Automotive_Prediction_V3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNIKOubLy+DGjscb1mG0Fzr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satyashah/Automotive-Prediction-Models/blob/main/Automotive_Prediction_V3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDpGIZZ2azlA"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#import btalib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "np.set_printoptions(precision=3, suppress=True)\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "# from tensorflow.keras.layers import Dense, Dropout, LSTM\n",
        "# from tensorflow.keras.models import Sequential\n",
        "from sklearn import tree\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6I7-JG2SYYMx",
        "outputId": "eacdd454-b0e4-4fe5-8330-fd207dc25af2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78CooM4cbpqw"
      },
      "source": [
        "# Data Collection & Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvq390TzdW0k"
      },
      "source": [
        "#Retreiving the data from my google drive\n",
        "copied_path = 'gdrive/MyDrive/carData.csv' #remove ‘content/’ from path then use \n",
        "data = pd.read_csv(copied_path, names=[\"stockNumber\",\"miles\",\"displayPrice\",\"year\",\"make\",\"model\",\"transmission\",\"drive\",\"exteriorColor\",\"interiorColor\",\"mpgCity\",\"mpgHighway\",\"engineSize\",\"engineCylinders\",\"storeName\",\"storeID\", \"features\"], header=0)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1cxoYX2ddpy"
      },
      "source": [
        "#deletes all incorrect or empty data\n",
        "data = data[data != \"None\"]\n",
        "data = data.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rawTrain = data.sample(frac=0.8, random_state=10)\n",
        "rawTest = data.drop(rawTrain.index)\n",
        "\n",
        "rawTrain_features = rawTrain.copy()\n",
        "rawTest_features = rawTest.copy()\n",
        "\n",
        "rawTrain_labels = rawTrain_features.pop('displayPrice')\n",
        "rawTest_labels =rawTest_features.pop('displayPrice')\n",
        "\n",
        "rawTrainFeatures = np.array(rawTrain_features)\n",
        "rawTestFeatures = np.array(rawTrain_features)\n",
        "\n",
        "rawTrainLabels = np.array(rawTrain_labels)\n",
        "rawTestLabels = np.array(rawTrain_labels)"
      ],
      "metadata": {
        "id": "4xuFySZPzgpr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1V7V0K3oAkc"
      },
      "source": [
        "#Turn things into arrays of [1,0,1,0]\n",
        "\n",
        "def encode(dataList, indexList):\n",
        "  encodedList = []\n",
        "  for data in dataList:\n",
        "    carIndex = indexList.index(data)\n",
        "    carArr = [0]*len(indexList)\n",
        "    carArr[carIndex] = 1\n",
        "    encodedList.append(carArr)\n",
        "  return encodedList\n",
        "\n",
        "def encodeSingle(value, indexList):\n",
        "  index = indexList.index(value)\n",
        "  carArr = [0]*len(indexList)\n",
        "  carArr[index] = 1\n",
        "  return carArr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTzZhSz-l4Gy",
        "outputId": "6c4f7f50-89a1-4c20-b6d4-1281c15e9c0d"
      },
      "source": [
        "components = [\"year\",\"make\",\"model\",\"transmission\",\"drive\",\"exteriorColor\",\"interiorColor\",\"storeName\",\"storeID\"]\n",
        "for component in components:\n",
        "  dataList = list(data[component].values)\n",
        "  \n",
        "  indexList = list(set(data[component]))\n",
        "  encodedList = encode(dataList, indexList)\n",
        "  data[component] = encodedList\n",
        "\n",
        "  print(\"\\\"\"+str(component)+\"\\\" :\", [str(index).lower() for index in indexList], \",\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"year\" : ['2016', '2017', '2018', '2019', '2020', '2021', '2010', '2011', '2012', '2013', '2014', '2015'] ,\n",
            "\"make\" : ['volkswagen', 'buick', 'ram', 'mazda', 'jeep', 'toyota', 'dodge', 'fiat', 'honda', 'smart', 'genesis', 'land rover', 'acura', 'chrysler', 'ford', 'lexus', 'nissan', 'hyundai', 'mercedes-benz', 'gmc', 'lincoln', 'scion', 'chevrolet', 'mini', 'bmw', 'infiniti', 'porsche', 'audi', 'subaru', 'jaguar', 'kia', 'cadillac', 'volvo', 'mitsubishi'] ,\n",
            "\"model\" : ['envision', 'a3', 'tribeca', 'quest', 'optima', 'ml400', 'lancer', 'beetle', 'mazda2', 'forte5', 'explorer', 'tundra', 'renegade', 'gti', 'ux 250h', 'c43 amg', 'optima hybrid', 'e350', '528', 'es 350', '300', 'golf alltrack', 'boxster', 'cayman', 'venue', 'grand caravan', 'c450 amg', 'xj', 'expedition el', '228', 'rc 300', '230', 'regal sportback', 'rdx', 'tiguan', 'im', '335', 'ss', 'f150', 'xc90', 'versa', 'g37', 'fj cruiser', '840', 'outback', 'lacrosse', 'qx60', 'm37', '320', 'gle53 amg', '370z', 'accord', 'mkz hybrid', 'e250', 'cooper countryman', 'm4', 'mkt', 'cls450', 'mustang', 'crosstrek', 'sequoia', 'camaro', 'e300', 'titan', 'supra', 'glc300', 'lucerne', 'trax', 'ls 460', 'iq', 'gls550', 'x3', 'santa fe', 'prius plug in hybrid', 'lc 500', 'e43 amg', 'sierra 1500', 'odyssey', '4runner', 'pacifica', 'accord crosstour', 'rx 350', 'navigator', 'rs3', 'c250', 'atlas', 'nx 300h', 'macan', 'xterra', 'range rover sport', 'gl450', 'rav4 hybrid', 'allroad', 'sienna', 's4', 'c63 amg', 'nautilus', 'malibu limited', 'challenger', 'transit connect', 'es 300h', 'a4', 'forte', 'escalade', 'sentra', 'cherokee', 'impala limited', 'tahoe', 'ilx', 'a8', '430', 'e-pace', 'xt6', 'fusion energi', 'trailblazer', '1500', 'cooper hardtop', 'discovery sport', 'cx-3', 'avalon', 'x4', 'm2', 'regal', 'outlander', 'xt4', 'patriot', 'cruze limited', 'santa fe xl', 'liberty', 'q50', '530', 'tacoma', 'spark', 'wrx', 'equinox', 'xv crosstrek', '1500 classic', 'camry hybrid', '500', 'mx-5 miata', 'eclipse', 'passat', 'legacy', 'touareg', 'avenger', 'cooper clubman', 'gl550', 'm3', 'mkx', 'colorado', 'telluride', 'mkc', 'tsx', 'x7', 'outlander sport', 'rs5', 'focus', 'x2', 'g90', 