMflgRjF81bxJGKfE2YYuhMFj40VuNlyiJGWiYjNRdrhR95hvBzS93Xme+bESj58cKfLXXpnasGD/1DMTzLb9wQbiRQlvXm/Q9CIsUw0jCKRyRpdK6OtvQntJWGMguVlJFONkCkkQb084PiJ6nDUW+U50DIkglhpXk9rg73WxEn6rdGUqIsUxdXKmxnJPeTUZukaapqQI/DDh8mIH0AjCGMvQBmaiRdsgSiR+qNo+iWAwZaoJ6PoxacaYLXY8okRl3pUcnW4AYZKs6Ly0vhWD0kWZhmJwbEOnkrOIk4SlrvJlS1GZno6hM9f20TSYquW5WXfphisxLIKMaUoBqSYp40SSt3Wmmx5v3Wzw7JEqjmHz9o0G3UDp55RWyaIXxpQEdAIFok6P5KnkTC4t9rY8FOQsnZGixURleHDAKtrKiHY50z1dnO/w3z93iL/7Y6c2bR9dWujwH1+/yX97bw5dEziGRsE2OFTNMZrp67pBfFuWevXBc6Jsc225RymnIoasTMd2ebHLi8eG7sh4b7WeHKo4zHcCJYy3DYRQ0Ty9IMYxdZ6YKJG3VZD19eUe371cZ7ETYOs6NxsuOVO1Xuu9MBu6UUJ8y1DP0NWl3hq7g4P66NUBcNphbaW7KTkGtbzFfNtDCEHe1nn+aFWdVnpq0xwuWHzi+PCeTWfA3U1oPIix2O309Vcvrh+bqq4ZvxZCCVLHyw5/9zOn+XBuo+/TTz611sdpR8LLwWVILFPD1DU6vmIO4lQSJAlFy6CWN7nZ8IhTybHhPDlT+cHMtX0MTRAkKUXb5PBQnht1D0MTOxrjX10jRZup4QJ//VPHmVrXIkhT1Xr6zGOj/OBqnYW2z7lsslGxcSbfvbJMnKgQ1jSVhOlKhMv6Wu3XxKrr7QvdU6l8iPoZd3eqU/oynzSvYQhJSzq8H09s+L5UQgSYmmCkYJFKSdiLeOpQiVsNXzmPC0ilQGgampT4UUKSgqEluKGKs1nTbhYqRkYkijXqW0n03ddtQ1P/P2PP4kRNzPWDuCUMnL37E36aENlovzJPTFLVXouSvjO5YvKiOOaD6TYS6FkJQlt7s9a0QbNBhrobqYy7FA5XVHbidMOlGySUbJNEqtDioYKJG8Z0QuUhVndDvnp+gWKmU9zqs94LY4Ik5fBQEV0T1HsBb1zvbLAvOTfb5mgtvwGo9Fmi68s9dA2Gi5Zy6/YiokQOPJDuxFKvPnj2mR9TV9vQatuDjh8PhgBux3hvld83k+mUlnshiZSUbXMwkTnfCThp61xa7OIGMbommCjnyJkaV5d7vH6tQc7UuLLkDYT4APVewnjZ4dnDFS4t9h5ILMpB7Y86AE47rK1Eif1JptXi3bGyw+MTRa4sCU6OFfm5T0zdlQXBVrXbCY39Mha7fjoxlXKwuIJqVZ0YyXM4drAyP6woTinnDH78iVFeOjFE0TYoOeYaELYVQNvKUXy66dF0I146PsRsy2e+7eNFCb1ATdlNlmyWuiHDRX3gRi1QDGOuolN0DOazyI3+xF/DjfDjhDiVmHo2nbdD9NT2Y549XB0k2fdrswGFnKU8ap47UmWi4iAlnJ9ts9gN0DL7g3X+k5vW6kvUhNrfTUOgAUGcxYWwMjG3vgSSjxvTPGPOAXAjqfJhvHk+Yx+khIlkrh0Msu768SWqvahYmSiLKun7UknUdFojY01yljFoZcVJiqFrJLECjVIyyLhLUqVjylkaSaosC8I4HUw9ptk1pWIlEy/JvvlI1UHXBNMNJRLP2zphLAmzG2ubGnoWidN0QwTqWdFY+94LlGM4rLi2Fx2DoWyjdiP1M01LoKOMGQft576nRJqB3Ts8U9u1L/mDd2c5OVpYs2asPsicHi2y1A1JMsDeN9G8vNhlKD90R5Z69cFTyhhD09Z4Rpm6YpDDJEVkrdU7Md6rmfSbdZcriys6pb4esv8zio5BvRvgh0n2+5vZIIdOOWfyieM1vnFhkW9frqMJKDomYZIOsglPjRbQNO2hDKY9qL2rA+C0w7odSzOUtzaId21D5+UTw/d8xHS3Exr3eyx2/Wbfn6CxTSX4dQydck6Z8jmmzrnZRharkQ7ytYYKJpfmO/yvX7mA0EBH48RIgZ94anwNO7aZ8PJ2Ycd9y4RqzsQNE5a7IV7mqWNogqU0IMwm45Ks9WqbCmiGcapMTtMUIaFaMGlnrTmBsoiwdB1Qo+7bBU+GUKD82aOVNe/lZgMKvSDmnekm0w2P8bI9uAfPT1X504tL+FGChiDdAe+lZ8xNkq6Aln4bayuNu0nMZ6yrHNVbALwbT/JWdCjTWKkW1WYC+f4kYb8W2j6g3MD74E2wEpSbCtBS1fYKk5T5dsBERV2rHyWZ2adEy5gMQxOkaUrTjUlTqBYMjg3n6QVpNpKe4EUKPBlZLEmSvX5/ziDNwFd/Mk7ThDLYjBQQsywN29BIpCTJlOBpNsFnrtM5iex/BqAOyFv6QLOXN3WljUqVDUYoUxo9FS1SK1jquUtSXjha5fRY8bYsyHbtS4Io2fAaq1miom1s8Hnqs0RtL2K+E9yWpV578Nz4WlGiJvRMTeyK8V7fEVj/M0xdoxlH+HFIJacA41jZGdzz4aLNS8eHePtmi24QK1sZXc8Gbgr3LZbloPZ3HQCnHdZqlubCfGfgFJykavJivXj3fo6Y7nZC436NxV6Yb/OvvnaZ5W7AZMWh4hi8dbNJI4tk+fixGo6pcXmxy+WFLjfrLromKDompqN0K9NNl3enI9I0pRvEyjIgSXnzZoOvnp/nC09N8HMvb9QBwZ1Dkf/M0xOEcco3Liyy1A2yCSg5aE+FmRP0bNNnvGxn7Q3lvJ1ICYnKKBMofZiuaQzlTWp5S7kzm+rku12duAYMl2xODhfWGA5uNqDQd45v9EIabsQ3Plzk2EiPEyNq2u65IxUuzHdZ7vo7e9MyuwLECmgB1uh1MuIDUKaWn7MuUdV8Yin4ZnSCa0ltJW4kBscQWIagF24FvRiEGKcZaBKaGEzKSVZ5JAFG1oIL4kBZP+iCOAO4qVSxIzlTVzYKiUDXVQ5gmAGjSt7EMjRu1l3czCC0D4wMTTmQm7pGGKd0gkhN5SUpAjgylCOIFWgzNOX5NVq0aXghvUBprxKpPKCkyH4XVqwevOweiOx3hhX/scmKQyVnUu+FaJZCrGGSqgw5qTRrw0WL02PFO7IgO7EvWf8aq8HIao+wvn6zL5S/tNjl2HDhtiz16oPnmbHimtcq2DpdP6FaMJlrKw+qnTLe6zsC66/XGrCXCV1fifpX23gATFZz1HshYSIZL9lU89aagRt49GJRDmpndfCu76JOj5X47ONj/Nq3rvH+TJsoG5E+PlLgLz8+NhAnP0x1r8diL8x1+Ke/f47Li13yls5SJ1DTXpB59ERcW+7x4rEhnjlU5r3pNi0/4omJ0sD929JV3pgbxpi6OulLlI9NnCgPmz96f47Zts/PfWKKT61qi24nFPntG02WuwHz7QCkEiNHyUaU0wki9C4YmoYfJRi6amF1/ZjxsvLYubrkcrjqEMYpU8PK6bjtR8T9vLhtgKfRksUnT9YQQluzQK8fUKj3gkHrxdQFhi7oBDEfzLQ5N9uhYOmUcmoEO5G3V1qt/9tBV0+unX9b7Te1+ustEooioJeavBaeZlkWNnxfkEhMudZ0c3UZGuRMHTdKFMOVibPT7HuEEBkLpkCHpoEQKiYnSdPMjkCBPEuDnGWsAnwpUgpMTbUTG27EeFk9X/EmESlSqqDlkqNAkZm5lCv3b/V+WoZqWsYSipZBOWeQypSuH3GomscxBUudECmg5SpX+m4QD9g1XUAlZxKlkpYXsdDxKedMNE3jY1ND/OnFRVp+TN40kGlKnCigYps6zx8dGnw+7sSCbNe+ZP1rbGYL8tzRKlcynyc/Ui7oTx2q8JdfPHJblnozecAzhyt8ONdhtuVj6IKhvMWzR6q7Yrw36wj0Pc0uzXe5XnexTRVFXC2YPDlZ2WDj4YUJQ3mL0ZLNzYa3ATQ9qrEoB7X9OgBOu6hLCx3++PwCBdvg1ZPDA91M24/54/MLHBvO35O23FbanP1elxY6/KuvXeLyYpdq3qRgG/SyXCvL0AliuUYUCqplESUJC50gS3TX6PoRTS8auP7EqaRoK72RhrIvWGj7vH51metLLj/99AQ/9cwEp8dKA7AxUXbWuB0XbTUJZBsa37u2zHxLMTL9OJR+e6i/kQrUhFYvTChY2eYtNJpeRNHJ0uOXXeJUcqvps+yGSlth6zQy5+6+MaLMXrDf/uq/vvr5KktuqRcNDAX71fEj6q46PadScmmhixcm5Cyd6YZHGCeDlpJEEntK1xLEisExdYg20Tn1NUtb1XaIsiVZ4LXwNI00j4eJhgI7qx0MUqneu63AY5IyANUrpSYDAYSQxJnruaEJhvImXtYuS6UCSkmg8uosQ8+AqhKJh4mybLAMQcE2MA2N5W5Ay4/wV4HkgTg+Y7OkH1G0TWoFK8vMU+yR8nAySTIA1d+Eg1j5QhUsHQRU8hZTQzneuNGgk10bqPfZMXRylk5JFyy0Q96faTNWssnbyhPu5EhBGXSmko4vMdKESs7gzHiJ4YI1sKHYDguyXfuS1a+xlS3IiZECk7HNlUWXxydL/MPPn8Wy9A0/c/26dXKkuEYeEMQJU7U8Hz8+xIvHa5v6Nm23ttJtmrpGJW/xUsXhC0+N86cXlrhR76Fnz8bqoPQ+KPr8E+P8+neufaRiUQ5qe3UAnHZYq5mLs+NrKd6JLMLkXkxb3E6bs59HYvv3q5+6XrANZS6oZ1ltmWfPRMUmTtOB27dlaFRyJtW8hR+l9IJ40NYo2DpulGLqYqCL6IYxSIlEULRNgjjh+9fqXFzo8jPPTWLpKmB3punRzNyOk1SSJBJdVxlmzV5EL1SMgBDK6RpWgNNKm0jgmKoVW7SVLYJt6gwXLNp+TJxKjo/kkVKNsHf9mCBOVJRI2SFF0OiF6KLvHaR+jmI0IGdoCE0FA3e8iM8/sbJAX1ro8KW3Zri80OXaUhddaLT8iJGixUI7oBvEg76ZECCyllA3VK2lgiUGk2Qb3qtdvL8aKS+bN7kQj7As1cTfTFpZ85qbsUqp3Mhu9e91X/StawISiaELDldzNF0VXdQfXVdsn8Z42ablxVRyec6MlbAMwRvXm1xb7mIbylut7wBfXLWxl2yDJyZLfDjfoeXGg1agJpQvW9gffZNKs6ghmarlOTVa5NxsGzdUAKjlhaplLzSEUM+zyoez6HgRCMF42eGxiRI36y7NDEAPQGWastwL0TXBZMXGNnRmmop9sQ2dzz0xzmefGKXlRvwfX7/ChQXFIk43lM3FUN7i1GiB5V54RxZkJ/YlsLUtyM1GjwvzHTRN4Jgaix2Lf/vNqxvWo9utW1vZHtxtbaXbfPZIhccmSpyf7bDYDbhZ97i65DJZcTg7USJn6mtA0Uc1FuWg7lwHwGmHtZNwyb1qfd1Jm7Of/UT692t96rouVACwAigJvc1cwA2dj01VEVl8ykLbp+mFA6GNrgmkVDqRNFV/Vu7sKuKm6SrzuvNzbYYLFh/MdshbOmNlmzgRzLY8/CjFMZUHkGVotH1JKiSOrikGZpUouV+plFRyBkN5k59/5Tiff2JMjZ6HMV96cxpD0xgpWlxa6ALxAGj5UUI1Z/GxqSpv3Wqx3AnAD9EzIXKcia/dSFJwBFO1HLWCTS7b7FeMAwPGSnaWt6ZYgoW2r3L60hUB93pGRwLdUKoNm7vLoNME2DLis9YlxvQeR7QWvxM8vcEFfKvair3SxEoLL82sAQxNY7zs8NxRFRkSxcpOYLqZYBkavSChnLN47mh1wPi8cAzmOz5JmjJezqYppSSMEvK2QceP8RPJjYarjEqzFqoaUNAGLFWUgcxEqik3L6PqRks2Lx6v8cLUEG9cb3B5scPFhS6LnZDJqsNj4yX8KOH1aw1AMlG2SaRc06KzDaGm/lJJFKfEmvrzseE8f/UTU0xUnA0+SgVHHTzCOKWcNxEIZpoetzIH7DuxIDuZpN3KFmSu7dHyIoJYMlww+dSpEXKWvmE9epDr1ma6TS9M+PXvqOuZquUZK9mDFuFiN+DseIkXpobWgKLTYyWO/2iBN282WO6FDBcsXjg6hGFs7zk/qEezDoDTDmsn4ZJ7UdvR5uxnP5H+/ToxXFgz3WIZSrDbDaJs3DvmaC1PyTGQmSA4TiGIEhzTYLhgYerK+boXxANfpCSVxGmKnpkIGplQv+2p163mTeJMOxKlKd1AUktMNVkloZJl0C11A47V8gRxQsuL6Sbp2uknVlinnGlwJhOUnhwtIITgSJbYvtQNyVsaP7ylEuzLORNTF/R8Ne59re5yaqzIc0eqfOPCIm4ks9BfBdIMXaAJjWpO+fJ0/JheGK9jOkuMlhzevtmk7UXoAtpezHYNve82tBdgiB6fsy9R0CICqfOt6Ni2QVO/NrUxWGUFgFDvTzVvcaPucrSW4xPHa3w41+FmwwOU8ex4JTfQ6PQrbxk8OVmm4Ua0vEhFtBga1YJF3jQ4VIW5ls9cK1gFmgSmnhmGrmqh9t/3at6i0Yv4xoVFnjtaHeh5fuJJ5ch/bq7ND64qM8WWp0TkP/HkOEhoehEfzLRpZO+XZfR1Vepn5ywdCSx21HN4arS45uDVf/+TVPKZsyNcWXSp9wL8OEWgWLnRos3JkeId7/t2mZT1h8RaweLFY1W+dUlNmqqwYJXZt349Ol4r7Mm6dTfyhNW6zTSV/JuvX15zPSXHZKRo0/YiLi12OTla4G/9yMk1oGgzxuz1q419z/Qf1L2tA+C0w9pJuORe1INguPay+vdrs9T1at6kG0S4UcpQQZ20u0HMxfludqqO+dqHixRsg9GinY1Dm/iRCs314xQzYxL6GWAl26DpRmhCMFayQQgWOwEhyn9nthVwq+EDMpt4ygJtUzhWK+BFCS2vuwZcrN7gTV1Nab15s8V42eY/vH6TnKnaD2cminhRTKMXbUiwLzoGlZzBci/kylKPHz0zQtFS+pv+BJfI5tMLtk6atX2PVnO0vYg/ubjIO9NNDmXtlVrB4vmjVd651WSu7W8bNK0vZZCwVmN1p5c6odf5tHkNQ6Q0U4fXwtO0pXOH71pblrZy3/tqJgWEV+wOKjmTT58ZoWgb/OB6g3ovwjE1pYc5NsRCO6DhhTx7uDIQSMNKy+lHzozy2cfH+M9vTnN5sUsqU6o5NYW23AvJmQYTZZu6F/KnF5ZIU0nBVuxLP8etYEMviAGBkbWlpISx0gpI6W/QR2t5Pnt2bAM70fcI+/qHC3ww26Zg6YSJxNJFBspU2zpOEtp+Qjlvbmi3rV4HSo4JQuDPJARxSCrU+/i9q8t8+/ISnz6zuV/W6trOJO1mh8RuoGJshotKd9hww0F7ffV69ObNxl2vW3spT9hqHRVCUMlbnB0vsdQJmW37g+tZzZhNlG2KqUHbj/j+tWWmmy5/49MnDsDTR7QOgNMOa7OpjX4IZBAnTDc9PnF8eM+mLe43w7XXtX78eHXqepwkWFl0xYmRPG0vYrET0PYjxsoOTxUtZpqKRr+23GO+7fPxY0Ms9wKuLbk03JBeqNpTmhDkTJXBFSYJExUbTVMTUGpcXlLOqUiOuVagxsmF0jYVbSPLG9NxTKWf8aJ0A4jQhBLydvwIKcVA79JvP1yY79D2Yha7wWAUul9CCMo5k64fM9tSi3gvitEFJAIsTUWFqOtPiZOYrh+jIfjN791gsRdwdaFHy404M15UXlNBjBvGWT6fxN0kf+6Olemg+j5Juna7EGLJC8Y0z2WmljeTCt8ITxDtYBnp35FErrBKBmTmnGpyThfKp+iVk8OcGCnS8SOenCzz+SfH0DVtAEiu1VX0xe2idU6Plfh//JnSGoAgpeR//epFDlWVLxEoIDTd9HDDBJGZTeYsjSQBxzQ4MpTj+aPVzKhRadfWb/p3YieO1vIYukbJNmi40cBaQBdK6xdEEk2DF44NbWBVVq8Dy72A16/Ws2k/g4JlECQJC+2A3/j+DSYqzrY29DtN0m52SAyTFbfvvufS6vZ6fz3qZ+3tdt3a6zbfTtfR1QzvcMHi/Fx3xU8u85jKmTr/z595cl8y/Qd1b+sAOO2w1msEclmS92I3oBfE5EyDUyMBV5a6e3Iaud8M117XZpqK1anrTx22+bs/eoqCo0KH+xqhvvD+yFB+AEpvNVxGihZfeHqcH1xr8P5Mi+mGRz0Laa0VLMo5FbRackyklHT9mKGCRS9Qk2Ulx8wYBZ2ibZIzNSSSIFYmgH6UMlKwmO8EOIZGnHnwaEJgaCpDLWfplB0D29TVZKCucXq0wMWFHl6Y0PEjhvJr3yuZvc7x0QJ+mLLUDVnuqggZxxAKOEiQWbxIx4+zYOiAQ1WHsYLDrOkx1/KYbfkESULXi/GiZCAC300lUhkzFmxdCdXj2zfyhjTVIns3muCN+LAK3d2ilD5payDWd14XmmJcdE1QtHXKOdVyu7bco5IzuF73QMJrHywQJOkaQLJZy+npQ2WeO1oljFO+f3WZomNQsk3OjpXQNMH5uTZ+nOBHGudnVeyIuo8KMCWpshZIfGU1UXJMTo8VGS05A8H/YjdQDEbZYbbtc262zR+8O0sQJRyq5jbd6E+MFKjm1LM4XrJpuBFelBDKdHAfJos5PnVqZMO96q8D0w2XN643qLshpq7hhgk5Uw0pVHMqj22vWvebHRL7TtxhrBz1V5tHwsp6NFywdr1u3Qt5wk7X0T5DlTNX2u6r/eQavZA/Pr/Ajz0+xo9sg+F71OphnfDeq9qfu+0+r75G4De+d4OvnV/AixIKtsHx4QKHqmoh3asQyAeRJ7fXtV5TsVnqOqi4hKVuyKHqCp3eZ2rqPUkvSPhv781xYaHLUM7k6UMV/sdPHscLEv7o/TnqbkjZMWn7EV0/yjLElNblymKPhY5PwTbImTo5y6DjR5i6TsNV7sGWoREnKUJAOWcyVrLRNTHI8XLDmJmmCg71opT3brUQGhhCGekN5S2Eprx+ljoB1cxGIUpSOr7SZY0WHQxN8MqpGjfqLrau0fJjOn5EKpM103ugFvDlXkQ5Z5Ck4IUxLS9GooJpU7lRBL6TMjVBJWeQSCjYBq0kvE2vTvAn4UkOaW2up0N3fG2J0mzFqVzj+yRYqx8yJDxzqEwrm0gsZS7fi52AP724jGkIJss5hgoWecvYAEj+9o+eHLTHvDBhuuHxa9++xo1lFy9SNg1TtTzPH6ny/FRVad7ciMsLHZJUtVFLjkOSSuZWibf7YnFLF1xd6gGw1AmZ76gonn/7J1f4d396FdsU3Fj26AYxx4bzjJZUJMv6jf5v/chJXjlR4yvn5gftLiklQbxipfBjZ0c5OrSRBTpczVHNm3z53TlafkjOMrB0LROcR7T9iJOjBU6OFPasdb+VkLxg6dxqeowW7TXmkavXoxeODvH61cau1q3tyhNuNVyEENvavHe6jvbCeMu2u20IRksWtxoer52bX+MX91Goh3XCey/rADjtsk6OFKnlLaaG8xyu5rANfeCHIvfQlmC/5Mndbe1WUwEMUspVICdMlB3yls7NhkcvTPjs42NMVnNcr7vcrHt0fDXufXK0yLNHqtmiB21fhfAeGcpxZqzIWzea3Gh4VHMmx4fzREk6aH/UCvqAuepvoH6k3tuGG2Kb6v2OU8lyN+BW08uE4zojRZteloXVkwoMqDw0eH+mRa1gM77QI5WSmbZPlLlcrxah90vTNFKg4ycYQtL0IjJJCW64k/CUjaUL5fPU9GIKlk7FMeh4yi+p/7qTWpujepPvR0cBQYS+LdDUL0MINCEzT6lsBD8DlzJN8RMl8G96EY9PllnshKolkqqWSCeIGS3l1XTlJszDb3zvBrW8xZWlHkvdgJt1d2B+qQs1Qh9EKZcWulyc7/I7b97iyFCOC/Md3CjhxHAe29Bxs6w2wYpNQC6LPIlTyVI3YKEdZOG+yh18sePT8tW9S6WkmjdZ7AR0g4Tnj1YHm21/o59t+/zcK1MsdAMuzHeUdURWtqlzdrzEz748tfVnWZLF5Qh0sQKvxSpH1Zyls9AJ9qx1v9mhZ6hgEacyc1QXxGm6YT0yDG3X69Z22mqXFrr8+29eo+1H29q8d7qOFiyDNGXTtjsoH7KCbTDb8rcNUh8FluZhnvDeyzoATrus6abHlaUep0aLG6jf9eLHw9nE1W4/MI+Kn8hONBXFbGQ8iBPOz3XUf3N0glhN4xVtg/GSzTvTLd683mCqluflE8Mk2eJ0brZNvRfSCxRbY+oaQwU7i0Gx6GRTfGPZaHvLi7B0jVNjSrMUx8qJfKkbZhOAGnFKFtarfIVSKVnoKOBTsHT8UOXQ5S0VyVHJmZQckxv1HmkqkVIwUrQ5O17kg7k2jV5InCrA1I8QWQ+E0iTBNDWCOCUVDEDT3QCmfgmR5aelEi9O0H3lX5S3NdI05XA0y0vmDTQBy2mey8nGFtJtXx/QdUEarvqztjJRFmWgSgCdQJksvnisSjdQGWxekPDdq0scGcpvyjzkTI2vnV9gajjPyZECM02PVEravgKXx4bz5C0DXcRcr7uq7WYbdIIYx9DwooQbdY/JSkrTiwnjFE0TmJoSgluGsgoIkhQvTEmkxDGdwec9lcr1fq4d4IYxo0WHog3LvZD3Z1o8PlHCzswtg1jpZx6fKPP3P3+GL787x7vTLdxIOYI/e6TCF56e2PKzPJ35jz1/pMoPbjSURi9R96FgGRQdHT9KWewEe96633S0P4r5o/fmb/s7nB4r8QufPMZv/2CtOP9O61bO1EkSya2Gy9AmcSezTY+bdRch4NRocdub907W0cPVHJOVHG/fam7adu/6MaMlGz1jvO5UjwJL87BPeO9lHQCnXdZ2xYbnZtv83tszd/2BuV95cg+y+nT6d68uE8cpDS8a0OXKSTzlyFCeKE35wbUG9V7AbNsnjFMcQ2OsbFMr2AwVLCYrNt+/2uD8XBc/szR49eQwn39ifE2OYF+j0v/zh3Md/t9fPk/HjxguqiDVXpCw3FPxFE4G2hxDY7YVqHagqQTDaAocPnWozJUlF5DMtjw6fkw1Z1LLWhtDeZMrC91BS8jQGDAy68tPIMnc1PvTd/26Gy8mAwXCTF0wWrRwMx+tgqXhBhHPcpWT1gIAl+JhriW1nf8MDWzTwAhVhl8iUwSCJO3HoQiQaiPKmco5XuW0qY3qetADIShvokmRUjLTVC0z1V4RNL2IgqWc4NMsTsUx1ORXn6Ap2AYtN0LTBcdqeWbbPgsdFRsikTiGGhBQLUOlh1NtGyXkruaVk/e5uQ5FR8Wi9ONXeqFqx/aCmMVOoExfTeUePlSwBmDm9FiJv/fjO/ss99ebU2NFWl7ETMuj5JgDgCeBRi9ktuXzyVMje966X3/oubTQYQ1NKjY+v5cWOnzl/QUWOz6JlOhCMFqy+fwTW699lxY6fPm9OW42XJZ7IUM5k6GCzekxZTeRpinvTbcxDW3NNOV2N+/trqOaJvj8k2N848LChra7yvbTOVRxAHFHkPqosDQP+4T3XtYBcNplbUdsGMQpf/DuLGGc7skH5l7nye11bUVNb/XfNU3w+GSJ3317WgGXbLOpd1XyumPqWLrIJopiNCGQ2UTdYlfltakWic1w0eGTp2pcWXJ59dQIj02UtjSu6zOCHT/i+1frTJRtRosWDTdCWIKcaVB0DOJULZpF22ShE9INYixDTUSFcTrwpoolnBkvcqvuMlywOFzNkbeUYWY5Z9LxY5p+jG0I/EiBocwke8Mknxy0nQTxOs8BTVPj/Lup/hk5iFLaKCDR9mMOFQWf0c5RSdtICT+Ij/BePA63EYFvVbom6GbarrwlCGMNQ1e7bT/fr+EqZ+2CpeHHK87x/ZZoNWehb2IP1fHV9GLBNgahu3GSYptK92WbGl6Y0A0SvEgJysNMv6ZahgLT0Dg6lGe5GyrT1CjNQJPSHKlaEZGVHIOPHa0SpZI4STEzUXTe0jF1Qb0XKjPL7OcUbRNDg1tNjziVeNHu22erbT1OjxfphjFemFB0DGW+GsS4YbKrYNyd1mog0H+23TDm/Zk2sy2fv/6p4wBrwMLhoTxuGHOz4fHr39lc/7n6dR+fUI7uPT9mJst5fGxc2UhEieRjU2stKGD7m/d219FPnhrhxx8f45sXlwZtdz3L9js5kme5F91RX/oosTQP+4T3XtYBcNpl3U5smKYplxe7dLyYME547kh1xyejh722oqYfn1SRB5sxcCdHipybaVPNqVN614+J0zQTiKtH9c0bTcI4zbLHlG7I0CBOdCX4XewxlFeg58J8h1sND/mB5K0bzU2N61ZfZ90NubzQZaxk88RkGTOzBjCz92e66fFet8WRCYe5tmIURKKhCQbtElCTRzlLZ9kNWOqGOIZOkqrQ4GrOJG/ryiZBKDXR6kG29af2vs8PCMQ6VLVZlAlsz4tp9Re7UYqRCsYNj08EF8kREKHznfQUl+Pydl9pUBowWbUp2iZdP6LhRnRDZZqoRUq31Z+iM3TF4rX9GENXo/kdP2K25XOkmufUSJHZtr9BZxLECb0g5vhwgZKj2rqGriFTlSUoEEipRudT2QenymneMTTytkHbi6jmTQxdYGg6mtBwwxg/StCyCTJdEyBj3DAliqUK9dW1QQC0bYiB3qfjqynHnKkjUbmAvSBltGhTdky++sECp0dLXFnq7rhtcztbj64f4YYpp8eKfPHHT91T9mI7QOAP35tDwo7AwmavW7ANLi/0WO4FLHcDzkt46XgVKeFQdXPgs5ebt6YJfu7lKfwoZbqp2oZlx0TXYK4dbEtf+iixNA/7hPde1qP/G96j2kpsONv0eG+6jRslhFFCKWcSJc0B1QwP3wdmp7UVNf3dK8v87tvTTFYczoxt1CY8c6TC//XeHEGcDHLnyjmTgmXSCSIavYBelrm2Ohw3kdALE6IUZlseN+s2lxZ7A5bqxHARQxd3jISwDI1rS12absQPb7V4/mgVTQg+nFceLmG2WZ+f6/D80QodP8bUlX+UqQsabsRY2abkGNxquMy3Q8I4pWAZA6bpwnyHKFH5dMEdRv9BtbOklISp8vgZuEWyNXC6nQXAZqWMKCVaGmFrAR3pcLn0LLrIkW96WYyNTpgkhLG8LShzDKXjOlRVRp2GLljqhUrfJEQWpZISpwJdwETF4dkjFT6c72JqgqVugGOu6E5AMRfrBb3TTY+caQwmMEuOmmpc6Pg4hkY3iDNApIBtEKVKKB6njJcdTo4W+OHNJoudAEMTlHLmoGWXSsUuKWF4SphIcpaOEJLLi11ePDbEUN5iseNj5pU3V/+zbWjqWdQ1DS+IqRVtnpgoESaSN67X+dLbDj+41qDh7qxtcydbj+GizRd/7DRnx+9ty2c7QOCdWy0QCuxtFyxs9rq1gs3QcaVHbGSWEX/mmUn+4+u37tvmfXqsxN/49IouarkX7Ehf+iixNI/ChPde1QFwuotaLza8tNDlZt3FNDQeGy9ybblH0TZY7Ph0g3gwaQMP1wdmJ7XVibRoq1ZXx48YLVqDD17/FPrWzSY/uFan6YZMVHJYhjrRd7MJubYf4UUrU2Q6KqIkleqfJE1JUi1jnbqZZgfGyw7VvGIr7hQJIaUyO7QNpVN5d7pJnEj8KKHomFiGypQLk5TvX21g6gI3TNGw6ASSvGVwarSIlJL3ZtrkTZ0jVYelboguEuq9cMB8pGmyLVbINjWEUGyTBsR+PACMW1WSruS93fH96o/eAzNJie9qZ2hoVcatInmhMvaCKCHNdEiTFYO5lo+/iVW5JgAhyFsr06XLXRVeW8tblGyDWy1ftdSE2kR1TdDyYj4+VeVnnj3ESMneoDvZTND7iePDnBpRGrd+uv3psSLdQIm8EynRsuyWVEKSBfjmLZ1TowVqBZvnjlT4/tWGMqV0TJZ6yp7CMpQ7eC9QXlpFx+DJyRIzTZ/ryy6HqzlGSzazLY+ry6odO1Z2WOoF2feDnemO6r2AP70YIDSBH8ZcXXIxNMEnTgwNNv6d6HO2Y+txL2s7QMDNWpL5LcDLZmvfVq/btyPJ2zrXlnqUHfO+b953oy99lFiaR2XCey9q/79b+7z6H6pbDZd//81rCAHPHq7QDRKmmz4ic0Gu90IuL3YZyg8NjPYelg/MTmqrE6k6NSrdUsON6PjxQAQM4AbqVKkWQwUubENgFSzm275iobIeVJ9x0oXAFBAmEj+WmFpKIGGpG2DpGqWcyaksSw7uHAlRcoxBnl7BNrjV8ChYOuNlFSdS7ynvHS+IubrsqkBdYKkbUitYPDZRwtQF70y3iOKUj01VKdgG3aDBdNMjkZK8qYwmt0E2AYopUXEtKnMvSiVhlBL3zZvkWoF4f3INqTRGhibwtvhhBgmvmDd4J57EFep3nNeG0TXVMrMMDUsTdLOWlCZU2zBKNkdkqdLH44YJ1bwkiBRDV3JMbFPjY8eGeDxM+GC2zUInIIzVgqtrCvRs5Xi91cZ1Zam7ho0q5wzOjBV4byYhlSamrtENEyo5kyhJyVkGZ8aKGfsXsdyLeOl4jZ96ZoLRks2b1xv82revARDGitkcKlg8OVlmuGgzWfH59uVl3rzRIMj0WAJBnChLiiBKibMJy1rRJk7U7+dnwvK8pfRTaUrGaApqBXvDs3k7FvpBD4lsBwjkTUO1gHcAFrYLMEqO+UA2793qSx81luZRmfC+23q0du0HVJomEELQ9iNOjRazSRsx2IRrBYuio6aGOn5MyTEeug/Mdmurk2NfvFvJm7S9aCACBgWqOkFM3jIoOar1YRX6TItiJaJEYmZTPJpQY/MamYcNigXy43TwtVPDyqupvzH163aREEKIQZ5eoxfghQm1gkWYTdIIAY1eRMMNMTMvoqJt0PVj2m7EWzeaPHWozMmR4kCHoWuCM2Ml5toBaQJBnJKkfTceVesn5dS1kDE1Ji+fGsbUNd671SJvOdyse8SxCjTuW/hIqYJenzsyRMnWeH9W+QTJLWinogj4nHWJmuYxrLn8fvgkUqpWWjGLnYFML5QxXkkqmWv7JFksSv8a01TptGT2Pruhej/VPRNUcmYGhHV0TcPUNcqOiW1aBFHCU4fKzLZubxq72ca11SL+5547xHMZu9sNYoqOwXI35Ic3m1xZ7HFtqbfpYl+wDF6/WsfQBZahK/C9ahS+LxgfLdlMVnKUHRNNSK4uu+RNtekvdALGyyraZ6mj2n5lR6ftJ9jZ8EA1b9Jcpcfrv/52WeitNvH74RO0HSDw7JEKEnh/RuXy9e0lLF2jaOubrn07ARiaJh6azftRZGkeNHjfD3UAnPao1gOG1ZtwvReSt9U4fcNVJowP4wdmO7XVybEvqPUy/cfqfKswSQkykezJkQKXFruDIGBT15BSIlMwLA3TEETZqV1oa6FH2TE5XMsN6PzVjFa/7hQJUSvYPH+0yg+uNZjvhHS8kCQ1GSvauFHMUtfLmCZBFKd4Wopj6cpxHMmJkQJ/7RNT/Ms/vjR47XwWh5G3dLwoZbHjb9pq0wChrThrm7pGLBXLY2gaXpxgCI3RkkPDDVTLTypwk7fU/R0pWSx11ORRnEjcTOy0WjA+rnX4rHUZR8S40uDb0TESqe6lF8WMlewBWCw4BlKATCXtICbImBgl7FaTcTECQ1OxNXFK5siuMVK0Bvqiw0M5irbOD6438aKEas7Aj1MMTaeaiW53MzCxk0X8U6dGbvt1h6s5To+VlAC7slaf0x+DtwyNTxyvrZnoei5n8cNbTfwoHQwmWIaGG8VZGLXEMXUMXbWD48ytvH+Q6j+nd8NC3y+foO0AgS88PQHA+bkOf/j+PInse8Qr486zE6UNa99OAca93rz3EoQ+iizNwzbhvdd1AJz2qDYDDP1N+PJCj/mOjx8pN+nnjlYf2g/MnWqrk6MS75pcWepxcriARA5aaoYGUSwZKRocreUpOsZgYqgXxCSZ6aRtqBBfN4yRUhKlqjXSJ1USJCXb4NRogbm2v8E4byeREH6UqPT6zPKg5UfMt326fqR0VQASNZau6yAE3UC1oYQm1tyDPmgMk5SmGypmLGOK1jfRhFT6byFR9gWOiRcltLyINIVq2WRqKM8Hs220rBWnZ9N5bT/m4rzyrSo5ykLhVt0jylqKAjirL/KKeQNNSJbSPK8Fp3GxBj8/SuBGvcd4OcfRoTzjZZtzs228KGHCNphuqKw6Q1ezfmpqDQyh0VegRSk03BA3jJWuyDY4OVKgGyTMtT28MLsPUUre0vlwrsPpseKuBya2u4jf6etut3lfXuzedgx+KG9xab7Li8eHWOyEzLa8zN5Ao5ABZy+KM4ZSTfMl6Vr7hd2y0PfbJ2g7QODSQie7OWSIXaz8+S5ed3Xdq837XoDQA5bm0aoD4LRHtRVgqBVsqsdM3plucWKkwF//1AmODuUf2Q/M7TYfQ9OwDZ2Zls9CN6B/CtWyqSiBCnYVQnBmvIgQSmtycb5DnEoavQiJyhITmRVBgmJqio7BaNEGBIuZIPl2J9etIiFmmx6vX2sAcLSWp+PFuFHCTMsbCKL775yhK8bHixLypmKTbjXUwrj6tSfKNpWcwYdzXcI4oWDpBJrSua2mnvrncjLvJinhp5+Z4M8+f5huEPOlt2a4Ue9xuOpwfdllsRtQy6vYi7qr3M7jOEEKjaKlE8cpp0YL3Gp6uEHEy+ZNHjcWAbgS1/hmdIwEfYNzeZhAq6fChUGSovRLZ0eLLHaCzKtIqhZddv3hOggYJpIwSdAFpAjOzbWp5kyWuiEiE3PnLZ3hopXFlMQ8c7iibAZ2OTCxFyzBVpv3iZHCbcfgS46RtZEFLx4fYqbl8Ma1BjlLGaaGSYqZ9hnVHoudEEMXa+wXdsNCPyifoNsBgf41JankC0+Ob2jVXVrsbXlNDxpg3EsQ+lFnaR6lOgBOe1R3opqPDOX5+VeOcWy48KAv9Z7XVpvPmfEimgazLZ8kXVH5hHFKlKTMtnzem26jaSp2YbzkkLN12l7MZEXlAdZ7iskI13kf6ZpgouJwYlixTYerKhT2ymJvy5Pr+uuca/lcW+6Rs3Q+cbxG24/55sUl/HXGhf0r74uv40SqtpOAME7o+jGfODG85rWlhChJsQyVZdd0VSDt6um3PoZS7JqGaWg8PzXEVPbMWIbG//rVi/zRuQW8MKHphSz3AkxNp2Dr9IKYKFXaJyklXphwqJrjxEiBW/Uuw9JDAm/Hh3k7mqDvEGXpysSzfy0S8OOUD+c6TDd9Xjo+pFp/sRrpX+oGxMntXctNTTExqVRmkTfrLguGRhAprU/B0hkp2eQtAykl9V7Ih/Mdpoby97VVtRnY2mzzTqXkX3z14pbiZUMTVHIWdTfkaC3PoUqOmYrPQsdHWiqiY6zscLSWp2AbfP9afVP7hZ1uyg/SJ2grILD6mjRNo5xby9Dd6ZoeFMB4lMwqD+re1gFw2sN6FHvZu631m0/e1Pm9t2eYtQx+8okxle8VJcSx5PJih4VOiCEElbxJEKmw3WvLPfKWwbHhPJ86PcK1ZZdvXVpaM2avoQBMx4/4wbUGN+outbwKIf0fP3kcTdw+PX31dV5e7PKb37vBoarKI7u82KPo6Ji6MvNbXQK10KIJdA2CKCFv6YNFdv1rvzfT4t9/8wqaUOP3WibsNrLptShRfkFG5gt1bDhPJWcxUlorbgdAKhBVzZnqNB+n1HsJKZJa3mKkaKvgXDfiVsNlouxwaKjAG83HqYke56IV8G4bijXrO5SLvjdWqkbqwzhhsRPwi588wYdzHZpeyEI7GICmrcw24xR0IdE01UYMopQgSgY2E26Y0Mj8nXKWQcHWmW36fPzY0H1rVd0JbK3evNNU3la8PNcOePVkDS9KBwen4yN5lnuBCpLOWxwbztMNYpZ7IS8eG+Knn5nc1H5hJ7UffYL24zVtpx4ls8qDurd1AJz2uB401byfavXJ8Wbd5cpSj5yp8caNFg03JE5Sml5EN4jRAcvUmCipiSTVmgtoeRESiZRSiaqlxDE0olT58kSpRAiJhjIrdMMEQcRiN+DD+Q4/+eTEtq/XixKCJCFvGQPTvVrBJk5S/Eg5UHeDzIEymzJLEjlwpi7YBlO1/BpGYvU9ODpUoJIzABXP8caN+sCNWumVJEdreR6fLFHNmbS8eMC+rGl/PLXS/jA0wTu3mlxdchHAeMlGImj4IRNymWrc44P6USqOQS6XZ2R4BOvKMn4sMTJWKIr7OpuVeyGBobyFpgmuLPb4rz+c4YufPcVLJ4b4f33pPa4s9YiTrc0wJRl7lemr0lS1WHOG0kK5UYKf2RWMlx2iDDS+eLy2rc9KnynqBBFfenOa5W7I2fHtswQ7BVvbES//7MtTAKt8lhIVJB2pKJi2FxFE6Z4epPajT9C9vKZ7OTn4sAK+g7r/dQCc7kEd9LI3Vi+MWeoGLPdCgsxQMtWF0swkEqGrllcK5E0dG9XaangRTVdpQBbaAalUDEWSicYBDE1DCMVyBFFKLS/wPckPrtX5/OO314xsjFzp0fFiJioOXhgPxu11TSh2KFEgLkmVKD1KUwxNaYoOD+V5YWpzxmR9ZEbJMVjoBMozytKV63jJ5pOnRhACLi50efpQhVRKzs+1aXsRlxY6g/ZHyRF0/Ji6G6qMuYrDQlcZQkZxyhl5i6eMWwAshgXm3CrFOMUazWPpGkGcIKVqk662RuiXcmSXlGyTME5Y6iq7gJKjs9ANMtPJtWBrffWz9DIfSgTKRiKvawNn/U4QQyfgxEieWsHmiYk7R7ysfc8CLi/0GCvZjJasNfYTW7EEu23JbJdRXn9wWh8kvZeb/X70CbpX13SvJwf3Iwg9qP1ZB0/AQd2Xypk6S90wYxgUq+SG2aYtJGmqBMar1RC2oaEJgR+luFFClKSAxDQ09Egok8LMgVqgGKg+KzRZdVhsB7el1dezDpMVh44XcW3Z5dqyix8lNL0o0zGlRKmkkjOQUnn6pFIyWlSj+0Xb5OzYxjHrfm3GWBwfyVPvBcx3Aqo5k8cmSvTCeGAKudwN+BdfvYgfJ/hhwtWlHk9MlpWlRcun4Ub0wphmL8QxNfwoJY4jXtWvclxXAveL6TjTSRk06IYJ37/WZLzs4DdcolXxKevxjyZUe9LQNNwg5sO5Lq9fayiWLftijdvn4q2eGhSAZQokSsNV0Q1iS8cNVLxO0Ta2BJ23e89UTI5L0w3XhDz3azOW4G5aMtthlDc7ON2rg9R+9Am6F9d0PyYH9yMIPaj9WQfAaY/qfpjPPcyl7oRErNpmdaGmikQ28r+e9xBZ0GqcpuQMHVNXW3UqFahSbTlVavJLEqdqxP2x8RItL9qSVt+KdTg8lOPyYldNAaqRMIQmiDKGSdcEZcdkoRMgAC9OyZs6nz4zws+9PLXl4p2mEtvQ+czZUX5wrcFixydMUtXKiVNsXWO25aMJGC7a9IKY2bbPZMXBj3TeWW4w1/aZa/sYmsA0NMZKNkM5i5argnTzMuAL9mVqwiWRgu/HU1xIRtE0GCna5EyNJJVMVnN4ccpc01uxVlj1PmmZ0WjLjWi70WBybv07lG54x9bWaoNxS4dKziJNJV7mpG1oAtvUBtlud9pMN3vPVEyOPsinW28quRlLcLctmf3GKO9HbeVurmmrNfR+ibb3Iwg9qP1ZB8BpD+p+mc89zOVGCSNFm+WMySg6BoauNs5uprs2dW2wiUsp6QYxpZxJxTHoBhGVvEHdDfGCGIRiLnRNqJy6rBU0XLB46UQNS1cMzFa0+masg5SSpU5IOWfimDodX+lykjgdaJME0PYiirbOVK3A2fESn3tijE+eGtlyQV3/fNi6xmjJ5sUTNZ6YKNMLI/7zGzNcXuwSpynv3mqSSPjE8RpRInl3uoWXRYcsd0OSrD1Y70WMl5RB6DAdfty+TE7E+NLgT+LTzCXFQa6dHyVZRIganR8r2bTdkIKlk6JMK71Itd9AsUnRJlTS+v+0nbw9ANPQGS8pu4h6L8CLUqIsR87SBD/x5J0/K5u9Z/1w38WOT8HW15hKbsUS7KYls98PRvtRW7mTa7rdGmob+n0Tbe9HEHpQ+68OgNNd1v02n3tYq2AZjBRtRooWc62AuqsAQM7SKScGbpgQp8qWwIsSWm5EKiXPHa3yFz52mHMzbb5zZVk5MUcSS1PeT6rdJNGlisL49OkRanmLiwvd29Lqm7EOfc1QrWBhaIKlbshUTU1CuYH6+iSFHzkzwo89NsYTk+U7bk7rn4+c6bDQ8flgts18xlp9/cNF6r2QY8N54kRyq+ERJ5K3bzbRNUHbiyg5ugqNFYq30zVlfTDX9lUOm4jJiZjlNMc3kzN0pbXSUssm99KsxXhpocvxkQIzTY/hokWcqim9uZZPnAUEJ7fzGthh9W+PbWhomsYhM0cQJXhZK7Ts2Lx6aviOr7PZe7Y63LfrxyRSPT9CsCVLsNOWzIM4GO0GqO03Jgy2d013WkM/c3b0voq29yMIPaj9VQfA6S5qOxTyH743h/WcEsN+lD+Aqzerjx+rrjHFC+OEb15axoti2l6EEDGVnMWrJ2uDSaVzs22KjsGx4RyLHeWBlKSSWKboQnBsJM/zR6pYhsbFhe4dafXNWIcwSYnTFFM3iJIUx1QbZMlRU3ZelDDf9vn5V47x5KHKHX/n9c9Hw404N9scTBReWezx9o0mR2s5XphS4c9L3QAhYLRkMdP0aGbxHW1feTNBf4JQ6ZOUizos6iN8OxFcjUqkQh+ItpUGSSKlmgKsZmHBLTei7JhUciZnxko0vIg3rzdoZ87maSLRuL1X01YlAENTU3UqLFjlBy50AoYKFnGiXON7QUycSsq2wR+8M8tPPT1xWyCyFVNUK1g8f7TKBzMtFjoB822fobx1W8fp7bZk7vfBKE0l3768xFc/WGC25WWeZsYjy2BvZw1943oDO0sMuF+i7f0IQg9q/9QBcLqLupPINGdq/MG7c7wz3ULXxEe6hbd6s7q02GOy4lDNm3hhQt2N+PTpEX7yyTEWuyp89/RYkRenalyr9wYb1+FqjjNjJXqBCkjVheC5qQo9P2apG9LyIvxtjntvxjqo+BcF5HpBwljZGcS2lHMmQqgR/c0W781q9fPRcCPevtnEC2OKjonpGAgRcqvhoWtwYqRIrWANrqEbxPTChCBWrJxt6ISJijcRSMbygsfiK7yTHGEx0MlZOk0xhq0pN3I3M9dUgnnFNOUsnVpBWQwsdgMmKg4jRZv5ToBtaFiGxnDJ4lbDR9Oyiblt9OLWAyyRvd+WrmFq6nPQ8ROklCxn71OcphhCMFq0OD1e4r3pNrMtf1MgMrAe8CNGijY36j3Ojq9liobypmp/Hq/x554/RMkxb3tI2U5L5n4bIl5a6PAb37vB184v4EYJRVu54eeq+iPLYG9HqL/Q9hkt2dxseAei7Y9Q7ef2+AFwuou6nchUOSF3qfcCHhsvcngo/5Fv4d1us3psosR7Mx0uLXRoeCFfO7/IayMLxGm6YeMq5yyeP6pCYS1d55d+6syOx723Yh0Kls6tpsdo0ebUaHGN/mknC3SaSi4vdlnsKruBS4tdvDCmVlgRLluGijsJY/W1Q/mhQabfh3Md0iRVbbNEkmgSSxcYukYxdfmY+x55fD6m+XzNeJw4SUlQAuycqXFj2SVOV3L8cpbORNlRVg5pSi+IOTNW5OdenuIr7y/wzq0mfpQghKBoG1TzBoudkE7ft+o21cdWAhVDY+mCgmWgayIDfgYSwasnh3njRgtNRJiaGgoIE8mF+S5jRYteGG8AIuvbZGGcstgJ6AUJZ8aLa5ii4aLNX37xyLY/V3dqydxPQ8RLCx3+/Tev8YNrdaSUHB3KEacoZi5MeO5IheVe+Mg5V29PqJ/y4okavXDxQLT9Ean9rhs+AE53UVu1DqSUXFro0vVjKjmTat5C18SBdT+bb1ZeFPPr377OjWUXN4zpBjFBnPLudJMwlrx4vHrbjWu27e9q41oP5ILYV62kVFJ2VAZcnKY7XqD7H/p3bjW5vNjj+rKLGyaMFK1sEkwSxmkWuSJwTG2NqHmi4vDedAs/TklS6AQqL0/TBMeMFi9xCZMED5u50uOUA5OGG6EJQdHWsXSdvKUTxGrKUAhlzgmKeWq6ETnT4HNPjHN2vMzp0RI3Gy6/+q2rnJtt03QjHENjqROhi7XTcZvZD6wGTrqmMuhMQyNOldg8TiSVnMlPPj3BrZaPG0b0ggSBAlZBHNJ0lSjfNrQBENmqTRbEKW0v4kbdxTa0uxLv3q4lc78MEfvM1nTTRQioFix0TUPXwCpY1HshV5Z6PDZefOScq7cr1H9ioszJkcKBaPsjUA+DbvgAON1FbSUy7fgxjV6AQDJctFUAaFYH1v1rN6s0lfybr1/mxrJLww3xM3PMck7Q9jSuLfd451abiXKO4eLa+JG92Li2AnJfeX9hsEBbusZULc/Hjw1hG7qKWoFNmYrVH/pDVYeWF3F92cULY5a7igFygwQvivGjFIlkuRtQypmEmSK7H6CLEBgGIJVO6XFmeZ5bCGBBlnjffpKjxSolEaELjUreIIglbhij6xqGTBBCQyJZ7AQsdgIcQ6fgGHz28VE+dWpk8H4cGy7w868c499/8yo/uN5guReCkMoCIkkH4Gk1aMpcC1badALiJMUNlRlplCj9mW1qHK3l0YTgVt2jGyTomsh8ulQrMYgVs3hpoUvHj27bJvvY0SoX5rtMDef48x87TMm+fVtuN5WmMnP6Tlho+0xswjrtlbamz2zV8hbTTS+z3VAlhKDoGNR7IXEq7yoEeT/WZNlhpGjxwWyb06PFrCW+OcuraeJAtP2I18OSF3gAnO6itmr3NNyQhhcxUrQ4NVrYsOA+rNb996LnPN30uLTQwQ1j/ChZ08rKmTo5U4XXfjDb5tOnR9bcy602rp1e52asw+nREtNNj3NzbX5wtc5C2+dLb0/z5ffmqOZNkND0ojU08k88Oc5XPlj50IMKNJ1t+0gfemFCL3AHG2Pe0ik6BoudgKVOwGLbRwDnZ9tEqaRk64yVHGQac7x3nkNyCYCrjLNQeZz/6TOnefJQmcVOwH97d456L6TkqBbZrYbLWzeamUmnhWnoREmKGySYmuCTpzfaJ5weK/E3Pn0Cx9D5ww/mCGIl3rd0nUSqSJpUKuF3yVGO4lEqiRM5CAuWgBuluKFqM6qvkxyr5bFNjaYbIqUkZ+qD91IXZDqomEYvpO2rjL13ppvkM1uIvtYMFJg4VHVY6oSUbHPPDx99xvDSQoebDY9zsx2O1fKcHi8OzDX3UlvTZ7ZGCjaGpnILbUMf/L2pK81bx48fKefq/n2+stTjRt3lSqZ9fGyihGPqm7K8B6LtR7selrzAR+MT+IBqM1PD+XZKnEhGChaPjZfWuBj362G07r9XPedeGNPwQrqBEk2v/rBYhkbBVqft1a0s2Hrj2qvr1DRBECd8I7MKUJSxwUzT5SsfzAPw0vEhTo4UBzTyhfkOvTBmqpan4UZcWujScEPSJFVu47H6d1lXJpq1go1jasSJpO3HvHWziUC15pQ/ldo0C45FzfeRseCKc5rz8Sh/7uwof/75w2ia4PEJBdD6v3cviKn3Qio5gzSVNL2YJI3QNUHFMSnnDL5/tc5kxdkgoj49VuIf//QTHBpy+LVvXSNF5ctJoOVHhGGKaSjvrCiR5CydsSGLha6adEylmsbTdEHR0ik5OqmUzLV93rzeIEpTNLEZiBVoCNKszf3ld+d571YLx9IwdZ1a3uLUWGHweeofPjp+xM26u2dgfn2b4MVjNd64XufKco+6G/LxY0Nbbuq7rX67StcEtbzFQsfHKmiDz0KUKCF9ww15+cTwIyGCXn2fp2p5xkoOH86pAYHFbsDZ8RIvTA0dtOE+YvWw5AU+PDv3PqvbmRo+Nl7iv/5whvdn2lmm18M9BXIve84Fy0AXGn6UZG7gaTbCrjaOkmPQ9VUQcMMNydtbi0L38jo3d6mWzLUCLF2AEMy1A44M5Qc08ps3Gix2AkaLNu/NtAcTdIYm6IQJfrTSiuvrnNwwxjF1NE3ghTGnRotMN11ypsFsy+d63eVQ1eF69Xm00GU2KeGIlJdODG/IUeu3MS4vdvm3f3KFJFUgfqioowkFSrpexI1lj5nmNB/OdxQgWQUsV7MtOUun5UUUChYTFYfryy65qq4Cl7shupZwdCiHYxnkbYOrSy5Sgp5N5CUSRkoOh6sOH851eXe6NQgUrieSgq2mBZPsXui6QEfw2rkFUilxLD1rgcNCx6cTRINIFS+bOPzSWzMsdYM9AfObveclBz5xYphLCx2uL7u8cb3Bk5PlPdXWHK7mODla4PVrdYYKJss9lelYcgwMTdDoRdimxuFq7pEQQW91n0eKIyqTcbHLqdEif+tHTmIY2p1f8KAemXpY8gIPgNMuaqsN+kbdZb4TwDPw3NEqM03/oZ8Cudc958PVHLVMANv2lCO4llk5DOWV58+RoTwS9aG5ttTbVBS619e5GWXcN8gsZazXahZM0cg5ri+7vDvdxI9SagULP0pZ6ASkqcQQEEsGLFAvTDk+nGm9/JSyYzJWUtYFh5J5juZD3gxGWeqGRDkDQyuTswWjlk01Z5KmctOMtI4fqfDkVGnsVseP+LEyiDRTwXjJpmAbA2D52cfH+OPzC4Pn+qXjw7xxvc5MxgIgwTZ03DChYKtN3TJVS0lKdSg4VM2RM3WiNKUXJExWHK4tuwSxsknImQZhkiCRdP2YyFQ6qoKlAFScqADl545UCGPJYkd9XvrPyOXFHtWcmqhsZzmCh6q5PQHzW7UJagWLl47XODKUo96L+NmXp3jxWG3PPr9XlrrUuyE3ll313ugCJCzHiWL1TIMfOT3Cz94m0udhqq3usxCCSt7i7HiJxU6w66GPg3p462HJCzwATjusrTboKJFKCHyrxfnZNk9OlhnKW0xWHJpu9NBOgdzrnvOVpS5uGCM0gR8nlGylfekEMW0/ZqxkUSvavHJimP/uuUm8TYxE01Tyg+t13rxRZ3iT1uhurnMzyni1QSZAN4gHgm5QzuW6BjNN5TsTRAn1XqjMNA2BFwkcTYGPsZJNkIUUu2GCbeqkEoZyBo/FV6n1riGBXrnKQlLg2HCeRi9kvu0TOZLf/N4NXr/a2JRd6QbKrHO1LkhKSb0XEKcSx9SIEkki5QBYXpjv8mvfukbB1jk7XkIIQZSEOKauNvFuSColfpxybDjPieEC5+Y6RInENtR0HKjfzTZ1iMExJLNNHy9MKDoGs00fKZRwHKlAn6EJxkoWQZziRynlnMGp0QKapg0cwfsRPXlb0fTvTLdoe8rAs3+tcPdg/nZtAiEEY2UHN0wo58w9A02rD2Efm1KHrcVuQDeIMTWNV07V+EsvHOFTt4n0edhqP7Zj9rNn0EepHpa8wAPgtMPaDEjUe+HA3LCaN0lSdYqebfsM5U3+4guHGSnZ+/YDebtF414ucn0Qmkr4zJkRvn+1QS+M0TWJlkWLRCkcHcrxhafHOTZc2PAa/dbSmzcavD/TppIzudWw1+hhdnOdm1HGpq5CZTueGv03MtNMKSUdP+ZGvUcQJrhBzK1YBdcqoa9GlICla+iayrsTQuCYOovdEA2JqetMlnSM69+h1psF4IZ5jJ5RIgxjrix08eKUobzJC1NKZ7MVu5K3dQxd0PZiTE1gm7qyP4j6Tu0ppq7aoWTXUnIM3p9p8crJGkKINc/04aEcI5HFTMsHIIpTCvZKRpyZN/GjRLUcRZYzmDFxvSDG0AVzrYAwSTNGLcQNE5IkpeWpNlTJNjk9ngcJBVvd774jeF8rFiUpfpQwWrQxs0nHvQTz97tNsNkh7MhQno4fE8QJ002PM6PFRwo0wf5rx+x3z6CPWj0MeYEHwGmHtR5I9D2b+uaGEmi6IaahcaZS5OJCl3dutfg7nzm1Lxe/Oy0aO13kdnJyWw1CS45JJafGkhu9EImatnJMnZ9+ZnLTD4syDbzKdNPDNjTyltq41+thNrvOO9V6yrjhqlH5lhfhBjFaJuRd6vqcn4uYa3nMtX00ocbsTV0jSJR2J0klQwWL0bLJQtunFybMtXyEkASxmkg7Xkw53ThPEnRB00mOfhw/HKLX9mi6ITlLvS+nx1YmuzZjVy4tdPi9t2bwwoRuENP0FFtTtAziVPlC6ZpGOWdg6yuTW7omlH2Apm14pkWmOatmbaSlXsi5uTZnxorUewE3Gh6VnEklb9JwQwxdI28ZHKrmOD/bHkxMDuVNRooWBUsfsCp+lGJqGv/9c4d4fqrK7745veZZU22yITVx5yoB+k8/O8lvv3GL/Bbv5W7B/P1uE2x2COu71IMKmr682HvgE0R7XfupHbOV7OLdaTXs8TPPTfLExJ0zKQ9qb2u/5wUeAKcd1nog0V/Q+xNhYcY0WLq2r8YnN6vtiKlPjhS3vcjt9OS2HoQOF1VIb8dXLTA9y24bKW1sv6Wp5De+e4MfXG+gAXGqJtPSVHKo6uCFCZcXewzlLYAdL8arKeO3bjZZaPvESUolZxBk03FeFPPNS8vkTA03TDA0weFqjoVuQJhIRksWyz11raYmsHWV2aZrIvMvEugCDuktXvAvIkgQVp784z+CXqzx8TTl9WvKOemFqSEmq7kNmpDVz1cQJ/zqt66x3FXxNAttnyiV9PyYrhcjgZJjYOkak5XcGn+xPkuapOmGZxrUZFfeMnhissSNZZeFdkDZMThayzMWK1YtjFM6npoIPDNWoGibRHFKy1MC+FrBxo8SGm5EGKcIFIunCcFzRyt86tQI79xsbXjW+ozYXNvnuaNVTo8W7wljcb/bBPuxZXU/ar+0Y7aWXaS03JDrdZfzc0p2cXqsdMBA3efaz9YTB8Bph7X+tBQmKXGSYjrGoEXRzziD3S9+97rnvl0x9d/5THFbi9yVpe6OJ9o2Y7NWTtzQ8SMcc/MN8FuXl/jahwtKF1SwMLM4kpmmx426x2jRYqkbMNvy6Qbxrhbj02MlfuHV4/zT3/+Ajh+Tt3QMXePUaAGZKsbADSOQ6r9PVCwqOXUt1+sejV5IztRxo2QwGahrgpO1ApqAuhsxWrJ50gwwZxLaepny2R9B5ot0/CiLEbGwDI3xSm5DWwpWjeUHEV87p6wTzo4XGS1ZhEmKGyqmp+3FgwiWvo5otf6p48ccHy7Q8WNMXRs80/2/7z/XR4fyTJQd3rrZ5FOnR3n2SIXnD1eZ7wb0wpilTsDbN5pcWeqx1A3QdQ0ETFRsQNkSRImKj5FSo2ALvDjht16/yaFqblvP2pGh/D1jLO5nm2C/tazuZ+2Hdszmsosga1EnA9mFoYt95Vp9UA++Hr1P5D2u9aeloq1GyXtBTJgFsq7elHaz+N2PnvtORN93WuROjhT5N1+/vOOJtt1S9mkqee3cPF6UcGQoh64prU4lZ2JqgumWR9OLMTS1EH78WG3Xi7FtauRMjacOlclbKkeunDNpuWHWlhLESYqtC0qZNidvmxyqSJZ6IYamgUzw4hRb15mo2ui6oOvHVPMWTx2qUCuMMofG+V6ZIy4Y/srk4DNHKhvaV6ur/3x1/XjN+1kr2Dx/tMrlBeU/ZBqCJJLUChZDBaURipJETS9lOW9/6ePH+PqHi9xquEhQrJCArh8PnuuGG/HBTIuFTsA3Ly3xwUyb10eVSP3xiTJMwCdPjQxA/7mZNv/iqxfo+urzEcUpVhZYbBoa1ZwFSHpBnAH1U9vaUO8lY3G/2gT7qWX1IOpBt2M2k11cXujhhclAdtFwQyxD50wlx4X5Lv/xBzfvmVv9QT08dQCcdlGrgcSlhQ4CaLoRx4bzazQou1n87ldOz07bBLdb5G7W3V1N3u2Wsp9uesy2fAq2QZxKViVUkLcNjg7labghR6p5/vZnTu16dPzSQof/87vXeW+2Tc7UMXVlkTBasjk/12au7ZNKiZTKQiFIJIcqOXKWTilnEqeSpw5V6AYRF+cVCE5SSAKP55Ir5CdfpFZQrcSRqTMcW+zyV16aYqLiDO4vsGn7CtY+X0Xb2PB+1go2Q8ctOr6asptv+/zVl45yeaHHmzfqXFlSm0TO0nEMnQtzXT77+BjnZtosduaYb/tUciZjZYdTowVA8NaNBovdgCNDOZ6aLONFyYZnczXFfnasxLvTLb52foFupg2LU6nE5TkTL0oYKzucHCmsAep32lDvNWNxP9oE+6Vl9SDrQbZjNpNd1F2lCezLLoxMdtFwQxY7Pu9nRre1vH0gIP8I1wFw2mWtXtzPzbX5gx/OEsSpanPsIhgW7m9Oz27aBFstcnej1djNBtgLYzQhGC3aLHWDNS7LoHQzYZzy2ETprkDTr37rGrcaLjlTp2DraEJjuuHywUybKE2QEgxNTdr1hwLSVPlOaRoYusZQXmXQfeHpca4vuZTSDuXZNyHyMWZ/CJVPA+p+O6bBqdHihnu8nc3VNvRN389+61MIGMoYLkMT/P47M3SDBEsXWJpypf7u1WVmWh6/8Opxnpuq8pvfv0EviDk5UiBn6XzvSp3FbsBo0eLJyTKGrlHStds+m5om+LmXp5ht+7x+tU6tYOFk5qa9ICZnqd85bxssdILBM7KdDfVBMxZ7UfuhZfVRrU1lF5ndyOr2dJQk/PBWCzdrtU+UHBIp+c6VJS4sdPjij5/i7Hj5Qf86B3Uf6wA47UFN1fL85RePDLQdu1387mdOz162Ce5Wq7GdDXC15qvtRTiGxqGqQy9c8flR7ad0oC363BNju9pEVwPYZw9XiGLJQsdnKK/E024Yo2mqjRdkwui8qdP0IjpBzHzbo2CbDBVM5tqqDfaXPz7Ff/36d+lc+C7IFC1Xxjn+3Lbu93Y21zSV23o/e2HE//bHl6i7IYcqNlaWYdfyImVSCXz1nGqZrY5xubbcY6GjmKYnJ8trrB7u9GyeHivxc5+Y4vqSMsJ0I3WSV0xWkVrBouNHuxZ071cB6XbrUQCAD2NtlF0YaGK17MLg5EiBK4u9zIvMpO1HfDjfxYsS4iTlymKPf9oL+Z9/5knOThyA3I9KHQCnXdZmOqSTowX+wguHGd2lZ9P9nLLZyzbBXoCw222Am8XbLHVDEPDckQpXFl3qbkgviDM7AJ1Pnxnhk6dGdn5jWAtgNU3j1FiBThCx0AnoBAlCoCgmSTYhJxCaoGjr9IKEphehaxpD+QLPHqny+SdGufHe9+h++G0E4OfHcM68Smo59DIR+J3u9/rNNWfqCMCNEm7WXQ7fRlg90/SwTaWT+/VvXWex4zNZdpRRJcq00ipo1Hshbhhzcb6zoWX23kyL//D6TZ7KmKb1dadn81OnRvjppyd4/Xqdw9UctqEPDDo/CnqeO9WjAAAfxlovu4DVsosShiaouyEFW4W3B3GKRkgpZ2E6BpYRc3mhy7/62iV++XOnDxjCj0gdAKdd1FY6pPdnVEjlX//U8V0tgvd7ymav2gT3Uqux+l5PlB2KqUHHj/CimKarNunHJookKbT9aKBt+rmXp7ZkrO4EavsANmc6tL2IVMLpsSKXF7sqPkUq76WCbVDJGbhhghelSASmrlHJm/zNHznBTzwxwUhO43d/9z9z8eJFAJ762CeYK5ziypLLQm/z+Jjb3eejtTyXFjr8/g9nNx0eWP9+BnFKEKfEqeQ3X7/Budk2QZQQp5LVJg9CCIqOQcePaXrRpi2zr+SVIL+0CXC607OpaYKfemaC2baffW5UxIoXxLd9Rg4cnQ/qXtca2cVsmz94d5Yg8yxTn+2YKNYIIsUur44xKtgGYZyw3Av2TEZxUPu/DoDTDute6pAexJTNXrUJ7oVWY/W9Hi6YnJ9tM98JiNIUUxMkicQNYppuRJik2IbOyyeGN/y8nU4pFiyDME757pVlemFCnKYYmpquq+aUiFTXBBMVB8fUqeZl5syd4EUJj0+U+IknJjhay+O6LouLixiGwZ/9s3+WZ5555q7AwHaGB/7uj51ao70ztJRDVRUXcnG+TS9Rz9LhqhKy98vUNcI4RBNsAEB78Wzu9Bk5cHQ+qPtV/QPC0Vqek6OFwXPXcEPSFHI5nRRJJWetefajJMXQdSYruX3r13dQe18HwGmHdS91SA9qykbLjBv7m/l009s1eNoMhAHcrLs7Bgr9e50zNb53tc5yVzmK9ytNJVGS8vOvHOOJQ+VNX3s3U4peFLPYCZhr+0yUbUqGRZSktL2IJE1JpMTSNSx9xaDRMjTlO2XoPHdkaPB75/N5fvZnf5Y4jjl06NDgfu9mcd2J99bhao7fe3uGMEk5O17MWmJQtE2SVAUWL/cCDpsr/lBhnBDGklNjxQ0AaK+eze0C9fs1XXpQB7W+Vj+jHT/iS2/N8P5ME78fwJzVagH5aMnm+nLvkTMrPajN6wA47bDutQ7pQUzZ7OXJfj0ouDDf5rd/MM3lxS6JTBnKWdt24e2Faox+puEx3w7QNRVnoly3wY9SGl7En1xY5M89f3jTNs9O2cE0lXzl/QXKjjK/6wUJQqgWXNExaXoRlqFj6ToLnZBK3gQkLVe19J6bLHIsneatt1qMnXgiAwdFDo/cPUu4GrQDtD3FtFm6Rskx1oB2YAPALzkGwwU7m+DTaXsRFcek4CiGba4dMFl2+L+9cHRTALSXrd3bAcf7OV16L+ugzfjw1upn1DI0/rc/9rm65GIZMQXbIErSNf5mfvTompUe1MZ6oO/yP/tn/4z//J//M+fPnyeXy/HJT36Sf/7P/zmPPfbY4Gt83+cf/sN/yG/91m8RBAFf+MIX+Nf/+l8zPj7+QK75fuiQ7ueUzb082b92bp5/+dpFFjsBlqFhGxodL2apG27rtQuWQZKm3Gi4CAE5c8V2QBdgGYIoUa6+NxvuhhDg3bCD/e85M14kSuQgYLYbxBiaxvGRApaucWQox/szHZa7AQDVnMknjlc55V/kjW+9B0Kjc9zF1fNqcGCkwPNT1bsKe+6Ddj/SOD/boe6GgzZiLW9xfCRPECcD0L4e4AshBkJ3KaUChlGMGyWEccpE2eGXP3fmttNB9+PZvJ/Tpfeq7nQYOQBVD0+dHivxxR8/xT/thlxe7Cp/J10f+JsN5S0uLnQ/0sMNH7V6oMDpG9/4Bl/84hd56aWXiOOYf/yP/zE/+ZM/yQcffEChoDbBf/AP/gF/8Ad/wH/6T/+JSqXCL/3SL/EX/+Jf5Fvf+tYDueb7pUO6H1M29/Jkf2Guw7987SJzbTXBZRoaUSKzsfcU4I6vfbiao+SYeGFCObf+XkOUSIqOQS+IubrU2wCc7sQOOqZGww14b6YFwGTZUQLwrk/RVi7h/YDZPrOTszSuL7v89U+foGAZXF3qqe/Nw7e+8vtcvnkTCXRqj1OuDjFhm8w0XX7vnRl+581bHK3lGSnuzjyvr71680aDOPvdTV2dfhc6Psu9gKO1/AC0bwbw+47iH8y0kW2fobyFrWucHCvwC6+e4PHJO/vR3Otn82HPcLvTYeSzj49xfrZzoN16iOrseJn/+b97kn/1tUss9wImKzlGSyp78eJC9yNhVnpQK/VAgdOXv/zlNX/+tV/7NcbGxnjjjTf40R/9UVqtFv/u3/07fuM3foPPfvazAPzqr/4qTzzxBN/97nd55ZVX7vs1P0puv/fqZJ+mkt9+4yaLnYCJsr1q7F1gFSw19h6tjL1v9dqaJvj4sSH+27uz+GGCsAW6ECRSibFNXaOSM+gFyabffzt2sN4LB9Eh/+H1m/yOdosgSkmRXFvsMdv0GSs5mRO8Nfi+vt9QyTY5WstzbLjA7Owsv/Vbv0W73UboJvWxj3Hs+EmEENR7IRcXeiSpREpJmKRUcuauGL3JskMQpTTciKmhHFoWNWMbOmZecKPhMR6nTJYdNE1sCfCH8hZ5Wx8E/aZI/DDltXMLGLp44Jv3w5zhdqfDyFs3mvzL1y4yWclxqHr/tFsHDNfd19mJEr/8udMDJvH68s6mYg/q0al9tfK0WurkX6vVAHjjjTeIoojPf/7zg695/PHHmZqa4jvf+c6mwCkIAoIgGPy53W7v+XU+Km6/9+pk3wdkliGwDH3N360dew/v+NqfPj3CZCXHci8gTlLCLN6kkDFCbS+imjM5OVLY8L1bsYP1XrgmOmSy7PDWzQYNV73WSNGm5YUsdFRA8PNHqyq7ahNG8f333+dLX/oScRxTqda4VfsYw9WhgT/RpYUuXhgzXFChu003AuDMWHHHjN5s28fOJvsabrTG9LOffWcZGrNtn6O1/JYA/+JCl7lWwETZ4fhIgbxl7Cvh9cOc4Xa7wwiAG6rBg48drQ5A4b3Wbh1MJ+5dHZiVHhTsI+CUpil//+//fT71qU/x9NNPAzA3N4dlWVSr1TVfOz4+ztzc3Kav88/+2T/jn/yTf3KvL/eR+ADdq5N9L4xJpGJCoswmYHWpsfcITWh3fO0jQ3k+89goX/lgHoEkZxmZQFzQ8ZUg++WTwxwZ2shabcYOOqbGBzOtQXTIE5Nlriz0iBPJ1FCOhhshNCg6Bl6Y0PYiLsx3eHKyxFw72MAo1ut14jjm9OnTPPPpn+D/+OZNJrLfqePHNNyQomMOBOa9QLX9dsPo9cIYy9D4+LEaV5d6a7RXY2WHY8N52qs8mDYD+JaugYSJssPHpqr7Unj9MLO6tzuMdPyYThArbV4q1/zdvdJuHUwn7n0dmJUe1L4BTl/84hd57733+OY3v3lXr/OP/tE/4ld+5VcGf2632xw9evRuL2/Tetg/QPfqZF+wVIBrx4toedGGLLkwTgnjlNObjL2vr37W2UIn4MJchziVxGEMCHRN47lDpQ1ml/1KU4lt6Hzm7Cg/uNZgsePT9MI10SGGpg2CPTVNo+gY+FHKExMl5tsB8x2fWw2Xas7kuaPVDYzipz/9aSqVCk8//TTTTX8NEA2TlDhJMR31MYuSFD0LDYWdM3p9oOuY2gbtVckx6AYxQZSuAaPrAX7bi/jN791gqGDta+H1w8rq3u4wEibqubcNffAMrK691m49KtOJB3VQ+632BXD6pV/6JX7/93+fP/mTP+HIkSOD/z4xMUEYhjSbzTWs0/z8PBMTE5u+lm3b2La96d89CrWXWoV7dbI/XM1xeqzEUi/EjxLm2z6OqWMbOgLJXEe1if7SxzdaCGxWp8dK/P3Pn+HL783x7nQLN0zIWzrPHq5u2W7YLKZltGTzzNEqksVBdMhSNxgEe4Jiw7pBTN42ePF4nqYbcXW5x8++PMWPnhml1Wry27/9O7zwI58jlIoxe/rpZwZeWKuBqKVrGLoSxVs6A8+XUgakdsrorX79M2NFyrmVjfl2QHc1wD8/1yZIUvJb/Mz9JLx+GFnd2x1GTE2FTw8XrcEzsLr2Wrv1KEwnHtRB7cd6oMBJSskv//Iv87u/+7t8/etf58SJE2v+/uMf/zimafLaa6/xl/7SXwLgww8/5MaNG7z66qsP4pIfaN0LrcK9ONn3AdkHsy2uLnZpezFR6iOEEncfGsrzP33uzI4SxU+Plfh72zTXvLLU3dCe6AUxH853uLDQIUokbphQzin2x9C0QUsxStR4v6UrlszQBaPZFNz169f4rf/wHwkDnzenuzRHnt7wHqwGohNlm0rOYK4VoGsKlKlA25iire+Y0dsLoPuwCa8fNlb3du/RXNtntGRvClr3Qru1/lDVCaKHejrxoA5qv9YDXR2/+MUv8hu/8Rv8l//yXyiVSgPdUqVSIZfLUalU+Jt/82/yK7/yK9RqNcrlMr/8y7/Mq6+++kAm6h5k3Uutwt2e7Ddjwa4vu1xa6NLyIpXtJgSGplGydU6PFjg2vPPNcP0mumnQ8kiBuhuuaU8s9wI+mGlT7wZ0siDghXbAyyeGqBVsanmLhY6PmRdrWKHVm9nspff48pe/jJQS36oQ1M4wUrDRNbHhPVgNRG1DJ05TukFK3tI4N9viHApAnp0o3RHorL+3J0eKdwV0H2bh9cNSWx1Gnj1S5S+/WOKPzy/ck0zH9Z+FkaJFGKcPDUg+qIN6WEpIKeWdv+we/fBNpk5AWQ784i/+IrBigPmbv/mbawwwt2rVra92u02lUqHValEub5/h2E7drxHfNJX8m69fHrRo1m92ffO1v/OZU/e9jbHZgl1xDL59ZZmmFzFRspFAEEv8KKZoG9QKNq+eGt7x9a6+34udgP/27hwNtw8k1WTY5cUuN5ZdPjZV5WitwNWlLt+7UqcXqnw5gfKAsgwNx9R56fgQjqnzxvUGTS+imrd4YapKztSZafrYBjwRX2b+yjkAFowxruQeI0IbGE+eGMlzq+lxYqTAX//UCY4OrZhpnptr81vfu8HNhke66qOma4Kz46oFuRXYuR3DeHJkZ0B39b1b6gT8X+/O0nCjTTfvA8Hw3tRW68Pq9zWIFXg5PVbcNcO78VClPgszTZ/ZlkfZMdcMAsCDXzcO6qD2W+0EKzxQ4HQ/6l4Bp/s54nuz7vK/fOUC1by56cmx40c03Yh/8BNn72tbY7MFuxdEfO3DRRbaAUdrDtX8it5MSkm9p2JKpoby/MpPPrbt6119v70o5vqyR5ykfOLEEMNFZ/B1ix2f184vcGKkwKmRIl89N083UIBN1wRxmtILEoYzcXTR1jk2XCBMUpV+biqH8yBOiXyfQ423cYI6EnifKaato4yVHUxNoxfG1HshcSLJmRoJ8MyhCs8eqQ7ATR/wnh4t0A2SgZi7aOtcWuxtuXFttRnuBtxs9qxW8yZIaHrRnmzeB7Wz2qtD150OVW/dbNL2FEA+lIU6H4DkgzqojbUTrHDA0e6i7veI7350Ut5qYgfUvyWSbpBQycnB3+3Uw6lf/fu93A0oOQaGptENIoSU/PBWi+ePCmoFBdBsQ6doGyy2fTp+TC9UeiJjVSCvY2oIAcNFk5GCzV956SinRotMlh1m2z7nZtv8wbuzRCk4SQ90kwv247zTcihpKW6Y4AYBLT/CCxNSCTlDUCsq9qb/HPyZpycG4lxN0yjn1k5SbSXO3ctpqK2e1dmWcg3/Cy8cZvQuYmAOane1V9qtOwnAz4wVuVF3maoVWOoGD8104kEd1H6uA+C0w3oQI777UdC71YIdJilIsA0NN1QZaH3ncNiZhxOs3O8byy5xmnJt2cUNYupuSNkxaLkRlxd7DOUVg1RyDEaKFpcXe3hRgq4p2wJQJ/AwVuP61byZAbuUiYoz2MQOV3P83tszhHHKmcNjpJXP0Ith7kZA3gzx45Sbyy4SZS+QSgUV3UgieyECMTC3fO3cAl6UcGiHgHevpqG286y+e6t10Kp5iGs7hyrb0PjzHztEyTHvi6zgYZqCPKiD2k0dAKcd1oMY8d2Pgt6tFmxL13BMDSfW6fgxcSpZbQ4RxilBlDBetun4ETfr7m0X1+mmx1s3Gyx0fJJUUnRMLF3Q8iI6foKmJaT1HmfHilQy8HS4muNWw8OLEgQQp8pwsh/TUisoh+2WF68BcFJKfv8Pv8r1WxGTE8fUFGBpmKDjEycuhi7oBilRKjE1QSpBov4B8KOED2abHK3lmKw4TDc8EOwY8O4Vw3gwjv7o13YPVSXHvOfv8YFD+UF9VOoAOO2wHkTbbD86KW+1YJccg6GCTduPMTSlvzJ0kTFNCbcaHqausdDx+d+/dumOi2sniLhRd0kSyXBRASM3SJES/CgmleAGMd++ssxTkxUOV22WeyFPTpa5stSj3gvpBQmOqQBSrWDhmJpq4wUxYyWLOE157+Yyb3zjj7h2+QI5oeNMHAJM6r2Q83Mdml5MnKQDx+f+v/t3XGb/c23Z48Zyj8O1PLoGY2WH2Za/I8B7p83QDWLiRDLX8m97qt9PLd4DJuLe1H45VN2tfOHg+Tioh6kOgNMOa/WmVrSNDe7N96pttt+clLdasIUQnBotcKvhMpS3qRVVSyyMwyyMV3B4KMdUbXsZaV0/xgsTSo76GX0/nBSJlJBk/1xb6jHdcDE0pXE6OZrH0jVyhoZjqFZdydFJ0pQbyx5NL8bUBd+7ssy5G/M86b2HHXVAaDRrT6Jj0umFvH2ziRvG5C2DjhduuA99tskQYOqCME35cL7LUMHCMQ0+98Q4X35vbkeA93ab4XI34PvX6pia4D+8fpOcuTXw3C8t3nvJROz3DfdeX99+OFTdrXzhgKk6qIetDoDTDqu/qX336jJxnNLwIuJUmSYO5UwMQ+PVk8P35IS3n5yUb7dgL/dCnjtaZaxk0+iFNL0IgaQTxDiGwfNHK3SDhIYbYukap0cLXFrsbbq4Fm2DnKkTRAkFS2em6dHyI6Jk7TBomEjCRGJokqGCYr2iJOVmwyVOJKYuaK/6voJtULJ1SnGLp9xzWESkuk19/AVuBHmW5zskqRwE9BasFC+MIUo3vR+pzFgoqVi2K0s9Xj4xzKdOjTBZcXYEeLe6t7NNj9evNQB4+vgQh6r52wLP/cBG3MtBiv2+4d6v63vQh6q7aQkfZOkd1MNYB8Bph6VpgscnS/zu29N0/IjhgkUlZ+KFCVeWe5Qdk8cmSvvq1HuvaqsQ2aO1HC8er/HYeCkTTieDjDQh4I3rTepuOACctbzFRMXedHEtOSZTw3kuLXQ5P9uhGybczj9DSslyN+RPLi6SppI0BZDoQiCEIEokJcdkpGAxGs5wOryoQJ1WZGHkBZ6bmqR5s8VSN6DlRVTzJhLQhPJe2vLnAnGqvq4bxBRsY3DS3w3gXX9v51oe15Y9cqa+xoLhdqf6PgCbbnr88FaTobxF2THRNTYNLN7rupeDFPt9w73f1/cgD1W7bQkfZOkd1MNaB8Bph5WmkvOzHSbLDqNFi4argmwNTePkSAFD0/hwrsOPPza25x/2/XjCXr1gn5ttqzDdts+X3ppec33lnMlyL2S5FxBEKUXHwNQNoiRloePT8kOGC/aGxfVwNcdULc+7t5oEyeagSbDSMksk9IIEU1fTfEVHx49UGn3RMThcNXHDhGU35JTpIZC0nAmuFh7H8wXdIOHMeJEoSfGjhCSVzLd9Ol6EGyVoQrFL60uuuhZTF/zfXzq65j3Zzfj56nt7ebHLb37/BocquUFGnZRy0Cou2joX5zubnuodQ2OxE3JxoYsAKjmTl0/W+Ozj48SpvKNAf7d1r8Tp+33DvZvru5vW3oOKp9ltS/hgeOGgHtY6AE47rP6H/cx4cVONUzeI78mHfT+fsDVNEMQJ37iwuMGw8b2ZFtNNl48fG+JWwyWIEg5Vc2iZRYBt6FgFjfm2DxLyq6wLBiVByxgjNoFO6/9LCgSJRNdTBDoFS6fpRfhRQtHOk0pY7oXcKp8isqu0nQkMIAoC6m5IztRASI6PFojilA9m23hxQppK7rSFlWyD0ZLD8eHCLu7kxupvhn3n84KtPrL1XsilhS4NNyROUrTMFf3cXHvw3K1+Zl4+MUSSQtuPmG54vHGtwc26pxzU7xEAv1fi9P2+4e72+vbjwWg7tduW8H4aXjiog9pJHQCnHdaD+LA/zCfsME75/rU6r1+ts9gJSCQI4VMr2OSs1SBJIBEbQNB00+NGwyVn6cSdnZncu2FKlITYuqZAR9pjvHGNsPg4AEEC7fwkAG03oN6LeONaA4kEKakWbK4v90hTSd7UcWVKmmyuceqXlJKRoo0bJTu61jvV6lN9lEjevtnEC2OKjonpGPSCmKYb8Qc/nOXkSIGTI8VN3xMJeFGXxW6AaWi8cmIYL0ruCQC/V+L0/b7h7ub69vPB6E61XpM3UbaJU8WGNtyQw9Xcpi3h/TK8cFAHtdM6eCJ3WP0P+0zTZa4VbKrV2esP+8N6wq73Qn54q0UQpaRpStE2COKUhhvhRSmTFUd5I/kxRcdguGDhrQMc5+baXJjvKDbK0ugG6W01TusrSSR+mnDcbPGKfhkzSol9h2nzEH6UIKWk0Qu5UVemlmkqSVJJydGZa3p4YZI5jQvSNCXZ5IeL7B+EEognUu75Yt8/1b873aTlRXhhTC2Ljekbex4bzhPEKX/0/jw/86y24T2RUnJpoYsfJUyUbXpBghsmlHP3BoDfK3H6ft9wd3p9+/1gtJ3qa/J+47s3+O7VOi0vBAnVvMmp0eKm37MfhhcO6qB2U9r/v707j66rvhJ8/z3jne/VPFmSZ2ODB8aAQyoDGAhJEygqXYEkHZLwOqsqpF+Rery8qlWhkupkhXR6WOlU16rUql4hpNeiqIQEuipVgRDC0BBwmEywMdiWZ1uyZunOZ/q9P450LdmSLMmabO/PWgL5TvpJ50pnn/3bv/0780PEWMuqYlTFLV45OMCJ4SJRS6c6HvYGOjEcrnqqiltz+st+8gp24hNBzDYoe/4SuMI+Ob4gCNh1fIjBgkM6aqDrGlHLoCEdoSpm4Xg+x4fCwKQhHeWixiR1yci4k10QKF490I/nK6riJgnbnFHQBBCg2GR28j59LxYBg0YVO90GGlIR4rbBgZ4cB/sKeCNZJU0DwwhrnQZLLoGCsheQK3l4EySbdMA2NSxTC/erG1mJ15yOnv7gszB6VR+xDA71FbBNfWTzZJ/+vEPMDveaa6kKg+gDvfnTjsloBiAZtbDNsDWDM5JBOzUAn8sx1yRs9nbnyJbCFajZksve7tysi9NHT7idQyVO3Wpz9IS7piG5aCfcmY5vJhdGS13JC6hP2bxnRQ3Xb2jkPStr6Rwq8eCLB9nXnR332Pl6fwgx3yRwmo1KJfApv9CjvYzm+MuNvYKdyFK6woYw0/TCvj72decYLrocHSyRL/vhdJuvWFYdo606TiZqsWlZhivaqyi6wWknu2ODRXqyZZozUQpOQMQ0ZvSGNfD5gLWfS81jaBp0qEb2praQSiRIR00Kjs9QMexuroCyH2Dq2sgKPGBkOxVUWBA+0R/wAAiCkzVYEdMgZpt0DpfO4ic6sTUNKT66qZlkxMQPFIMFh5Ib0JCOcmlbVWX6s+yFWbtT3zOOH+D5AZah4foBhq5jGyd/ovMRgI9mIja2ZBgsuBzszTNYcNm0LDPr6aelfsKd6fiW+oXRdIxmzQYKDltaq2ivTVCdsEnHLNY2JOnPO/xy1wmCU1ZWzMf7Q4j5JlN1M3RssMhg0eWqFdUcHyzRnS3jBgGWrtOYjtCciTJQcM/7LVcmG5/jBbx5NMw0aRrELJ2iE6Dp4Prhhr/9eYd4xED3NbxAsa8nP+HJLu94lP2Ai5pSvHKwn76Cgx7WbU84ZTZWQnO4zt5HnV4gUBovu+0coIHmckBNwuREtoxlatQkbXJlj4ih4/oBBdfH1MOC6bIX4PkKX4GpqbAdgQLvlK+tUNiGgWFotFXHyIzsCTYfNjSnuaQljWXoWKZeWZQw+p4YDaJX1SVOe8/Yho5p6LheQK7s0ZCOkoqe/BMwn81b53qp/GL3LprL8S31qcfpOJtygqXUn06I6Vi6v4lL1OjVYVXMRtNG6lvUySxTxDIYLrrn/ZYrE43v2GCB3x7sp+wGVMXDFYZFJyBi6TSmo5UO4FHLoDtbpuT6FB2fLW1VE57sRk8oEVMnETGxDR3PD/ADMPWw6eVk8ZOJT1orU1Imzzqr6SFFzNTJxMxwo2DHwxrJthgamKaOYWgMFFw0TWEaYVDi+mH2RtM0dE1D6VSiNl2DxEiBezJqUh23R+o5tHk7yS2rirGmIcXO40OszSQnDaJbq+MTvGd0ErbB0cEi9Umb1fWJcfVP8xmAz8dS+aV+wp3u+Jb6hdF0nG3B/mK1UhBiNiRwmqGEbeJ4Aa8d6g+LiGPWyNSHoidbpj/v0FYTP++3XJlofB/e2MRvD/TjK8Vw0Q2nvAxoTEWI2yaGrlF0Aza3ZrBNnZV1CT537UraquMTnuxGTyi/PdhHyfFpr4lTcn1OZMsEgULXA4qTdPIeUjGedlaTVRGKKkLE1HACRW8u3DYlHTUpuQFlN0DXQeETMXV0DXw/wA8UtqlTcPyw7kkPp7fGLqrTAHekFUB9MsLGZWn68u68nuRmEkSf+p4pez7VCRsvUKRjFpah4wXBkgnAZ2Opn3CnM76lfmE0HedD1kyI6ZJ38Qw1p6OU3YDBokt79dh+RBpW3OLwQJFGL5jz4mBYuCvs2TTh29ed5dfvdFNwfCxDQ9d06tMWnq8ougGG4WPoGiXXo6Mnz/LaBJ++ZjnLp+h3pOsaN1zSwCuH+ujOlWlIRUhGTRSK44Ml3DFRjIbiSvMoR4MMnUEagK6R/wN4SmHqGnHbpOg6mLoeBlN+QKAUrueffCUV4PoBZS8gbhu4vsL1g0pxeMQMa5mUClfQ6Wg0pKP05d05PclNdhzWNKS4a+sKHn3tCB09OQIFVTFrwiB6ovdM0fF56u2lGYBfqJb6hdGZnA9ZMyGmSwKnGeocLhGxdKrjFgMFd6QDdlgfkyt5VMUsbFOnc7g0L1fC832FPZsmfKM9aI6O9FtKRAx0TSdXctE0jeRIdifsxA0Xt6T5wyvbzngy2Ned5ald3WHg4gUcHShi6BoqCPel0zUNHYWJxwft/Swzhlmj+ni0tBH3lLe2UoR1QYZOoGC47FVaD2iEdUuRkUpwL4CiGxbrJiIGuZJHthRgaBAxdWxTR9c0LEMn74QB19GBAh/d1DJnzQqnOg4AT719YqQvlsLQdOpTUbZd3DDp3nen15Us3Smuc9FcbOa71Kcep3I+ZM2EmC4JnGYo73jYps7l7dUc7C3QX3DIlz0MXachHWVFbZyhOa5xWiizacI3tgfN5mUZXE/RnS1RkzCojlt0Z8NO3Jc0pejKltm4LMP/e+NFmObU6+PGjmVdfYp82eNQX56iE+AFCtvQSMcsVHGY9xl7SetlXKXzG2c5HiamFpYiKcIpNT8Ir3wNDTw/XIZv6hqmrhE1DfKuH76uaRAxoTZhh5sLez5VcYtsKezcHbMNbCOcwlMoWjIx2mpimLrOv9nSPGUGbS6Ow+6uYQD8QNGcibKsOtzo98hAgYd+c2jaK5GW+hTXuWQuO36fy8flXM+aCTFdEjjN0OhcftQyuHJF9YRbrpTc4Jyby59tE76xq2l0XWd1Q4Js2aVruITrhavUenIluoZLNGdivGdlzRmDprFjqU1YvHsiS/dwieGiV1lN5weKRnp4r7kfW/PJBTa/ctYwoMKTztiVb6Of5h2fo4Nh1soMwPMVtqVjWzoKha7rNKQifPbaFezvyfPEzi6Mka1M4rZBxNLxg7Cvk65rGLrGJS1pWqpjHOzNn9a8c66PQ8I2eHLXCdDgposbK9PEc90ocS6yJxeKc7nj93w4l7NmQkzXuXV2XwLGzuWvbUhWNlyFxZ/LP9MJb6r7J1pOPJ1NZPOOR9H1SfomvbkylqFRn7Q53B/uS2eOBB51yQjpqMWv3+lmeW18ypPJ6Fhils6bR4cYKrgUnLEdwxWXmF1caYT9mbpViue8VeSVhTGyCe/YTXfDZ4S3DxU9UlEDyzTQg7DuabTH1PKaGNWJCO9bU8+Wtir2dmdpSsfw/IC3jg0RtYwwe6XC6b2C4xGPmHNa+DrVsu5c2cdX4TeXK/ukYycD0LnqIH+u7pe2GM6Hjt/z4VzOmgkxHRI4zdBSncs/0wnvTPefupx4OpvIAvRmyxzqy7PnRBbXDxgueuTLLr4Kl+sHCmKWwbqGFO218TOeTIJA0dGToztbIlfyyJdcAqUoez5je+dVayU0Dd716nnFb8M2LfQg3B6lNLLSzhjJCsUsg6LrU3TD4CtX8qlP2cRsE9cPiFkGm1ozNGdiHOrLk3c8UhGLmnjYXTwZiXBssERPtlTZ5qTs+ZiGgaVrcxosT7WsO+zyHU4+OhPsmXe2e7RJ9mRmlvpWSEKI+SGB0ywstbn8M53wPnRRPf/8u076cmEX7pW1idM2dp3pJrKjwdi/vtWJFyiKjk/B8ciXfbwgzPQYuoYfKEqez67OYVIxa8qTyb7uLE/s7OK3B/p5u3OYkuuTsE3Knj9uBR1o/MZdztEgw+GgJuzqHQTYhobjhQ0rbWO0i7eBqYfhRnSkx5avYLjkUXB8opZBfSpCMmJSck9mjk7NLK5pSJIre/TnHRIRk1zJpSph0zVcpjZ55mB5utNfUy3rDrt8a2M+H+9sMl9znT25EKb7lvpmw0KI+SGB0ywtlbn8M53w3jg8yDd+vhvH80lETHpzDtXxcK+stQ3JygnxC7+3akabyK6oSYxsseBy1fJqnnr7BPmyD6NNQbVwSss2dQxdoy9fZl93lsvaqybcPmJfd5bv/move7qy+EG4NUjJCfB8Fz9Q1JBjrdXLS+5yFBo+Ogf8GoyRH7frK+qSNkrB4Mhqx4ihEwBFN8AydDLRMAgrOgFVMYv6VBRdg1zJ443DA1QnImxdVVs5jqdmFjcuS7OnK0vnUAnT0KiO22xuPXOwPJPpr6mWdScjBsZI19VkxBj3vLOdJp7L7MmFMt0nvYuEuDDJb/RZWApz+VOd8AYKDscGC3RnSyyrilGdsEcadZbIlT0ubauqnBA7h0vctLGRPd1ZDh0doipuoQDH88mVvMomspahs687x+tHBipfVymwrbDztgoUvnZyNVvMNjANnbIb0D1cpidbPu1kEgSKh7cf5s0jg9iGRipmYRg6B908nq9YafTxXusgpqYYDGK87TdWnqtGV84pWFEb5/L2Gh7efgjPV6ACNC3s4h22jwh3bDd1DV3X0PWwRUECk67hEqaus23DyczRRA0k22viXLmihiuWV7OhOX3GYHmm019nmgpe1xQ+dl9Pfk6niecqe3IhTfdJ7yIhLkwSOJ3jJjvhKaXo6M7jeAG6Fm4Fo2saEVPDTtj05x06enJc2papZIDWN6X56KZmdh8frmwiO9pmYXV9gppEBC8IODFcoi/vVL7uQMFBQyNq6tiGRt4JG0iOdtw2RnbMdfyAzqES711dN+5kcnSgwMv7+zA0qE1GKvuqVccsVjj7udjoAuCQX8U+v46R/XcrdMDQwfHC16+K2wyXXJozUSwj7LvkeGGHbAXUpWzaquMMFl1yZQ9T12mtjlEdt4nZ4zM5Z5NZnO3015mmgoE5nyaei+zJhVYsvVTrHYUQ80sCp3PcZCe8bMmjv+AQsw2GSzqBOhlqjDal7M87p2WApruJbG3Crnxde6SxpKZp6LpOIqKRLSm8QBEo8AKFUlD2fGKWUWm+OBqA7O/NM1RwqU3Zla9jKI9r9XfJGH0A7HCbecNrQUcbFziNVj5pCgbyDoZeIBkzGSyGLREa01EMQ2O45JJzPOKWwVUralhRmxjXSiJm6xzqK0yYUZltZnE+Nz6d62niucieXIjF0kut3lEIMf8kcDrHTXbCc/wg3KBWaVTFLBzXR4253zJ0ciXvtAzQdDeRvbytmlcODLDz+BBr6hM0pCIMFhzKXkDU1Ee6dIMKFDnXQ9M0qiyDQCke33GMJ3Z2jeuErTRGQiKwvTxtA28Q8Qt46Gz3V7LXq0bjZKA0lk5Y+J1zAlS2TCpq0piOkC25dA+XUICuaaSjJpe2VbOyLgkwrpVEtuTOeT3KdKe/smWXI/2F04KgqQK2uZ4mnovsyYVaLL1U6h0nciEU6Qux0CRwOsdNdsILp6YCUjGLS5alOdCbpz/vVLaIyZfDVWW1yci4E+J0T6CmqVcet68nT0tVlN5cma7hMiXXJWYZ1CVt8uUwaLItnbaaBMtrE8Rtc1zdy42XNFIVsxksuDSmdXTlYfklynqEF4N1DOgJquIaq+vivN2VpeQG41oTJKMmUVOn5AUUnDAI8hUEShExdSKmTnMmRswyKLkBSqkFqUeZzvRX2Qt4/PVj9OacRS+kPtvsyYVcLL0U6h1PdaEU6Qux0DSllDrzw85dw8PDZDIZhoaGSKfTZ37COWrsH8my52MbOr05B4DL2qsYKDh0dOfpLzh4vk/BCVjTkOSr/2YD6xpP/7mc+noRM5xiO/UEOvZxvbkyxwaKZEsuaGDqOnHbCAuwIwaXtKTxAirTfwB7u3NsbMnQmy3xq3e6sc3wvmqvnz4/zv4hD9BY05Dg0tYqnn23m4GCS67kMdqn2zI0dMJu4r4CA2Ck8Ls5E0UDLFMnETEZKno0p6OsbUyeFhDe9d7lxCxzzq7Og0Dxt892VFoanBqsvXF4kOGSS1XcoiZuk4paGLpG13A4nskKqec7izDb1z/T97u3O8emZRn+6AOrJesxz04v0g8vVkbf6+dTkb4Qc2EmsYIETueRU094Rdfjod8cqvzxjFoGPdkynUNFapMR7vngmsoqrem83mQn0LGPi1kGSil+09HH64cGOTFU5J0Tw3hBWCgetw3itklN3GZ1QwLL0BnMlbg42M/Oci1vDxn4o+kkpRgseliGxrYNjQwWXZ7f04NSirIb4Abj37qnvpFjlsZFjWlsU6c/71CfimCObFNSl7Rx/KASEF7UlOKdzuycX52PnsD6cuEU4mhvq+Giy8G+AiU3IGrp+Eph6jo1cZtV9XH68u6EQcZSzyKcesI+NTiVE/b8kwBWiJmbSaxw/uXML2ATTReMX04fTr28d3XdtKZeTn29IFDTqsXZ151l1/HhcENkK5xC0xQEgUaRcN+37mxY27OpIUL80EscKw/SmkqzetPN7OzMUXA94pZJS1WUnpxDb67M0YECQaCwDI0SUwdNEPZ28gNVKYYfKITBiOcH3Hl1O+mYFQaYjs9DL83PEvo1DSmuW9/AD188yK7jw7j+SE+peDieuKUTG8nKuX5Q+bmsbUieVkh9Liz1l2LpxXchFukLsZAkcDrPzVXh6nQzHWOXpK+pT/Divj6UgrhtYuiQL/sM5F2a0hEoDqLt2YUVlLEjUW6/7VZWrFh52lj39+b48StH2XlsCNPQcIOwsWYwUus0WcpUKejJlWkfCUzyZQ9D1yg4AemYxfqmdOXqfOwSeqXCVYDVcYujAwWe3HmCVR+c3RL6fd1Zfv1ON4mIydZVtei6hu8HbD/YT7bkUp9KEDHDFggR08BOhNmx40MlauJ2pZD6XFrqv5SLpS8EF2qRvhALRQKnC8DZFq6eKdNx19YVxGyDvOMxXHTZ152lORMlVw77QyWjJoWyTxAEeAEUXJ96r5vL6cAggGiK/+vzn6G+vg5gwqX5t13Wwp7uLDHLYH9PjqGiS9ktTxo0aYSBU7bkMVR0K/2k/ECNK1A+9eq8P18+WQsWBGHwlT3OlrYM71tbP6Of29hgZ13jyWBnuOiGTTg16Mu7JOyTqx1Hs2M92TKZkYzYROMc970uwSzCUiyWvlBcyEX6QiwE+c0RUzrjli5HBvnGz9+mLmlT9gPKrs+RgSJXLq+pbLuStE0GC+HWKZYOm/UjXELY1PK4quKaaz5cCZomk4qGBdRVcYu6ZIRdx4cYKrp47vgGBeEeeeHmwoEKTxLHB4sooDpm0TVcZOuqk+0Xxl6d9+fLI3v0+SOrD8MtWrqHyzz828M0ZaIzmmqaLNhx/AANSEYt8iWXsmsTHXMSM3WNfNmjOROdcJwTkSyCGCUdzYWYX6fvFCrEGGfa0qV7uERHTw7T0FhVl6Q2ESFX8njtUD+FsoehaeRGmmRGTZ1AQUYrAtChL+N1YwOdeZ8gmHqNwujJoHOoRHXc4trVtbRWx9ABWwdLh5ilE7X0sN/TaH05YVsCU9cpuAFdQ2UuakpVpo1Gr87zZY+O7jxFx6cmYRMxw07ruhb2wcqXPX6568QZxznWyWBn/PWJbehYpkFVzELXNPoLLmXPJ1CKsufTk3WIWSbXj9n+ZWwWYSKSRRCjRluK1CRs9nbnyJZcvCAgW3LZ252TjuZCnCUJnMSUJjv5j27p4vkBcdvANg10LSz8rkva9OUdOgeLxEy9MjUVtcJao7ft9XQkN3Estpr2ugQ9w2WODRanHMepJ4O847O6PolpaGELAl0jGTGJ28bJbuIj/zd1neqExbrGJE3pKO92ZSsB0GhAtr83R1++THJMl3SlFLmSR00ywqq6RGUqbLomC3ZSUZPquE3JDahO2NSnIpTcgIGCQ8nxiVg6162v59rVJ7NwYwPHUxfCjmYR1jQkJYsggJNF+htbMgwWXA725hkcWRyxFBYRCHEuk8tTMaUzbekSsQwCBYWyx/6ePAMFh4LjUXZ9fndsiHTMosYfpFUN8prfhmUa2JEox4kSjxhc1JhiqOhOa4rp9E13A9JRk4LrAxqOF+6PZ+rhvnmGDnHL5OqVNdQkI6SiJrmyN64WaDQg29UZTv1FRrqbu34wsrmxyer6JPGISXe2PKOpsMmmTDRNY3V9gqMDBWKmweZlafyReqyBgsOyqhh3Xt0+LiMg+6KJmZIifSHmhwROYkrT2dIlFQtXdZVcn2TUIhU10TWNowMF6otHuNw6jK7BIAkOqXoG8i4r6uJsWpbBMnRKbjDtKaaxJ4NsyeXxN46z6/gQBcdjsOBS9gK8QJGJmhi6TmtNnOV1icq4Y7ZB11A4/Th6MllVl+TO97RzsLcw0lE9zFKFmxsnqUnYs9qSZapgpy/vsKWtamSrGrfSZPTqlbWTLtuXpf5ipkaL9Ed7re3pzkoAJcRZksBJTOlMW7okRzqAl9ywNmh0Ob/julxrH2KV1gNAp9FAtKad1UonWwo3+62KWezryc+4UHXsii3b1HnwRY++XJk1DUlKbsCbRwdBQSZus7p+fAPAzsEiB/uK/MNvD2PoWqWtwg0XN/KRjU28cqifZVUxIqZR2dz4bApqzxTsrKqbWUZAsghippZ601QhzjXSOfwsLPQGmkGgODJQ4EBvHoBVdQlaq+MLctIc98fX9fB8RW8uXLJfdn1iEYuIGZbMeaUCLQM7qNNyKKDDXkV3pA1fhfvKBQryZY/W6hit1fGzrrkYPzafg315vEDxnhU11CYjlcf15co8t6eHmGVw7Zo6EpHx21Bct76BX7/TPS9dr2WzVbEYZOsVIaZHOocvgH3dWZ7Y2cVbx8JporhtsmlZhg9vbJqXP0T7urM8/PJhXj7Qz2DRQVOQiVtcs6qWT17dPu9//EYzHS929PL07hN0DpVIRA06ekoMFV2WVYFl2JjlIdYNvklUK+NpJkczm+jy01zUlKY/59BfcHB9n5IbsKouyaeuOfuxn5qF6c2W+de3OunLO9hm2Jm7UPb47cF+AN6zspp0LKzXGttA8t2uLHe9dzlP7eqe86kw6WskFtq51DRViHOJBE6zsK87y3d/tZc9XVl8pQgXvWsc6MnzTleWe7etPetgYGyGojdb5h9+e5jfHR1CKUhGTAwtzNo89fYJurPlOfmaZ7K/N8cTO7voz4cFzHHbJGGbvLivl66hMmU3oEkrE8GhoMU4mLmUshnHUAH1IyvTRgugi67P565dQXttYk7GNi4waYKmTHTc9JjnKyxdY+OKamqT0XHPHdtA8pYtLfzxB1dLdkic8861pqlCnCskcJqhIFA8vP0wbx4ZxNIhYoeF0IFSlB2PN48M8vD2w3z1oxfP+mQ7duqp6Poc7M1xfKiMUipsjuh46JpGzNLRNNjTlT2rbUGmY7Kr13WNKQYLDvt68lTFbTa0rSFWSLF3yKY374Pr0piJVeqFUlGTruESW1qraK2evz/Wp2ahuoZK/OMrR2ipmvhrjm0gKdkhcT6QpqlCzA8JnGbo6ECBl/f34QcBSmkMDZcJlKoEMoFSbN/fx9GBwqyyKafWJCR9kx2H+8mXXTQgHbOImga+UuQdn9E46XfHBuf1ynHSq9fA46LyuxSjTfSMLNdP1CyjUS9yeGgAgKZ0BF8pimVvQZfOjw2AErZJzJJtKMSFQ7ZeEWJ+yG/MDO3vzdObLeONbAQbMfWRjBPkHR9NCzeW3d+bn3HgdOoGubmyT2+uTK7sA6BrGo6niJhhryLDMig6HvnyyMcsrhynW7Q80dVrUMpReOd59MIQl0aHeClyKf15l8LIH+QbL25EQaUB32IunZdtKMSFRt7zQswPCZxmKFCKouuj6RpJ26j8MTK0cMuPnOPjeQHBLBYrjmZ1YpbOa4cG6S84ZEthIAKg6eAFAX6gMA0NTQPD0Cm5PrqmTXnlOFGAtL83N+1lyqdevXpDJyi++wLKc9CsKPryy7mEFHde3U56ZHPa0T/IC10vNFkwKA0kxYVE3vNCzA8JnGYoPhIsqSAsCB8vvF3TNOK2MePXzjsevbkyffmw0DoZNTF0i55sCc8Hx1eYmkKNFKMrBZ4XbjCysj4x6ZXjRH1cqmIW3bkyfqBGlinHKDgeO48PcXyoeNoy5bFXr/bAAcoHXwel0BM1xC56Hx1DAZuWpbhyec1pf4gXsl7oTD1rpIGkuJDIe16IuSeB0wyloxY1cZu+fJmi62ObOoam4SuFMxLE1CRs0hPUFJxJ3DLozZUplD0a0iO1RIpK7xXXBw/wAwUElN0AXykyMYttGya+chytmerLlUlFTdJRC88P+D/7eii5AR9YV1epfzh1mfKKmgSdI8WjCdvk+ovqOLHrN5T7DwBg1Lbjt11Bx5C7JK5eT+9ZM3EwKA0kxYVE3vNCzC0JnGYoFbVY05iEbhguuZVgCUDToDYZYU1DcsJizDMJJ/c01JhMlm3qJCMWfqBQ+AQBFEem5qKWQZVtsm1Dw7gNYUeN1kwd7ivgBQEH+wp4foBSMFR0sU2N/b0FahKRcfuoNWeivH54gO88+S69uXIlc7OqNkaVViALFOsvJpteSaQUzNvV60yaRs60Z42smhMXEnnPCzF3JHCaoWVVMS5rq6bsBbieT0/WwQ0CLF2nIWVjmgaXt1fPquCy6PrUJW00DfrzDsmoiWXoJCMGwyWNqGUSMzXWN2eIWuEeb63Vp28IO+rYYJE3jgzQnS3hB4pk1MKKmmRLLkXXJ0Cnc6hItpSqNISEcPuUPSeylFyfdY2pSuZmV1eOmoYruOHqCKtXr5nXq9eZbhOx1HrWXKidwi/U71sIceGQwGmGxhZc9uUcWqvjlLwAxwsouT6t1bFZT1klbJO6ZIS6pE3nUJmBgkOu7GHqOqvqEzheQLbkYeiQjFhc2pacMNMzevL63bFBOnpy6GjUJu1KQBE1DSKmjhcohovhBrMQBk5KKd7tyuL5ijX1SaL5LtzuAVLtmyuZm7dzcd4/jyfE6U65jbWUetZcqHuDXajftxDiwiKB0yyMFlyOboEyVHRAQVXcYk1DctavO7YA+8rlVeTKPo4fYI9knfZ252mvjXHbZctIRawJr+bHnryODRU4MVQiGTFJRAxiI6vubFMnbpsMFR2cQI2bbhwuunQOlWjORIj0vkvx6E4AzFQdZnXLvGduZrtNxFLpWTOboG+uTJXtme9M0GJ+30IIsZAkcDoLJS+gPmVzUWNY02ToGp1DJR588eCsThRjs1n7evI0Z6JUxS2Kjs++njy1SZs/vLJt0tfd153lBy8c4Nhgkeq4TX0igm3q5EYaT4bLkcN+LtUJi6Gig1Lg+gFeEFAohyc6TXlcVOzA6ekEwG5eh1HVBMx/5ma2U25LoWfNYu4NNlW2B5jXTJDsiSaEuJBI4DQLoyeKgYLDltaqcSfpVNRkz4kcP371yJSZocnMdvlwECgefvkwrx4aQFOKg715vEARKLAMjYLj050tU5+iMq1Ym7Qx9LBW6q2jQ/TmyuhugWvct4i4BRQaQevlRNvXVb7OfGduJptyCwJF53CRXMljsOgyXHCh5uT9S6FnzWLVWU2V7dndNQww7bYTs7HU6suEEGI+SeA0C1OdKAYKDj3ZEruOD7HnRJaaeGTGV/ezWT78Ykcvz7zbTdkLCFC4XoDrB5TcAAVEDY2BvEO25KFrYcfzRMTi/evq+NBFDTzyyhFiTj/N2R1oODhYvB29hMJwirU9OeqTEZIRY94zNxNNuR3ozbHj8CCDRRfXD9CAr//zLv7og6u5fkPjuJ/bYvasWYw6q6myPQnb4MldJ0CDmy5uRNf1yn1zmQlaSvVlQggx3yRwmoXJThT9+TI7jgxSKHsYukZTOkbcNmZ1dT+T5cNBoHh69wmyJRdN0/ACRcTUiZg6lh4wWHLJuQpTg3TUJG6beEHY3bx7uMyz7/agFKzKmJS6HVQsw5v6Bo4VwMkX6cmWqYpbmIbOusbUjDI3M62tOXXK7UBvnuf39lD2AqIj35Nt6hzqz/PAL94BOC14WqyeNYtRZzVVEJ8r+/hKgQo/T8f0yn1zmQlaKvVlQgixEOQv2SxMdKJQStHRnafo+CSjFmUvIGYZc3Z1P1UAcmywyPHBIr6CIAhIjNkKJmIZGI6L74GnYLDoknd8MjGLS9uqGCo67OnKcnFLhlKihei695K1a8ntH8IPHHSg7Pl4gYmpTzHACcxmldXYKbfXDw3w5tFB8mUfy4C8o7CNsO1DJmZyeKDIQ785yAfW1mOOGdxi9axZjDqrqbI9jh/ASJf58PPx5ioTtBTqy4QQYqFI4DQLE50osiWP/oJDImKQH+n8nYqGP96zvbo/UwCSdzzKXoChaQSnbAVTdn38cKs7LB1aq2OYho7j+uzr7GdlcQ995WXs8ANitknEjNE93E+27GEZOkoDXWmsqU/QVhOnoyfPj189wlduXD8uWJlozLNdZbWmIcV16xv41r/uJu/4GLqGrmtYuoauaQwWXaKWQW3C5kBvntePDPCelbWV5y9WL6HFqLOaKttjGzqj74Xw8/HmKhO0FOrLhBBioUjgNAsTnSiKrk/R9XA9nXjEZHV9ctyV92yv7qcTgCRsE1MPp7E0TVW2gtGBgusTqPD0aVsGUcsgbpsEQZ6VQ2+S0MpcY5ToMC8HoKMnR9kNSERNYpaO4ysKjs/vjg5zoK9AoBSH+gqgNP7wqtYJg5+zXWUVBIp3OrOkIia2oYV79mkaxshji25Af96hIW3Tnw/oyzvjfl5P7OzirWNDFByPuG2yaVmGD29sWpDl8AtdZzVVticZMTA0DbTw87HmOhO02PVlQgixUCRwmqVTTxQDBYcggKq0xcXNGWoS9rjHz+bqfroByBd+bxWr65Ps7hymNhkhV/Ipuj6eH+CNTNGM1jwZmkayeIKW4Z2Ymk9eRdilrUR5AUHgV3JVvh+gTD3s8aTCYMw0NVoyUfrzLm93DvHgi96EmaOzXWU1+vyW6hj7e/OAhjkmY2KbOkXXZ7gYZsVqR37W+7qzfPdXe9nTlQ1re0amqQ705HmnK8u929YuWPC0UHVWZ8r2rGsKv9/R9hbzmQmSPdGEEBcCCZzOwtgTRbbk8vgbxzncn6c6Pn7KZLZX92MDEAibU442xExFzUoA0jlc4uNXLmP7gT66hks0pSIobIaLDo4fYOphyilm6rSUDtKY7wCgV0vzgr+Gxuo0AXB8sIht6Dh+QNkL0DQfP1CYhk7MMnA9RdENiFoGa+qTnMiWJ8wcne0qq9Hnr6hJsDM2RF/ewTa0ShBmaOAEAf0Fh40tGS5vqw7bMWw/zJtHBrENjVTMwjJ0XD8gW3R588ggD28/zFc/evGCTdstVJ3VmbI9wIJlgmRPNCHE+U4Cp7M09kRhmzoPvnhwzuo8RgOIkmuwu3OAgYKD5weYhk513GZFXZyy55N3PNY3pfm/r1/L957eS0+2jG1qaLpGzDIwDQ3leWz23qXR6QWgg0be9NvRDIMNLWlcX9GbLZ/MOAUKy9DC1gWWEQYrviJX8miriZOOWei6NmHm6GxXWY0+v+T5XNpexf/Z28tQ0SNmG1iGRskNKLgBzZkod713Baapc7gvz8v7+zBGNlquFMebBnZS58Rwie37+zg6UKC9NjHtY3CuOFO2RzJBQggxNyRwOkunFiLftXUFT709N1f3CdvE8QJeO9Q/bpNe11f0ZEv058u01cQrAcj1Gxppq47z6GtH6OjJ4QeKbrOMFyguakiTOvQ2QaCxx17NzlIthqaxoi5BW3WcbMmjIRVB1zVSjkdv1kEjDKA0wqDJ9QPi9sn6rckyR2e7ymrs89eObGEz2sepUA77UjVnovzFRzdUWhHs780zVHCpTdkTTg9m4hZ9OYf9vfnzMnCCqbM9kgkSQoi5IYHTWZhstdsNlzTwMavlrK/um9NRym7AYNGlvTpWaWAYMTWsuMXhgSKNXkBzOlp5zrqmFH9284ZKMNeTLfOLt7oYKDhE1r0Pv1ygwchQe3QIP1BsbEmjaRqpqElNIsKRgTz5so9phtNcRden7IVZrrqkzVUrqiv1W5Nljs52ldVEz/83m1s42J+jc7BMXcrm/9l2Eetb0uOepzTQmOznLNkVIYQQZ2+GnXnEqNHVbjuPD1EVt1hVl6QqbrHz+BAP/eYQZc9nfVOatpr4rKdEOodLRCyd6rjFQMGl7PkESlH2fAYKLlUxC9vU6RwujXveaHah2NmB2buPz79vBZe0pDleMjhQjlPyAq67qIEtbVX05V2yJRdfKRIRg/6CS8H1SUdMWqrCbFagwmm7La1V1CYjwMnM0ZqG5ISZo9G6m40tGQYLLgd78wwWXDYty0yrEeipzz/UnydqmtxwcSP/34fXnxY0raxLUBWzGSy4KKXG3aeUYqjgkonZrKw7P7NNQgghFoZknGZhoTY1zTsetqlzeXs1B3sL9Bcc8mUPQ9dpSEdZURtnqOieNlUWBAG//OUv2b59OwA33V4bpmMUqPA/1CRsrl1bxzudWTp6cnQNlTg2WCQTDQOlwaJbWeYfGenXdHyoSFtNjJIbTCtzdLarrGby/LbqONesrOGp3SfoyzukoubJ4vCSR6AUW1fV0FYt01VCCCFmTwKnWVioTU1Hi6SjlsGVK6rJlrxxq+pyZY+SG4ybKisWizz66KPs378fgE1XbuWf95boyzukYybVCRs/CNh5fIjO4RJ3vXc5H7Na6OjJ8ffP78fzA3wFNXEdTQcVQLbsUXR9jg0U2R0dpjoemXbd1tjamtk0ppxubY6ua3zymna6c2X2nMiSLZ0MJg1dY0tbFXde3S4F0UIIIc6KBE6zsFCbmo4tkl5TP36KaaIi656eHh555BH6+/uxLItbb72NZ0/YHO7swwsCDvYVxqzKs8iXfX71djd/9IHVZMsuPbkyfgC1yfEF1smoSW/OQaH4+JVtbF5WNeO6ram6n6+qm5sVX2saUty7bS1PvDXSANP1iFsmm1sz3LRADTCFEEKc3yRwmoWF2tR0tEh6d9cwT+46Ma6po6FprGs6ueHunj17+OlPf4rjOGQyGe644w5cO80br+6iO1uaYFVeGUPXeP3wAMcGi+RKHkXHJxU1J8yiRS2dbMmjPhmZ1ZYxk3U/3901TEMywmDRnfZ+dlNZ05Diix+SpfdCCCHmx6IWhz///PPccssttLS0oGkajz/++Lj7lVL85V/+Jc3NzcRiMbZt28bevXsXZ7BjjGaCOodKExYiT1U0PWvaKZ+cEgcMDQ3hOA7t7e38+3//72lqaiJbdjncX8DzFTUJm4ipo2saEVOnJmHj+Yoj/QWyZZdkxCRmGZRdf8Lvqez6xG2DZGRmweCp9WCpqIWha6SiFrUJizePDPJCRy+ZmDmuwP7BFw+yrzs7qx/V6PTe2RbnCyGEEKda1MApn8+zZcsW/uZv/mbC+7/zne/wve99j+9///ts376dRCLBTTfdRKlUmvDxC2U0E1STsNnbnSNbcvGCgGzJZW93bs62shgNOvxAcdPFjbx/bT3XrKrl/WvrueniRvxA8ctdJwgCxVVXXcXtt9/OZz7zGWKxOEf6C7x9bJhs0Q2bYU6QRYpYOgXHJ1cKM2fttXEsU6c/74xbwdefdzANnbaa+IQZtqlMVg+mlGJ/TwFdG30TapWAam1Dkv68U/nehBBCiKViUafqbr75Zm6++eYJ71NK8d3vfpevfvWr3HrrrQD86Ec/orGxkccff5w77rhjIYd6moXY1HRs0KHrOunYyTg3KBdo6t9Bh76JY4MttNXE2bRp07haou5sKSwg9wJMPdx8eNSpWaRlVTEua6um7AV4XsBA0a2s4KtPRTCNcHXfTLNok9WDZUse/QWHTNyi4Pg4I3vqwdwW2AshhBBzacnWOB04cICuri62bdtWuS2TyXD11Vfz0ksvTRo4lctlyuVy5d/Dw8PzNsb53tR0sqDDy/ZSfOcFcIvYrk/eWQ+cXkuUjJgc6suP9EEq0FIVJRW1cP2AXMkbl0Ua23SyL+fQWhPH0DX8QJEtedQmZ5dFm6wezPEDvCDAxsTUdWxjfPJzrgrshRBCiLm0ZBtgdnV1AdDY2Dju9sbGxsp9E3nggQfIZDKVj7a2tnkd53zW04wNOkY53Qco7Hwa5RYhmsZpuCRsUjlBLVFV3KKtOkFV3MLUNXpzDgP5MiU3oD4VoSEdHZdFGs2ibVqWGQmYXPxAsbl1ek0rJzJZPZht6BiaxlDBpSZhk4qOj+HnqsBeCCGEmEvn3Vnpz//8z/nTP/3Tyr+Hh4fnPXiaL2PbESRsHefw73COvwOAWb2MY1Wb2Nhay7Kq2IS1RJqmsbohQbbsomsuCljfnCFmGZNmkeY6izbZ9iugUAoCpVhVl5jxfnZCCCHEYliygVNTUxMAJ06coLm5uXL7iRMnuPTSSyd9XiQSIRKJzPfwFkQl6Ogfou/NZ4gUe8I7mtZzNL6ammSkEvhMNq1Xk4hwaVsVe0/kODJQYKjoYBtRNrdOXos11xvCTlYP9r61dXRny/TlHWxTn9F+dkIIIcRiWLKB08qVK2lqauLpp5+uBErDw8Ns376dP/7jP17cwS2gNQ0p7ryqjcf2PYOvGQw3bIGqNjY1JMcFPlP1lqpJRNjQrJGJW9z5nnZW1ycXvLfRZJms/b25eS2wF0IIIebSogZOuVyOffv2Vf594MABduzYQU1NDe3t7dx7771885vfZO3ataxcuZL777+flpYWbrvttsUb9CLYuLyB2rs+TddQkVhV7YTTZ2On9ZIR87Spr67hMltaq3j/2vpFy+JMlMma7wJ7IYQQYi4tauD06quv8qEPfajy79HapLvuuosf/vCHfOUrXyGfz/OFL3yBwcFB3ve+9/HEE08QjUYXa8gLQinFyy+/TDQa5bLLLgOgubmJMTOWp5msluhcmPqa66lBIYQQYr5o6tQ20eeZ4eFhMpkMQ0NDpNPpxR7OGXmex89//nPefPNNdF3ni1/8IrW1tdN+/tg+TmUvXJm25pRpPSGEEEKcNJNYYcnWOF2IstksP/7xjzl69CiapnHjjTdSU1Mzo9eQqS8hhBBi/kjgtEQcP36cRx55hGw2SzQa5eMf/zirV6+e1WvJ1JcQQggxPyRwWgLeeust/umf/gnP86irq+OOO+6Y0fScEEIIIRaGBE5LQH9/P57nsXbtWm6//fbzvvhdCCGEOFdJ4LQEvP/976e6upqNGzei60t2FxwhhBDigidn6UXQ39/PT3/6UxzHAcKtUTZv3ixBkxBCCLHEScZpge3fv5+f/OQnlEolotEoH/3oRxd7SEIIIYSYJgmcFohSit/+9rc8+eSTKKVYtmwZ73//+xd7WEIIIYSYAQmcFoDnefzrv/4rb7zxBgCbN2/mlltuwTTlxy+EEEKcS+TMPc/y+Tz/+I//yJEjR9A0jW3btrF169Zxe8kJIYQQ4twggdM88zyP/v5+IpEIf/AHf8DatWsXe0hCCCGEmCUJnOZZJpPhjjvuIBqNUldXt9jDEUIIIcRZkPXvc0wpxTPPPMPu3bsrt7W2tkrQJIQQQpwHJOM0hxzH4bHHHuOdd97Bsiza2tpIJpOLPSwhhBBCzBEJnObIwMAAjzzyCN3d3RiGwUc+8hEJmoQQQojzjAROc+DgwYP8+Mc/plgskkwm+cQnPkFra+tiD0sIIYQQc0wCp7P0yiuv8MQTTxAEAc3Nzdxxxx2k0+nFHpYQQggh5oEETmept7eXIAjYuHEjH/vYx7Asa7GHJIQQQoh5IoHTWbrppptobW1l48aN0tRSCCGEOM9J4HSWdF1n06ZNiz0MIYQQQiwA6eMkhBBCCDFNEjgJIYQQQkyTBE5CCCGEENMkgZMQQgghxDRJ4CSEEEIIMU0SOAkhhBBCTJMETkIIIYQQ0ySBkxBCCCHENEngJIQQQggxTRI4CSGEEEJMkwROQgghhBDTJIGTEEIIIcQ0SeAkhBBCCDFNEjgJIYQQQkyTBE5CCCGEENMkgZMQQgghxDRJ4CSEEEIIMU0SOAkhhBBCTJMETkIIIYQQ0ySBkxBCCCHENJmLPYD5ppQCYHh4eJFHIoQQQoilaDRGGI0ZpnLeB07ZbBaAtra2RR6JEEIIIZaybDZLJpOZ8jGamk54dQ4LgoDjx4+TSqXQNG2xhzMnhoeHaWtr48iRI6TT6cUezgVNjsXSIsdj6ZBjsXTIsTgzpRTZbJaWlhZ0feoqpvM+46TrOq2trYs9jHmRTqfll2CJkGOxtMjxWDrkWCwdciymdqZM0ygpDhdCCCGEmCYJnIQQQgghpkkCp3NQJBLha1/7GpFIZLGHcsGTY7G0yPFYOuRYLB1yLObWeV8cLoQQQggxVyTjJIQQQggxTRI4CSGEEEJMkwROQgghhBDTJIHTEvb8889zyy230NLSgqZpPP744+PuV0rxl3/5lzQ3NxOLxdi2bRt79+5dnMGe5x544AGuuuoqUqkUDQ0N3Hbbbbz77rvjHlMqlbjnnnuora0lmUzyB3/wB5w4cWKRRnz++tu//Vs2b95c6UmzdetWfvGLX1Tul+OweL797W+jaRr33ntv5TY5Hgvj61//OpqmjftYv3595X45DnNHAqclLJ/Ps2XLFv7mb/5mwvu/853v8L3vfY/vf//7bN++nUQiwU033USpVFrgkZ7/nnvuOe655x5efvllnnrqKVzX5cYbbySfz1ce8+Uvf5l//ud/5ic/+QnPPfccx48f5/bbb1/EUZ+fWltb+fa3v81rr73Gq6++ynXXXcett97Krl27ADkOi+WVV17h7/7u79i8efO42+V4LJxLLrmEzs7OyscLL7xQuU+OwxxS4pwAqMcee6zy7yAIVFNTk/rP//k/V24bHBxUkUhE/cM//MMijPDC0t3drQD13HPPKaXCn71lWeonP/lJ5TG7d+9WgHrppZcWa5gXjOrqavU//+f/lOOwSLLZrFq7dq166qmn1Ac+8AH1J3/yJ0op+b1YSF/72tfUli1bJrxPjsPckozTOerAgQN0dXWxbdu2ym2ZTIarr76al156aRFHdmEYGhoCoKamBoDXXnsN13XHHY/169fT3t4ux2Me+b7PI488Qj6fZ+vWrXIcFsk999zDRz/60XE/d5Dfi4W2d+9eWlpaWLVqFZ/61Kc4fPgwIMdhrp33e9Wdr7q6ugBobGwcd3tjY2PlPjE/giDg3nvv5dprr2Xjxo1AeDxs26aqqmrcY+V4zI+33nqLrVu3UiqVSCaTPPbYY1x88cXs2LFDjsMCe+SRR3j99dd55ZVXTrtPfi8WztVXX80Pf/hDLrroIjo7O/mrv/orfu/3fo+dO3fKcZhjEjgJMUP33HMPO3fuHFc/IBbWRRddxI4dOxgaGuLRRx/lrrvu4rnnnlvsYV1wjhw5wp/8yZ/w1FNPEY1GF3s4F7Sbb7658vnmzZu5+uqrWb58OT/+8Y+JxWKLOLLzj0zVnaOampoATlsVceLEicp9Yu596Utf4uc//znPPPMMra2tldubmppwHIfBwcFxj5fjMT9s22bNmjVcccUVPPDAA2zZsoX//t//uxyHBfbaa6/R3d3N5ZdfjmmamKbJc889x/e+9z1M06SxsVGOxyKpqqpi3bp17Nu3T34v5pgETueolStX0tTUxNNPP125bXh4mO3bt7N169ZFHNn5SSnFl770JR577DF+/etfs3LlynH3X3HFFViWNe54vPvuuxw+fFiOxwIIgoByuSzHYYFdf/31vPXWW+zYsaPyceWVV/KpT32q8rkcj8WRy+Xo6OigublZfi/mmEzVLWG5XI59+/ZV/n3gwAF27NhBTU0N7e3t3HvvvXzzm99k7dq1rFy5kvvvv5+WlhZuu+22xRv0eeqee+7h4Ycf5n//7/9NKpWq1AVkMhlisRiZTIa7776bP/3TP6WmpoZ0Os1/+A//ga1bt3LNNdcs8ujPL3/+53/OzTffTHt7O9lslocffphnn32WJ598Uo7DAkulUpU6v1GJRILa2trK7XI8FsZ9993HLbfcwvLlyzl+/Dhf+9rXMAyDO++8U34v5tpiL+sTk3vmmWcUcNrHXXfdpZQKWxLcf//9qrGxUUUiEXX99derd999d3EHfZ6a6DgA6sEHH6w8plgsqi9+8YuqurpaxeNx9fu///uqs7Nz8QZ9nvr85z+vli9frmzbVvX19er6669Xv/zlLyv3y3FYXGPbESglx2OhfOITn1DNzc3Ktm21bNky9YlPfELt27evcr8ch7mjKaXUIsVsQgghhBDnFKlxEkIIIYSYJgmchBBCCCGmSQInIYQQQohpksBJCCGEEGKaJHASQgghhJgmCZyEEEIIIaZJAichhBBCiGmSwEkIIYQQYpokcBJCiGlYsWIF3/3udxd7GEKIRSaBkxBiXmiaNuXH17/+9QUZx6ZNm/ijP/qjCe/7X//rfxGJROjt7V2QsQghzn0SOAkh5kVnZ2fl47vf/S7pdHrcbffdd1/lsUopPM+bl3HcfffdPPLIIxSLxdPue/DBB/nYxz5GXV3dvHxtIcT5RwInIcS8aGpqqnxkMhk0Tav8+5133iGVSvGLX/yCK664gkgkwgsvvMBnP/tZbrvttnGvc++99/LBD36w8u8gCHjggQdYuXIlsViMLVu28Oijj046jk9/+tMUi0V++tOfjrv9wIEDPPvss9x99910dHRw66230tjYSDKZ5KqrruJXv/rVpK958OBBNE1jx44dldsGBwfRNI1nn322ctvOnTu5+eabSSaTNDY28u/+3b8bl9169NFH2bRpE7FYjNraWrZt20Y+n5/6ByuEWFQSOAkhFs2f/dmf8e1vf5vdu3ezefPmaT3ngQce4Ec/+hHf//732bVrF1/+8pf59Kc/zXPPPTfh4+vq6rj11lv5wQ9+MO72H/7wh7S2tnLjjTeSy+X4yEc+wtNPP80bb7zBhz/8YW655RYOHz486+9tcHCQ6667jssuu4xXX32VJ554ghMnTvCHf/iHQJiRu/POO/n85z/P7t27efbZZ7n99tuRfdeFWNrMxR6AEOLC9R//43/khhtumPbjy+Uy3/rWt/jVr37F1q1bAVi1ahUvvPACf/d3f8cHPvCBCZ939913c/PNN3PgwAFWrlyJUoqHHnqIu+66C13X2bJlC1u2bKk8/hvf+AaPPfYY//RP/8SXvvSlWX1v/+N//A8uu+wyvvWtb1Vu+8EPfkBbWxt79uwhl8vheR633347y5cvB8J6LCHE0iYZJyHEornyyitn9Ph9+/ZRKBS44YYbSCaTlY8f/ehHdHR0TPq8G264gdbWVh588EEAnn76aQ4fPsznPvc5AHK5HPfddx8bNmygqqqKZDLJ7t27zyrj9Oabb/LMM8+MG+f69esB6OjoYMuWLVx//fVs2rSJf/tv/y1///d/z8DAwKy/nhBiYUjGSQixaBKJxLh/67p+2lSV67qVz3O5HAD/8i//wrJly8Y9LhKJTPp1dF3ns5/9LA899BBf//rXefDBB/nQhz7EqlWrALjvvvt46qmn+C//5b+wZs0aYrEYH//4x3EcZ9LXA8aNdew4R8d6yy238J/+03867fnNzc0YhsFTTz3Fb37zG375y1/y13/91/zFX/wF27dvZ+XKlZN+L0KIxSUZJyHEklFfX09nZ+e428YWYF988cVEIhEOHz7MmjVrxn20tbVN+dqf+9znOHLkCD/72c947LHHuPvuuyv3vfjii3z2s5/l93//99m0aRNNTU0cPHhwynEC48Y6dpwAl19+Obt27WLFihWnjXU0YNQ0jWuvvZa/+qu/4o033sC2bR577LEpvw8hxOKSwEkIsWRcd911vPrqq/zoRz9i7969fO1rX2Pnzp2V+1OpFPfddx9f/vKXeeihh+jo6OD111/nr//6r3nooYemfO2VK1dy3XXX8YUvfIFIJMLtt99euW/t2rX87Gc/Y8eOHbz55pt88pOfJAiCSV8rFotxzTXXVArbn3vuOb761a+Oe8w999xDf38/d955J6+88godHR08+eSTfO5zn8P3fbZv3863vvUtXn31VQ4fPszPfvYzenp62LBhwyx/ekKIhSCBkxBiybjpppu4//77+cpXvsJVV11FNpvlM5/5zLjHfOMb3+D+++/ngQceYMOGDXz4wx/mX/7lX6Y1vXX33XczMDDAJz/5SaLRaOX2//bf/hvV1dW8973v5ZZbbuGmm27i8ssvn/K1fvCDH+B5HldccQX33nsv3/zmN8fd39LSwosvvojv+9x4441s2rSJe++9l6qqKnRdJ51O8/zzz/ORj3yEdevW8dWvfpX/+l//KzfffPMMfmJCiIWmKVn7KoQQQggxLZJxEkIIIYSYJgmchBBCCCGmSQInIYQQQohpksBJCCGEEGKaJHASQgghhJgmCZyEEEIIIaZJAichhBBCiGmSwEkIIYQQYpokcBJCCCGEmCYJnIQQQgghpkkCJyGEEEKIaZLASQghhBBimv5/QUZrbnTPki8AAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "width": 590,
       "height": 590
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1400x300 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1389,
       "height": 290
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 600x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 590,
       "height": 590
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x350 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 1190,
       "height": 340
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/211d68e4-f007-4bb9-91ca-ba2d34be3d24",
   "content_dependencies": null
  },
  {
   "cellId": "4b25090aa3e1404ca0755888032a06c1",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "4b25090aa3e1404ca0755888032a06c1",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "Ok so its still 0.388.... maybe there are just too much noice in the voice data",
   "block_group": "8dd6118a1ccb44c1be32084a94cc4f00"
  },
  {
   "cellId": "a3611d741b2e41e3b1b083decedc7b7a",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "a3611d741b2e41e3b1b083decedc7b7a",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Ensemble + age (stratified)",
   "block_group": "5fa7bf09dfb94793a2258875ab853bdd"
  },
  {
   "cellId": "41e9f3b0e89140b7bd589e2f41749160",
   "cell_type": "code",
   "metadata": {
    "source_hash": "bd62a31c",
    "execution_start": 1754055866835,
    "execution_millis": 1321,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "41e9f3b0e89140b7bd589e2f41749160",
    "deepnote_cell_type": "code"
   },
   "source": "seed = 42\nnp.random.seed(seed)          \ntf.random.set_seed(seed) \nparkinsons_voice = fetch_ucirepo(id=189)\n\n\nage = parkinsons_voice.data.features['age']\nX  = parkinsons_voice.data.features.copy()\nbins = [age.min() - 1, 40, 50, 60, 70, age.max()]\nlabels = ['<40', '40–50', '50–60', '60–70', '70+']\nage_cat = pd.cut(age, bins=bins, labels=labels)\nfeatures_to_drop = [\n    'Jitter(%)', 'Jitter:RAP', 'Jitter:DDP',\n    'Shimmer(dB)', 'Shimmer:APQ5', 'Shimmer:DDA',\n    'Shimmer', 'NHR',\n    'test_time', 'subject#'\n]\n\nX = X.drop(columns=[col for col in features_to_drop if col in X.columns])\ny = parkinsons_voice.data.targets['total_UPDRS']\n\nX_train, X_temp, y_train, y_temp, age_train, age_temp = train_test_split(\n    X, y, age_cat,\n    test_size=0.5,         # leaves 50% for temp\n    random_state=42,\n    stratify=age_cat\n)\n\n# 4. Second split: split the temp half into val & test equally\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp,\n    test_size=0.5,         # splits temp into 25% each\n    random_state=42,\n    stratify=age_temp\n)\n\nprint(\"Train age-bin distrib:\\n\", age_train.value_counts(normalize=True))\nprint(\"Val   age-bin distrib:\\n\", age_temp.loc[X_val.index].value_counts(normalize=True))\nprint(\"Test  age-bin distrib:\\n\", age_temp.loc[X_test.index].value_counts(normalize=True))\n",
   "block_group": "34486baa6f9340a3b4efa17257b90466",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "text": "Train age-bin distrib:\n age\n60–70    0.335376\n70+      0.318352\n50–60    0.285325\n40–50    0.043582\n<40      0.017365\nName: proportion, dtype: float64\nVal   age-bin distrib:\n age\n60–70    0.335602\n70+      0.318584\n50–60    0.285228\n40–50    0.043567\n<40      0.017018\nName: proportion, dtype: float64\nTest  age-bin distrib:\n age\n60–70    0.335602\n70+      0.318584\n50–60    0.285228\n40–50    0.043567\n<40      0.017018\nName: proportion, dtype: float64\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/eee60132-33c8-4bde-9337-e636b7af680f",
   "content_dependencies": null
  },
  {
   "cellId": "612ef4a9a6684999bb899b5088ee95ea",
   "cell_type": "code",
   "metadata": {
    "source_hash": "9e24f257",
    "execution_start": 1754055868959,
    "execution_millis": 14,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "612ef4a9a6684999bb899b5088ee95ea",
    "deepnote_cell_type": "code"
   },
   "source": "#Build a DataFrame with age & target (or whatever y)\ndf = parkinsons_voice.data.features.copy()\ndf['target'] = parkinsons_voice.data.targets['total_UPDRS']\n\n#Compute count per age\nage_counts = df['age'].value_counts().sort_values(ascending=False)\n# This gives a Series: index=age, value=#samples\n\n#Define your target sizes (50%,25%,25%)\ntotal = len(df)\ntargets = {\n    'train': total * 0.7,\n    'val':   total * 0.15,\n    'test':  total * 0.15,\n}\n\n#  keep track of current sum per split; sort ages by descending count\ncurrent = {k: 0 for k in targets}\nassignment = {}  # age -> split\n\nfor age, count in age_counts.items():\n    # compute “fill ratio” = current[split] / targets[split]\n    ratios = {s: current[s] / targets[s] for s in targets}\n    # pick the split with the smallest ratio (i.e. farthest below its target)\n    best_split = min(ratios, key=ratios.get)\n    assignment[age] = best_split\n    current[best_split] += count\n\n#filter df into X/y for each split\nsplits = {s: {'X': [], 'y': []} for s in targets}\nfor split in splits:\n    ages_in_split = [age for age, s in assignment.items() if s == split]\n    mask = df['age'].isin(ages_in_split)\n    splits[split]['X'] = df.loc[mask].drop(columns=['age','target'])\n    splits[split]['y'] = df.loc[mask,  'target']\n\nfor split in ['train','val','test']:\n    print(f\"{split.upper():5} | n_samples = {len(splits[split]['X']):4} | \"\n          f\"pct = {100*len(splits[split]['X'])/total:5.1f}%\")\n\nfor name, split_df in [('Train', splits['train']['X']),\n                       ('Val',   splits['val']['X']),\n                       ('Test',  splits['test']['X'])]:\n    freqs = split_df.copy()\n    freqs['age'] = df.loc[split_df.index, 'age']\n    print(f\"\\n{name} age distribution:\")\n    print(freqs['age'].value_counts(normalize=True).sort_index())\n\nages = df['age']\n\nQ1, Q3 = np.percentile(ages, [25,75])\nIQR = Q3 - Q1\nlower_bound = Q1 - 1.5*IQR\nprint(\"Lower outlier cutoff =\", lower_bound)",
   "block_group": "df4743bf471a44dc880602a06fb53427",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "text": "TRAIN | n_samples = 4055 | pct =  69.0%\nVAL   | n_samples =  861 | pct =  14.7%\nTEST  | n_samples =  959 | pct =  16.3%\n\nTrain age distribution:\nage\n49    0.063132\n55    0.065845\n56    0.034525\n57    0.094945\n58    0.105795\n59    0.073736\n60    0.038471\n61    0.036991\n62    0.058200\n68    0.078175\n72    0.076449\n73    0.094945\n74    0.066831\n76    0.035512\n78    0.041430\n85    0.035018\nName: proportion, dtype: float64\n\nVal age distribution:\nage\n63    0.181185\n66    0.490128\n67    0.328688\nName: proportion, dtype: float64\n\nTest age distribution:\nage\n36    0.105318\n65    0.424400\n71    0.172054\n75    0.298227\nName: proportion, dtype: float64\nLower outlier cutoff = 37.0\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/34c71224-cf17-4bb5-89b8-700b37c6874e",
   "content_dependencies": null
  },
  {
   "cellId": "f8e946bdc8904cd5b648305872f35ca6",
   "cell_type": "code",
   "metadata": {
    "source_hash": "4d27cf06",