'compass', 'ls 500', 'a220', 'gle43 amg', '124 spider', 'rogue sport', 'v60', 'venza', 'sorento', 'explorer sport trac', 'cts', 'xts', 'cc', 'gs 200t', 'insight', 'highlander hybrid', 'rc 350', 'q8', 'maxima', '650', 'fusion', 'xd', 'wrangler', '718 boxster', 'corvette', '340', 'expedition', 'rio5', 'flex', 'm240', 'a7', 'azera', 'qx60 hybrid', 'equus', 'xc60', 'xt5', 'suburban 1500', 'versa note', 'traverse', 'q60', 'sierra 1500 limited', 'rav4', 'xe', '540', 'fortwo', 'glc43 amg', 'camry', 'e550', 'range rover velar', 'sportage', 'ml350', 's550', 'm8', 'rogue', 'mazda6', 'cascada', 'arteon', 'prius', 'brz', 'prius v', 'range rover evoque', 'canyon', 'ex35', 's450', 'glk350', 'cls550', 'panamera', '86', '750', 'avalon hybrid', 'ridgeline', 's3', 'corolla hatchback', 'fiesta', 'is 300', 'cooper', 'malibu', 'malibu hybrid', 'tucson', 'ct 200h', 's60', 'avalanche 1500', 's6', 'slk350', 'rx 450h', 'c-hr', 'civic', 'tlx', 'corolla', 'cx-9', 'c-max', 'gle350', '535', 'journey', 'silverado 1500 ld', 'a5', 'range rover', 'jetta hybrid', 'gx 460', 'x1', 'nv 200', 'f-pace', 'm340', 'v60 cross country', 'glc300 coupe', 'mazda3', 'q3', '330', 'qx56', 'glc63 amg coupe', 'gladiator', 'discovery', 'eos', 'm235', 'nx 200t', 'a6', 'xb', '718 cayman', 'terrain', 'ascent', 'qx50', 'sl450', 'mks', 'promaster city', 'sl550', 'cruze', 'srx', 'lx 570', 'kona', 'c400', 'sonata hybrid', 'is 250', '435', 'tt', 's7', 'grand cherokee', 'gls450', 'q5', 'fusion hybrid', 'sq5', 'cla45 amg', 'altima', 'sonic', 'rogue select', 'q7', 'c350', 'pilot', 's560', 'rogue hybrid', 'nx 300', 'ats', 'frontier', 'ecosport', '328', 'cadenza', 'genesis', 'cls400', 'continental', 'impala', 'fr-s', 'sedona', 'accord hybrid', 'navigator l', 'silverado 1500', 'encore', 'jetta', 'impreza', 'fit', 'g80', 'armada', 'corolla hybrid', 'pathfinder', '200', 'cts-v', 'ia', 'dart', 'aviator', 'q70', 'veloster', 'niro', 'c300', '640', 'gls63 amg', 'encore gx', 'highlander', 'ct6', 'gs 350', 'cayenne', 'charger', 'passport', 'seltos', '911', 'prius c', 'q50 hybrid', 'blazer', 'palisade', 'glb250', 'mdx', '500x', 'soul', 'g25', 'cx-5', 's90', 'volt', 'gla45 amg', 'escape hybrid', 'mirage g4', 'sonata', 'accent', 'tl', 'yaris', 'tiguan limited', 'juke', 'enclave', 'ranger', 'eclipse cross', 'kicks', 'grand cherokee srt-8', 'f-type', 'mirage', 'durango', 'x5', 's5', '500l', 'sts', '740', 'santa fe sport', 'yaris ia', 'ioniq hybrid', 'cla250', 'e450', 'verano', 'xf', 'elantra', 'corsair', 'acadia limited', 'z4', 'e400', 'yukon xl 1500', 'edge', 'is 350', 'jetta gli', 'stinger', 'ux 200', 'corolla im', 'mkz', 'town and country', 'qx30', 'forester', 'escape', 'rio', 'captiva sport', 'regal tourx', 'yukon', 'murano', 'x6', 'g70', 'qx70', 'acadia', 'rlx', 'a35 amg', 'hr-v', 'gla250', 'tts', 'tc', 'slc43 amg', 'atlas cross sport', 'golf', 'c-max energi', 'slk250', 'xc40', '428', 's8', 'qx80', '500c', 'gle43 amg coupe', 'is 200t', 'cr-v', 'rc f', 'taurus'] ,\n",
            "\"transmission\" : ['manual 7 speed', 'manual 5 speed', 'automatic', 'manual 6 speed'] ,\n",
            "\"drive\" : ['4wd', '2wd'] ,\n",
            "\"exteriorColor\" : ['gray', 'brown', 'gold', 'orange', 'purple', 'white', 'tan', 'silver', 'burgundy', 'maroon', 'yellow', 'copper', 'creme', 'green', 'black', 'red', 'blue', 'pearl'] ,\n",
            "\"interiorColor\" : ['gray', 'brown', 'cream', 'orange', 'tan', 'champagne', 'white', 'burgundy', 'maroon', 'black', 'red', 'taupe', 'blue'] ,\n",
            "\"storeName\" : ['augusta', 'ontario', 'fort bend', 'norco', 'austin south', 'naples', 'capitol expressway', 'independence', 'shreveport', 'lynchburg', 'langhorne', 'cy-fair', 'el paso', 'glencoe (edens expy)', 'cleveland east', 'maplewood', 'buffalo', 'jacksonville west', 'mobile', 'huntsville', 'lithia springs', 'ft. lauderdale', 'gainesville', 'hillside (eisenhower expy)', 'bristol', 'knoxville', 'tupelo', 'baton rouge', 'live oak', 'des moines', 'lafayette', 'laurel', 'kearny mesa', 'salem', 'torrance', 'oak lawn', 'newport news', 'dothan', 'costa mesa', 'germantown', 'tyler', 'schaumburg', 'gastonia', 'ocala', 'easton', 'manchester', 'west sahara', 'gulf freeway', 'st. louis - south lindbergh', 'wichita', 'doral', 'madison', 'lexington', 'warwick', 'jacksonville', 'north attleboro', 'tucson', 'jackson (ms)', 'albany', 'independence boulevard', 'southlake', 'winston-salem', 'fairfield', 'king of prussia', 'jensen beach', 'lakeland', 'cincinnati', 'fremont', 'greensboro', 'merrillville', 'corpus christi', 'capital boulevard', 'irvine', 'columbus sawmill', 'tinley park', 'south portland', 'clackamas', 'clearwater', 'tampa', 'salt lake (south jordan)', 'murrieta', 'rochester', 'wilmington', 'santa rosa', 'sacramento south', 'miami lakes', 'memphis', 'reno', 'melbourne', 'hickory', 'centennial', 'columbus', 'newark', 'south boulevard', 'fort wayne', 'fresno', 'pharr', 'raleigh', 'myrtle beach', 'waterbury', 'riverside', 'parker', 'lubbock', 'burbank', 'cool springs', 'greenville nc', 'fayetteville', 'salisbury', 'roswell', 'harrisonburg', 'potomac mills', 'buena park', 'white marsh', 'san gabriel valley/duarte', 'escondido', 'texas stadium (irving)', 'lax', 'west broad', 'tulsa', 'phoenix-gilbert', 'grand rapids (kentwood)', 'albuquerque', 'columbus easton', 'golden', 'phoenix-west valley', 'westborough', 'arlington/ft. worth', 'norwood', 'greenville', 'murfreesboro', 'west 104th ave', 'montgomery', 'rivergate', 'athens', 'san antonio', 'maple shade', 'austin north', 'birmingham', 'fredericksburg', 'killeen', 'scottsdale', 'town center', 'plano', 'lynnwood', 'gulfport', 'denton', 'bloomington', 'oklahoma city', 'southwest freeway', 'omaha', 'mckinney', 'milwaukee', 'oxnard', 'east haven', 'serramonte', 'charleston', 'nashville', 'turnersville', 'henderson', 'brooklyn park', 'norman', 'boynton beach', 'hulen mall', 'norcross', 'spokane', 'pleasant hill', 'houston katy freeway', 'north shore', 'warner robins', 'north houston', 'royal palm beach', 'st. louis - mid rivers mall', 'danvers', 'boise (meridian)', 'indianapolis', 'loveland', 'chattanooga', 'pleasanton', 'midlothian', 'garland', 'dulles', 'bradenton', 'brandywine', 'springfield', \"o'fallon\", 'charlottesville', 'virginia beach', 'south broadway', 'ft. myers', 'buford', 'dayton', 'colorado springs', 'pensacola', 'kansas city', 'orlando', 'sanford', 'santa fe', 'frederick', 'vancouver', 'kenner', 'canoga park', 'sacramento/roseville', 'savannah', 'ellicott city', 'mechanicsburg', 'waukesha', 'beaverton', 'tallahassee', 'hartford', 'kenosha', 'lancaster', 'modesto', 'naperville', 'sample rd (pompano beach)', 'palmdale', 'puyallup', 'daytona', 'louisville', 'columbia', 'palm desert', 'gaithersburg', 'renton', 'jackson (tn)', 'bakersfield'] ,\n",