    "execution_start": 1754056041545,
    "execution_millis": 130394,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "f8e946bdc8904cd5b648305872f35ca6",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import PredefinedSplit, HalvingRandomSearchCV\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\n\n# 1) Glue train+val and build PredefinedSplit\nX_tv = pd.concat([splits[\"train\"][\"X\"], splits[\"val\"][\"X\"]], axis=0)\ny_tv = pd.concat([splits[\"train\"][\"y\"], splits[\"val\"][\"y\"]], axis=0)\n\nfolds = np.array(\n    [-1] * len(splits[\"train\"][\"X\"]) +\n    [ 0] * len(splits[\"val\"][\"X\"])\n)\nps = PredefinedSplit(test_fold=folds)\n\n# 2) Define RF search space (exclude n_estimators)\nrf_param_dist = {\n    \"max_depth\":         [None] + list(range(3, 31, 3)),\n    \"min_samples_split\": list(range(2, 21)),\n    \"min_samples_leaf\":  list(range(1, 11)),\n    \"max_features\":      [1.0, \"sqrt\", \"log2\"],\n}\n\n# 3) HalvingRandomSearchCV over n_estimators\nhalving_rf = HalvingRandomSearchCV(\n    estimator=RandomForestRegressor(random_state=42),\n    param_distributions=rf_param_dist,\n    resource=\"n_estimators\",   # budget parameter\n    min_resources=50,          # start with 50 trees\n    max_resources=1500,        # up to 1500 trees\n    factor=3,                  # keep top third each round\n    cv=ps,\n    scoring=\"neg_mean_squared_error\",\n    random_state=42,\n    n_jobs=1,\n    verbose=2\n)\n\n# 4) Run the search\nhalving_rf.fit(X_tv, y_tv)\n\nprint(\"Best RF params (excluding n_estimators):\",\n      {k: v for k, v in halving_rf.best_params_.items() if k != \"n_estimators\"})\nprint(\"Best n_estimators:\", halving_rf.best_params_[\"n_estimators\"])\nprint(\"Best CV MSE:\", -halving_rf.best_score_)\n\n# 5) Retrain & test\nbest_rf = halving_rf.best_estimator_\nbest_rf.set_params(n_estimators=halving_rf.best_params_[\"n_estimators\"])\nbest_rf.fit(X_tv, y_tv)\n\nX_test, y_test = splits[\"test\"][\"X\"], splits[\"test\"][\"y\"]\nmse_test = mean_squared_error(y_test, best_rf.predict(X_test))\nprint(\"RF Test MSE:\", mse_test)\n",
   "block_group": "7fbb5425152447c5961d70f66794a334",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "text": "n_iterations: 4\nn_required_iterations: 4\nn_possible_iterations: 4\nmin_resources_: 50\nmax_resources_: 1500\naggressive_elimination: False\nfactor: 3\n----------\niter: 0\nn_candidates: 30\nn_resources: 50\nFitting 1 folds for each of 30 candidates, totalling 30 fits\n[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=6, min_samples_split=7, n_estimators=50; total time=   0.3s\n[CV] END max_depth=27, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=50; total time=   0.6s\n[CV] END max_depth=27, max_features=1.0, min_samples_leaf=6, min_samples_split=3, n_estimators=50; total time=   2.0s\n[CV] END max_depth=27, max_features=1.0, min_samples_leaf=4, min_samples_split=6, n_estimators=50; total time=   2.1s\n[CV] END max_depth=18, max_features=sqrt, min_samples_leaf=9, min_samples_split=12, n_estimators=50; total time=   0.5s\n[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=3, min_samples_split=16, n_estimators=50; total time=   0.5s\n[CV] END max_depth=30, max_features=1.0, min_samples_leaf=2, min_samples_split=17, n_estimators=50; total time=   2.1s\n[CV] END max_depth=30, max_features=log2, min_samples_leaf=10, min_samples_split=16, n_estimators=50; total time=   0.5s\n[CV] END max_depth=None, max_features=log2, min_samples_leaf=5, min_samples_split=12, n_estimators=50; total time=   0.5s\n[CV] END max_depth=27, max_features=sqrt, min_samples_leaf=1, min_samples_split=16, n_estimators=50; total time=   0.6s\n[CV] END max_depth=21, max_features=log2, min_samples_leaf=3, min_samples_split=20, n_estimators=50; total time=   0.5s\n[CV] END max_depth=27, max_features=log2, min_samples_leaf=4, min_samples_split=13, n_estimators=50; total time=   0.5s\n[CV] END max_depth=30, max_features=log2, min_samples_leaf=8, min_samples_split=20, n_estimators=50; total time=   0.5s\n[CV] END max_depth=18, max_features=1.0, min_samples_leaf=2, min_samples_split=7, n_estimators=50; total time=   2.3s\n[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=7, min_samples_split=19, n_estimators=50; total time=   0.5s\n[CV] END max_depth=15, max_features=1.0, min_samples_leaf=4, min_samples_split=14, n_estimators=50; total time=   1.9s\n[CV] END max_depth=None, max_features=1.0, min_samples_leaf=7, min_samples_split=18, n_estimators=50; total time=   1.8s\n[CV] END max_depth=6, max_features=log2, min_samples_leaf=9, min_samples_split=15, n_estimators=50; total time=   0.3s\n[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=11, n_estimators=50; total time=   0.2s\n[CV] END max_depth=12, max_features=1.0, min_samples_leaf=6, min_samples_split=18, n_estimators=50; total time=   1.9s\n[CV] END max_depth=27, max_features=log2, min_samples_leaf=6, min_samples_split=8, n_estimators=50; total time=   0.5s\n[CV] END max_depth=12, max_features=1.0, min_samples_leaf=9, min_samples_split=3, n_estimators=50; total time=   1.8s\n[CV] END max_depth=27, max_features=1.0, min_samples_leaf=10, min_samples_split=12, n_estimators=50; total time=   1.8s\n[CV] END max_depth=24, max_features=log2, min_samples_leaf=6, min_samples_split=18, n_estimators=50; total time=   0.5s\n[CV] END max_depth=6, max_features=1.0, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.2s\n[CV] END max_depth=21, max_features=log2, min_samples_leaf=10, min_samples_split=16, n_estimators=50; total time=   0.5s\n[CV] END max_depth=15, max_features=log2, min_samples_leaf=9, min_samples_split=5, n_estimators=50; total time=   0.5s\n[CV] END max_depth=21, max_features=1.0, min_samples_leaf=7, min_samples_split=15, n_estimators=50; total time=   1.9s\n[CV] END max_depth=24, max_features=sqrt, min_samples_leaf=5, min_samples_split=19, n_estimators=50; total time=   0.5s\n[CV] END max_depth=15, max_features=1.0, min_samples_leaf=3, min_samples_split=18, n_estimators=50; total time=   2.0s\n----------\niter: 1\nn_candidates: 10\nn_resources: 150\nFitting 1 folds for each of 10 candidates, totalling 10 fits\n[CV] END max_depth=27, max_features=log2, min_samples_leaf=6, min_samples_split=8, n_estimators=150; total time=   1.6s\n[CV] END max_depth=27, max_features=log2, min_samples_leaf=4, min_samples_split=13, n_estimators=150; total time=   1.8s\n[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=7, min_samples_split=19, n_estimators=150; total time=   1.5s\n[CV] END max_depth=21, max_features=log2, min_samples_leaf=3, min_samples_split=20, n_estimators=150; total time=   1.5s\n[CV] END max_depth=21, max_features=log2, min_samples_leaf=10, min_samples_split=16, n_estimators=150; total time=   1.5s\n[CV] END max_depth=24, max_features=log2, min_samples_leaf=6, min_samples_split=18, n_estimators=150; total time=   1.5s\n[CV] END max_depth=30, max_features=log2, min_samples_leaf=10, min_samples_split=16, n_estimators=150; total time=   1.4s\n[CV] END max_depth=15, max_features=log2, min_samples_leaf=9, min_samples_split=5, n_estimators=150; total time=   1.5s\n[CV] END max_depth=None, max_features=log2, min_samples_leaf=5, min_samples_split=12, n_estimators=150; total time=   1.6s\n[CV] END max_depth=27, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=150; total time=   1.6s\n----------\niter: 2\nn_candidates: 4\nn_resources: 450\nFitting 1 folds for each of 4 candidates, totalling 4 fits\n[CV] END max_depth=None, max_features=log2, min_samples_leaf=5, min_samples_split=12, n_estimators=450; total time=   5.0s\n[CV] END max_depth=24, max_features=log2, min_samples_leaf=6, min_samples_split=18, n_estimators=450; total time=   4.8s\n[CV] END max_depth=21, max_features=log2, min_samples_leaf=3, min_samples_split=20, n_estimators=450; total time=   4.6s\n[CV] END max_depth=27, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=450; total time=   4.8s\n----------\niter: 3\nn_candidates: 2\nn_resources: 1350\nFitting 1 folds for each of 2 candidates, totalling 2 fits\n[CV] END max_depth=27, max_features=sqrt, min_samples_leaf=4, min_samples_split=15, n_estimators=1350; total time=  14.4s\n[CV] END max_depth=24, max_features=log2, min_samples_leaf=6, min_samples_split=18, n_estimators=1350; total time=  13.6s\nBest RF params (excluding n_estimators): {'min_samples_split': 18, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 24}\nBest n_estimators: 1350\nBest CV MSE: 63.90142787033376\nRF Test MSE: 310.10015035337017\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/e054f661-7e4e-4a73-9b3d-debea7c46fab",
   "content_dependencies": null
  },
  {
   "cellId": "8e0368a7e2d542e082a0bec31ebe9d3e",
   "cell_type": "code",
   "metadata": {
    "source_hash": "14065712",
    "execution_start": 1754055872352,
    "execution_millis": 169143,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "8e0368a7e2d542e082a0bec31ebe9d3e",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport pandas as pd\nfrom sklearn.experimental import enable_halving_search_cv  # noqa\nfrom sklearn.model_selection import PredefinedSplit, HalvingRandomSearchCV\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.base import BaseEstimator, RegressorMixin\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Input\nfrom tensorflow.keras.optimizers import Adam\n\n# ─── 0) Load or build your DataFrame `df` with features + `age` + target column ───\n# e.g. df = your_dataframe.copy()\n#      df['target'] = ...\n\n# ─── 1) Create age‐stratified splits dict ───────────────────────────────────────────\ntotal = len(df)\nage_counts = df['age'].value_counts().sort_values(ascending=False)\ntargets = {'train': total*0.7, 'val': total*0.15, 'test': total*0.15}\ncurrent = {k: 0 for k in targets}\nassignment = {}\n\nfor age, cnt in age_counts.items():\n    # pick split that’s furthest below its target proportion\n    frac = {s: current[s]/targets[s] for s in targets}\n    pick = min(frac, key=frac.get)\n    assignment[age] = pick\n    current[pick] += cnt\n\n# build the actual X/y splits\nsplits = {s: {'X': None, 'y': None} for s in targets}\nfor s in splits:\n    ages = [age for age, grp in assignment.items() if grp == s]\n    mask = df['age'].isin(ages)\n    splits[s]['X'] = df.loc[mask].drop(columns=['age','target'])\n    splits[s]['y'] = df.loc[mask, 'target']\n\n# ─── 2) Glue train+val and build PredefinedSplit ──────────────────────────────────\nX_tv = pd.concat([splits['train']['X'], splits['val']['X']], axis=0)\ny_tv = pd.concat([splits['train']['y'], splits['val']['y']], axis=0)\n\n# -1 for train rows, 0 for val rows\nfolds = np.array(\n    [-1] * len(splits['train']['X']) +\n    [ 0] * len(splits['val']['X'])\n)\nps = PredefinedSplit(test_fold=folds)\n\n# ─── 3) Define your 3-layer Keras regressor ──────────────────────────────────────\nclass KerasRegressor3L(BaseEstimator, RegressorMixin):\n    def __init__(self,\n                 hidden_units_1=64,\n                 hidden_units_2=32,\n                 hidden_units_3=16,\n                 lr=1e-3,\n                 epochs=50,\n                 batch_size=32,\n                 verbose=0):\n        self.hidden_units_1 = hidden_units_1\n        self.hidden_units_2 = hidden_units_2\n        self.hidden_units_3 = hidden_units_3\n        self.lr = lr\n        self.epochs = epochs\n        self.batch_size = batch_size\n        self.verbose = verbose\n        self.model_ = None\n\n    def build_model(self, input_dim):\n        m = Sequential([\n            Input(shape=(input_dim,)),\n            Dense(self.hidden_units_1, activation='relu'),\n            Dense(self.hidden_units_2, activation='relu'),\n            Dense(self.hidden_units_3, activation='relu'),\n            Dense(1)\n        ])\n        m.compile(optimizer=Adam(self.lr), loss='mse')\n        return m\n\n    def fit(self, X, y):\n        X, y = np.asarray(X), np.asarray(y)\n        self.model_ = self.build_model(X.shape[1])\n        self.model_.fit(X, y,\n                        epochs=self.epochs,\n                        batch_size=self.batch_size,\n                        verbose=self.verbose)\n        return self\n\n    def predict(self, X):\n        return self.model_.predict(np.asarray(X), verbose=0).ravel()\n\n    def score(self, X, y):\n        preds = self.predict(X)\n        return -mean_squared_error(np.asarray(y), preds)\n\n# ─── 4) Set up HalvingRandomSearchCV over epochs ─────────────────────────────────\nnn_param_dist = {\n    \"hidden_units_1\": [32, 64, 128],\n    \"hidden_units_2\": [16, 32, 64],\n    \"hidden_units_3\": [16, 32, 64],\n    \"lr\":             [1e-4, 1e-3, 1e-2],\n    \"batch_size\":     [16, 32, 64],\n}\n\nhalving_nn = HalvingRandomSearchCV(\n    estimator=KerasRegressor3L(verbose=0),\n    param_distributions=nn_param_dist,\n    resource=\"epochs\",\n    min_resources=20,\n    max_resources=200,\n    factor=3,\n    cv=ps,\n    scoring=\"neg_mean_squared_error\",\n    random_state=42,\n    n_jobs=1,\n    verbose=2\n)\n\n# ─── 5) Run the hyperparameter search ───────────────────────────────────────────\nhalving_nn.fit(X_tv, y_tv)\n\nprint(\"Best params (excl. epochs):\",\n      {k:v for k,v in halving_nn.best_params_.items() if k!=\"epochs\"})\nprint(\"Best epochs:\", halving_nn.best_params_[\"epochs\"])\nprint(\"Best CV MSE:\", -halving_nn.best_score_)\n\n# ─── 6) Retrain best model & test ───────────────────────────────────────────────\nbest_nn = halving_nn.best_estimator_\nbest_nn.set_params(epochs=halving_nn.best_params_[\"epochs\"])\nbest_nn.fit(X_tv, y_tv)\n\nX_test, y_test = splits['test']['X'], splits['test']['y']\nprint(\"NN Test MSE:\", mean_squared_error(y_test, best_nn.predict(X_test)))\n",
   "block_group": "f4a6d3f303a34608807b64345ee149a7",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "text": "n_iterations: 3\nn_required_iterations: 3\nn_possible_iterations: 3\nmin_resources_: 20\nmax_resources_: 200\naggressive_elimination: False\nfactor: 3\n----------\niter: 0\nn_candidates: 10\nn_resources: 20\nFitting 1 folds for each of 10 candidates, totalling 10 fits\n[CV] END batch_size=16, epochs=20, hidden_units_1=32, hidden_units_2=64, hidden_units_3=64, lr=0.0001; total time=   6.6s\n[CV] END batch_size=16, epochs=20, hidden_units_1=32, hidden_units_2=16, hidden_units_3=64, lr=0.0001; total time=   5.6s\n[CV] END batch_size=32, epochs=20, hidden_units_1=128, hidden_units_2=32, hidden_units_3=64, lr=0.01; total time=   3.6s\n[CV] END batch_size=64, epochs=20, hidden_units_1=128, hidden_units_2=32, hidden_units_3=64, lr=0.001; total time=   2.1s\n[CV] END batch_size=64, epochs=20, hidden_units_1=128, hidden_units_2=64, hidden_units_3=32, lr=0.001; total time=   2.1s\n[CV] END batch_size=64, epochs=20, hidden_units_1=32, hidden_units_2=32, hidden_units_3=32, lr=0.001; total time=   2.3s\n[CV] END batch_size=64, epochs=20, hidden_units_1=64, hidden_units_2=64, hidden_units_3=64, lr=0.01; total time=   2.3s\n[CV] END batch_size=32, epochs=20, hidden_units_1=128, hidden_units_2=32, hidden_units_3=64, lr=0.0001; total time=   3.4s\n[CV] END batch_size=16, epochs=20, hidden_units_1=32, hidden_units_2=32, hidden_units_3=16, lr=0.0001; total time=   5.6s\n[CV] END batch_size=32, epochs=20, hidden_units_1=64, hidden_units_2=16, hidden_units_3=32, lr=0.01; total time=   3.3s\n----------\niter: 1\nn_candidates: 4\nn_resources: 60\nFitting 1 folds for each of 4 candidates, totalling 4 fits\n[CV] END batch_size=32, epochs=60, hidden_units_1=64, hidden_units_2=16, hidden_units_3=32, lr=0.01; total time=   9.0s\n[CV] END batch_size=32, epochs=60, hidden_units_1=128, hidden_units_2=32, hidden_units_3=64, lr=0.01; total time=   8.4s\n[CV] END batch_size=64, epochs=60, hidden_units_1=64, hidden_units_2=64, hidden_units_3=64, lr=0.01; total time=   4.8s\n[CV] END batch_size=64, epochs=60, hidden_units_1=128, hidden_units_2=64, hidden_units_3=32, lr=0.001; total time=   4.8s\n----------\niter: 2\nn_candidates: 2\nn_resources: 180\nFitting 1 folds for each of 2 candidates, totalling 2 fits\n[CV] END batch_size=64, epochs=180, hidden_units_1=64, hidden_units_2=64, hidden_units_3=64, lr=0.01; total time=  12.5s\n[CV] END batch_size=32, epochs=180, hidden_units_1=64, hidden_units_2=16, hidden_units_3=32, lr=0.01; total time=  23.6s\nBest params (excl. epochs): {'lr': 0.01, 'hidden_units_3': 32, 'hidden_units_2': 16, 'hidden_units_1': 64, 'batch_size': 32}\nBest epochs: 180\nBest CV MSE: 84.73360653507366\nNN Test MSE: 302.36672016657633\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/744e392e-de02-4cbf-8ea3-ca54353bb1a8",
   "content_dependencies": null
  },
  {
   "cellId": "b548a8f11b31446db250c366530073ce",
   "cell_type": "code",
   "metadata": {
    "source_hash": "7fdb9da4",
    "execution_start": 1754057355915,
    "execution_millis": 61320,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "b548a8f11b31446db250c366530073ce",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.ensemble import VotingRegressor\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nimport numpy as np\n\n# --- assume best_rf, best_nn, X_tv, y_tv, splits are already defined and fitted ---\n\n# 1) Build and fit the Voting ensemble\nensemble = VotingRegressor([\n    (\"rf\", best_rf),\n    (\"nn\", best_nn)\n])\nensemble.fit(X_tv, y_tv)\n\n# 2) Evaluate on the held-out test set\nX_test, y_test = splits[\"test\"][\"X\"], splits[\"test\"][\"y\"]\npreds = ensemble.predict(X_test)\n\nmse  = mean_squared_error(y_test, preds)\nrmse = np.sqrt(mse)\nmae  = mean_absolute_error(y_test, preds)\nr2   = r2_score(y_test, preds)\n\nprint(\"Voting Ensemble performance on test set:\")\nprint(f\"  RMSE = {rmse:.3f}\")\nprint(f\"  MAE  = {mae:.3f}\")\nprint(f\"  R²   = {r2:.3f}\")\n",
   "block_group": "db3d9ce394c44edcae4658bca89b8ab6",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "text": "Voting Ensemble performance on test set:\n  RMSE = 17.620\n  MAE  = 15.576\n  R²   = -0.204\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/ff25dd16-167b-48bc-9f21-e81e1b3ac593",
   "content_dependencies": null
  },
  {
   "cellId": "14554fafe30047daaf0b8c775e5117ad",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "14554fafe30047daaf0b8c775e5117ad",
    "deepnote_cell_type": "text-cell-h2"
   },
   "source": "## Very Bad -  Will not be used",
   "block_group": "da09230bae414c5c94d941dee5c54554"
  },
  {
   "cellId": "d8c4379c5e384a4ca5dcb10fba259d2b",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "d8c4379c5e384a4ca5dcb10fba259d2b",
    "deepnote_cell_type": "text-cell-h1"
   },
   "source": "# Converting into a classification task",
   "block_group": "1d77ef24822045ff8ee83ee7f1b31656"
  },
  {
   "cellId": "e7f1603169224b1fa232bdce059ba503",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "e7f1603169224b1fa232bdce059ba503",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Classifying using total UPDRS",
   "block_group": "6d0825262067482c8fd4d64ffe19f95e"
  },
  {
   "cellId": "48631088597e4ea48545b52c414c3246",
   "cell_type": "code",
   "metadata": {
    "source_hash": "61af330d",
    "execution_start": 1754059662635,
    "execution_millis": 0,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "48631088597e4ea48545b52c414c3246",
    "deepnote_cell_type": "code"
   },
   "source": "y_target = y[\"total_UPDRS\"]\n#y_target = y[\"motor_UPDRS\"]\n\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X_filtered, y_target, test_size=0.5, random_state=seed\n)\n\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, random_state=seed\n)",
   "block_group": "b0b4bfd7953143199e0c9d94678f8ba2",
   "execution_count": 72,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "50b7c42261364ed682ea682fb84bb7fa",
   "cell_type": "code",
   "metadata": {
    "source_hash": "eba21b97",
    "execution_start": 1754059664498,
    "execution_millis": 300,
    "execution_context_id": "d7a07521-7027-4016-a696-da7e8a558f72",
    "cell_id": "50b7c42261364ed682ea682fb84bb7fa",
    "deepnote_cell_type": "code"
   },
   "source": "print(y_target.describe())\nprint(y_target.unique())\nprint(y_target.head(10))\nsns.stripplot(x=y_target, color='dodgerblue', size=3, jitter=0.2)\nplt.title(\"Strip Plot of total_UPDRS\")\nplt.xlabel(\"total_UPDRS\")\nplt.show()",
   "block_group": "c4e58863fe204946943fc150ff013598",
   "execution_count": 75,
   "outputs": [
    {
     "name": "stdout",
     "text": "count    5875.000000\nmean       29.018942\nstd        10.700283\nmin         7.000000\n25%        21.371000\n50%        27.576000\n75%        36.399000\nmax        54.992000\nName: total_UPDRS, dtype: float64\n[34.398 34.894 35.389 ... 35.863 36.961 35.401]\n0    34.398\n1    34.894\n2    35.389\n3    35.810\n4    36.375\n5    36.870\n6    37.363\n7    37.857\n8    38.353\n9    38.849\nName: total_UPDRS, dtype: float64\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 520,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/745f2261-1549-41f5-8d0d-7ecac8f21e25",
   "content_dependencies": null
  },
  {
   "cellId": "3b0aab21f29b44bbb9975058f8ccc61e",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "3b0aab21f29b44bbb9975058f8ccc61e",
    "deepnote_cell_type": "text-cell-p"
   },
   "source": "",
   "block_group": "5885dba56495474291c26651744526fb"
  },
  {
   "cellId": "2da0f87c00764b3883839f63613d4dea",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "2da0f87c00764b3883839f63613d4dea",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### 4-way split",
   "block_group": "bc3071acb0544b89b26a3683b89eca29"
  },
  {
   "cellId": "33c448e73be14b7da4eced5f6aa6ccd7",
   "cell_type": "code",
   "metadata": {
    "source_hash": "8a724aa4",
    "execution_start": 1754182237305,
    "execution_millis": 186,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "33c448e73be14b7da4eced5f6aa6ccd7",
    "deepnote_cell_type": "code"
   },
   "source": "#4-way Split\nbins = [0, 20, 30, 40, float('inf')]\nlabels = [0, 1, 2, 3]  \ny_classify = pd.cut(y_target, bins=bins, labels=labels).astype(int)\n\n### this is just a repeat of making traing/val of X and y\nX_train, X_temp, y_train, y_temp = train_test_split(X_filtered, y_classify, test_size=0.5, stratify=y_classify, random_state=42)\nX_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)\n#making categorical y\ny_train_cat = to_categorical(y_train, num_classes=4)\ny_val_cat = to_categorical(y_val, num_classes=4)\n###\n\nsns.countplot(x=y_classify)\nplt.title(\"Class Distribution After Binning\")\nplt.xlabel(\"Severity Stage\")\nplt.ylabel(\"Count\")\nplt.show()",