            "\"storeID\" : ['6146', '7171', '7633', '6149', '7174', '7175', '7176', '7177', '7178', '7179', '7180', '7181', '6156', '7183', '7185', '7186', '6161', '7188', '7187', '7190', '7191', '7192', '7193', '7194', '7195', '7196', '7197', '7198', '7199', '7201', '7202', '7203', '7204', '7206', '7207', '7208', '7209', '7210', '7211', '7213', '7218', '7224', '7227', '7230', '7231', '7233', '7234', '7240', '7241', '7242', '7243', '7244', '7247', '7248', '7249', '7250', '7252', '7255', '7257', '7258', '7260', '7261', '7262', '7264', '7265', '7267', '7268', '7270', '7271', '7272', '7274', '7276', '7278', '7279', '7280', '7281', '7282', '7283', '7284', '7285', '7286', '7287', '7290', '7291', '7294', '7295', '7807', '7297', '7298', '7810', '6135', '6141', '7164', '7172', '6095', '7173', '6104', '6077', '6001', '6002', '6003', '6004', '6005', '6007', '6008', '6010', '6011', '6084', '6013', '6014', '6080', '6016', '6018', '6020', '6021', '6022', '6024', '6025', '6027', '6031', '6032', '6034', '6035', '6036', '6037', '6038', '6039', '6040', '6041', '7103', '6043', '6042', '6045', '6047', '6048', '6049', '6050', '6051', '6052', '6054', '6055', '6056', '6057', '6058', '6059', '6089', '6061', '6063', '6065', '6090', '6067', '6068', '6070', '6071', '6072', '6073', '6074', '6075', '7100', '7101', '7102', '7110', '7104', '7105', '7106', '7107', '7108', '6081', '7109', '7111', '7112', '7113', '6082', '7115', '7116', '7117', '6094', '6086', '7120', '7121', '7122', '7123', '6092', '7124', '7114', '6103', '7128', '7129', '7130', '6107', '7132', '7126', '6110', '6106', '7136', '6112', '6113', '6108', '6116', '7653', '7654', '7118', '7144', '6121', '7146', '6123', '7148', '7149', '7662', '7663', '7150', '7147', '7154', '6128', '7152', '7157', '6133', '7159', '6134', '6137', '6138', '6140', '7165', '6142', '7167'] ,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pC9twUNyl3LP"
      },
      "source": [
        "data = data.drop(columns=[\"stockNumber\", \"storeName\",\"storeID\", \"features\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = data.sample(frac=0.8, random_state=10)\n",
        "test_dataset = data.drop(train_dataset.index)\n",
        "\n",
        "train_features = train_dataset.copy()\n",
        "test_features = test_dataset.copy()\n",
        "\n",
        "train_labels = train_features.pop('displayPrice')\n",
        "test_labels = test_features.pop('displayPrice')"
      ],
      "metadata": {
        "id": "lz6hwn4gmbKJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def flatten(arr):\n",
        "  flatArr = []\n",
        "  for i in arr:\n",
        "    if isinstance(i, float) or isinstance(i, int) or isinstance(i, str):\n",
        "      flatArr.append(float(i))\n",
        "    else:\n",
        "      flatArr.extend(i)\n",
        "  return flatArr"
      ],
      "metadata": {
        "id": "fj6_uhKQJroR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "array = list(np.asarray(train_features))\n",
        "\n",
        "flatTrain = []\n",
        "for i in array:\n",
        "  flatTrain.append(flatten(i))\n",
        "\n",
        "array = list(np.asarray(test_features))\n",
        "\n",
        "flatTest = []\n",
        "for i in array:\n",
        "  flatTest.append(flatten(i))\n",
        "\n",
        "\n",
        "flatTrain = np.array(flatTrain).astype('float32')\n",
        "flatTest = np.array(flatTest).astype('float32')"
      ],
      "metadata": {
        "id": "T-uL8T3HJ9PS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainLabels = np.array([[val] for val in train_labels.values.astype('float32')])\n",
        "testLabels = np.array([[val] for val in test_labels.values.astype('float32')])"
      ],
      "metadata": {
        "id": "r8n2w178LFm3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnoRU004pSXg"
      },
      "source": [
        "# Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oS_F1GNMpVUZ"
      },
      "source": [
        "normalizer = preprocessing.Normalization(axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EB0OQTYFrOcW"
      },
      "source": [
        "normalizer.adapt(np.asarray(flatTrain).astype('float32'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yZp6Z25r-4f"
      },
      "source": [
        "def build_and_compile_model(norm):\n",
        "  model = keras.Sequential([\n",
        "      norm,\n",
        "      layers.Dense(556, activation='relu'),\n",
        "      layers.Dense(192, activation='relu'),\n",
        "      layers.Dense(48, activation='relu'),\n",
        "      layers.Dense(12, activation='relu'),\n",
        "      layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  model.compile(loss='mean_absolute_error',\n",
        "                optimizer=tf.keras.optimizers.Adam(0.001))\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lf8WcvdIsBnc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22c442dd-c1a7-4530-c01e-6a7d617be87d"
      },
      "source": [
        "dnn_model = build_and_compile_model(normalizer)\n",
        "dnn_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizatio  (None, 527)              1055      \n",
            " n)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 556)               293568    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 192)               106944    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 48)                9264      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 12)                588       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 13        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 411,432\n",
            "Trainable params: 410,377\n",
            "Non-trainable params: 1,055\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print( np.asarray(flatTrain).astype('float32'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNOTLCO6NUCh",
        "outputId": "174a2904-f7a1-4946-f780-55548aa460a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[110627.       0.       0.  ...     35.       2.4      4. ]\n",
            " [ 72675.       1.       0.  ...     33.       2.5      4. ]\n",
            " [ 92937.       1.       0.  ...     26.       2.5      4. ]\n",
            " ...\n",
            " [ 53449.       0.       0.  ...     37.       2.       4. ]\n",
            " [ 15719.       0.       1.  ...     33.       2.5      4. ]\n",
            " [ 33483.       0.       0.  ...     25.       3.6      6. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26kCflJPsFCg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af910387-a026-4075-843b-a961cc3cdedb"
      },
      "source": [
        "%%time\n",
        "history = dnn_model.fit(\n",
        "    np.asarray(flatTrain).astype('float32'), np.asarray(trainLabels).astype('float32'),\n",
        "    validation_split=0.2,\n",
        "    verbose=1, epochs=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "358/358 [==============================] - 5s 9ms/step - loss: 11557.4268 - val_loss: 4545.5459\n",
            "Epoch 2/30\n",
            "358/358 [==============================] - 2s 7ms/step - loss: 3944.5334 - val_loss: 3913.0007\n",
            "Epoch 3/30\n",
            "358/358 [==============================] - 2s 7ms/step - loss: 3468.8000 - val_loss: 3537.1289\n",
            "Epoch 4/30\n",
            "358/358 [==============================] - 2s 7ms/step - loss: 3150.4695 - val_loss: 3257.3713\n",
            "Epoch 5/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2875.3447 - val_loss: 3090.1443\n",
            "Epoch 6/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2645.1001 - val_loss: 2912.1125\n",
            "Epoch 7/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2478.5305 - val_loss: 2726.5554\n",
            "Epoch 8/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2324.6267 - val_loss: 2683.4224\n",
            "Epoch 9/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2222.4492 - val_loss: 2512.1453\n",
            "Epoch 10/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2126.2388 - val_loss: 2396.5015\n",
            "Epoch 11/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 2061.1294 - val_loss: 2368.8621\n",
            "Epoch 12/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1989.2306 - val_loss: 2327.2178\n",
            "Epoch 13/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1929.9432 - val_loss: 2232.2725\n",
            "Epoch 14/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1878.0304 - val_loss: 2221.5857\n",
            "Epoch 15/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1843.0021 - val_loss: 2218.2178\n",
            "Epoch 16/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1818.8190 - val_loss: 2197.2441\n",
            "Epoch 17/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1789.6155 - val_loss: 2194.5610\n",
            "Epoch 18/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1779.5426 - val_loss: 2155.2842\n",
            "Epoch 19/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1748.8136 - val_loss: 2218.8130\n",
            "Epoch 20/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1716.5831 - val_loss: 2219.1865\n",
            "Epoch 21/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1703.4020 - val_loss: 2189.5576\n",
            "Epoch 22/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1697.2612 - val_loss: 2192.0317\n",
            "Epoch 23/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1680.4215 - val_loss: 2130.0615\n",
            "Epoch 24/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1664.6611 - val_loss: 2149.7639\n",
            "Epoch 25/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1633.0690 - val_loss: 2134.8000\n",
            "Epoch 26/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1612.3473 - val_loss: 2166.6177\n",
            "Epoch 27/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1602.5242 - val_loss: 2125.9033\n",
            "Epoch 28/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1577.2041 - val_loss: 2058.1833\n",
            "Epoch 29/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1568.7385 - val_loss: 2074.5464\n",
            "Epoch 30/30\n",
            "358/358 [==============================] - 2s 6ms/step - loss: 1572.2378 - val_loss: 2066.9141\n",
            "CPU times: user 1min 26s, sys: 6.49 s, total: 1min 32s\n",
            "Wall time: 1min 9s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPC8UQ7bsF5Q"
      },
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error [$]')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q17qM0cjsOD2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52c550fe-c78f-4325-d62a-e8ed38a9f80c"
      },
      "source": [