   "block_group": "c878b72da84d4686aa198cd5ef0221d8",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 580,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/368b0e32-cb08-410e-a381-dfdf7fccf5cf",
   "content_dependencies": null
  },
  {
   "cellId": "bb35877f3b2840368bae7e83bf0553eb",
   "cell_type": "code",
   "metadata": {
    "source_hash": "36fc1689",
    "execution_start": 1754181158865,
    "execution_millis": 6354,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "bb35877f3b2840368bae7e83bf0553eb",
    "deepnote_cell_type": "code"
   },
   "source": "# Build the final model with best grid parameters\nfinal_model = RandomForestClassifier(\n    n_estimators=700,\n    max_depth=20,\n    max_features='log2',\n    min_samples_leaf=2,\n    min_samples_split=2,\n    class_weight='balanced',\n    random_state=42\n)\n\nfinal_model.fit(X_train, y_train)\n\ny_val_pred = final_model.predict(X_val)\n\nprint(\"Final Validation Classification Report:\")\nprint(classification_report(y_val, y_val_pred))\n\ncm = confusion_matrix(y_val, y_val_pred)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\nplt.title(\"Final Confusion Matrix (Validation Set)\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.show()",
   "block_group": "ab48cf30d7ae4ddcaf01452bb4783cac",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "text": "Final Validation Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.66      0.63      0.65       319\n           1       0.56      0.67      0.61       517\n           2       0.56      0.52      0.54       382\n           3       0.59      0.47      0.53       251\n\n    accuracy                           0.59      1469\n   macro avg       0.60      0.57      0.58      1469\nweighted avg       0.59      0.59      0.59      1469\n\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 539,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/9a2cd965-50ba-42bc-80c1-e2dad9e79288",
   "content_dependencies": null
  },
  {
   "cellId": "7ce550d68edd49e789ec99efd59edc3b",
   "cell_type": "code",
   "metadata": {
    "source_hash": "dde9d8d5",
    "execution_start": 1754182237555,
    "execution_millis": 61275,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "7ce550d68edd49e789ec99efd59edc3b",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LeakyReLU\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom tensorflow.keras.optimizers import Adam\n\n# Build model\nmodel = Sequential([\n    Dense(256, input_shape=(X_train_scaled.shape[1],)),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.4),\n\n    Dense(128),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(64),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.2),\n\n    Dense(32),\n    LeakyReLU(alpha=0.01),\n    \n    Dense(4, activation='softmax')  \n])\n\n# Compile model\nmodel.compile(\n    optimizer=Adam(learning_rate=0.001),\n    loss='categorical_crossentropy',\n    metrics=['accuracy']\n)\n\n# Callbacks\nearly_stop = EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True, verbose=1)\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=20, verbose=1)\n\n# Train\nhistory = model.fit(\n    X_train_scaled, y_train_cat,\n    validation_data=(X_val_scaled, y_val_cat),\n    epochs=200,\n    batch_size=32,\n    callbacks=[early_stop, reduce_lr],\n    verbose=1\n)\n\ny_val_pred = model.predict(X_val_scaled)\ny_val_pred_classes = tf.argmax(y_val_pred, axis=1)\n\nprint(\"Keras MLP Classification Report:\")\nprint(classification_report(y_val, y_val_pred_classes))\n\ncm = confusion_matrix(y_val, y_val_pred_classes)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\nplt.title(\"Final Confusion Matrix (Validation Set)\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.show()\n",
   "block_group": "1cf0159b02a241bbae27969c942134fe",
   "execution_count": 38,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/200\n92/92 [==============================] - 2s 8ms/step - loss: 1.4784 - accuracy: 0.3214 - val_loss: 1.3217 - val_accuracy: 0.4261 - lr: 0.0010\nEpoch 2/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.2876 - accuracy: 0.4195 - val_loss: 1.2619 - val_accuracy: 0.4472 - lr: 0.0010\nEpoch 3/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.2407 - accuracy: 0.4474 - val_loss: 1.2012 - val_accuracy: 0.4997 - lr: 0.0010\nEpoch 4/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1843 - accuracy: 0.4678 - val_loss: 1.1312 - val_accuracy: 0.4997 - lr: 0.0010\nEpoch 5/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1647 - accuracy: 0.4668 - val_loss: 1.0805 - val_accuracy: 0.5269 - lr: 0.0010\nEpoch 6/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1554 - accuracy: 0.4866 - val_loss: 1.0618 - val_accuracy: 0.5453 - lr: 0.0010\nEpoch 7/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1202 - accuracy: 0.5056 - val_loss: 1.0428 - val_accuracy: 0.5351 - lr: 0.0010\nEpoch 8/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1304 - accuracy: 0.5049 - val_loss: 1.0372 - val_accuracy: 0.5398 - lr: 0.0010\nEpoch 9/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.1085 - accuracy: 0.5131 - val_loss: 1.0282 - val_accuracy: 0.5548 - lr: 0.0010\nEpoch 10/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0897 - accuracy: 0.5182 - val_loss: 1.0150 - val_accuracy: 0.5548 - lr: 0.0010\nEpoch 11/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0884 - accuracy: 0.5121 - val_loss: 1.0236 - val_accuracy: 0.5589 - lr: 0.0010\nEpoch 12/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0734 - accuracy: 0.5264 - val_loss: 1.0075 - val_accuracy: 0.5630 - lr: 0.0010\nEpoch 13/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0933 - accuracy: 0.5233 - val_loss: 1.0003 - val_accuracy: 0.5793 - lr: 0.0010\nEpoch 14/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0539 - accuracy: 0.5380 - val_loss: 0.9833 - val_accuracy: 0.5827 - lr: 0.0010\nEpoch 15/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0541 - accuracy: 0.5349 - val_loss: 0.9847 - val_accuracy: 0.5725 - lr: 0.0010\nEpoch 16/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0501 - accuracy: 0.5441 - val_loss: 0.9780 - val_accuracy: 0.5827 - lr: 0.0010\nEpoch 17/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0363 - accuracy: 0.5529 - val_loss: 0.9896 - val_accuracy: 0.5643 - lr: 0.0010\nEpoch 18/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0436 - accuracy: 0.5407 - val_loss: 0.9742 - val_accuracy: 0.5882 - lr: 0.0010\nEpoch 19/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0601 - accuracy: 0.5349 - val_loss: 0.9756 - val_accuracy: 0.5807 - lr: 0.0010\nEpoch 20/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0324 - accuracy: 0.5506 - val_loss: 0.9664 - val_accuracy: 0.5888 - lr: 0.0010\nEpoch 21/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0190 - accuracy: 0.5499 - val_loss: 0.9647 - val_accuracy: 0.5759 - lr: 0.0010\nEpoch 22/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0092 - accuracy: 0.5587 - val_loss: 0.9627 - val_accuracy: 0.5779 - lr: 0.0010\nEpoch 23/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0186 - accuracy: 0.5601 - val_loss: 0.9624 - val_accuracy: 0.5800 - lr: 0.0010\nEpoch 24/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0130 - accuracy: 0.5618 - val_loss: 0.9560 - val_accuracy: 0.5909 - lr: 0.0010\nEpoch 25/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0089 - accuracy: 0.5533 - val_loss: 0.9621 - val_accuracy: 0.5895 - lr: 0.0010\nEpoch 26/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0102 - accuracy: 0.5662 - val_loss: 0.9600 - val_accuracy: 0.5807 - lr: 0.0010\nEpoch 27/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0018 - accuracy: 0.5594 - val_loss: 0.9474 - val_accuracy: 0.5916 - lr: 0.0010\nEpoch 28/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9890 - accuracy: 0.5686 - val_loss: 0.9472 - val_accuracy: 0.5984 - lr: 0.0010\nEpoch 29/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9801 - accuracy: 0.5781 - val_loss: 0.9440 - val_accuracy: 0.5997 - lr: 0.0010\nEpoch 30/200\n92/92 [==============================] - 0s 4ms/step - loss: 1.0005 - accuracy: 0.5577 - val_loss: 0.9411 - val_accuracy: 0.5888 - lr: 0.0010\nEpoch 31/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9946 - accuracy: 0.5720 - val_loss: 0.9477 - val_accuracy: 0.5861 - lr: 0.0010\nEpoch 32/200\n92/92 [==============================] - 0s 5ms/step - loss: 0.9949 - accuracy: 0.5700 - val_loss: 0.9426 - val_accuracy: 0.6031 - lr: 0.0010\nEpoch 33/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9884 - accuracy: 0.5747 - val_loss: 0.9369 - val_accuracy: 0.6065 - lr: 0.0010\nEpoch 34/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9762 - accuracy: 0.5904 - val_loss: 0.9398 - val_accuracy: 0.5950 - lr: 0.0010\nEpoch 35/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9860 - accuracy: 0.5666 - val_loss: 0.9277 - val_accuracy: 0.6072 - lr: 0.0010\nEpoch 36/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9576 - accuracy: 0.5873 - val_loss: 0.9337 - val_accuracy: 0.5990 - lr: 0.0010\nEpoch 37/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9752 - accuracy: 0.5724 - val_loss: 0.9382 - val_accuracy: 0.5990 - lr: 0.0010\nEpoch 38/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9581 - accuracy: 0.5884 - val_loss: 0.9252 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 39/200\n92/92 [==============================] - 0s 5ms/step - loss: 0.9605 - accuracy: 0.5870 - val_loss: 0.9133 - val_accuracy: 0.5997 - lr: 0.0010\nEpoch 40/200\n92/92 [==============================] - 1s 6ms/step - loss: 0.9555 - accuracy: 0.5935 - val_loss: 0.9242 - val_accuracy: 0.6018 - lr: 0.0010\nEpoch 41/200\n92/92 [==============================] - 1s 10ms/step - loss: 0.9725 - accuracy: 0.5822 - val_loss: 0.9230 - val_accuracy: 0.6099 - lr: 0.0010\nEpoch 42/200\n92/92 [==============================] - 1s 8ms/step - loss: 0.9575 - accuracy: 0.5890 - val_loss: 0.9284 - val_accuracy: 0.5963 - lr: 0.0010\nEpoch 43/200\n92/92 [==============================] - 1s 10ms/step - loss: 0.9560 - accuracy: 0.6033 - val_loss: 0.9237 - val_accuracy: 0.6147 - lr: 0.0010\nEpoch 44/200\n92/92 [==============================] - 1s 12ms/step - loss: 0.9457 - accuracy: 0.5993 - val_loss: 0.9242 - val_accuracy: 0.6133 - lr: 0.0010\nEpoch 45/200\n92/92 [==============================] - 1s 10ms/step - loss: 0.9521 - accuracy: 0.5901 - val_loss: 0.9255 - val_accuracy: 0.6072 - lr: 0.0010\nEpoch 46/200\n92/92 [==============================] - 1s 11ms/step - loss: 0.9671 - accuracy: 0.5802 - val_loss: 0.9143 - val_accuracy: 0.6161 - lr: 0.0010\nEpoch 47/200\n92/92 [==============================] - 1s 9ms/step - loss: 0.9461 - accuracy: 0.5996 - val_loss: 0.9386 - val_accuracy: 0.5970 - lr: 0.0010\nEpoch 48/200\n92/92 [==============================] - 1s 14ms/step - loss: 0.9543 - accuracy: 0.5955 - val_loss: 0.9172 - val_accuracy: 0.6181 - lr: 0.0010\nEpoch 49/200\n92/92 [==============================] - 1s 10ms/step - loss: 0.9356 - accuracy: 0.6013 - val_loss: 0.9130 - val_accuracy: 0.6188 - lr: 0.0010\nEpoch 50/200\n92/92 [==============================] - 1s 9ms/step - loss: 0.9444 - accuracy: 0.5962 - val_loss: 0.9007 - val_accuracy: 0.6113 - lr: 0.0010\nEpoch 51/200\n92/92 [==============================] - 1s 11ms/step - loss: 0.9437 - accuracy: 0.5935 - val_loss: 0.9194 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 52/200\n92/92 [==============================] - 1s 7ms/step - loss: 0.9444 - accuracy: 0.5921 - val_loss: 0.9155 - val_accuracy: 0.6018 - lr: 0.0010\nEpoch 53/200\n92/92 [==============================] - 1s 9ms/step - loss: 0.9418 - accuracy: 0.5989 - val_loss: 0.9054 - val_accuracy: 0.6052 - lr: 0.0010\nEpoch 54/200\n92/92 [==============================] - 1s 11ms/step - loss: 0.9235 - accuracy: 0.6071 - val_loss: 0.9094 - val_accuracy: 0.6018 - lr: 0.0010\nEpoch 55/200\n92/92 [==============================] - 1s 6ms/step - loss: 0.9365 - accuracy: 0.6020 - val_loss: 0.9231 - val_accuracy: 0.6004 - lr: 0.0010\nEpoch 56/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9096 - accuracy: 0.6170 - val_loss: 0.9112 - val_accuracy: 0.6072 - lr: 0.0010\nEpoch 57/200\n92/92 [==============================] - 0s 5ms/step - loss: 0.9108 - accuracy: 0.6118 - val_loss: 0.9091 - val_accuracy: 0.6113 - lr: 0.0010\nEpoch 58/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9089 - accuracy: 0.6088 - val_loss: 0.9154 - val_accuracy: 0.6201 - lr: 0.0010\nEpoch 59/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9130 - accuracy: 0.6047 - val_loss: 0.9098 - val_accuracy: 0.6195 - lr: 0.0010\nEpoch 60/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9160 - accuracy: 0.6067 - val_loss: 0.9064 - val_accuracy: 0.6113 - lr: 0.0010\nEpoch 61/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9160 - accuracy: 0.6023 - val_loss: 0.9087 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 62/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9126 - accuracy: 0.6156 - val_loss: 0.9067 - val_accuracy: 0.6208 - lr: 0.0010\nEpoch 63/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9167 - accuracy: 0.6105 - val_loss: 0.9055 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 64/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9221 - accuracy: 0.6153 - val_loss: 0.8958 - val_accuracy: 0.6195 - lr: 0.0010\nEpoch 65/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9131 - accuracy: 0.6142 - val_loss: 0.9113 - val_accuracy: 0.6079 - lr: 0.0010\nEpoch 66/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9031 - accuracy: 0.6200 - val_loss: 0.8993 - val_accuracy: 0.6093 - lr: 0.0010\nEpoch 67/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9079 - accuracy: 0.6166 - val_loss: 0.9077 - val_accuracy: 0.6113 - lr: 0.0010\nEpoch 68/200\n92/92 [==============================] - 0s 5ms/step - loss: 0.8906 - accuracy: 0.6146 - val_loss: 0.9029 - val_accuracy: 0.6147 - lr: 0.0010\nEpoch 69/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9055 - accuracy: 0.6071 - val_loss: 0.9004 - val_accuracy: 0.6154 - lr: 0.0010\nEpoch 70/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8817 - accuracy: 0.6313 - val_loss: 0.9009 - val_accuracy: 0.6093 - lr: 0.0010\nEpoch 71/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9191 - accuracy: 0.6057 - val_loss: 0.9095 - val_accuracy: 0.6059 - lr: 0.0010\nEpoch 72/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8965 - accuracy: 0.6166 - val_loss: 0.9072 - val_accuracy: 0.6099 - lr: 0.0010\nEpoch 73/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.9016 - accuracy: 0.6207 - val_loss: 0.9078 - val_accuracy: 0.6059 - lr: 0.0010\nEpoch 74/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8752 - accuracy: 0.6306 - val_loss: 0.9029 - val_accuracy: 0.6154 - lr: 0.0010\nEpoch 75/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8905 - accuracy: 0.6214 - val_loss: 0.9089 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 76/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8865 - accuracy: 0.6204 - val_loss: 0.9063 - val_accuracy: 0.6140 - lr: 0.0010\nEpoch 77/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8984 - accuracy: 0.6163 - val_loss: 0.9032 - val_accuracy: 0.6154 - lr: 0.0010\nEpoch 78/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8885 - accuracy: 0.6193 - val_loss: 0.8904 - val_accuracy: 0.6195 - lr: 0.0010\nEpoch 79/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8874 - accuracy: 0.6207 - val_loss: 0.9001 - val_accuracy: 0.6167 - lr: 0.0010\nEpoch 80/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8816 - accuracy: 0.6241 - val_loss: 0.8930 - val_accuracy: 0.6140 - lr: 0.0010\nEpoch 81/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8680 - accuracy: 0.6272 - val_loss: 0.8917 - val_accuracy: 0.6201 - lr: 0.0010\nEpoch 82/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8755 - accuracy: 0.6193 - val_loss: 0.8855 - val_accuracy: 0.6215 - lr: 0.0010\nEpoch 83/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8885 - accuracy: 0.6255 - val_loss: 0.8936 - val_accuracy: 0.6208 - lr: 0.0010\nEpoch 84/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8806 - accuracy: 0.6275 - val_loss: 0.8926 - val_accuracy: 0.6025 - lr: 0.0010\nEpoch 85/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8853 - accuracy: 0.6296 - val_loss: 0.8919 - val_accuracy: 0.6093 - lr: 0.0010\nEpoch 86/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8625 - accuracy: 0.6377 - val_loss: 0.8950 - val_accuracy: 0.6310 - lr: 0.0010\nEpoch 87/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8828 - accuracy: 0.6217 - val_loss: 0.8948 - val_accuracy: 0.6249 - lr: 0.0010\nEpoch 88/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8668 - accuracy: 0.6326 - val_loss: 0.8889 - val_accuracy: 0.6181 - lr: 0.0010\nEpoch 89/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8763 - accuracy: 0.6272 - val_loss: 0.8914 - val_accuracy: 0.6140 - lr: 0.0010\nEpoch 90/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8788 - accuracy: 0.6248 - val_loss: 0.9038 - val_accuracy: 0.6201 - lr: 0.0010\nEpoch 91/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8650 - accuracy: 0.6306 - val_loss: 0.8875 - val_accuracy: 0.6201 - lr: 0.0010\nEpoch 92/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8635 - accuracy: 0.6316 - val_loss: 0.8910 - val_accuracy: 0.6290 - lr: 0.0010\nEpoch 93/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8651 - accuracy: 0.6289 - val_loss: 0.9014 - val_accuracy: 0.6127 - lr: 0.0010\nEpoch 94/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8737 - accuracy: 0.6313 - val_loss: 0.8942 - val_accuracy: 0.6147 - lr: 0.0010\nEpoch 95/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8742 - accuracy: 0.6241 - val_loss: 0.8972 - val_accuracy: 0.6174 - lr: 0.0010\nEpoch 96/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8562 - accuracy: 0.6330 - val_loss: 0.8993 - val_accuracy: 0.6188 - lr: 0.0010\nEpoch 97/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8571 - accuracy: 0.6279 - val_loss: 0.9046 - val_accuracy: 0.6093 - lr: 0.0010\nEpoch 98/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8691 - accuracy: 0.6207 - val_loss: 0.8981 - val_accuracy: 0.6208 - lr: 0.0010\nEpoch 99/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8645 - accuracy: 0.6326 - val_loss: 0.8896 - val_accuracy: 0.6167 - lr: 0.0010\nEpoch 100/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8509 - accuracy: 0.6411 - val_loss: 0.8943 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 101/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8571 - accuracy: 0.6374 - val_loss: 0.8977 - val_accuracy: 0.6174 - lr: 0.0010\nEpoch 102/200\n86/92 [===========================>..] - ETA: 0s - loss: 0.8620 - accuracy: 0.6344\nEpoch 102: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n92/92 [==============================] - 0s 4ms/step - loss: 0.8613 - accuracy: 0.6357 - val_loss: 0.8928 - val_accuracy: 0.6167 - lr: 0.0010\nEpoch 103/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8428 - accuracy: 0.6357 - val_loss: 0.8851 - val_accuracy: 0.6270 - lr: 5.0000e-04\nEpoch 104/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8526 - accuracy: 0.6360 - val_loss: 0.8826 - val_accuracy: 0.6201 - lr: 5.0000e-04\nEpoch 105/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8237 - accuracy: 0.6452 - val_loss: 0.8858 - val_accuracy: 0.6154 - lr: 5.0000e-04\nEpoch 106/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8199 - accuracy: 0.6524 - val_loss: 0.8823 - val_accuracy: 0.6188 - lr: 5.0000e-04\nEpoch 107/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8264 - accuracy: 0.6473 - val_loss: 0.8853 - val_accuracy: 0.6120 - lr: 5.0000e-04\nEpoch 108/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8226 - accuracy: 0.6592 - val_loss: 0.8836 - val_accuracy: 0.6154 - lr: 5.0000e-04\nEpoch 109/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8258 - accuracy: 0.6452 - val_loss: 0.8878 - val_accuracy: 0.6201 - lr: 5.0000e-04\nEpoch 110/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8282 - accuracy: 0.6476 - val_loss: 0.8900 - val_accuracy: 0.6181 - lr: 5.0000e-04\nEpoch 111/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8241 - accuracy: 0.6442 - val_loss: 0.8903 - val_accuracy: 0.6195 - lr: 5.0000e-04\nEpoch 112/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8268 - accuracy: 0.6496 - val_loss: 0.8897 - val_accuracy: 0.6208 - lr: 5.0000e-04\nEpoch 113/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8306 - accuracy: 0.6527 - val_loss: 0.8829 - val_accuracy: 0.6181 - lr: 5.0000e-04\nEpoch 114/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8226 - accuracy: 0.6428 - val_loss: 0.8888 - val_accuracy: 0.6215 - lr: 5.0000e-04\nEpoch 115/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8207 - accuracy: 0.6479 - val_loss: 0.8918 - val_accuracy: 0.6188 - lr: 5.0000e-04\nEpoch 116/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8262 - accuracy: 0.6452 - val_loss: 0.8934 - val_accuracy: 0.6236 - lr: 5.0000e-04\nEpoch 117/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8158 - accuracy: 0.6565 - val_loss: 0.8899 - val_accuracy: 0.6270 - lr: 5.0000e-04\nEpoch 118/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8274 - accuracy: 0.6619 - val_loss: 0.8865 - val_accuracy: 0.6276 - lr: 5.0000e-04\nEpoch 119/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8113 - accuracy: 0.6479 - val_loss: 0.8902 - val_accuracy: 0.6229 - lr: 5.0000e-04\nEpoch 120/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8286 - accuracy: 0.6517 - val_loss: 0.8894 - val_accuracy: 0.6215 - lr: 5.0000e-04\nEpoch 121/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8170 - accuracy: 0.6513 - val_loss: 0.8934 - val_accuracy: 0.6174 - lr: 5.0000e-04\nEpoch 122/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8134 - accuracy: 0.6510 - val_loss: 0.8857 - val_accuracy: 0.6161 - lr: 5.0000e-04\nEpoch 123/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8156 - accuracy: 0.6513 - val_loss: 0.8849 - val_accuracy: 0.6215 - lr: 5.0000e-04\nEpoch 124/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8255 - accuracy: 0.6483 - val_loss: 0.8880 - val_accuracy: 0.6236 - lr: 5.0000e-04\nEpoch 125/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8092 - accuracy: 0.6602 - val_loss: 0.8928 - val_accuracy: 0.6181 - lr: 5.0000e-04\nEpoch 126/200\n76/92 [=======================>......] - ETA: 0s - loss: 0.8100 - accuracy: 0.6521\nEpoch 126: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n92/92 [==============================] - 0s 4ms/step - loss: 0.8099 - accuracy: 0.6541 - val_loss: 0.8914 - val_accuracy: 0.6133 - lr: 5.0000e-04\nEpoch 127/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8046 - accuracy: 0.6629 - val_loss: 0.8885 - val_accuracy: 0.6174 - lr: 2.5000e-04\nEpoch 128/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.7869 - accuracy: 0.6585 - val_loss: 0.8886 - val_accuracy: 0.6229 - lr: 2.5000e-04\nEpoch 129/200\n92/92 [==============================] - 0s 3ms/step - loss: 0.8045 - accuracy: 0.6691 - val_loss: 0.8909 - val_accuracy: 0.6249 - lr: 2.5000e-04\nEpoch 130/200\n92/92 [==============================] - 0s 3ms/step - loss: 0.7998 - accuracy: 0.6639 - val_loss: 0.8900 - val_accuracy: 0.6242 - lr: 2.5000e-04\nEpoch 131/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.7963 - accuracy: 0.6633 - val_loss: 0.8860 - val_accuracy: 0.6229 - lr: 2.5000e-04\nEpoch 132/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.7867 - accuracy: 0.6680 - val_loss: 0.8858 - val_accuracy: 0.6283 - lr: 2.5000e-04\nEpoch 133/200\n92/92 [==============================] - 0s 3ms/step - loss: 0.7950 - accuracy: 0.6663 - val_loss: 0.8849 - val_accuracy: 0.6304 - lr: 2.5000e-04\nEpoch 134/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.8053 - accuracy: 0.6537 - val_loss: 0.8894 - val_accuracy: 0.6242 - lr: 2.5000e-04\nEpoch 135/200\n92/92 [==============================] - 0s 4ms/step - loss: 0.7936 - accuracy: 0.6646 - val_loss: 0.8865 - val_accuracy: 0.6256 - lr: 2.5000e-04\nEpoch 136/200\n79/92 [========================>.....] - ETA: 0s - loss: 0.7899 - accuracy: 0.6630Restoring model weights from the end of the best epoch: 106.\n92/92 [==============================] - 0s 4ms/step - loss: 0.7886 - accuracy: 0.6616 - val_loss: 0.8871 - val_accuracy: 0.6283 - lr: 2.5000e-04\nEpoch 136: early stopping\n46/46 [==============================] - 0s 2ms/step\nKeras MLP Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.71      0.64      0.67       319\n           1       0.60      0.66      0.63       517\n           2       0.60      0.56      0.58       382\n           3       0.60      0.59      0.59       251\n\n    accuracy                           0.62      1469\n   macro avg       0.62      0.61      0.62      1469\nweighted avg       0.62      0.62      0.62      1469\n\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 539,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/16a161a8-9440-4a53-8818-d2f2582e0061",