        "plot_loss(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HKf8d7zRRMBERGfxg4pzbBPwE2IAXRGqAZcBu51zMP60aGONvjwE2+tfG/POHJqd3cs2ACwUDFOeH9BS8iAheDWFQmdkQvFrFBGA38L94zVQD+ZmXAJcAVFRUUFVVldJ96uvr97o2PxDnzXerqara1g+5HHz7licbZFuZsq08kH1lyrbyQGplGvRgAnwGeNc5tw3AzB4CTgTKzCzk1z7GApv88zcB44Bqv1msFNiRlN4m+Zq9OOfuAu4CmD59uqusrEwp41VVVSRfW/H6c0RKIlRWHpPS/dJt3/Jkg2wrU7aVB7KvTNlWHkitTOnoM9kAHGdmBX7fx6nAG8BS4Gz/nHnAw/72En8f//jTzjnnp5/rj/aaAEwCXhqkMgDeiC5NQy8ikoaaiXPuRTN7EFgOxIBX8WoNjwILzewGP+1u/5K7gV+b2TpgJ94ILpxzq81sMV4gigGXOefig1mWsmgea2tqB/MjRUQOSOlo5sI5dy1w7T7J6+lkNJZzrgk4p4v73Ajc2O8Z7CG9IEtExKMn4Pug7QVZXqubiEjuUjDpg7KCMLGEY0/LoLauiYgccBRM+qDjwUU9ayIiuU3BpA9K/ZmD1W8iIrlOwaQP2iZ71Ot7RSTX7Xc0l5l1N+7VgM3OuY/3X5Yyh16QJSLi6W5o8DvOuaP2d4KZvdqP+ckoHS/IUjARkdzWXTPXF3twj56ck5U6aibqgBeR3LbfYOKcW9/dDXpyTraKhIPkhQLqMxGRnNdtB7yZzTWzif72J/w3G35gZjlbI0lWFtVT8CIiPRnNdTUds/H+O3Al8Ek+Oh1KTiorCKvPRERyXnejua4FRgPX+K/E/TTeJIzTgVIz+zegyjn37IDn9ABVFs1Tn4mI5Lz9BhPn3I/MbCbwLjAceNw5dx2Amc12zl0/8Fk8sJUWhNm4syHd2RARSaueNHNdCpwJHAn8C4CZHY43ZXzOU5+JiEgPpqB3zq0B5u6T9gbee0RyXmlUfSYiIvutmZjZmd3doCfnZLOygjCNrXGaY5o5WERyV3c1k1vMbBPetCld+Q/gkf7LUmYpLeiY7HFEcTDNuRERSY/ugskW4NZuznm7n/KSkcqiHZM9jiiOpDk3IiLp0d1orspBykfG0mSPIiKagr7PNNmjiIiCSZ/pbYsiIj2bmytgZicMRmYyUWnbC7LUzCUiOazbYOKcSwA/H4S8ZKTi/BABUzARkdzW02aup8zsi2a2vyHCOSkQMD24KCI5r6fB5OvA/wItZlZrZnU9eKVvzigryNNoLhHJad1OpwLgnCse6IxkMq9mog54EcldPQomAGZ2FjDD361yzuXsU+/7Ko2G2aVgIiI5rEfNXGZ2E95LsdomeLzSzP5zIDOWSfSCLBHJdT2tmZwBHOmP7MLMFuC9JOu7A5WxTKJp6EUk1/XmocWypO3S/s5IJistyKO2qZV4wqU7KyIiadHTmsl/AK+a2VK8GYRnAPMHLFcZpiwaxjmoa2qlzJ9FWEQkl/ToCXggARwHPAT8DjjeObco1Q81szIze9DM1prZGjM73szKzexJM3vbXw/xzzUz+5mZrTOz183s6KT7zPPPf9vM5qWan75qn+xR/SYikqN6+gT8vzrnNjvnlvjLh3383Nvx3ic/GTgCWINX03nKOTcJeIqOms/pwCR/uQS4A8DMyoFrgU8BxwLXtgWgwdY+P5f6TUQkR/W0z+QvZvYvZjbOr0GU+z/mvWZmpXjNZHcDOOdanHO7gTnAAv+0BcDn/e05wH3O8wJQZmajgNnAk865nc65XcCTwGmp5KmvOmomGh4sIrmpp30mbe+AvywpzQETU/jMCcA24F4zOwJYhjfsuMI5t9k/50Ogwt8eA2xMur7aT+sq/SPM7BK8Wg0VFRVUVVWlkG2or6/v9NoP6hMAvLDsddjc40d30q6r8mSybCtTtpUHsq9M2VYeSK1M3f7y+X0m8/vSR9LJZx4NXO6ce9HMbmefznznnDOzfhsa5Zy7C7gLYPr06a6ysjKl+1RVVdHZtdvrm/ne839h9IRDqDx+fOoZHWRdlSeTZVuZsq08kH1lyrbyQGpl6mmfydUp5qkz1UC1c+5Ff/9BvOCyxW++wl9v9Y9vAsYlXT/WT+sqfdB1vNNEfSYikpsGvc/E77zfaGaH+kmn4j1VvwRoG5E1D3jY314CfM0f1XUcUOM3hz0BzDKzIX7H+yw/bdCFgwGK8kMKJiKSs9LRZwJwOXC/meUB64EL8QLbYjO7CHgf+JJ/7mN4T+CvAxr8c3HO7TSzfwde9s+73jm3M8X89FlpNMzuRnXAi0hu6umswRP680OdcyuA6Z0cOrWTcx17B7HkY/cA9/Rn3lJVGg1To5qJiOSo/TZzmdm/Jm2fs8+x/xioTGWisgLNzyUiuau7PpNzk7b3ndQxLc90HKjKCsJ6aFFEclZ3wcS62O5sP6eVRvPUAS8iOau7YOK62O5sP6d5zVwteF08IiK5pbsO+CP8d70bEE1677sBkQHNWYYpjYZpjTsaWuIU5mfOU/AiIv1hv796zrngYGUk05UlTfaoYCIiuaY3L8eS/Wib7FHDg0UkFymY9JPSqPdSLD24KCK5SMGkn6hmIiK5TMGkn7S/00TPmohIDlIw6SeaOVhEcpmCST+JhoPkBQPqMxGRnKRg0k/MjNKCMLVq5hKRHKRg0o/KomE1c4lITlIw6UdlBQomIpKbFEz6UWk0T6O5RCQnKZj0I+8FWeqAF5Hco2DSj/ROExHJVQom/agsGqahJU5LLJHurIiIDCoFk37UPqWKaicikmMUTPpRaYE32WONHlwUkRyjYNJTznnLfpRpShURyVF6i1NPxFthyRUcVBsEZnZ5mubnEpFcpZpJTwRCkGhl4ru/hjeWdHmaZg4WkVylYNITZnDWf1Nb/HH4/dfhgxWdnlYWbeszUTARkdyiYNJT4Qirpn4PouXwwHlQu/kjpxRHQpihBxdFJOcomPRCS/4Q+PJCaKqBhedBS8NexwMBozSqBxdFJPcomPTWyGnwxV96TV0PfwMSez+gqJmDRSQXKZikYvIZ8A8/gtW/h2du2uuQaiYikos0NDhVJ1wB296CZ34Mwz4O084GvAcX1WciIrlGNZNUmcGZt8FBJ8AfvgHVrwBeM5dGc4lIrklbMDGzoJm9amaP+PsTzOxFM1tnZovMLM9Pz/f31/nHxyfd47t++ptmNnvQCxHKg7m/geKR3givmmrNHCwiOSmdNZMrgTVJ+z8GbnPOHQLsAi7y0y8Cdvnpt/nnYWaHA+cCU4DTgF+YWXCQ8t6hcCh8eTHEmuC35zI8r5WaxlYSif1PvSIikk3SEkzMbCzwWeCX/r4BpwAP+qcsAD7vb8/x9/GPn+qfPwdY6Jxrds69C6wDjh2cEuxjxGQ4+17Yupqz1l8HLkFdUywtWRERSYd0dcD/X+BfgWJ/fyiw2znX9gtcDYzxt8cAGwGcczEzq/HPHwO8kHTP5Gv2YmaXAJcAVFRUUFVVlVKm6+vr93NtiDEfu4hJ6/6Ha0LFPPB4AZPLB7+i1Bv7L09myrYyZVt5IPvKlG3lgdTKNOjBxMzOBLY655aZWeVgfKZz7i7gLoDp06e7ysrUPraqqor9XutOpuH3Lfzz6wv4w2u15M+9neMPn5DSZw2GbsuTgbKtTNlWHsi+MmVbeSC1MqWjmetE4Cwzew9YiNe8dTtQZmZtwW0ssMnf3gSMA/CPlwI7ktM7uSY9zCiYcyv1n/oOn7PnGLtoFs8+/WhasyQiMhgGPZg4577rnBvrnBuP14H+tHPufGApcLZ/2jzgYX97ib+Pf/xp55zz08/1R3tNACYBLw1SMboWDFF0+r/ReP4fyQ8ZJzzzFZYv+FeIqw9FRLLXgfScyTXAVWa2Dq9P5G4//W5gqJ9+FTAfwDm3GlgMvAE8DlzmnIsPeq67UDTp05R8+0VeKfkMR7/7/6i+9WQS29enO1siIgMircHEOVflnDvT317vnDvWOXeIc+4c51yzn97k7x/iH1+fdP2NzrmPOecOdc79KV3l6EqkaAjHfHsxi8f/iJL69bT84gRiy37T7RsbRUQyzYFUM8lKwYBxzrwr+f1xi1kRG0/oj5fRumgeNOxMd9ZERPqNgskgMDPmnX4SGz+3kB/HzsPWPkL8FyfA+mfSnTURkX6hYDKIzjlmPMd+9Xrmxm+gek8Ad98ceOQqqNuS7qyJiPSJgskgm3noCH54yZc5z37MQjsNt+xX8LMj4anroXF3urMnIpISBZM0OHJcGb+5dCZ3RL/OzKabWVFwAjz3U7j9CHj+to+8wVFE5ECnYJImE4cX8diVJzHrpBM5e9tFnMMtVBd/Av5ynVdTefmXENN7UUQkMyiYpFFRfojvnXEYf7ryJEKjP8GnN/4z15TcTH3hQfDod+Dnx8Driz/yamARkQONgskBYFJFMb+9+FP87LyjWNr4MaZt+Bb3HnwzsVAhPHQx3PlpWPMIxPWeFBE5MOm1vQcIM+OsI0ZzyuQR/Oypt7nx+QC35/0b/33Ee5y48f9hi86HSBkcejpMPhM+dgrkFaQ72yIigILJAaet6evsT47l3x5exVdeHMeRo2/jtlM/ZMK2pfDmn+C1ByAUhUNO9QLLx2dDQXm6sy4iOUzB5AD18YpiHrj4OP74+mZueOQNZj5azGenfZ1vz7uJQxpfg7WP+ssjYEEYfyJM/hxMPgNKx6Y7+yKSYxRMDmDJTV93PfMOdz//Ln9atZkvHDWWb33mR4w7/Wb4YLnXn7L2UfjT1d4y5pMw5Qtw+OehbFz3HyQi0kcKJhmgKD/EVbMOZd4J47nzmXe47+/v8/CKTcw9ZhyXnzKFkZ/5JHzmWtj+Nqz5I7zxMPz5B94y9hg/sMxRjUVEBoxGc2WQoUX5fP+zh/PM1TM599hxLH5lIyffspQbHnmDHfXNMGwSnHQVfP0ZuOJVOPVaiDXDE9+D26bA3bPghTug9oN0F0VEsoyCSQYaWRrhhs9P4+nvVPK5I0Zzz1/fZcbNS/npn9+kptEfPlw+0Qss//wcXL4cTvmh92T94/Ph1sPgntMYU/2I5gUTkX6hYJLBxpUX8JNzjuDP3z6Zyskj+K+n13HSj5/mtiffYmtdU8eJQz8GM/4FLn0evvkKzPwBNNUyad3/wK2T4b458OpvoKkmfYURkYymYJIFDhlRxM+/fDSPXvFpjp0wlNufepsTb3qaqxatYGX1PgFi2CQ4+Wr4xt946Zj/gpO+A7veh4cvg1smwcLzYfUfoLUxPYURkYykDvgsMmV0Kb+cN5312+pZ8Lf3eHBZNQ+9uonpBw/hwhMnMHtKBaFgx98PDYUHQeXXYOb3YdNyWPm/sPohb7hxXjEcdiZMOxsmVEJQ/6mISNf0C5GFJg4v4kdzpvKd2Yey+OWNLPj7e1z22+WMLo3w1ePHc96x4ygryOu4wAzGftJbZt8I7z0HKx+EN5Z4D0gWDPOGGxcOg4Kh/jp521/nFXn3EpGco2CSxUoiYf7ppIlceOIEnlqzhXv/+h4/fnwttz/1Fv949FimhDqZQDIQhImV3vLZn8LbT3q1le1vw5ZVsGc7xJs7/8BgPpSMgkmz4LCz4OATvPuJSNZTMMkBwYAxa8pIZk0ZyZrNtdz713d5cFk1v40lWLDuGWZPGcmsw0cydUwJllyzCOV7TV2HndmR5hy01EPDDtizAxq2ewGmbb1jHSy/D166y6u9TP6sF1gmzIBQ3kczJyJZQcEkxxw2qoSbzz6Ca06bzE8ffJZ3mvP4+dJ1/NfT6xhdGvGDTgXHji/fq3+lnRnkF3vLkPGdf0jLHq9Gs2YJrPodLF8A+aXeJJWHfc6bUywcHdByisjgUjDJUUOL8pk1Pkxl5fHs3NPCU2u28MTqLTzw0gZ+9bf3KCsIc+rkCmZNqWDGpOFE83rRXJVXCFM+7y2tTbC+ygssax+F1xdCuBAm/YMXVMYeA8MOhYAGFopkMgUTobwwj3Omj+Oc6eNoaInx7Fvb+PPqLfxlzRZ+t7yaSDjApw8ZzszJw6k8dARjynpRqwhH4NDTvCXeCu897wWWNY/AG3/wzskvgdFHeYFl7DEwdrrXoS8iGUPBRPZSkBfitKmjOG3qKJzk9YYAABHVSURBVFrjCV56dyd/Xv0hT63dyl/WeE/LH1pRzMzJI5h56HCOPngI4c6awzoTDMPHZnrLZ2+FHe9A9csdy/O3gYt75w4Z3xFcRh0B0SHeaLH8Im+tjn2RA4qCiXQpHAxw4iHDOPGQYVx3luOdbfUsXbuNpW9u5ZfPrefOZ96hOBJixqThzJw8gpM/Ppzhxfk9u7kZDDvEW448z0traYDNKzqCy7vPec++dJq5Qi+w5Bf7Qcbrx5m8uwman9x72HLyEilTk5rIAFAwkR4xMw4ZUcwhI4q5eMZE6ppa+eu67Ty9ditL39zGoys3AzBldAmfGFvG1DElTBldyuSRxUTCPaxF5BV4w4kPPsHbdw5qN8GWN6C5FprrvJFkzfX+dl3Sdj3s3kDZ7g/h5b9BrKnzz7AARMuhaAQMnwwVU6BiqrcuHavnZERSpGAiKSmOhNubwxIJxxuba6l6cyt/XbeDR17/gAde2gB4w5IPGV7ElNElTBlTypTRJRw+uoSSSLj7DzHzfuB7MXX+C1VVVFZWerWcBn/ocsMOaNjpr/2l9gPY9Ir3DE2b/FKoONwPMH6QGXGYV+sRkf1SMJE+CwSMqWNKmTqmlG+eMgnnHNW7Glm1qYbVH9Sy+oManlu3nYde3dR+zcFDCzh8VAmTR5Zw2KhiDhtVwtgh0b2fc+mLvAJv6e7lYE01sHUNbFndsby2yKv1tAkXQCDsTSkTzOvYDoS9fqBAW3rIq/mY+UvAW7CkdD8tmOfdNxz1lrxCf7sgaV3AkJ1vwjvOrzFZ0jqwd5oFvHtEy71XOId62NwIXg2waTfs3gg11VCzEXZv8LY7naPNfTTJAh1DxvOLvUEV+SXedqRkr/RI4xbY+a53H+c68uASSWnO+/ctGeWVSw54CibS78yMceUFjCsv4PRpo9rTt9Y1ecHFDzJrNtfy+OoP239PivJDTB5ZzGQ/uEweWcLkkcUU5g/gf6aRUjjoOG9p45z3Y7r1De+p/8bdkIhBvMUbkZaI+etWiMf8dQsk4t4PYiKR9MPob7t9tuMt3g91a0PH0okjAF5PoVzhQi+oRIf46/KO/VC+VzOrqfYDyEavmTBZKOLVCPOKOr//vkE/Eetocmyu63qWBOA4gBd7UZZouV9DHddRU01eiiq8ARmJuPdvGmvy/039dfJ+otUL5ME8798hFOnYDuZ5+23bsSbvj43mWm/dVJu037F92AcbYc8jfr9cecc6Wt7RV5dX0IsCZ6ZBDyZmNg64D6jA+xPnLufc7WZWDiwCxgPvAV9yzu0y70/V24EzgAbgAufccv9e84Af+Le+wTm3YDDLIr0zojjCiEMjzDx0RHvanuYYb26pY+3mOtZ+6AWYh1/9gN+8sKH9nHHlUcYNKWDskChjyvz1kChjh0QZWRLp/OHKvjCDIQd7y6Gn9++9u+Kc/6PXFmC89fKX/sbRRx3VEYTaA9Q+2855tamGndC4Exp2+Wt/f/dGb92427suOsT7cR76MZh4srdd1vZjfZA3eKEvtcRYsx9Y/L6uptr2/bVvrGbyYYfReU2LjrR4i9dnVlPtLbvehXef3bvWCB01wnhL6vlNhV/7KmmJwaqV0Lir63NDES+olIyGsoOgzP/vq+xgb790XMbPEJGOmkkM+I5zbrmZFQPLzOxJ4ALgKefcTWY2H5gPXAOcDkzyl08BdwCf8oPPtcB0vKC0zMyWOOf2843KgaYwP8TRBw3h6IOGtKe1NZOt/bCOtZtreWtrPdW7Gqh6cxtb6/b+izcYMEaWRNqDS7ymhR3F1Rw8tICDhhYwvCi//5rOBpJZR5MX5e3JtaW79q419VUi7v3oDvQMBKF8b+nkeaEPd1Ux+cjK1O/dVNMRYGr8pjnnOv79QtGO7XDU+yFvaz4Mhr3yx5q9pX27qWM73uLth6J+E12JV4ON+Ou25jt/ePqLbf108ZjXXJjcP9eY3Fe308vrpmXeq7UTsY4yWQCK/UAz5GBvgEi81ctHW/72WidtgxdQA0F/CfkBdp/9QBDO+VXvmkB7YdCDiXNuM7DZ364zszXAGGAOUOmftgCowgsmc4D7nHMOeMHMysxslH/uk865nQB+QDoNeGDQCiMDIrmZ7B8Or9jrWFNrnM01TWza1Uj1rgY27W70txt54Z0dbK5p5eF3Xms/PxoOclC5F1gO9tcHlXvL6LJoz0eaZYtAEAIZPpVNpNRbKqakOyd7C4a84NmTB27jMajbDLvf994ntHuDt717gzckfs+2joDc1vTW3iQX8fqRCob6tRnzAlMi7j2nlYj5S8ILNq6hI42B+8PKXFuDdRqY2XjgWWAqsME5V+anG7DLOVdmZo8ANznnnvePPYUXZCqBiHPuBj/9h0Cjc+4nnXzOJcAlABUVFZ9cuHBhSvmtr6+nqKiLNuQMlG3lAdhdW09TsICtDQm2Nji2NSTY2ujY2pBgW4OjZZ+JkovCMCQSoDxiDI0YQyJGecQo99OGRIy8YPpqNtn4HWVbmbKtPNB5mWbOnLnMOTe9q2vS1gFvZkXA74BvOedqk5sinHPOzPotyjnn7gLuApg+fbqrrKxM6T5VbdXZLJFt5YH9l8k5x7a6Zt7f2cDGnQ1srmnig92NfFjTxAc1TSzf0cjuhtaPXFdWEGZEcb7X51Ocz/CSju0RxfmMKPG2B2KgQK59R5ko28oDqZUpLcHEzMJ4geR+51zbQP8tZjbKObfZb8ba6qdvApLHd4710zbR0SzWll41kPmWzGZm3g9/SYRjxpd3ek5jS5zNNR0BZvPuRrbUNbG1tpmtdc28u30P2+qaaYl/9F0wBXlBSqNhSqNhSvz1vktJNERpNExRfpiCvCCF+SEK84JE84IU5IUIBjKgf0ekE+kYzWXA3cAa59ytSYeWAPOAm/z1w0np3zSzhXgd8DV+wHkC+A8za+u5nQV8dzDKINkrmhdk4vAiJg7vutnCOcfuhla21jWzNSnQbK9vpqaxtX3ZuLOBVf52Q0u8R58fCQcozAsRzQtSmBci1tTI3e+8SHEkRFF+iOJI2F97+0Xt6SHyQ0FCQSMUCBAOGqFggFDAvCXop/nHMmJQgmSUdNRMTgS+Cqw0sxV+2vfwgshiM7sIeB/4kn/sMbxhwevwhgZfCOCc22lm/w687J93fVtnvMhAMjOGFOYxpDCPQ0f27On4lliC2qaOQLOnOUZDS5yGlhh7muM0tsTZ0xJrXzc0x2loibPxwz3UN8f4sKaJuqYY9c3e0rf8Q1GeF4CKI2GKIh3bxf52iR+0SqIhivO9mlZJ1DunJBKiMC9EQLUoSZKO0VzP0/WQglM7Od8Bl3Vxr3uAe/ovdyIDIy8UYFhRPsOKejcs02u7PnGvtETCsafFDyxNMeqaY9Q1xWiJJYjFE7QmHLF4gljc0Zrw1/EEMT+9OZZoD0x1Ta3UNcXYUd/Ce9u9wFXr32t/zKDYrymVRMMU54fIC3m1nnAwQDgUID8Y8LeNvGDQXwfYuKGFlfG3CQSMYMAImnnb5g31DvhpoWCA/JC/hIMd26Eg+eEAkaS0vFCAvGBANa400hPwIhkmEDC/FhGG0oH5jOZYnLqmmL+0Utvor/3gU9vYSm1TrH2/vilGU2uc2qYELbEErfEELfEErTHnr739lnjCe95y3VsDku+2wJIf2jvQJAehkqQaWFtNq6NW5tXASiJen1Y0L0gkFFQtrAcUTETkI/JDQfKLgr2uSfXE00uXctKMk4knHAnnvHUC4m3b/jqecDTHEjS1xmmOJWiO+etWf9tfN7V6Qaq5NU5z3Dvestc63r5f1xTjg92N7YGysbVnfVn5oQDRvCDRsLdEwsH2/fqaJhZuXEYwaIT36Z8KBqy9/yocMCJ+X1jb4IuCvCBF+SEK8kIU5gfb19FwMONqWQomIjKoAuY3hR0Az4u2xhPtta86v6bVVgtrbPX6shpbvaWpfTtBY4sXiBpaYuxscjRsr/ebEf0mxoQjlvCaF+N+emsiQW8e6wv6zYBhfx0KesEp1Lbvr8GfUQdIOC8Yt82w07btpcNf588kPzQw//AKJiKSs8LBAOWFeZQXpj4vltevdXK35znn1bQaWuLsaY6xxx980TYIwxuUEWNPizf4Ip7wglI87gWmeKJtnejYjztvajO8IB0wb4CImbffnh4AMGwAn4BXMBERGQRmRsRvIutL8DpQ6f2lIiLSZwomIiLSZwomIiLSZwomIiLSZwomIiLSZwomIiLSZwomIiLSZwomIiLSZ2l9bW86mNk2vCnuUzEM2N6P2Um3bCsPZF+Zsq08kH1lyrbyQOdlOtg5N7yrC3IumPSFmb2yv3cgZ5psKw9kX5myrTyQfWXKtvJAamVSM5eIiPSZgomIiPSZgknv3JXuDPSzbCsPZF+Zsq08kH1lyrbyQAplUp+JiIj0mWomIiLSZwomIiLSZwomPWBmp5nZm2a2zszmpzs//cHM3jOzlWa2wsxeSXd+UmFm95jZVjNblZRWbmZPmtnb/npIOvPYG12U5zoz2+R/TyvM7Ix05rE3zGycmS01szfMbLWZXemnZ/J31FWZMvJ7MrOImb1kZq/55fmRnz7BzF70f/MWmVm3b/NSn0k3zCwIvAX8A1ANvAyc55x7I60Z6yMzew+Y7pzL2IetzGwGUA/c55yb6qfdDOx0zt3kB/4hzrlr0pnPnuqiPNcB9c65n6Qzb6kws1HAKOfccjMrBpYBnwcuIHO/o67K9CUy8HsyMwMKnXP1ZhYGngeuBK4CHnLOLTSzO4HXnHN37O9eqpl071hgnXNuvXOuBVgIzElzngRwzj0L7NwneQ6wwN9egPc/ekboojwZyzm32Tm33N+uA9YAY8js76irMmUk56n3d8P+4oBTgAf99B59Rwom3RsDbEzaryaD/+NJ4oA/m9kyM7sk3ZnpRxXOuc3+9odARToz00++aWav+81gGdMklMzMxgNHAS+SJd/RPmWCDP2ezCxoZiuArcCTwDvAbudczD+lR795Cia569POuaOB04HL/CaWrOK8NtxMb8e9A/gYcCSwGfhperPTe2ZWBPwO+JZzrjb5WKZ+R52UKWO/J+dc3Dl3JDAWryVmcir3UTDp3iZgXNL+WD8toznnNvnrrcDv8f4jygZb/HbttvbtrWnOT58457b4/7MngP8hw74nvx3+d8D9zrmH/OSM/o46K1Omf08AzrndwFLgeKDMzEL+oR795imYdO9lYJI/uiEPOBdYkuY89YmZFfqdh5hZITALWLX/qzLGEmCevz0PeDiNeemzth9d3xfIoO/J79y9G1jjnLs16VDGfkddlSlTvyczG25mZf52FG+g0Rq8oHK2f1qPviON5uoBf5jf/wWCwD3OuRvTnKU+MbOJeLURgBDw20wsk5k9AFTiTZe9BbgW+AOwGDgI71UDX3LOZUSndhflqcRrOnHAe8DXk/obDmhm9mngOWAlkPCTv4fXx5Cp31FXZTqPDPyezOwTeB3sQbzKxWLn3PX+b8RCoBx4FfiKc655v/dSMBERkb5SM5eIiPSZgomIiPSZgomIiPSZgomIiPSZgomIiPSZgolIPzOzeNLssSv6c6ZpMxufPKuwyIEi1P0pItJLjf70FCI5QzUTkUHiv0PmZv89Mi+Z2SF++ngze9qfJPApMzvIT68ws9/775p4zcxO8G8VNLP/8d8/8Wf/yWWRtFIwEel/0X2aueYmHatxzk0D/htvVgWA/wIWOOc+AdwP/MxP/xnwjHPuCOBoYLWfPgn4uXNuCrAb+OIAl0ekW3oCXqSfmVm9c66ok/T3gFOcc+v9yQI/dM4NNbPteC9cavXTNzvnhpnZNmBs8jQW/rTnTzrnJvn71wBh59wNA18yka6pZiIyuFwX272RPEdSHPV9ygFAwURkcM1NWv/d3/4b3mzUAOfjTSQI8BRwKbS/wKh0sDIp0lv6i0ak/0X9N9e1edw51zY8eIiZvY5XuzjPT7scuNfMrga2ARf66VcCd5nZRXg1kEvxXrwkcsBRn4nIIPH7TKY757anOy8i/U3NXCIi0meqmYiISJ+pZiIiIn2mYCIiIn2mYCIiIn2mYCIiIn2mYCIiIn32/wEjXPJltSZmjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OmFji_9t9Hj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c906076b-2d3a-46f6-8bd7-99cf476e810a"
      },
      "source": [
        "test_predictions = dnn_model.predict(np.asarray(flatTest).astype('float32')).flatten()\n",