   "content_dependencies": null
  },
  {
   "cellId": "3a9aa2fcc18c493dbfc2d938316c8b4f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "539f83a6",
    "execution_start": 1754182298895,
    "execution_millis": 117,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "3a9aa2fcc18c493dbfc2d938316c8b4f",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.metrics import roc_auc_score\nfrom sklearn.preprocessing import label_binarize\n\n# Predict softmax probabilities from Keras model\ny_val_probs = model.predict(X_val_scaled)\n\n# Binarize the true labels to one-hot (if not already)\ny_val_bin = label_binarize(y_val, classes=[0, 1, 2, 3])  # shape: (n_samples, 4)\n\n# ROC AUC Scores\nauc_macro = roc_auc_score(y_val_bin, y_val_probs, average='macro', multi_class='ovr')\nauc_weighted = roc_auc_score(y_val_bin, y_val_probs, average='weighted', multi_class='ovr')\nauc_per_class = [roc_auc_score(y_val_bin[:, i], y_val_probs[:, i]) for i in range(4)]\n\nprint(f\"ROC AUC (macro): {auc_macro:.4f}\")\nprint(f\"ROC AUC (weighted): {auc_weighted:.4f}\")\nfor i, auc in enumerate(auc_per_class):\n    print(f\"Class {i} AUC: {auc:.4f}\")\n",
   "block_group": "b040a76cb5e84b269bb4938551bf4caa",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stdout",
     "text": "46/46 [==============================] - 0s 1ms/step\nROC AUC (macro): 0.8579\nROC AUC (weighted): 0.8489\nClass 0 AUC: 0.9055\nClass 1 AUC: 0.8066\nClass 2 AUC: 0.8371\nClass 3 AUC: 0.8823\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/39875066-a479-4f64-8ea5-4163909b5cd3",
   "content_dependencies": null
  },
  {
   "cellId": "d81611ff79ef4139824440f200a8eda8",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "d81611ff79ef4139824440f200a8eda8",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### 3-way split",
   "block_group": "6487d3287d1540558c30c9b0b7929722"
  },
  {
   "cellId": "08355cbb5e024bf48941d032e6b28be9",
   "cell_type": "code",
   "metadata": {
    "source_hash": "cefb3166",
    "execution_start": 1754182635981,
    "execution_millis": 217,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "08355cbb5e024bf48941d032e6b28be9",
    "deepnote_cell_type": "code"
   },
   "source": "import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom tensorflow.keras.utils import to_categorical\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# --- Binning: 3-class severity levels based on total_UPDRS ---\nbins = [0, 25, 32, float('inf')]\nlabels = [0, 1, 2]\ny_classify = pd.cut(y_target, bins=bins, labels=labels).astype(int)\n\n# --- Train/Val/Test split (with stratification on class labels) ---\nX_train, X_temp, y_train, y_temp = train_test_split(\n    X_filtered, y_classify, test_size=0.5, stratify=y_classify, random_state=42\n)\nX_val, X_test, y_val, y_test = train_test_split(\n    X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42\n)\n\n# --- Scale input features ---\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)\nX_test_scaled = scaler.transform(X_test)\n\n# --- One-hot encode class labels for classification ---\nnum_classes = len(np.unique(y_classify))\ny_train_cat = to_categorical(y_train, num_classes=num_classes)\ny_val_cat = to_categorical(y_val, num_classes=num_classes)\n\n# --- Plot class distribution ---\nsns.countplot(x=y_classify)\nplt.title(\"Class Distribution After Binning (3 Classes)\")\nplt.xlabel(\"Severity Stage\")\nplt.ylabel(\"Count\")\nplt.tight_layout()\nplt.show()",
   "block_group": "fc5cafba42944fd0ae1d5593ecd17c08",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 630,
       "height": 470
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/ef80bedb-fa0b-47e1-93fb-7ecea9246c0c",
   "content_dependencies": null
  },
  {
   "cellId": "cf59868adbce4bec8dab557bbcbfc83f",
   "cell_type": "code",
   "metadata": {
    "source_hash": "63203421",
    "execution_start": 1754182859774,
    "execution_millis": 108884,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "cf59868adbce4bec8dab557bbcbfc83f",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LeakyReLU\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom tensorflow.keras.optimizers import Adam\n\n# Build model\nmodel = Sequential([\n    Dense(256, input_shape=(X_train_scaled.shape[1],)),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.4),\n\n    Dense(128),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(64),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.2),\n\n    Dense(32),\n    LeakyReLU(alpha=0.01),\n    \n    Dense(3, activation='softmax')  \n])\n\n# Compile model\nmodel.compile(\n    optimizer=Adam(learning_rate=0.01),\n    loss='categorical_crossentropy',\n    metrics=['accuracy']\n)\n\n# Callbacks\nearly_stop = EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True, verbose=1)\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=15, verbose=1)\n\n# Train\nhistory = model.fit(\n    X_train_scaled, y_train_cat,\n    validation_data=(X_val_scaled, y_val_cat),\n    epochs=300,\n    batch_size=16,\n    callbacks=[early_stop, reduce_lr],\n    verbose=1\n)\n\ny_val_pred = model.predict(X_val_scaled)\ny_val_pred_classes = tf.argmax(y_val_pred, axis=1)\n\nprint(\"Keras MLP Classification Report:\")\nprint(classification_report(y_val, y_val_pred_classes))\n\ncm = confusion_matrix(y_val, y_val_pred_classes)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\nplt.title(\"Final Confusion Matrix (Validation Set)\")\nplt.xlabel(\"Predicted\")\nplt.ylabel(\"Actual\")\nplt.show()\n",
   "block_group": "f25c1fbbf4174f309d6ce3442a44e4dc",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/300\n184/184 [==============================] - 3s 6ms/step - loss: 1.1795 - accuracy: 0.4205 - val_loss: 1.0281 - val_accuracy: 0.4826 - lr: 0.0010\nEpoch 2/300\n184/184 [==============================] - 1s 4ms/step - loss: 1.0381 - accuracy: 0.4743 - val_loss: 0.9809 - val_accuracy: 0.5099 - lr: 0.0010\nEpoch 3/300\n184/184 [==============================] - 1s 4ms/step - loss: 1.0018 - accuracy: 0.4923 - val_loss: 0.9358 - val_accuracy: 0.5405 - lr: 0.0010\nEpoch 4/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9691 - accuracy: 0.5182 - val_loss: 0.9216 - val_accuracy: 0.5650 - lr: 0.0010\nEpoch 5/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9405 - accuracy: 0.5475 - val_loss: 0.8973 - val_accuracy: 0.5664 - lr: 0.0010\nEpoch 6/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9262 - accuracy: 0.5495 - val_loss: 0.8780 - val_accuracy: 0.5800 - lr: 0.0010\nEpoch 7/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9260 - accuracy: 0.5359 - val_loss: 0.8658 - val_accuracy: 0.5793 - lr: 0.0010\nEpoch 8/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9037 - accuracy: 0.5591 - val_loss: 0.8636 - val_accuracy: 0.5922 - lr: 0.0010\nEpoch 9/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9162 - accuracy: 0.5485 - val_loss: 0.8519 - val_accuracy: 0.6025 - lr: 0.0010\nEpoch 10/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.9060 - accuracy: 0.5635 - val_loss: 0.8478 - val_accuracy: 0.5848 - lr: 0.0010\nEpoch 11/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8863 - accuracy: 0.5577 - val_loss: 0.8276 - val_accuracy: 0.6072 - lr: 0.0010\nEpoch 12/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8778 - accuracy: 0.5778 - val_loss: 0.8235 - val_accuracy: 0.6181 - lr: 0.0010\nEpoch 13/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8703 - accuracy: 0.5863 - val_loss: 0.8318 - val_accuracy: 0.6127 - lr: 0.0010\nEpoch 14/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8546 - accuracy: 0.5918 - val_loss: 0.8379 - val_accuracy: 0.6025 - lr: 0.0010\nEpoch 15/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8713 - accuracy: 0.5754 - val_loss: 0.8168 - val_accuracy: 0.6120 - lr: 0.0010\nEpoch 16/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8586 - accuracy: 0.5826 - val_loss: 0.8113 - val_accuracy: 0.6242 - lr: 0.0010\nEpoch 17/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8545 - accuracy: 0.5822 - val_loss: 0.8276 - val_accuracy: 0.6188 - lr: 0.0010\nEpoch 18/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8625 - accuracy: 0.5877 - val_loss: 0.8258 - val_accuracy: 0.5970 - lr: 0.0010\nEpoch 19/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8481 - accuracy: 0.5819 - val_loss: 0.8166 - val_accuracy: 0.6147 - lr: 0.0010\nEpoch 20/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8429 - accuracy: 0.5986 - val_loss: 0.8098 - val_accuracy: 0.6086 - lr: 0.0010\nEpoch 21/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8558 - accuracy: 0.5894 - val_loss: 0.8010 - val_accuracy: 0.6263 - lr: 0.0010\nEpoch 22/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8552 - accuracy: 0.5819 - val_loss: 0.8136 - val_accuracy: 0.6195 - lr: 0.0010\nEpoch 23/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8500 - accuracy: 0.5924 - val_loss: 0.8067 - val_accuracy: 0.6208 - lr: 0.0010\nEpoch 24/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8245 - accuracy: 0.6010 - val_loss: 0.7910 - val_accuracy: 0.6167 - lr: 0.0010\nEpoch 25/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8278 - accuracy: 0.6078 - val_loss: 0.8052 - val_accuracy: 0.6195 - lr: 0.0010\nEpoch 26/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8258 - accuracy: 0.6020 - val_loss: 0.8042 - val_accuracy: 0.6222 - lr: 0.0010\nEpoch 27/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8255 - accuracy: 0.6098 - val_loss: 0.7934 - val_accuracy: 0.6263 - lr: 0.0010\nEpoch 28/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8230 - accuracy: 0.6067 - val_loss: 0.7775 - val_accuracy: 0.6474 - lr: 0.0010\nEpoch 29/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8268 - accuracy: 0.6078 - val_loss: 0.7898 - val_accuracy: 0.6215 - lr: 0.0010\nEpoch 30/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8166 - accuracy: 0.6166 - val_loss: 0.7872 - val_accuracy: 0.6317 - lr: 0.0010\nEpoch 31/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8335 - accuracy: 0.5918 - val_loss: 0.7821 - val_accuracy: 0.6338 - lr: 0.0010\nEpoch 32/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.8063 - accuracy: 0.6003 - val_loss: 0.7894 - val_accuracy: 0.6215 - lr: 0.0010\nEpoch 33/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8141 - accuracy: 0.6132 - val_loss: 0.7841 - val_accuracy: 0.6236 - lr: 0.0010\nEpoch 34/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8037 - accuracy: 0.6214 - val_loss: 0.7769 - val_accuracy: 0.6344 - lr: 0.0010\nEpoch 35/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8185 - accuracy: 0.6006 - val_loss: 0.7731 - val_accuracy: 0.6399 - lr: 0.0010\nEpoch 36/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8066 - accuracy: 0.6176 - val_loss: 0.7758 - val_accuracy: 0.6392 - lr: 0.0010\nEpoch 37/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8059 - accuracy: 0.6224 - val_loss: 0.7747 - val_accuracy: 0.6338 - lr: 0.0010\nEpoch 38/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.8063 - accuracy: 0.6071 - val_loss: 0.7848 - val_accuracy: 0.6249 - lr: 0.0010\nEpoch 39/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7946 - accuracy: 0.6176 - val_loss: 0.7813 - val_accuracy: 0.6304 - lr: 0.0010\nEpoch 40/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8016 - accuracy: 0.6156 - val_loss: 0.7716 - val_accuracy: 0.6290 - lr: 0.0010\nEpoch 41/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8013 - accuracy: 0.6224 - val_loss: 0.7787 - val_accuracy: 0.6201 - lr: 0.0010\nEpoch 42/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.8046 - accuracy: 0.6159 - val_loss: 0.7779 - val_accuracy: 0.6440 - lr: 0.0010\nEpoch 43/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7941 - accuracy: 0.6156 - val_loss: 0.7879 - val_accuracy: 0.6263 - lr: 0.0010\nEpoch 44/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7998 - accuracy: 0.6227 - val_loss: 0.7708 - val_accuracy: 0.6358 - lr: 0.0010\nEpoch 45/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7839 - accuracy: 0.6333 - val_loss: 0.7683 - val_accuracy: 0.6460 - lr: 0.0010\nEpoch 46/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7827 - accuracy: 0.6224 - val_loss: 0.7654 - val_accuracy: 0.6365 - lr: 0.0010\nEpoch 47/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7850 - accuracy: 0.6319 - val_loss: 0.7582 - val_accuracy: 0.6549 - lr: 0.0010\nEpoch 48/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7730 - accuracy: 0.6381 - val_loss: 0.7750 - val_accuracy: 0.6508 - lr: 0.0010\nEpoch 49/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7829 - accuracy: 0.6309 - val_loss: 0.7690 - val_accuracy: 0.6399 - lr: 0.0010\nEpoch 50/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7845 - accuracy: 0.6313 - val_loss: 0.7724 - val_accuracy: 0.6521 - lr: 0.0010\nEpoch 51/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7827 - accuracy: 0.6340 - val_loss: 0.7659 - val_accuracy: 0.6494 - lr: 0.0010\nEpoch 52/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7763 - accuracy: 0.6299 - val_loss: 0.7713 - val_accuracy: 0.6426 - lr: 0.0010\nEpoch 53/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7861 - accuracy: 0.6255 - val_loss: 0.7721 - val_accuracy: 0.6392 - lr: 0.0010\nEpoch 54/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7755 - accuracy: 0.6411 - val_loss: 0.7562 - val_accuracy: 0.6501 - lr: 0.0010\nEpoch 55/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7726 - accuracy: 0.6350 - val_loss: 0.7613 - val_accuracy: 0.6372 - lr: 0.0010\nEpoch 56/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7709 - accuracy: 0.6422 - val_loss: 0.7568 - val_accuracy: 0.6562 - lr: 0.0010\nEpoch 57/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7643 - accuracy: 0.6391 - val_loss: 0.7556 - val_accuracy: 0.6528 - lr: 0.0010\nEpoch 58/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7586 - accuracy: 0.6319 - val_loss: 0.7662 - val_accuracy: 0.6460 - lr: 0.0010\nEpoch 59/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7676 - accuracy: 0.6367 - val_loss: 0.7664 - val_accuracy: 0.6351 - lr: 0.0010\nEpoch 60/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.7708 - accuracy: 0.6306 - val_loss: 0.7500 - val_accuracy: 0.6535 - lr: 0.0010\nEpoch 61/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7566 - accuracy: 0.6411 - val_loss: 0.7596 - val_accuracy: 0.6501 - lr: 0.0010\nEpoch 62/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7550 - accuracy: 0.6398 - val_loss: 0.7584 - val_accuracy: 0.6406 - lr: 0.0010\nEpoch 63/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7552 - accuracy: 0.6425 - val_loss: 0.7652 - val_accuracy: 0.6521 - lr: 0.0010\nEpoch 64/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7601 - accuracy: 0.6445 - val_loss: 0.7801 - val_accuracy: 0.6419 - lr: 0.0010\nEpoch 65/300\n184/184 [==============================] - 1s 5ms/step - loss: 0.7676 - accuracy: 0.6428 - val_loss: 0.7494 - val_accuracy: 0.6569 - lr: 0.0010\nEpoch 66/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7569 - accuracy: 0.6350 - val_loss: 0.7541 - val_accuracy: 0.6487 - lr: 0.0010\nEpoch 67/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7616 - accuracy: 0.6462 - val_loss: 0.7557 - val_accuracy: 0.6494 - lr: 0.0010\nEpoch 68/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7611 - accuracy: 0.6391 - val_loss: 0.7478 - val_accuracy: 0.6610 - lr: 0.0010\nEpoch 69/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7535 - accuracy: 0.6387 - val_loss: 0.7471 - val_accuracy: 0.6576 - lr: 0.0010\nEpoch 70/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7691 - accuracy: 0.6462 - val_loss: 0.7540 - val_accuracy: 0.6501 - lr: 0.0010\nEpoch 71/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7556 - accuracy: 0.6469 - val_loss: 0.7470 - val_accuracy: 0.6610 - lr: 0.0010\nEpoch 72/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7428 - accuracy: 0.6442 - val_loss: 0.7477 - val_accuracy: 0.6487 - lr: 0.0010\nEpoch 73/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7463 - accuracy: 0.6561 - val_loss: 0.7399 - val_accuracy: 0.6603 - lr: 0.0010\nEpoch 74/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7399 - accuracy: 0.6500 - val_loss: 0.7562 - val_accuracy: 0.6542 - lr: 0.0010\nEpoch 75/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7549 - accuracy: 0.6394 - val_loss: 0.7566 - val_accuracy: 0.6569 - lr: 0.0010\nEpoch 76/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7466 - accuracy: 0.6452 - val_loss: 0.7640 - val_accuracy: 0.6338 - lr: 0.0010\nEpoch 77/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7594 - accuracy: 0.6323 - val_loss: 0.7516 - val_accuracy: 0.6651 - lr: 0.0010\nEpoch 78/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7402 - accuracy: 0.6582 - val_loss: 0.7452 - val_accuracy: 0.6678 - lr: 0.0010\nEpoch 79/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7480 - accuracy: 0.6534 - val_loss: 0.7608 - val_accuracy: 0.6644 - lr: 0.0010\nEpoch 80/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7491 - accuracy: 0.6456 - val_loss: 0.7433 - val_accuracy: 0.6590 - lr: 0.0010\nEpoch 81/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7444 - accuracy: 0.6571 - val_loss: 0.7497 - val_accuracy: 0.6528 - lr: 0.0010\nEpoch 82/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7264 - accuracy: 0.6575 - val_loss: 0.7484 - val_accuracy: 0.6596 - lr: 0.0010\nEpoch 83/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7355 - accuracy: 0.6571 - val_loss: 0.7615 - val_accuracy: 0.6576 - lr: 0.0010\nEpoch 84/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.7355 - accuracy: 0.6622 - val_loss: 0.7410 - val_accuracy: 0.6664 - lr: 0.0010\nEpoch 85/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7159 - accuracy: 0.6554 - val_loss: 0.7658 - val_accuracy: 0.6413 - lr: 0.0010\nEpoch 86/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7381 - accuracy: 0.6507 - val_loss: 0.7723 - val_accuracy: 0.6583 - lr: 0.0010\nEpoch 87/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7332 - accuracy: 0.6513 - val_loss: 0.7639 - val_accuracy: 0.6596 - lr: 0.0010\nEpoch 88/300\n176/184 [===========================>..] - ETA: 0s - loss: 0.7338 - accuracy: 0.6538\nEpoch 88: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n184/184 [==============================] - 1s 3ms/step - loss: 0.7344 - accuracy: 0.6503 - val_loss: 0.7596 - val_accuracy: 0.6692 - lr: 0.0010\nEpoch 89/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7280 - accuracy: 0.6670 - val_loss: 0.7461 - val_accuracy: 0.6671 - lr: 5.0000e-04\nEpoch 90/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7274 - accuracy: 0.6660 - val_loss: 0.7525 - val_accuracy: 0.6658 - lr: 5.0000e-04\nEpoch 91/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7156 - accuracy: 0.6639 - val_loss: 0.7393 - val_accuracy: 0.6603 - lr: 5.0000e-04\nEpoch 92/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7196 - accuracy: 0.6677 - val_loss: 0.7512 - val_accuracy: 0.6739 - lr: 5.0000e-04\nEpoch 93/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6933 - accuracy: 0.6806 - val_loss: 0.7434 - val_accuracy: 0.6692 - lr: 5.0000e-04\nEpoch 94/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6991 - accuracy: 0.6786 - val_loss: 0.7490 - val_accuracy: 0.6698 - lr: 5.0000e-04\nEpoch 95/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7006 - accuracy: 0.6684 - val_loss: 0.7467 - val_accuracy: 0.6644 - lr: 5.0000e-04\nEpoch 96/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6985 - accuracy: 0.6711 - val_loss: 0.7628 - val_accuracy: 0.6535 - lr: 5.0000e-04\nEpoch 97/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6926 - accuracy: 0.6830 - val_loss: 0.7532 - val_accuracy: 0.6658 - lr: 5.0000e-04\nEpoch 98/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7123 - accuracy: 0.6718 - val_loss: 0.7534 - val_accuracy: 0.6603 - lr: 5.0000e-04\nEpoch 99/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6917 - accuracy: 0.6830 - val_loss: 0.7542 - val_accuracy: 0.6658 - lr: 5.0000e-04\nEpoch 100/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7179 - accuracy: 0.6745 - val_loss: 0.7523 - val_accuracy: 0.6678 - lr: 5.0000e-04\nEpoch 101/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7111 - accuracy: 0.6687 - val_loss: 0.7500 - val_accuracy: 0.6624 - lr: 5.0000e-04\nEpoch 102/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6970 - accuracy: 0.6687 - val_loss: 0.7590 - val_accuracy: 0.6637 - lr: 5.0000e-04\nEpoch 103/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7081 - accuracy: 0.6748 - val_loss: 0.7478 - val_accuracy: 0.6726 - lr: 5.0000e-04\nEpoch 104/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6949 - accuracy: 0.6759 - val_loss: 0.7563 - val_accuracy: 0.6630 - lr: 5.0000e-04\nEpoch 105/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6770 - accuracy: 0.6908 - val_loss: 0.7564 - val_accuracy: 0.6555 - lr: 5.0000e-04\nEpoch 106/300\n180/184 [============================>.] - ETA: 0s - loss: 0.6940 - accuracy: 0.6743\nEpoch 106: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n184/184 [==============================] - 1s 3ms/step - loss: 0.6925 - accuracy: 0.6745 - val_loss: 0.7508 - val_accuracy: 0.6705 - lr: 5.0000e-04\nEpoch 107/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.7026 - accuracy: 0.6799 - val_loss: 0.7509 - val_accuracy: 0.6698 - lr: 2.5000e-04\nEpoch 108/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6921 - accuracy: 0.6772 - val_loss: 0.7562 - val_accuracy: 0.6692 - lr: 2.5000e-04\nEpoch 109/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6830 - accuracy: 0.6956 - val_loss: 0.7422 - val_accuracy: 0.6651 - lr: 2.5000e-04\nEpoch 110/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6788 - accuracy: 0.6912 - val_loss: 0.7407 - val_accuracy: 0.6678 - lr: 2.5000e-04\nEpoch 111/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6732 - accuracy: 0.6959 - val_loss: 0.7383 - val_accuracy: 0.6705 - lr: 2.5000e-04\nEpoch 112/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6761 - accuracy: 0.6898 - val_loss: 0.7422 - val_accuracy: 0.6705 - lr: 2.5000e-04\nEpoch 113/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6778 - accuracy: 0.6803 - val_loss: 0.7422 - val_accuracy: 0.6767 - lr: 2.5000e-04\nEpoch 114/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6778 - accuracy: 0.6905 - val_loss: 0.7441 - val_accuracy: 0.6590 - lr: 2.5000e-04\nEpoch 115/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6744 - accuracy: 0.6939 - val_loss: 0.7523 - val_accuracy: 0.6671 - lr: 2.5000e-04\nEpoch 116/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6681 - accuracy: 0.6902 - val_loss: 0.7553 - val_accuracy: 0.6624 - lr: 2.5000e-04\nEpoch 117/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6697 - accuracy: 0.6895 - val_loss: 0.7500 - val_accuracy: 0.6760 - lr: 2.5000e-04\nEpoch 118/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6663 - accuracy: 0.6990 - val_loss: 0.7469 - val_accuracy: 0.6746 - lr: 2.5000e-04\nEpoch 119/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6711 - accuracy: 0.6977 - val_loss: 0.7390 - val_accuracy: 0.6760 - lr: 2.5000e-04\nEpoch 120/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6632 - accuracy: 0.6949 - val_loss: 0.7482 - val_accuracy: 0.6726 - lr: 2.5000e-04\nEpoch 121/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6703 - accuracy: 0.6977 - val_loss: 0.7460 - val_accuracy: 0.6644 - lr: 2.5000e-04\nEpoch 122/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6743 - accuracy: 0.6966 - val_loss: 0.7419 - val_accuracy: 0.6746 - lr: 2.5000e-04\nEpoch 123/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6590 - accuracy: 0.6953 - val_loss: 0.7442 - val_accuracy: 0.6767 - lr: 2.5000e-04\nEpoch 124/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6696 - accuracy: 0.6905 - val_loss: 0.7429 - val_accuracy: 0.6739 - lr: 2.5000e-04\nEpoch 125/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6686 - accuracy: 0.6912 - val_loss: 0.7428 - val_accuracy: 0.6678 - lr: 2.5000e-04\nEpoch 126/300\n179/184 [============================>.] - ETA: 0s - loss: 0.6801 - accuracy: 0.6917\nEpoch 126: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n184/184 [==============================] - 1s 3ms/step - loss: 0.6757 - accuracy: 0.6942 - val_loss: 0.7423 - val_accuracy: 0.6732 - lr: 2.5000e-04\nEpoch 127/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6590 - accuracy: 0.6987 - val_loss: 0.7433 - val_accuracy: 0.6767 - lr: 1.2500e-04\nEpoch 128/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.6679 - accuracy: 0.6973 - val_loss: 0.7439 - val_accuracy: 0.6678 - lr: 1.2500e-04\nEpoch 129/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6716 - accuracy: 0.6908 - val_loss: 0.7415 - val_accuracy: 0.6739 - lr: 1.2500e-04\nEpoch 130/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6706 - accuracy: 0.6834 - val_loss: 0.7412 - val_accuracy: 0.6739 - lr: 1.2500e-04\nEpoch 131/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6720 - accuracy: 0.6898 - val_loss: 0.7439 - val_accuracy: 0.6671 - lr: 1.2500e-04\nEpoch 132/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6665 - accuracy: 0.6959 - val_loss: 0.7430 - val_accuracy: 0.6664 - lr: 1.2500e-04\nEpoch 133/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6842 - accuracy: 0.6816 - val_loss: 0.7425 - val_accuracy: 0.6617 - lr: 1.2500e-04\nEpoch 134/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6625 - accuracy: 0.6939 - val_loss: 0.7441 - val_accuracy: 0.6644 - lr: 1.2500e-04\nEpoch 135/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6627 - accuracy: 0.6891 - val_loss: 0.7451 - val_accuracy: 0.6644 - lr: 1.2500e-04\nEpoch 136/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6743 - accuracy: 0.6922 - val_loss: 0.7460 - val_accuracy: 0.6685 - lr: 1.2500e-04\nEpoch 137/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6710 - accuracy: 0.6956 - val_loss: 0.7461 - val_accuracy: 0.6671 - lr: 1.2500e-04\nEpoch 138/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6660 - accuracy: 0.6939 - val_loss: 0.7456 - val_accuracy: 0.6698 - lr: 1.2500e-04\nEpoch 139/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6555 - accuracy: 0.6994 - val_loss: 0.7436 - val_accuracy: 0.6678 - lr: 1.2500e-04\nEpoch 140/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6642 - accuracy: 0.6966 - val_loss: 0.7451 - val_accuracy: 0.6698 - lr: 1.2500e-04\nEpoch 141/300\n180/184 [============================>.] - ETA: 0s - loss: 0.6660 - accuracy: 0.6927\nEpoch 141: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n184/184 [==============================] - 1s 3ms/step - loss: 0.6649 - accuracy: 0.6932 - val_loss: 0.7474 - val_accuracy: 0.6671 - lr: 1.2500e-04\nEpoch 142/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6580 - accuracy: 0.7024 - val_loss: 0.7499 - val_accuracy: 0.6678 - lr: 6.2500e-05\nEpoch 143/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6721 - accuracy: 0.6912 - val_loss: 0.7474 - val_accuracy: 0.6685 - lr: 6.2500e-05\nEpoch 144/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6654 - accuracy: 0.6959 - val_loss: 0.7464 - val_accuracy: 0.6664 - lr: 6.2500e-05\nEpoch 145/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6681 - accuracy: 0.6915 - val_loss: 0.7467 - val_accuracy: 0.6698 - lr: 6.2500e-05\nEpoch 146/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6548 - accuracy: 0.6990 - val_loss: 0.7483 - val_accuracy: 0.6678 - lr: 6.2500e-05\nEpoch 147/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6715 - accuracy: 0.6973 - val_loss: 0.7473 - val_accuracy: 0.6746 - lr: 6.2500e-05\nEpoch 148/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6575 - accuracy: 0.7075 - val_loss: 0.7490 - val_accuracy: 0.6644 - lr: 6.2500e-05\nEpoch 149/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6462 - accuracy: 0.7021 - val_loss: 0.7474 - val_accuracy: 0.6705 - lr: 6.2500e-05\nEpoch 150/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6651 - accuracy: 0.7055 - val_loss: 0.7458 - val_accuracy: 0.6732 - lr: 6.2500e-05\nEpoch 151/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6658 - accuracy: 0.6898 - val_loss: 0.7435 - val_accuracy: 0.6719 - lr: 6.2500e-05\nEpoch 152/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.6453 - accuracy: 0.7034 - val_loss: 0.7437 - val_accuracy: 0.6712 - lr: 6.2500e-05\nEpoch 153/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6541 - accuracy: 0.6987 - val_loss: 0.7446 - val_accuracy: 0.6739 - lr: 6.2500e-05\nEpoch 154/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6464 - accuracy: 0.7075 - val_loss: 0.7451 - val_accuracy: 0.6739 - lr: 6.2500e-05\nEpoch 155/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.6505 - accuracy: 0.7021 - val_loss: 0.7470 - val_accuracy: 0.6760 - lr: 6.2500e-05\nEpoch 156/300\n178/184 [============================>.] - ETA: 0s - loss: 0.6641 - accuracy: 0.7029\nEpoch 156: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n184/184 [==============================] - 1s 3ms/step - loss: 0.6628 - accuracy: 0.7017 - val_loss: 0.7473 - val_accuracy: 0.6732 - lr: 6.2500e-05\nEpoch 157/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6687 - accuracy: 0.6868 - val_loss: 0.7476 - val_accuracy: 0.6746 - lr: 3.1250e-05\nEpoch 158/300\n184/184 [==============================] - 1s 3ms/step - loss: 0.6711 - accuracy: 0.6905 - val_loss: 0.7450 - val_accuracy: 0.6732 - lr: 3.1250e-05\nEpoch 159/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.6486 - accuracy: 0.7038 - val_loss: 0.7456 - val_accuracy: 0.6712 - lr: 3.1250e-05\nEpoch 160/300\n184/184 [==============================] - 1s 4ms/step - loss: 0.6416 - accuracy: 0.7085 - val_loss: 0.7460 - val_accuracy: 0.6685 - lr: 3.1250e-05\nEpoch 161/300\n173/184 [===========================>..] - ETA: 0s - loss: 0.6402 - accuracy: 0.7077Restoring model weights from the end of the best epoch: 111.\n184/184 [==============================] - 1s 3ms/step - loss: 0.6395 - accuracy: 0.7072 - val_loss: 0.7454 - val_accuracy: 0.6746 - lr: 3.1250e-05\nEpoch 161: early stopping\n46/46 [==============================] - 0s 2ms/step\nKeras MLP Classification Report:\n              precision    recall  f1-score   support\n\n           0       0.74      0.70      0.72       547\n           1       0.61      0.60      0.61       403\n           2       0.65      0.69      0.67       519\n\n    accuracy                           0.67      1469\n   macro avg       0.67      0.66      0.67      1469\nweighted avg       0.67      0.67      0.67      1469\n\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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/9m/SqFEjAMDly5fLvW9ZvO6a3hbr6/4xe3WyLPDiE+utW7cQFhYGHR0dzJgxA40bN8aFCxfKH/BrvOt1vGrQoEGIi4tDaGgomjdvDmdn51L73b17F127dkVqaiq++eYbHDhwAFFRUfJ7kJT1kyigWCF5G5lMJp+Ue/fuXflk3rcxMzN77X+OlpaW6NatG3bv3o38/Hzs378fz58/l8/nAIDvvvsOw4YNQ926dbF582YcOnQIUVFR6NKlS7mutbzmz5+P4OBgdOjQAd999x0OHz6MqKgoNGnSRNTzvkwZ7y2JRIIGDRpg3LhxiImJgYaGBnbs2AHg3/fK4sWLS1Raix8GBgZlOk/xz/jlFVWk3rj0tZravXs3dHR0cPjwYUilUnn71q1b3+l4fn5+CAsLw3fffScfGnid4sl/N2/eLLHtxo0bMDc3h76+/jvF8ariT6Cv3lzo1U+FxerWrYtJkyZh0qRJuH37Nlq0aIGlS5e+9kZajo6OuHTpEoqKihSqGzdu3JBvF0O7du3g4OCAEydOYOHCha/tt3//fshkMuzbt0/hk29pqzOU+Slz1qxZuH79OpYsWYKpU6di2rRpWLly5Vv3a9SoEXbv3v3a7YMHD8ahQ4dw8OBBREREwMjICH5+fvLtu3btQp06dfDzzz8rXM+sWbPKfQ3FP7vbt2+jTp068vaUlJQSCdGuXbvQuXNneRWg2LNnzxT+Qy3Pa+zo6IgjR47g+fPnCtUNsd9bxerUqYOaNWsiISEBAORDNEZGRm+trr7tOu/fvw8A5Z7ETdUXKxvVlKamJiQSicIn/AcPHmDv3r3vdDx3d3d0794dmzZtKvUYeXl5mDx5MoAXJdsWLVpg27ZtCknAlStX8Ntvv8HHx+edYiiNo6MjNDU1Fcb5AZS4XXJ2dnaJOxrWrVsXhoaGkMlkrz2+j48PEhMT8cMPP8jbCgoKsGrVKhgYGKBjx45KuIqSJBIJVq5ciVmzZr1xBVDxp92XP92mp6eXmlTq6+uXSMrexZkzZ7BkyRJMmDABkyZNwpQpU7B69eoS81dK4+7ujqdPn752noG/vz/09PSwdu1aHDx4EL1791ZYmlna9Z45cwaxsbHlvg4PDw9oaWlh1apVCsdbvnx5ib6ampolKgg//fQTHj16pNBWnESX5XX28fFBYWEhVq9erdC+bNkySCQSeHt7l/FK3uzMmTMlhi0B4OzZs0hLS5MPd7q6uqJu3bpYsmRJqXcVTUlJkf/9bdd5/vx5SCQSuLu7K+EKqDpgZaOa8vX1xTfffIPu3btj0KBBSE5Oxpo1a1CvXr13vuXz//73P3h6eqJ3797w8/ND165doa+vj9u3b2Pnzp1ISEiQ32tj8eLF8Pb2hru7O0aMGIGcnBysWrUKxsbGCt9X8V8ZGxujX79+WLVqFSQSCerWrYvIyEgkJycr9Lt16xa6du2K/v37w9nZGTVq1MCePXuQlJSEAQMGvPb4o0aNwrfffothw4bh/PnzqF27Nnbt2oU//vgDy5cvL9OE2XfVq1cv9OrV6419PD09oa2tDT8/P3z22WfIzMzExo0bYWlpKf/EWszV1RXr1q3DvHnzUK9ePVhaWqJLly7liik3NxcBAQGoX78+vv76awDA7NmzsX//fgwfPhyXL19+Y9XK19cXNWrUwJEjRzBq1KgS2w0MDODv7y+ft/HyEAoA9OjRAz///DM+/PBD+Pr64v79+1i/fj2cnZ3Lfdvt4vvThIWFoUePHvDx8cGFCxdw8ODBEuX/Hj16YM6cORg+fDg++OADXL58GTt27FCoiAAvElgTExOsX78ehoaG0NfXh5ubW6nzYfz8/NC5c2d89dVXePDgAZo3b47ffvsNv/zyCyZMmKAwGfS/2L59O3bs2IEPP/wQrq6u0NbWxvXr17Flyxbo6OjI5/doaGhg06ZN8Pb2RpMmTTB8+HDUqlULjx49wvHjx2FkZIT9+/cDePFeAl7cRn7AgAHQ0tKCn5+f/GcfFRWFtm3bwszMTCnXQNWAStbAkCAI/y7FO3fuXKnbX7f0VV9fv0Tf4iWkL9u8ebNQv359QSqVCo0aNRK2bt1aar/y3Jo5OztbWLJkidC6dWvBwMBA0NbWFurXry+MGzdOYVmeIAjCkSNHhLZt2wq6urqCkZGR4OfnJ1y7dq3UuFNSUhTaS1tyWdqyT0F4cRv1Pn36CHp6ekLNmjWFzz77TLhy5YrCa5eamioEBgYKjRo1EvT19QVjY2PBzc1N+PHHHxWO9erSV0EQhKSkJGH48OGCubm5oK2tLbi4uJRY2lj8syptaS1eWYJZmpeXvr5Jaa/Bvn37hGbNmgk6OjpC7dq1hYULFwpbtmwp8folJiYKvr6+gqGhoQBAfp1veh+++nOYOHGioKmpKZw5c0ah359//inUqFFDGDNmzBvjFwRB6Nmzp9C1a9fXbj9w4IAAQLCxsSmx3LSoqEiYP3++4OjoKEilUqFly5ZCZGRkiWWlgvD2pa+CIAiFhYXC7NmzBRsbG0FXV1fo1KmTcOXKlRK/E7m5ucKkSZPk/dq2bSvExsaW+n755ZdfBGdnZ6FGjRoK78HSYnz+/LkwceJEwdbWVtDS0hLq168vLF68uMQt1QEIgYGBJV6rsvzuXrp0SZgyZYrw3nvvCaampkKNGjUEGxsboV+/fsJff/1Vov+FCxeE3r17C2ZmZoJUKhUcHR2F/v37C0ePHlXoN3fuXKFWrVqChoaGwuv67NkzQVtbW9i0adMb4yL1IhGEd5gtSET0jn7//Xd06tQJN27cKLEShKq+5cuXY9GiRbh79265Jh1T9cZkg4gqnLe3N+zs7LBx40ZVh0JKlJ+fj7p162LatGkYO3asqsOhSoTJBhEREYmKq1GIiIhIVEw2iIiISFRMNoiIiEhUTDaIiIhIVEw2iIiISFTV8g6iui2DVB0CVTKP/1ih6hCoEsnOK/lFfaS+aploi34OZf2/lHNh9ds7VUKsbBAREZGoqmVlg4iIqFKRqPdneyYbREREYpNIVB2BSjHZICIiEpuaVzbU++qJiIhIdKxsEBERiY3DKERERCQqDqMQERERiYeVDSIiIrFxGIWIiIhExWEUIiIiIvGwskFERCQ2DqMQERGRqDiMQkRERCQeVjaIiIjExmEUIiIiEpWaD6Mw2SAiIhKbmlc21DvVIiIiItGxskFERCQ2DqMQERGRqNQ82VDvqyciIiLRsbJBREQkNg31niDKZIOIiEhsHEYhIiIiEg8rG0RERGJT8/tsMNkgIiISG4dRiIiIiMTDygYREZHYOIxCREREolLzYRQmG0RERGJT88qGeqdaREREJDpWNoiIiMTGYRQiIiISFYdRiIiIqLpZt24dmjVrBiMjIxgZGcHd3R0HDx6Ub+/UqRMkEonCY/To0QrHiI+Ph6+vL/T09GBpaYkpU6agoKCg3LGwskFERCQ2FQyj2NnZYcGCBahfvz4EQcC2bdvQq1cvXLhwAU2aNAEAjBw5EnPmzJHvo6enJ/97YWEhfH19YW1tjVOnTiEhIQFDhw6FlpYW5s+fX65YmGwQERGJTQXDKH5+fgrPv/76a6xbtw6nT5+WJxt6enqwtrYudf/ffvsN165dw5EjR2BlZYUWLVpg7ty5mDp1KkJDQ6GtrV3mWDiMQkREVM0VFhZi586dyMrKgru7u7x9x44dMDc3R9OmTRESEoLs7Gz5ttjYWLi4uMDKykre5uXlhYyMDFy9erVc52dlg4iISGxKGkaRyWSQyWQKbVKpFFKptNT+ly9fhru7O3Jzc2FgYIA9e/bA2dkZADBo0CA4OjrC1tYWly5dwtSpU3Hz5k38/PPPAIDExESFRAOA/HliYmK54mayQUREJDYlJRthYWGYPXu2QtusWbMQGhpaav+GDRsiLi4O6enp2LVrFwICAhAdHQ1nZ2eMGjVK3s/FxQU2Njbo2rUr7t69i7p16yol3mJMNoiIiKqIkJAQBAcHK7S9rqoBANra2qhXrx4AwNXVFefOncOKFSvw7bfflujr5uYGALhz5w7q1q0La2trnD17VqFPUlISALx2nsfrcM4GERGR2CQSpTykUql8KWvx403JxquKiopKDMMUi4uLAwDY2NgAANzd3XH58mUkJyfL+0RFRcHIyEg+FFNWrGwQERGJTQVLX0NCQuDt7Q0HBwc8f/4cEREROHHiBA4fPoy7d+8iIiICPj4+MDMzw6VLlzBx4kR06NABzZo1AwB4enrC2dkZQ4YMwaJFi5CYmIjp06cjMDCwXAkOwGSDiIhIfCpY+pqcnIyhQ4ciISEBxsbGaNasGQ4fPoxu3brh4cOHOHLkCJYvX46srCzY29ujT58+mD59unx/TU1NREZGYsyYMXB3d4e+vj4CAgIU7stRVhJBEARlXlxloNsySNUhUCXz+I8Vqg6BKpHsvEJVh0CVSC2Tst8v4l3p+m9QynFy9o56e6dKiJUNIiIisfGL2IiIiEhU/CI2IiIiIvGwskFERCQyiZpXNphsEBERiUzdkw0OoxAREZGoWNkgIiISm3oXNphsEBERiY3DKEREREQiYmWDiIhIZOpe2WCyQUREJDImG1RljOzXDiP7toejrSkA4Pq9RMzfcBC//XENAGBlZoj5Ez5ElzaNYKgvxa0HyVi0+TD2Ho0rcSxtrRqI2T4ZzRvawe2jMFy69agiL4VE4u/jgcSExyXa+/QfiCkhM7Bg3iycO3MaqSnJ0NXVg0vzFgj8fBJqO9VRQbRUEbKzsrDl29U4GX0Uz54+Qb0GjRAUPA2NnJsCAARBQPiGNTjwy25kZj5H02YtMOGLGbBzcFRx5NULkw2qMh4lPcOMVb/gTnwKJJDgYz83/LRsFNoMWIDr9xKxae5QmBjqot+Eb5H6LBMfebfCdws/QdvBi3Dx5j8Kx5o/oRcSUtLRvKGdiq6GxLD1ux9RVPTvl4zdvXMb48d8ii7dvAAAjRo3gZe3H6xsbJCRno5N69fg87Gf4ufIKGhqaqoqbBLRkvmzcP/uHYSEzoe5uSWiDkViStBIbNm5FxaWVti5fQt+/jEC02bOg7VtLWz9djWmfv4Ztu78Bdrl/BpxotfhBNEq5NeYKzh88hruxqfgTnwyQtfsR2a2DO83cwIAtGleB2t3RuPPq3/jwaM0LNx0GM+e56Cls73CcTzbOqNrm8YIWbZHFZdBIqppagozcwv544/fo2Fnb4/3XFsDAPz79EdL11awta2FRo2d8VngeCQlJiLhMStb1ZEsNxcxx4/gs6BgNG/ZCrXsHTBs5FjY2tlj388/QBAE7N75HT4ePgptO3ZB3foNMS10PlJTU3Ay+piqw69eJEp6VFEqrWykpqZiy5YtiI2NRWJiIgDA2toaH3zwAYYNGwYLCwtVhlepaWhI0Kfbe9DX1caZS/cBAKcv3kNfT1cc+v0qnj3PQV/P96AjrYGYP2/L97M0NcTaGQPRP3gjsnPyVBU+VYD8/Dwc+nU/Bn4cUGoJNycnGwf27YFtLTtYWVurIEISW2FhIYoKC6EtVfwKdalUB1cuXkDC43/wJC0Vru+3kW8zMDBE4yYuuHb5Irp4eld0yNUWh1FU5Ny5c/Dy8oKenh48PDzQoEEDAEBSUhJWrlyJBQsW4PDhw2jVqpWqQqyUmtSzxYltk6CjXQOZOTJ8NGkjbtx7kah9/MUWbF/4CR5HL0J+fiGyc/PwUfBG3HuYKt9/w5yPsXHXSfx1LR4ONqaqugyqANHHjyLz+XP4+n2o0L7rx++xZvkS5OTkwLG2E1au2wQtLe3XHIWqMj19fTi7NMf2Ld/CoXYd1DQ1w7HffsW1Kxdha+eAJ2lpAICapmYK+9U0NcOTJ6mlHZLonags2Rg3bhz69euH9evXl8j4BEHA6NGjMW7cOMTGxr7xODKZDDKZTHH/okJINKrn+POtB0lwGxAGYwNdfOjREhvnDIHnpytw414iZgX2gImhLrw/W4m0Z1nw69QM3y36BB6fLMfVO48xdmBHGOrpYPGW31R9GVQB9u/9GW3atoeFpaVCe3fvHnjfzR1pqanY8b+t+GpqMDZs3QEpx+erpZDQMCyeNwP9e3SFhqYm6jdsjC6e3rh145qqQ1MrrGyoyMWLFxEeHl7qD0AikWDixIlo2bLlW48TFhaG2bNnK7RpWrWGls37Sou1MskvKJRXKi5cfwjXJg4IHNgJ32w7gjEDOuK9PvNw/f8rHZdvPULb9+ris486YPzXO9GpdQO4NXNC+pnlCsf8Y8cX2HnwT4ycub2iL4dEkvD4Ec6dicWCJStKbDMwNISBoSEcHGujabNm6NbBHdHHjsDT21cFkZLYatnZY/n6cOTkZCM7Kwtm5haY89Vk2NjawdTsRUXj6ZM0mJn/O2z99Eka6tVvpKqQqyV1TzZUNkHU2toaZ8+efe32s2fPwsrK6q3HCQkJQXp6usKjhpWrMkOt1DQkEki1a0BP50UZvEgQFLYXFgrQ+P83+aRFu/D+R2FwG7AAbgMWwH/cOgDAkGlbEbp6f8UGTqKK3LcHNU1N8UH7jm/sJwiAAAF5+Zy/U93p6urBzNwCzzPSce70KbTt0Pn/Ew5z/HXujLxfVmYmrl+9DGeX5iqMlqoblVU2Jk+ejFGjRuH8+fPo2rWrPLFISkrC0aNHsXHjRixZsuStx5FKpSXKv9V1CGXOuJ44/MdVPEx4CkN9HXzk3QodWtWH39i1uPkgEXfik7F6+kCEfLMHaelZ6Nm5Gbq2aYjen68HADxMfKpwvMzsF8NP9x6m4FHys4q+HBJJUVERDvyyBz49/FGjxr+/4o/+eYgjhw/Czb0tTGrWRHJSEv63dROkUik+aNdBhRGTmM6d/gOCIMDesTYePYzHt6u+gYOjE7r7+UMikaDPgI/x3dZvUcveATb/v/TV3NwC7Tp2UXXo1Yq6VzZUlmwEBgbC3Nwcy5Ytw9q1a1FY+OLeAJqamnB1dUV4eDj69++vqvAqJQtTA2yeOxTW5kZIz8zFlduP4Dd2LY6duQEA8B+3DvPG98KuFZ/BQE+Kuw9T8OnM7Th8kmOz6uTcmVgkJibAz7+3Qru2thRxF85jZ8R2PM9Ih6mZOVq854qN4REwfWWCIFUfWZnPsXHtCqQmJ8HQyBjtO3tgxJjxqFFDCwAwYMgnyM3JwTdhs5GZ+RwuzVtiwYr1vMeGsql3rgGJILxSd1eB/Px8pKa+mIdgbm4OLS2t/3Q83ZZBygiLqpHHf5Scu0DqKzuv8O2dSG3UMhF/NZZZwPdKOU7atoFKOU5FqxR3ENXS0oKNjY2qwyAiIhIFh1GIiIhIVEw2iIiISFTqnmzwu1GIiIhIVKxsEBERiU29CxtMNoiIiMTGYRQiIiIiEbGyQUREJDJ1r2ww2SAiIhKZuicbHEYhIiIiUbGyQUREJDJ1r2ww2SAiIhKbeucaHEYhIiIicbGyQUREJDIOoxAREZGomGwQERGRqNQ92eCcDSIiIhIVKxtERERiU+/CBpMNIiIisXEYhYiIiEhErGwQERGJTN0rG0w2iIiIRKbuyQaHUYiIiEhUrGwQERGJjJUNIiIiEpdESY9yWLduHZo1awYjIyMYGRnB3d0dBw8elG/Pzc1FYGAgzMzMYGBggD59+iApKUnhGPHx8fD19YWenh4sLS0xZcoUFBQUlPvymWwQERFVQ3Z2dliwYAHOnz+PP//8E126dEGvXr1w9epVAMDEiROxf/9+/PTTT4iOjsbjx4/Ru3dv+f6FhYXw9fVFXl4eTp06hW3btiE8PBwzZ84sdywSQRAEpV1ZJaHbMkjVIVAl8/iPFaoOgSqR7LxCVYdAlUgtE23Rz1En+FelHOfeNz7/aX9TU1MsXrwYffv2hYWFBSIiItC3b18AwI0bN9C4cWPExsaiTZs2OHjwIHr06IHHjx/DysoKALB+/XpMnToVKSkp0NYu++vGygYREZHIJBKJUh7vqrCwEDt37kRWVhbc3d1x/vx55Ofnw8PDQ96nUaNGcHBwQGxsLAAgNjYWLi4u8kQDALy8vJCRkSGvjpQVJ4gSERGJTFnzQ2UyGWQymUKbVCqFVCottf/ly5fh7u6O3NxcGBgYYM+ePXB2dkZcXBy0tbVhYmKi0N/KygqJiYkAgMTERIVEo3h78bbyYGWDiIioiggLC4OxsbHCIyws7LX9GzZsiLi4OJw5cwZjxoxBQEAArl27VoERv8DKBhERkciUtfQ1JCQEwcHBCm2vq2oAgLa2NurVqwcAcHV1xblz57BixQp89NFHyMvLw7NnzxSqG0lJSbC2tgYAWFtb4+zZswrHK16tUtynrFjZICIiEplEopyHVCqVL2Utfrwp2XhVUVERZDIZXF1doaWlhaNHj8q33bx5E/Hx8XB3dwcAuLu74/Lly0hOTpb3iYqKgpGREZydnct1/axsEBERVUMhISHw9vaGg4MDnj9/joiICJw4cQKHDx+GsbExRowYgeDgYJiamsLIyAjjxo2Du7s72rRpAwDw9PSEs7MzhgwZgkWLFiExMRHTp09HYGBguRIcgMkGERGR6FRxB9Hk5GQMHToUCQkJMDY2RrNmzXD48GF069YNALBs2TJoaGigT58+kMlk8PLywtq1a+X7a2pqIjIyEmPGjIG7uzv09fUREBCAOXPmlDsW3meD1ALvs0Ev43026GUVcZ+NRtMOK+U4NxZ4KeU4FY1zNoiIiEhUHEYhIiISmYaGen8RG5MNIiIikan5l75yGIWIiIjExcoGERGRyFSxGqUyYbJBREQkMjXPNZhsEBERiU3dKxucs0FERESiYmWDiIhIZOpe2WCyQUREJDI1zzU4jEJERETiYmWDiIhIZBxGISIiIlGpea7BYRQiIiISFysbREREIuMwChEREYlKzXMNDqMQERGRuFjZICIiEhmHUYiIiEhUap5rMNkgIiISm7pXNjhng4iIiERVLSsbT8+tVnUIVMkE7Lig6hCoEgnzaazqEEjNqHlho3omG0RERJUJh1GIiIiIRMTKBhERkcjUvLDBZIOIiEhsHEYhIiIiEhErG0RERCJT88IGkw0iIiKxcRiFiIiISESsbBAREYlM3SsbTDaIiIhEpua5BpMNIiIisal7ZYNzNoiIiEhUrGwQERGJTM0LG0w2iIiIxMZhFCIiIiIRsbJBREQkMjUvbDDZICIiEpuGmmcbHEYhIiIiUbGyQUREJDI1L2ww2SAiIhKbuq9GYbJBREQkMg31zjU4Z4OIiIjExcoGERGRyDiMQkRERKJS81yDwyhERETVUVhYGFq3bg1DQ0NYWlrC398fN2/eVOjTqVMnSCQShcfo0aMV+sTHx8PX1xd6enqwtLTElClTUFBQUK5YWNkgIiISmQQVX9qIjo5GYGAgWrdujYKCAnz55Zfw9PTEtWvXoK+vL+83cuRIzJkzR/5cT09P/vfCwkL4+vrC2toap06dQkJCAoYOHQotLS3Mnz+/zLEw2SAiIhKZKlajHDp0SOF5eHg4LC0tcf78eXTo0EHerqenB2tr61KP8dtvv+HatWs4cuQIrKys0KJFC8ydOxdTp05FaGgotLW1yxQLh1GIiIjUQHp6OgDA1NRUoX3Hjh0wNzdH06ZNERISguzsbPm22NhYuLi4wMrKSt7m5eWFjIwMXL16tcznZmWDiIhIZMpajSKTySCTyRTapFIppFLpG/crKirChAkT0LZtWzRt2lTePmjQIDg6OsLW1haXLl3C1KlTcfPmTfz8888AgMTERIVEA4D8eWJiYpnjZrJBREQkMmWtRgkLC8Ps2bMV2mbNmoXQ0NA37hcYGIgrV67g5MmTCu2jRo2S/93FxQU2Njbo2rUr7t69i7p16yonaHAYhYiIqMoICQlBenq6wiMkJOSN+wQFBSEyMhLHjx+HnZ3dG/u6ubkBAO7cuQMAsLa2RlJSkkKf4uevm+dRGiYbREREItOQSJTykEqlMDIyUni8bghFEAQEBQVhz549OHbsGJycnN4aZ1xcHADAxsYGAODu7o7Lly8jOTlZ3icqKgpGRkZwdnYu8/VzGIWIiEhkqripV2BgICIiIvDLL7/A0NBQPsfC2NgYurq6uHv3LiIiIuDj4wMzMzNcunQJEydORIcOHdCsWTMAgKenJ5ydnTFkyBAsWrQIiYmJmD59OgIDA986T+RlTDaIiIhEporbla9btw7Aixt3vWzr1q0YNmwYtLW1ceTIESxfvhxZWVmwt7dHnz59MH36dHlfTU1NREZGYsyYMXB3d4e+vj4CAgIU7stRFkw2iIiIqiFBEN643d7eHtHR0W89jqOjI3799df/FAuTDSIiIpGp+3ejMNkgIiISmYaaZxtcjUJERESiYmWDiIhIZOpd12CyQUREJDpVrEapTDiMQkRERKJiZYOIiEhkqviK+cqkTMnGvn37ynzAnj17vnMwRERE1ZG6D6OUKdnw9/cv08EkEgkKCwv/SzxERERUzZQp2SgqKhI7DiIiompLzQsbnLNBREQkNg6jvIOsrCxER0cjPj4eeXl5CtvGjx+vlMCIiIiqC04QLacLFy7Ax8cH2dnZyMrKgqmpKVJTU6GnpwdLS0smG0RERKSg3PfZmDhxIvz8/PD06VPo6uri9OnT+Pvvv+Hq6oolS5aIESMREVGVJpFIlPKoqsqdbMTFxWHSpEnQ0NCApqYmZDIZ7O3tsWjRInz55ZdixEhERFSlSZT0qKrKnWxoaWlBQ+PFbpaWloiPjwcAGBsb4+HDh8qNjoiIiKq8cs/ZaNmyJc6dO4f69eujY8eOmDlzJlJTU7F9+3Y0bdpUjBiJiIiqNH7FfDnNnz8fNjY2AICvv/4aNWvWxJgxY5CSkoINGzYoPUAiIqKqTiJRzqOqKndlo1WrVvK/W1pa4tChQ0oNiIiIiKoX3tSLiIhIZFV5JYkylDvZcHJyeuOLdu/evf8UEJXPujWrsH7taoW22k5O+CXyRcXpYXw8li5ZiLi/ziMvLw9t27XHtC9nwMzcXBXhkpL5N7XC+47GsDXWQV5BEW6lZGHH+cdIyJCV2n9a17poaWeExcfu4c+H6QAAx5q66OVihYaW+jCS1kBKZh6ibqXi4PWUirwUUoLLceexKyIcd25ex5O0FMyYvwwfdOgi3y4IArZvXotD+39G1vPncHZpgaDJX6GWvSMAICnhESLCN+DiX2fxNC0NpuYW6OLliwFDR0JLS0tVl1UtqHmuUf5kY8KECQrP8/PzceHCBRw6dAhTpkxRVlxUDnXr1ceGTVvlzzVraAIAsrOzMXrUJ2jQsBE2btkGAFizagXGBY7Gd9//KF9VRFVXY2sDHL6Rirtp2dCUSDDgPRt81a0eJv1yHbICxe808nG2ACCUOIaTmS7Sc/Kx+ve/kZaVhwaW+hjl7oAiQcDhG6kVdCWkDLk5OahTryE8ff0x76vgEtt/2rEV+3Z9j0lfzYW1TS38b9MaTA8eg2+/2wNtqRQP/34AQSjCuCkzYFvLAX/fv4MVC2cjNycHI4MmqeCKqLood7Lx+eefl9q+Zs0a/Pnnn/85ICq/GpqaMLewKNEed+EvPH70CD/s2gsDAwMAwNz5C9HevTXOnjmNNu4fVHSopGRhR+4qPF97Mh6bBrigjpkuridlydsda+qih7MlQiJvYsNHLgr7nLjzROF5cmYeGljo430HEyYbVUxr93Zo7d6u1G2CIGDvTzswYOhIuLfvDACYPH0eBvbsglO/H0MnD2+0atMWrdq0le9jU8sO/8Q/wIE9PzLZ+I+4GkVJvL29sXv3bmUdjsrh7/i/4dGpHXy8uiLki0lIePwYAJCXlweJRAJtbW15X6lUCg0NDVz467yqwiUR6Wm/+JXOlBXK27Q1JRjfwRFbzvyD9NyCsh1HSxOZsrL1paoh8fEjPE1LRcvWbvI2fQNDNHR2wY0rl167X1ZmJgyNjCsixGpN3VejKC3Z2LVrF0xNTZV1OCojl2bNMPfrMKz9dhO+mhGKR48eYfjQwcjKykSz5i2gq6uL5UsXIycnB9nZ2Vi6eCEKCwuRksLx+OpGAiCgtR1uJGXi4bNceXtAazvcSs6Sz9F4mwYW+nB3qomjt9NEipRU4emTF1WqmjXNFNpr1jSTb3vV43/isW/39/Du1Vf0+Ko7db9d+Tvd1OvlCxYEAYmJiUhJScHatWuVGtzDhw8xa9YsbNmy5bV9ZDIZZDLFyXCCphRSqVSpsVRW7dp3lP+9QcNGcGnWHN7dOuPwoYPo3acfFn+zAl/PDUXEju3Q0NBAdx9fNHZuAg11/wrCauiTNnawr6mDWQdvy9tc7Y3QxMYAU/ffLNMx7E10MKWLE3ZfTMClx8/FCpWqgNSUJEyfNBbtO3eDd88+qg6HqrhyJxu9evVSSDY0NDRgYWGBTp06oVGjRkoN7smTJ9i2bdsbk42wsDDMnj1boe2rGbMwfWaoUmOpKoyMjODoWBsP//828h+0bYcDh47g6dMn0NSsASMjI3Tp0BZ23j4qjpSUabibHd6zM0boodt4kp0vb29qbQgrQym2Dmym0H9SJydcT87EnMN35G21jHUw3bMejtxKw8+XkiosdqoYNU1frEB7+vTFKpNiT5+moW69hgp901KTMW3cp3Bu2hzjv5hZoXFWV+o+Hb/cyUZoaKjSTr5v3743bi/LMtqQkBAEByvOuhY01aOqUZrsrCw8fPgQvj0VJ4zWrPliiOvM6Vg8eZKGTp27lLY7VUHD3ezwvoMxZh+6g5TMPIVtey8n4dgrwyFLejXGtnOPcP6ff4dV7Ex0MMOzHmLuPsEPFxIqJG6qWNa2tVDTzBxxf55B3fovPhhmZWXi5rXL8PXvJ++XmpKEaeM+Rb2Gzpj45RyuWlOSqjwEogzlTjY0NTWRkJAAS0tLhfa0tDRYWlqisLDwNXuW5O/vD4lEAkEouRyv2Nt+QFJpySGTMs6BqxaWLl6Ijp06w8bWFinJyVi3ZhU0NTXg7dMDALB3z27UqVMXNWua4uLFC1gUNh8fDx2G2k51VBw5KcMINzu0rVMTi4/dR05+IYx1XvxKZ+cXIr9QQHpuQamTQlOz8uSJif3/JxoXHz9H5NVk+TGKBOA5J4lWKTnZ2Xj8KF7+PCnhEe7evgFDQ2NYWtvAv99g7Ny2EbXsHWFlUwvbN62BmZkFPmj/4sNHakoSpo77FJZWNvg0KBjpz57Kj2Vqxnvz0Lsrd7LxusRAJpMprHooCxsbG6xduxa9evUqdXtcXBxcXV3LG6JaSUpKxLQpwXj27Blqmpqi5Xuu2B7xo3yy7oP797Fy2TdIT0+Hba1a+HTUaAwJGKbaoElpPBu9qGCFdq+v0L725N+IvvuktF1KcHM0gbGuFjrUNUWHuv9O8k7OlGHc7mvKC5ZEd/vGVUwd/6n8+YZVSwAAHt49Memrueg3eDhyc3OwctEcZGY+RxOXlpi7dC20//8D24Vzp/H4n3g8/iceQz70VDj2wZMXK+5CqiF1nyYnEd5UVnjJypUrAQATJ07E3Llz5fdtAIDCwkLExMTgwYMHuHDhQplP3rNnT7Ro0QJz5swpdfvFixfRsmVLFBUVlbr9ddSpskFlE7Cj7O9Lqv7CfBqrOgSqROpY6Ih+juB9N5RynG96KnduZEUpc2Vj2bJlAF5UNtavXw9NTU35Nm1tbdSuXRvr168v18mnTJmCrKys126vV68ejh8/Xq5jEhERUeVS5mTj/v37AIDOnTvj559/Rs2aNf/zydu3b//G7fr6+ujYseMb+xAREVV2nCBaTqw0EBERlY+6z9ko95qmPn36YOHChSXaFy1ahH79+pWyBxEREamzcicbMTEx8PEpeUMob29vxMTEKCUoIiKi6kTdvxul3MMomZmZpS5x1dLSQkZGhlKCIiIiqk74ra/l5OLigh9++KFE+86dO+Hs7KyUoIiIiKoTDSU9qqpyVzZmzJiB3r174+7du+jS5cVd544ePYqIiAjs2rVL6QESERFR1VbuZMPPzw979+7F/PnzsWvXLujq6qJ58+Y4duwYv2KeiIioFGo+ilL+ZAMAfH194evrCwDIyMjA999/j8mTJ+P8+fPl+m4UIiIidcA5G+8oJiYGAQEBsLW1xdKlS9GlSxecPn1ambERERFRNVCuykZiYiLCw8OxefNmZGRkoH///pDJZNi7dy8nhxIREb2Gmhc2yl7Z8PPzQ8OGDXHp0iUsX74cjx8/xqpVq8SMjYiIqFrQkCjnUVWVubJx8OBBjB8/HmPGjEH9+vXfvgMRERERylHZOHnyJJ4/fw5XV1e4ublh9erVSE1NFTM2IiKiakFDIlHKo6oqc7LRpk0bbNy4EQkJCfjss8+wc+dO2NraoqioCFFRUXj+/LmYcRIREVVZqrhdeVhYGFq3bg1DQ0NYWlrC398fN2/eVOiTm5uLwMBAmJmZwcDAAH369EFSUpJCn/j4ePj6+kJPTw+WlpaYMmUKCgoKyhVLuVej6Ovr45NPPsHJkydx+fJlTJo0CQsWLIClpSV69uxZ3sMRERGRCKKjoxEYGIjTp08jKioK+fn58PT0RFZWlrzPxIkTsX//fvz000+Ijo7G48eP0bt3b/n2wsJC+Pr6Ii8vD6dOncK2bdsQHh6OmTNnlisWiSAIwn+9oMLCQuzfvx9btmzBvn37/uvh/rPc8iVcpAYCdlxQdQhUiYT5NFZ1CFSJ1LHQEf0cXx+9o5TjfNW13jvvm5KSAktLS0RHR6NDhw5IT0+HhYUFIiIi0LdvXwDAjRs30LhxY8TGxqJNmzY4ePAgevTogcePH8PKygoAsH79ekydOhUpKSmlfldaaZRyq3VNTU34+/tXikSDiIiospEo6Y9MJkNGRobCQyaTlSmG9PR0AJDf7fv8+fPIz8+Hh4eHvE+jRo3g4OCA2NhYAEBsbCxcXFzkiQYAeHl5ISMjA1evXi3z9Vfl73UhIiKqEpS19DUsLAzGxsYKj7CwsLeev6ioCBMmTEDbtm3RtGlTAC/unaWtrQ0TExOFvlZWVkhMTJT3eTnRKN5evK2s3ul25URERFTxQkJCEBwcrNAmlUrful9gYCCuXLmCkydPihXaGzHZICIiEpmybsgllUrLlFy8LCgoCJGRkYiJiYGdnZ283draGnl5eXj27JlCdSMpKQnW1tbyPmfPnlU4XvFqleI+ZcFhFCIiIpFJJBKlPMpDEAQEBQVhz549OHbsGJycnBS2u7q6QktLC0ePHpW33bx5E/Hx8XB3dwcAuLu74/Lly0hOTpb3iYqKgpGRUbm+poSVDSIiomooMDAQERER+OWXX2BoaCifY2FsbAxdXV0YGxtjxIgRCA4OhqmpKYyMjDBu3Di4u7ujTZs2AABPT084OztjyJAhWLRoERITEzF9+nQEBgaWq8LCZIOIiEhkqvhek3Xr1gEAOnXqpNC+detWDBs2DACwbNkyaGhooE+fPpDJZPDy8sLatWvlfTU1NREZGYkxY8bA3d0d+vr6CAgIwJw5c8oVi1Lus1HZ8D4b9CreZ4Nexvts0Msq4j4b38TcU8pxgjvUUcpxKhrnbBAREZGoOIxCREQksqr8JWrKwGSDiIhIZKqYs1GZcBiFiIiIRMXKBhERkcjUfBSFyQYREZHYNKDe2QaTDSIiIpGpe2WDczaIiIhIVKxsEBERiUzdV6Mw2SAiIhKZut9ng8MoREREJCpWNoiIiESm5oUNJhtERERi4zAKERERkYhY2SAiIhKZmhc2mGwQERGJTd2HEdT9+omIiEhkrGwQERGJTKLm4yhMNoiIiESm3qkGkw0iIiLRcekrERERkYhY2SAiIhKZetc1mGwQERGJTs1HUTiMQkREROJiZYOIiEhkXPpKREREolL3YQR1v34iIiISGSsbREREIuMwChEREYlKvVMNDqMQERGRyFjZICIiEhmHUaqhzNwCVYdAlczq3i6qDoEqEYfeS1UdAlUiOVFTRT+Hug8jVMtkg4iIqDJR98qGuidbREREJDJWNoiIiESm3nUNJhtERESiU/NRFA6jEBERkbhY2SAiIhKZhpoPpDDZICIiEhmHUYiIiIhExMoGERGRyCQcRiEiIiIxcRiFiIiISESsbBAREYmMq1GIiIhIVOo+jMJkg4iISGTqnmxwzgYREVE1FRMTAz8/P9ja2kIikWDv3r0K24cNGwaJRKLw6N69u0KfJ0+eYPDgwTAyMoKJiQlGjBiBzMzMcsXBZIOIiEhkEiX9Ka+srCw0b94ca9aseW2f7t27IyEhQf74/vvvFbYPHjwYV69eRVRUFCIjIxETE4NRo0aVKw4OoxAREYlMQ0XDKN7e3vD29n5jH6lUCmtr61K3Xb9+HYcOHcK5c+fQqlUrAMCqVavg4+ODJUuWwNbWtkxxsLJBRERURchkMmRkZCg8ZDLZfzrmiRMnYGlpiYYNG2LMmDFIS0uTb4uNjYWJiYk80QAADw8PaGho4MyZM2U+B5MNIiIikSlrGCUsLAzGxsYKj7CwsHeOq3v37vjf//6Ho0ePYuHChYiOjoa3tzcKCwsBAImJibC0tFTYp0aNGjA1NUViYmKZz8NhFCIiIpEpazVKSEgIgoODFdqkUuk7H2/AgAHyv7u4uKBZs2aoW7cuTpw4ga5du77zcV/FygYREVEVIZVKYWRkpPD4L8nGq+rUqQNzc3PcuXMHAGBtbY3k5GSFPgUFBXjy5Mlr53mUhskGERGRyFS1GqW8/vnnH6SlpcHGxgYA4O7ujmfPnuH8+fPyPseOHUNRURHc3NzKfFwOoxAREYlMVatRMjMz5VUKALh//z7i4uJgamoKU1NTzJ49G3369IG1tTXu3r2LL774AvXq1YOXlxcAoHHjxujevTtGjhyJ9evXIz8/H0FBQRgwYECZV6IArGwQERFVW3/++SdatmyJli1bAgCCg4PRsmVLzJw5E5qamrh06RJ69uyJBg0aYMSIEXB1dcXvv/+uMDSzY8cONGrUCF27doWPjw/atWuHDRs2lCsOiSAIglKvrBJIzSxQdQhUyVS/dzn9Fw69l6o6BKpEcqKmin6O3289Vcpx2jeoqZTjVDQOoxAREYlM3b8bhckGERGRyNQ81+CcDSIiIhIXKxtEREQi01DzcRQmG0RERCJT71SDwyhEREQkMlY2iIiIxKbmpQ0mG0RERCKriFuNV2YcRiEiIiJRsbJBREQkMjVfjMJkg4iISGxqnmtwGIWIiIjExcoGERGR2NS8tMFkg4iISGTqvhqFyQYREZHI1H2CKOdsEBERkahY2SAiIhKZmhc2mGwQERGJTs2zDQ6jEBERkahY2SAiIhIZV6MQERGRqLgahYiIiEhErGwQERGJTM0LG0w2iIiIRKfm2QaHUYiIiEhUrGwQERGJjKtRiIiISFTqvhqFyQYREZHI1DzX4JwNIiIiEhcrG1Vcnx7dkJjwuER7734DMGnaDPzzMB5rli/Bpbi/kJefhzbu7TDxiy9hamaugmhJbIWFhdiyYQ1+OxiJtLRUmJtbwsevFwJGjIbk/+u40ceisHf3j7h54yoy0tOxdccu1G/YWMWRkzKM7NECI/1awtHKGABw/e9UzP/uFH47dw8AcHjJQHRo7qCwz8bICxi/4jf5804tHTEroD2aOJkjKzcfO6KuYNaWGBQWCRV3IdWRmpc2mGxUcZu2/4CiwkL583t372DC2E/R2cMLOTnZmBg4CvUaNMTK9VsAABvXrcIXEwOxIfx7aGiwsFXd7Ni2GXt3/YCvZs+HU516uHHtCubPmQ59A0P0G/AxACAnJwfNWrREl25eWDhvloojJmV6lPocMzZH486jp5AA+NizKX6a3RttxoTj+t+pAIDNB+Iwd9tJ+T7Zsnz5313qWGDvvL5Y+H0sRiyKhK25IVZ97gVNDQ2EbDhe0ZdTrXCCKFVpNWuaKjzfHr4Jtezs0dK1Nc6ePoXEhEcIj9gFfQMDAMD02fPRvbM7zp87g9Zu7qoImUR05VIc2nXsgg/adQQA2NjWwpHDv+L61cvyPt19ewIAEh4/UkmMJJ5fT99VeB669XeM7NES7ze2lScbObICJD3NKnX/vp0a48r9FIR9dwoAcO/xM3y18Ti+m94LX2//A5k5eeJeAFVb/GhbjeTn5+G3XyPh26s3JBIJ8vPzIJFIoKWtLe+jLZVCQ0MDl+L+UmGkJJamzVrg/LnTiP/7AQDg9q0buHTxAtp80F61gVGF09CQoF+nxtDX0cKZa/8mlh91ccbDXePw54ZPMOeTDtCV/vuZU6qlidy8AoXj5MgKoCvVQsv6VhUWe3UkkSjnUVWxslGNxBw/hszM5/Dx8wcANHFpDh0dXaxduRSjAydAgIB1q5ahsLAQaakpqg2WRPHxsE+RlZWJwX17QENDE0VFhRg19nN4evdQdWhUQZrUNseJlUOgo10DmTl5+Gj2HtyITwMA/HDsGuKTM5CQ+hwudSwx79NOaGBvigGz9wIAov68j6APW6F/58bYFX0D1jX18eXHbQEANmYGqrqkaqEK5wlKofJkIycnB+fPn4epqSmcnZ0VtuXm5uLHH3/E0KFDX7u/TCaDTCZTbMvXhFQqFSXeyizyl91o80E7WFhYAngxxDJ34TdYEjYXu3bugIaGBjy8fNCwkTMkEha1qqNjUYcQdegAZs1bBKe69XD75g2s/GYBzC0s4N3DX9XhUQW49c8TuI3eCmN9KT5s3xAbp/jCc1IEbsSnYcuvF+X9rj5IRcKTTBxaPBBONia4n/AMR88/wJcbT2Dl517YPLUHZHkFWLDjFNo1s0cRJ4jSf6DS/3Fu3bqFxo0bo0OHDnBxcUHHjh2RkJAg356eno7hw4e/8RhhYWEwNjZWeKxYulDs0CudxITH+PPsafj591Vod3Nvi5/2HUJk1O84cPQkZs5dgJSUJNja2akoUhLT2pVLMThgBDy8fFC3XgN09+2J/gOHYvvWTaoOjSpIfkER7j1+hgu3kzBzSwwu30tG4IetSu177saLf2/r1qopb1u5+xys/ZejweB1sOu7Cvtj7wAA7ic8Ez32ak2ipEcVpdJkY+rUqWjatCmSk5Nx8+ZNGBoaom3btoiPjy/zMUJCQpCenq7w+HzSVBGjrpwO7NuDmjVN4d6uQ6nbTWrWhKGhEc6fPY2nT56gXYfOFRwhVYTc3JwSq4w0NTVRJBSpKCJSNQ2JBFJtzVK3Na/7ogqamJZZYltCWiZy8wrQv3NjPEzOwIU7SaLGWd1JlPSnqlLpMMqpU6dw5MgRmJubw9zcHPv378fYsWPRvn17HD9+HPr6+m89hlQqLTFkkpdZ8Jre1VNRUREO7NsD7x69UKOG4o/0wL49cHSqAxOTmrh6+SKWLwnDR4OGwrG2k4qiJTG1bd8J/9uyAVbWNnCqUw+3bl7HDzu2wafnh/I+GenPkJSYgNSUF/N2iieTmpqZw8zcQhVhk5LM+aQDDp+7h4fJGTDU1cZHXZzRobkD/EJ+hJONCT7q4ozDZ+8iLSMHLnUssWh0F/x+KR5X7v87h2tiv/fx27l7KBKAXu0aYPJHbfDxvF84jEL/iUqTjZycHIX/HCUSCdatW4egoCB07NgRERERKoyu6jh3JhZJiQnw7dW7xLb4B/exfvUyZKSnw8a2FgI+GYWPBgeoIEqqCBOnfIWN61di6YK5ePr0CczNLdGzdz8MHzlG3udkzHHMnz1d/nzWl5MBAMNHjsWIzwIrPGZSHgsTfWz+ogesTfWRniXDlfsp8Av5Ecf+egA7C0N0ec8RQb1bQV9HC/+kZGDv77ewIOKUwjE8W9fBF4PcIdXSxOV7Keg362f5TcHo3VXllSTKIBEEQWXp6vvvv49x48ZhyJAhJbYFBQVhx44dyMjIQOFLN60qi1Q1q2zQ26nuXU6VkUPvpaoOgSqRnCjxh95vJWYr5TgNrPWUcpyKptI5Gx9++CG+//77UretXr0aAwcOhApzISIiIuVQ8wmiKq1siIWVDXpV9XuX03/Byga9rEIqG0lKqmxYVc3Khsrvs0FERFTdVeWVJMrAZIOIiEhk6j5BlLeRJCIiIlGxskFERCQyNS9ssLJBREQkOhWtRomJiYGfnx9sbW0hkUiwd+9ehe2CIGDmzJmwsbGBrq4uPDw8cPv2bYU+T548weDBg2FkZAQTExOMGDECmZkl7zr7Jkw2iIiIqqmsrCw0b94ca9asKXX7okWLsHLlSqxfvx5nzpyBvr4+vLy8kJubK+8zePBgXL16FVFRUYiMjERMTAxGjRpVrji49JXUQvV7l9N/waWv9LKKWPp6LyX37Z3KoI6FzjvvK5FIsGfPHvj7+wN4UdWwtbXFpEmTMHnyizsJp6enw8rKCuHh4RgwYACuX78OZ2dnnDt3Dq1avfhCv0OHDsHHxwf//PMPbG1ty3RuVjaIiIhEJpEo5yGTyZCRkaHwkMlk7xTT/fv3kZiYCA8PD3mbsbEx3NzcEBsbCwCIjY2FiYmJPNEAAA8PD2hoaODMmTNlPheTDSIioioiLCwMxsbGCo+wsLB3OlZiYiIAwMrKSqHdyspKvi0xMRGWlpYK22vUqAFTU1N5n7LgahQiIiKRKWs1SkhICIKDgxXaXv3m88qIyQYREZHYlJRtSKVSpSUX1tbWAICkpCTY2NjI25OSktCiRQt5n+TkZIX9CgoK8OTJE/n+ZcFhFCIiIpFJlPRHmZycnGBtbY2jR4/K2zIyMnDmzBm4u7sDANzd3fHs2TOcP39e3ufYsWMoKiqCm5tbmc/FygYREVE1lZmZiTt37sif379/H3FxcTA1NYWDgwMmTJiAefPmoX79+nBycsKMGTNga2srX7HSuHFjdO/eHSNHjsT69euRn5+PoKAgDBgwoMwrUQAmG0RERKJT1Xej/Pnnn+jcubP8efF8j4CAAISHh+OLL75AVlYWRo0ahWfPnqFdu3Y4dOgQdHT+XWK7Y8cOBAUFoWvXrtDQ0ECfPn2wcuXKcsXB+2yQWqh+73L6L3ifDXpZRdxn4+GTd1ue+ip708o/GbQ0nLNBREREouIwChERkcjU/SvmmWwQERGJTr2zDQ6jEBERkahY2SAiIhIZh1GIiIhIVGqea3AYhYiIiMTFygYREZHIOIxCREREolL295pUNUw2iIiIxKbeuQbnbBAREZG4WNkgIiISmZoXNphsEBERiU3dJ4hyGIWIiIhExcoGERGRyLgahYiIiMSl3rkGh1GIiIhIXKxsEBERiUzNCxtMNoiIiMTG1ShEREREImJlg4iISGRcjUJERESi4jAKERERkYiYbBAREZGoOIxCREQkMnUfRmGyQUREJDJ1nyDKYRQiIiISFSsbREREIuMwChEREYlKzXMNDqMQERGRuFjZICIiEpualzaYbBAREYmMq1GIiIiIRMTKBhERkci4GoWIiIhEpea5BpMNIiIi0al5tsE5G0RERCQqVjaIiIhEpu6rUZhsEBERiUzdJ4hyGIWIiIhEJREEQVB1EKR8MpkMYWFhCAkJgVQqVXU4VAnwPUEv4/uBKhKTjWoqIyMDxsbGSE9Ph5GRkarDoUqA7wl6Gd8PVJE4jEJERESiYrJBREREomKyQURERKJislFNSaVSzJo1ixO/SI7vCXoZ3w9UkThBlIiIiETFygYRERGJiskGERERiYrJBhEREYmKyQYRERGJislGNbVmzRrUrl0bOjo6cHNzw9mzZ1UdEqlITEwM/Pz8YGtrC4lEgr1796o6JFKhsLAwtG7dGoaGhrC0tIS/vz9u3ryp6rCommOyUQ398MMPCA4OxqxZs/DXX3+hefPm8PLyQnJysqpDIxXIyspC8+bNsWbNGlWHQpVAdHQ0AgMDcfr0aURFRSE/Px+enp7IyspSdWhUjXHpazXk5uaG1q1bY/Xq1QCAoqIi2NvbY9y4cZg2bZqKoyNVkkgk2LNnD/z9/VUdClUSKSkpsLS0RHR0NDp06KDqcKiaYmWjmsnLy8P58+fh4eEhb9PQ0ICHhwdiY2NVGBkRVUbp6ekAAFNTUxVHQtUZk41qJjU1FYWFhbCyslJot7KyQmJiooqiIqLKqKioCBMmTEDbtm3RtGlTVYdD1VgNVQdARESqERgYiCtXruDkyZOqDoWqOSYb1Yy5uTk0NTWRlJSk0J6UlARra2sVRUVElU1QUBAiIyMRExMDOzs7VYdD1RyHUaoZbW1tuLq64ujRo/K2oqIiHD16FO7u7iqMjIgqA0EQEBQUhD179uDYsWNwcnJSdUikBljZqIaCg4MREBCAVq1a4f3338fy5cuRlZWF4cOHqzo0UoHMzEzcuXNH/vz+/fuIi4uDqakpHBwcVBgZqUJgYCAiIiLwyy+/wNDQUD6Xy9jYGLq6uiqOjqorLn2tplavXo3FixcjMTERLVq0wMqVK+Hm5qbqsEgFTpw4gc6dO5doDwgIQHh4eMUHRColkUhKbd+6dSuGDRtWscGQ2mCyQURERKLinA0iIiISFZMNIiIiEhWTDSIiIhIVkw0iIiISFZMNIiIiEhWTDSIiIhIVkw0iIiISFZMNompo2LBh8Pf3lz/v1KkTJkyYUOFxnDhxAhKJBM+ePavwcxNR5cFkg6gCDRs2DBKJBBKJBNra2qhXrx7mzJmDgoICUc/7888/Y+7cuWXqywSBiJSN341CVMG6d++OrVu3QiaT4ddff0VgYCC0tLQQEhKi0C8vLw/a2tpKOaepqalSjkNE9C5Y2SCqYFKpFNbW1nB0dMSYMWPg4eGBffv2yYc+vv76a9ja2qJhw4YAgIcPH6J///4wMTGBqakpevXqhQcPHsiPV1hYiODgYJiYmMDMzAxffPEFXv0WgleHUWQyGaZOnQp7e3tIpVLUq1cPmzdvxoMHD+Tfo1KzZk1IJBL592UUFRUhLCwMTk5O0NXVRfPmzbFr1y6F8/z6669o0KABdHV10blzZ4U4iUh9MdkgUjFdXV3k5eUBAI4ePYqbN28iKioKkZGRyM/Ph5eXFwwNDfH777/jjz/+gIGBAbp37y7fZ+nSpQgPD8eWLVtw8uRJPHnyBHv27HnjOYcOHYrvv/8eK1euxPXr1/Htt9/CwMAA9vb22L17NwDg5s2bSEhIwIoVKwAAYWFh+N///of169fj6tWrmDhxIj7++GNER0cDeJEU9e7dG35+foiLi8Onn36KadOmifWyEVFVIhBRhQkICBB69eolCIIgFBUVCVFRUYJUKhUmT54sBAQECFZWVoJMJpP33759u9CwYUOhqKhI3iaTyQRdXV3h8OHDgiAIgo2NjbBo0SL59vz8fMHOzk5+HkEQhI4dOwqff/65IAiCcPPmTQGAEBUVVWqMx48fFwAIT58+lbfl5uYKenp6wqlTpxT6jhgxQhg4cKAgCIIQEhIiODs7K2yfOnVqiWMRkfrhnA2iChYZGQkDAwPk5+ejqKgIgwYNQmhoKAIDA+Hi4qIwT+PixYu4c+cODA0NFY6Rm5uLu3fvIj09HQkJCXBzc5Nvq1GjBlq1alViKKVYXFwcNDU10bFjxzLHfOfOHWRnZ6Nbt24K7Xl5eWjZsiUA4Pr16wpxAIC7u3uZz0FE1ReTDaIK1rlzZ6xbtw7a2tqwtbVFjRr//hrq6+sr9M3MzISrqyt27NhR4jgWFhbvdH5dXd1y75OZmQkAOHDgAGrVqqWwTSqVvlMcRKQ+mGwQVTB9fX3Uq1evTH3fe+89/PDDD7C0tISRkVGpfWxsbHDmzBl06NABAFBQUIDz58/jvffeK7W/i4sLioqKEB0dDQ8PjxLbiysrhYWF8jZnZ2dIpVLEx8e/tiLSuHFj7Nu3T6Ht9OnTb79IIqr2OEGUqBIbPHgwzM3N0atXL/z++++4f/8+Tpw4gfHjx+Off/4BAHz++edYsGAB9u7dixs3bmDs2LFvvEdG7dq1ERAQgE8++QR79+6VH/PHH38EADg6OkIikSAyMhIpKSnIzMyEoaEhJk+ejIkTJ2Lbtm24e/cu/vrrL6xatQrbtm0DAIwePRq3b9/GlClTcPPmTURERCA8PFzsl4iIqgAmG0SVmJ6eHmJiYuDg4IDevXujcePGGDFiBHJzc+WVjkmTJmHIkCEICAiAu7s7DA0N8eGHH77xuOvWrUPfvn0xduxYNGrUCCNHjkRWVhYAoFatWpg9ezamTZsGKysrBAUFAQDmzp2LGTNmICwsDI0bN0b37t1x4MABODk5AQAcHBywe/du7N27F82bN8f69esxf/58EV8dIqoqJMLrZpERERERKQErG0RERCQqJhtEREQkKiYbREREJComG0RERCQqJhtEREQkKiYbREREJComG0RERCQqJhtEREQkKiYbREREJComG0RERCQqJhtEREQkKiYbREREJKr/A93hfYh2bAv6AAAAAElFTkSuQmCC"
     },
     "metadata": {
      "image/png": {
       "width": 539,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/4e29ad80-0dca-4cca-971b-8f0dc2bce2ad",
   "content_dependencies": null
  },
  {
   "cellId": "781211ef9edc402c8e7f8b1867cf1d39",
   "cell_type": "code",
   "metadata": {
    "source_hash": "1e4d4dfc",
    "execution_start": 1754182990075,
    "execution_millis": 129,
    "execution_context_id": "1c21859c-8cfb-47f6-bb66-049f34d3a756",
    "cell_id": "781211ef9edc402c8e7f8b1867cf1d39",
    "deepnote_cell_type": "code"
   },
   "source": "from sklearn.metrics import roc_auc_score\nfrom sklearn.preprocessing import label_binarize\n\n# Predict class probabilities (softmax output)\ny_val_probs = model.predict(X_val_scaled)\n\n# Binarize the true labels (in case it's not already)\ny_val_bin = label_binarize(y_val, classes=[0, 1, 2])  # shape: (n_samples, 3)\n\n# Compute ROC AUC (macro, weighted, per-class)\nauc_macro = roc_auc_score(y_val_bin, y_val_probs, average='macro', multi_class='ovr')\nauc_weighted = roc_auc_score(y_val_bin, y_val_probs, average='weighted', multi_class='ovr')\nauc_per_class = [roc_auc_score(y_val_bin[:, i], y_val_probs[:, i]) for i in range(3)]\n\nprint(f\"ROC AUC (macro): {auc_macro:.4f}\")\nprint(f\"ROC AUC (weighted): {auc_weighted:.4f}\")\nfor i, auc in enumerate(auc_per_class):\n    print(f\"Class {i} AUC: {auc:.4f}\")",
   "block_group": "9e80669e1efc42e683b8517b04548aa6",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stdout",
     "text": "46/46 [==============================] - 0s 2ms/step\nROC AUC (macro): 0.8478\nROC AUC (weighted): 0.8486\nClass 0 AUC: 0.8559\nClass 1 AUC: 0.8405\nClass 2 AUC: 0.8470\n",
     "output_type": "stream"
    }
   ],
   "outputs_reference": "dbtable:cell_outputs/f9c08941-9151-4209-be45-a52ca6348a81",
   "content_dependencies": null
  },
  {
   "cellId": "f8ca0ec82ea843b984e935469190af4f",
   "cell_type": "markdown",
   "metadata": {
    "formattedRanges": [],
    "cell_id": "f8ca0ec82ea843b984e935469190af4f",
    "deepnote_cell_type": "text-cell-h3"
   },
   "source": "### Categorical using motor - 3-way since its range is small",
   "block_group": "7eb173fe03a74f4d902ae2846c2c6c74"
  },
  {
   "cellId": "695f13b1aad94f6f91fb2ca021236abd",
   "cell_type": "code",
   "metadata": {
    "source_hash": "93644879",
    "execution_start": 1753997479820,
    "execution_millis": 0,
    "execution_context_id": "1b751dc0-b309-45dc-9efd-30a3c874b135",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "695f13b1aad94f6f91fb2ca021236abd",
    "deepnote_cell_type": "code"
   },
   "source": "#y_target = y[\"total_UPDRS\"]\ny_target = y[\"motor_UPDRS\"]\n",
   "block_group": "fe3e9ad0b5fa4f8681d5b2451c601b7c",
   "execution_count": 10,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  },
  {
   "cellId": "c73aeccd31ad4f07a9206bbb7829f46a",
   "cell_type": "code",
   "metadata": {
    "source_hash": "973f6dfc",
    "execution_start": 1753997485268,
    "execution_millis": 537,
    "execution_context_id": "1b751dc0-b309-45dc-9efd-30a3c874b135",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "c73aeccd31ad4f07a9206bbb7829f46a",
    "deepnote_cell_type": "code"
   },
   "source": "bins = [0, 15, 25, float('inf')]\nlabels = [0, 1, 2]  \ny_classify = pd.cut(y_target, bins=bins, labels=labels).astype(int)\n\n### this is just a repeat of making traing/val of X and y\nX_train, X_temp, y_train, y_temp = train_test_split(X_filtered, y_classify, test_size=0.5, stratify=y_classify, random_state=42)\nX_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_val_scaled = scaler.transform(X_val)\n#making categorical y\ny_train_cat = to_categorical(y_train, num_classes=3)\ny_val_cat = to_categorical(y_val, num_classes=3)\n###\nsns.countplot(x=y_classify)\nplt.title(\"Class Distribution After Binning\")\nplt.xlabel(\"Severity Stage\")\nplt.ylabel(\"Count\")\nplt.show()",
   "block_group": "4427cc2135204d4ebeca073966c9e9e2",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 580,
       "height": 455
      }
     },
     "output_type": "display_data"
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/9e9ca250-e6db-47b4-9947-5a9e0066d4b6",
   "content_dependencies": null
  },
  {
   "cellId": "2bff7b6c24bc4400b00c4d5409d7d1f5",
   "cell_type": "code",
   "metadata": {
    "source_hash": "5296f32f",
    "execution_start": 1753997502430,
    "execution_millis": 1592,
    "execution_context_id": "1b751dc0-b309-45dc-9efd-30a3c874b135",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "2bff7b6c24bc4400b00c4d5409d7d1f5",
    "deepnote_cell_type": "code"
   },
   "source": "from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LeakyReLU\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\nfrom tensorflow.keras.optimizers import Adam\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# Build model\nmodel = Sequential([\n    Dense(256, input_shape=(X_train_scaled.shape[1],)),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.4),\n\n    Dense(128),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.3),\n\n    Dense(64),\n    LeakyReLU(alpha=0.01),\n    BatchNormalization(),\n    Dropout(0.2),\n\n    Dense(32),\n    LeakyReLU(alpha=0.01),\n    \n    Dense(4, activation='softmax')  \n])\n\n# Compile model\nmodel.compile(\n    optimizer=Adam(learning_rate=0.001),\n    loss='categorical_crossentropy',\n    metrics=['accuracy']\n)\n\n# Callbacks\nearly_stop = EarlyStopping(monitor='val_loss', patience=30, restore_best_weights=True, verbose=1)\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=20, verbose=1)\n\n# Train\nhistory = model.fit(\n    X_train_scaled, y_train_cat,\n    validation_data=(X_val_scaled, y_val_cat),\n    epochs=200,\n    batch_size=32,\n    callbacks=[early_stop, reduce_lr],\n    verbose=1\n)\n\ny_val_pred = model.predict(X_val_scaled)\ny_val_pred_classes = tf.argmax(y_val_pred, axis=1)\n\nprint(\"Keras MLP Classification Report:\")\nprint(classification_report(y_val, y_val_pred_classes))\n",
   "block_group": "5c786e31fc95417e84c7aaaacd18a4fc",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "text": "Epoch 1/200\n",
     "output_type": "stream"
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n        return self.compiled_loss(\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 143, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 270, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 2221, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/backend.py\", line 5573, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 3) and (None, 4) are incompatible\n",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 42\u001b[0m\n\u001b[1;32m     39\u001b[0m reduce_lr \u001b[38;5;241m=\u001b[39m ReduceLROnPlateau(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, factor\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Train\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     43\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_train_scaled\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train_cat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     44\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_val_scaled\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val_cat\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     47\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mearly_stop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_lr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     48\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m     49\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m y_val_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_val_scaled)\n\u001b[1;32m     52\u001b[0m y_val_pred_classes \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39margmax(y_val_pred, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/venv/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/tmp/__autograph_generated_filedwsamhlh.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n        return self.compiled_loss(\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 143, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 270, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/losses.py\", line 2221, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"/root/venv/lib/python3.10/site-packages/keras/src/backend.py\", line 5573, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 3) and (None, 4) are incompatible\n"
     ]
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/534fcddb-0837-4918-8fb6-88eed1a87c20",
   "content_dependencies": null
  },
  {
   "cellId": "37844e438c924f82871bc6c3a77db901",
   "cell_type": "code",
   "metadata": {
    "source_hash": "cc06a249",
    "execution_start": 1753997321749,
    "execution_millis": 544,
    "execution_context_id": "1b751dc0-b309-45dc-9efd-30a3c874b135",
    "deepnote_to_be_reexecuted": true,
    "cell_id": "37844e438c924f82871bc6c3a77db901",
    "deepnote_cell_type": "code"
   },
   "source": "# Save the model as HDF5 (.h5)\nmodel.save('mlp_model.h5')\n\n# For download link (works in Deepnote and Jupyter)\nfrom IPython.display import FileLink\n\n# Create clickable download link\nFileLink('mlp_model.h5')\n",
   "block_group": "b4b6e0086eac448d825f26e0490cd4ad",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Save the model as HDF5 (.h5)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmlp_model.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# For download link (works in Deepnote and Jupyter)\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FileLink\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "outputs_reference": "s3:deepnote-cell-outputs-production/2ad1279c-2be7-486f-b76b-559a76c1d6f3",
   "content_dependencies": null
  },
  {
   "cellId": "60ccb46deb1b4e55b34af6bab06de8f4",
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": true,
    "cell_id": "60ccb46deb1b4e55b34af6bab06de8f4",
    "deepnote_cell_type": "code"
   },
   "source": "",
   "block_group": "4c8058bdd642417ea85d58e1831cc27d",
   "execution_count": null,
   "outputs": [],
   "outputs_reference": null,
   "content_dependencies": null
  }
 ],
 "metadata": {
  "deepnote_notebook_id": "a4e6c5d693964007b3b2b85943462fbc"
 },
 "nbformat": 4,
 "nbformat_minor": 5,
 "version": "0"
}