        "\n",
        "#a = plt.axes(aspect='equal')\n",
        "plt.scatter(np.asarray(testLabels).astype('float32'), np.asarray(test_predictions).astype('float32'))\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8587bf7450>]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FG266z7tueR"
      },
      "source": [
        "y_true = []\n",
        "y_pred = []\n",
        "\n",
        "for i in range(len(test_predictions)):\n",
        "  # if test_predictions[i]> 150000:\n",
        "  #   pass\n",
        "  # else:\n",
        "    y_pred.append(test_predictions[i])\n",
        "    y_true.append(testLabels[i][0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_true)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhVvkE6aGkVD",
        "outputId": "f9168f22-ab39-4505-ab2d-bde45d10ef11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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34893.484, 25337.965, 33853.84, 30875.967, 48639.695, 16215.048, 18636.53, 28093.332, 23252.133, 22385.582, 35380.645, 27173.97, 48948.668, 22355.248, 18280.16, 33501.234, 26254.357, 14959.015, 42819.164, 20955.19, 27748.727, 28830.64, 35968.32, 26967.66, 20273.3, 26706.932, 34016.316, 18994.857, 34736.81, 34719.703, 36967.152, 29356.262, 23606.12, 32777.85]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9DmbExNtvuS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddd9b69c-59a5-41ef-8150-8748b4ed65a9"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def mape(actual, pred): \n",
        "    actual, pred = np.array(actual), np.array(pred)\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100\n",
        "\n",
        "\n",
        "mape(y_true, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.471286714076996"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = open(\"annData.csv\",\"w+\")\n",
        "\n",
        "f.write(\"True, Predicted,\\n\")\n",
        "for i in range(len(y_true)):\n",
        "  f.write(str(y_true[i])+\",\"+str(y_pred[i])+\",\\n\")\n",
        "\n",
        "f.close()\n"
      ],
      "metadata": {
        "id": "RUyrkVhg9wJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeIvpgsIbHxA"
      },
      "source": [
        "# KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKkxuIDrbbQq"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# Train the model using the training sets\n",
        "model.fit(flatTrain, trainLabels.ravel())\n",
        "\n",
        "#Predict Output\n",
        "knnResults= model.predict(flatTest) # 0:Overcast, 2:Mild"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGYX1REMcXnp"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def mape(actual, pred): \n",
        "    actual, pred = np.array(actual), np.array(pred)\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqvCgkEze4V1",
        "outputId": "f87955d6-3859-4df0-8125-68edfd1d3cb3"
      },
      "source": [
        "mape(testLabels.ravel(), knnResults)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.31399428844452"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f = open(\"dataDT.csv\",\"w+\")\n",
        "# for i in range(len(testlabels)):\n",
        "#   f.write(str(testlabels[i][0])+\",\"+str(predicted[i])+\"\\n\")\n",
        "\n",
        "# f.close()"
      ],
      "metadata": {
        "id": "_egx4wGnUEZJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(np.asarray(testLabels).astype('float32'),np.asarray(knnResults).astype('float32'))\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "mB3mY1UVPXVI",
        "outputId": "af6d6e20-83c5-4ddd-9765-3232ea312379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb15bf8d810>]"
            ]
          },
          "metadata": {},
          "execution_count": 171
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7Xl98X1sheB"
      },
      "source": [
        "# Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkJrfydf0QpO"
      },
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeRegressor()\n",
        "clf = clf.fit(flatTrain, trainLabels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQW45BUR0ZOD"
      },
      "source": [
        "dtResults = clf.predict(flatTest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgMX2swI0m_b",
        "outputId": "e704c124-7f92-45ef-bc23-bcf1aedb1eeb"
      },
      "source": [
        "mape(dtResults, testLabels.ravel())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.36952650880495"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(np.asarray(dtResults).astype('float32'), np.asarray(testLabels).astype('float32'))\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "8NFzUZwSIMEg",
        "outputId": "c5311189-f97b-4930-bae8-cef9efb32610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb15bee0ad0>]"
            ]
          },
          "metadata": {},
          "execution_count": 175
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uxjA6MUFB54SHU3Ly29YWjq26h4sG3M+K5t6MXXR9W5bmVGlmjM6DGRvDiCFBiy8rRQLlt4U2Nnlb4LhhGbx2Kpf1/Qd/azyEK5b/mPWt3dveTpVmxug8mEDAMGJIUDA6KOVKoeW3iZhSQ2MTyhYPK2MRQ/0j8NTesOZNLq6/iPPqL2tnaCB1mhmj82DGxjBiSFDlyKCUK9nKb7NVvOW8wLH5M5j5PfjXidBjMBw1l1n8L4m6M15SpZkxOg82jWYYMSUofYo3ZgPZy29zUbzltMBx9evw6smwYQnscSXsdRVUVDF+1NZ5uzYjfpixMYxORD7lt6m8k3THyyqhZ2sLfHADvHM1VPeDI16C7Q/pkHxz66oKGjc2WzLMEsOMjWF0MtIljAxLLt7J+FGDM/NCNiyD174DK1+GupPggN9C1xrf5JvVVZVMPmlf32u0ZJidFzM2hlEEwjyd56tPELmUG8jIw6p/BGadBboZRtwNA08DV2GWLvaTfPxcvDGjuNiiThdb1GkUiuSnc3C8Am/24nz1yXUcOdH8Ocw5Hxb/EbYbDgfdD9vu2q7LzhOeDNy9uqqyw9iCskILsGTSMbmP2ciYvCzqFJEnQpxrjaqeEXZghlHuhHk6z1efVESafmX16/DqKfD5ItjjCtjraqio6tCtUiRQ2ux3bUH9LRlm/Ek3jfYV4Acp3hfgtvwNxzBKnzCxklz7hJ1eyyX+43uOfXZsLwL4+kuw/aGBx8h0DU2Lqq/HY4q1+JPO2Fyhqi+n6iAiP8/jeAyj5AkTK8mlT023qsiD6H6B+ilPzODgpbfzpY0z24kAUhFUwjnIgynl3HClTspFnar6cLoDhOljGMYWwqSqz6WPqv8UVLoFl5ks8Eyewju657+YNvBsun3+tiMCGPlAWkOTuAZvWQGAqgph3PABgdc/Zmgtr0w4nCWTjuGVCYcHqtZyKc8QRXmHcidtBgER2UNE+rjb24nIH0TkQRHZPfrhGUbpEZQdwPulmUufdU3NvudNJWnONP1M4ljdKpr4Zf9fc/tOk1iyqZajP5oCu5zepjYLRXJXgWE79U57/fm6lnzvb/iTVo0mIv8CvqWqq0Tkt8BqYB5wkaqOKMAYC4Kp0YxSIVV5gqBU/WFLGmxdVcEXm1tpVdinej6/rvsVO3X9lNtWnsgUn3IA3rhOTbcqVGFdU3M7KXOmY43i+vO5f7kRVo2W0rMRkauBXYGz3e1vAZXAEKC/iFwlIsHRP8MwCk42FSWD0vgnnuoTT/lNza2gLZzT52Ee3e0yuspmxi6eyE0rvkNV1VbtzpHsIazd2ExjU3M7byHovLkkFs0plU4e9jf8SSkQUNWfi8g3gXuAHYBDVfVyABH5H1X9RQHGaBhlQzpPIBtJc+I4Fz00lxunz/c9TlgJct+qVUwecBMjur/L3xsP4acNP2Z9S/c2D8h7XD9pdvJxs5Eyp1Pa5bJYNR/7dxYKnfYnTAaBa4HXgE3AOHDiOMDKyEZlGGVIssJr7cYtsZdMFWUJSXPY9C5hJMhH9/wXE2tvoVJauWTZRTy29nASARe/6aUwnkDQeQ8b0se3Pcz1ZJxKJ4lc9+8MFCPtT1qBgKr+RVX7qerOqvqa2/aeqh6X7UlFpEZEHhWRD0XkAxE5UER6i8hzIrLA/d3L7SsiMlVEForIOyKyn+c4p7v9F4jI6Z72/UVknrvPVBGrvmTEnzCeQNgU/gk11YUPzQ2lTEuVxj9ZBHDMgik8tvYI/MoBeAnjCQQVRnvxw1W+7WFKG4QRV6Qi1/07AzmXiMiCdBkEdlTVT3Pt48MU4BlVPUFEugLdgJ8CL6jqJBGZAEwAfgIcBQxyf4YDvwGGi0hv4GpgGKDAHBF5QlXXun3OBGYBTwGjgaczHKNhFJQwnkCYPn5paNIdx+9pHmDv6o+YUncjO3X9lFtWnMSUFePYHDKlYtAxE6RKP5Np3CS5PddkpflKdhpXihGXSufZPBXiGGH6tCEiPYFDgTsBVHWTqjYCx+LEhnB/j3G3jwXuVYeZQI2I9AVGAc+p6hrXwDwHjHbf66GqM9WR2t3rOZZhxJYwnkCYPuk8JL/jeJ/mASpwRACP7Ta+nQjAz9AEeSfJHkKvblXUVFflVAwu03bDn2J8jukeUfYRkfUp3hcg1ft+DARWAX8UkX2AOcAFwA6q+onb51McQQJALbDMs/9yty1V+3Kf9o6DFzkLOAugrq4uw8swjPyQCNQ2NDYhOG66H2HjBumeTqsqxPc4iaf5g352Nzf7iACCaFFl5KQZvgHmMB5CJvGRcoinFIJifI7p1GiVqd7P4Zz7Aeep6iwRmYIzZeY9r4pI5OmoVfUO4A5w1tlEfT7DSCZ5ykuhzeD0ylKNFqSmaiNVqKX+EZ4ZfB4V2tJBBJCKbAPMmSYDjTR5aBlRjM+xGPVslgPLVXWW+/pRHGOzQkT6quon7lRYQu3WAAzw7N/fbWsAvpbU/pLb3t+nv2HEDr8pr8RTT7euXbL6Ahg/ajDjH3mb5lb/56fmFu2YGdpTDmBzt30Z9+55zN+4Q9vb1VWVbF1V0U4hl0y2dWUyjY+UejylUBT6c0yrRss3rphgmYgk/LUjgPeBJ4CEoux04K/u9hPAaa4qbQSwzp1umw4cKSK9XOXakcB09731IjLCVaGd5jmWYcSKVFNeOaVJSeOMtDvvf96Ap4fC4rthjyvoPeZ1zv7GqA5qrFSGxjtmw/CjWJU6zwPud5Voi4Hv4hi+h0Xk+8BS4ES371PA0cBCYKPbF1VdIyL/B7zh9vuFqq5xt88B7gaqcVRopkQzYkm6Ka9svIUbp8+nuSX1rHC/mmpobYEPfgnvXAXVfduVA/B76r3k4bfTrscJEgskE7SgsNALDY3CEcrYiMiuOFNfX4jI14C9cRRijdmcVFXn4kiWkznCp68CPw44zl3AXT7ts4E9sxmbYRSSdPJgyNxbSCcQqK6q5KrDt4UZR8DKl6HuRLccQK+U+4VZ+BmmT9CCwtlL1/DYnIaCLjQ0CkdYz+YxYJiI7IYTUP8r8Gccj8MwjCzxBmqDjEpYbyFBKm+ptqaayQcu4ICl40A3O+UABp4GIlw5bR4PzFpGiyqV4qT5v3bMXu32TWf4Ui0OTRC0oDBx7uT2bOJARvwIG7NpVdXNOIk4b1HV8UDf6IZlGOVDoj5LEJlWswxKxHnLtwfxytfu44CGH0GPL8NRb7WVA7hy2jzum1nfdq4WVe6bWc+V0+alPG7yOXKRZgddpyXALA3CejbNIjIOJ3D/DbetY0FxwzBC4RebCEpKWZnkdYhAdZcKmppb6VldhQg0buwoj/Ye/7pDm/hawzHw+SLmb3c+Z751DMtenE+/mnrGjxrMA7OWdTgvwAOzlrV5N8nH9Z47sZ0q2WeCIM8rm6ScRuchbT0bALdQ2o+A11T1AREZCJyoqjdEPcBCYfVsjELhl04mVeqWTKiuqmyfxytJBPDPPpM565ltMjr3x5OOSXnOoOsJyicW1P/4/WvbxWzSHceIB2Hr2YTybFT1feB8z+slQMkYGqPwxF11FOX4gmIWqdL8h8WbTPGe5//JT3pex4ju7zL9s0O57N1z+Ly1mhbteO4gwsSLUiV19PvMUi0oHLZT71j/XRjZE1aNNhK4BtjJ3UdwhGK7RDc0o1QpRnrzTIh6fEFB9lwNjff4Lz53O3/ccSpVsplLl13Io21ZmjM7x7jhA9L2ySapY9CCQluwWbqEFQjcCdwMHAx8FUe2/NWoBmWUNsVIb54JUY8vU3VZJnSraOLG/lOY0v96Pt7Uj6MXTOXRtV8n3SrP2ppqTh1R1za2ShFOHVHXTo0WhCXHNMIQViCwTlVtYaSRF/KZ3tw73ZUqWB6H8SXGlC8PJpm9qz9iat2vqOv6SUblABIqsjFDa0MZl2TGjxrM+EffbreQtKrSP9mnUb6ENTYvisiNwOPAF4lGVX0zklEZJU2+yu4mT3c1NmVf2bIQ40uMqVtVBRubWzM6lpeEGi1xjApa+FGfx7hox/tZ2dybM+pv4B/rdk95jEoRWlXzFxdJtp+W1tZIIqyxGe7+9ioOFAheHGAYAeQrvXnYypbZJLK8+OG5ePNYVojTHkY44JUp+40pl1m02ppqDhvSp02q3LdqFZOTygF81tI9pcIsXwovb2mEZJpbfZJ9FpC4C1DKkbBqtMOiHohRPuQrvXm+KlsmM3vpGpITJrcqPDK7njfr16UUDiQWR6Yil1m0hsamtuMf1fNfTKy91UcEABOP2yvv04tesqkGWijiLkApV8Kq0XrilGA+1G16GSfx5bqoBmaUNvlQHaWt20J2QeqgBY6vLFrToS3ZewraN590q2ji6n53cFLv55i7cRAX1I9n6aZ+7frk+vmm8wyyqQZaKDKVYhuFIawa7S7gM5xMzCfiVOf8Y1SDMoww5Ct9SjKZBvC9T/BRBf8T7F39EU8OOp9v93qeW1acxAkLb+xgaHIl4Rk0NDah+Jc6SGfki1k9M58CDyN/hI3Z7Kqqx3te/1xE5kYxIMMIS6r0KYWcp1doK4ucj4WZflTQwg/7PM7FO97HyubejF08kdc3+Cc2rxTJKWaRq2dQW+QYSb4EHkZ+CWtsmkTkYFX9F7Qt8rTHBKPoxGURYOLpf8QuvXyn23Khb9Uqbh5wMwd2n9cmAljf0j2wf4tqTjGLXD2DVElFC0G+BChGfglrbM4G7nFjNwKsAc6IalCGUUzCpNL3o6m5hVcWrWGrLhV8sTl7abOXVCKAICpFQnsmfh5QkGfg9eDiTL4EKEZ+CZWIs62zSA8AVV0f2YiKhCXiNBKEUVplQjZJNsOIADJFgCWepJqZJMT0kmviTqO0yEsiThE5VVXvE5GLk9oBUNWbcxqlYWRBNvGITPYJU9AsEzI1NHtXf8SUuhvZqeun3LriRH694uRQmQDSkRyzCIrN3DeznprqKrauqmDtxmaSSZU0NEzxNCNzSmHdULq/4G3c39v6vGdrhI2Ck80aimz2ScSCdp7wZL4vIZBkEcC4xdcza0Pm6WP88ItZpIrBNDY1p1T6tah28HAsLhINpbJuKKX0WVV/524+r6o/9/4AL0Q/PMNoTzZJMnNJrFmoJ/W+Vau4f5cr+Unfe5i+7iCOWnBL3gxNbU21b8aAdOqshAeT6pi1NdVIinMYuRP3xLVhCeub3wLsF6LNMCIhVWoUSP2UHlZd5TdVMX7UYC555G1aklMK5BGvCGD8sgt4JESW5rAIweowP9VWMsX0YEph6igflMq6oXQxmwOBg4A+SXGbHkCwj20YeSRMwD7VU3qYdRdBUxX71fWMzNBEIQJIJtXnEiY2lVgzk/ylD0Q6tVMqU0f5oGd1Vbsks972zkQ6z6Yr0N3t543brAdOiGpQhuElXWqUdE/a40cN5uKH5uIVI1e47anOkZAyR8He1R/x67pfsXPXT/IqAvASxgNJxKaClGkJbyL5C37kpBmRpoSxlDNbCErcGmFZpEhI+detqi8DL4vI3aq6tEBjMox2pJouCLNaffbSNSSveml12xP7FWpKwisCWNXcK68iAC+ZruLPdG1K1FM7pTJ1lA8afRSBqdrjSthHqT+IyLdVtRFARHoBD6rqqOiGZhgOQdNgtTXVoVarByXHfGDWMpas+jwy7yWZHatWM3nATW4mgIP5acO5KTMB5EJi6uuih+aGjncEZWPIZOFnvlLC5Hr8Uor3lEr6nbCJOL+UMDQAqroW2D6aIRlGe/wSbmYSpA7KVdaiWjBDM7rHKzwz6Fz2rl7A+GUXcG79TyIzNEDaRJphCUrKediQPjndk3Tkcs/DJBLtTOT69x8Xwno2rSJSp6r1ACKyE7bOxigQY4bW8sjs+naGYb+6nqFSr4wZWhtZcswwJIsALqy/lI835f6EPXLX3sxcvDbwulLFO7yfU023KlRhXZN/8tJUCz97datiqy4VgfuGJZUXko13UmrxnlJJvxPW2FwB/EtEXsZRUx4CnBXZqAzDw5XT5nXwQF5ZtIYrp83j2jFOvCOVeimK5Jhh2Kt6AVPqboxEBPBm/bqMr+vfjU0dPidvhgA/xVeqGMnajc7Cz8kn7Zv1F1861Vk2xy3FeE9cEs7mQqhpNFV9BmdNzTBipIwAAByLSURBVEPAg8D+qjo9yoEZRoKgmMt9M+sZOWlG25Ox39PshQ/NLbihqaCFs/s8wuO7XcrWsolxi6/nVytOy6varKm5hY//08SpI+raFl5WinDqiLrAhaj9aqpDl9L27pNuHLksLoxiwWLQmDtbjKPUSGlsRGSI+3s/oA74t/tT57YZRuSkmgJLPAnnI4dZPtixarUnE8CBjF5wayRqM3Cu/doxe7Fo4tF8POkYFk08mmvH7JVyjj/TUtrpCtQl98+UKLyQUolxlBrpHrUuAc4EbvJ5T4HiFq4wYk8+VEHpYi75ys6cK6N7vMKk/rdEkgnAj6BUMqnm+MMkF/V6AGEWfubiMUShtCqVGEepkVGJgVLGSgzkn6CFgpnm0Lpy2jzum1kfxRDzQreKJq7q93vG9n42ryKAMGSazj9dNoZU9ydf9zPqYxqFJV8lBo5L9b6qPp7pwIzyIV+qoIQI4IFZy4qmKgvCKwK4beW3mfzpKXnPBBBENklCk5/606nRUu2bD4/BvJDyId1/xTfc39vj5Eib4b4+DHgVMGNjBJJLAsxkiW6/mmpuOnEfLnxobiGGnpZCZQJIEFUyzG5du2ScaSDfhqAUlFZGetKlq/kugIg8C+yuqp+4r/sCd0c+OqNTk0sCzNlL17SrFplo71opbGoprndTyEwAAL26VXH1N/bIy9O/Jbg0ikWomI2IfKCqX/G8rgDe87ZlfGKRSmA20KCq/ysiA3Fk1dsBc4DvqOomEdkKuBfYH/gPcJKqfuwe43Lg+0ALcH5Cji0io4EpOJmp/6Cqk9KNx2I2ueHnnQBp5+NHTprha5CKuRAzFV4RwDX//mHkIgCAmuoq5l59ZNvrK6fNa5tSrBRh3PABbVONCYIWblakqLAZJvVP0Dls+qt8CRuzCZuu5gURmS4iZ4jIGcCTwPO5DBC4APjA8/oGYLKq7gasxTEiuL/Xuu2T3X6IyO7AWGAPYDRwu4hUukbsNuAoYHdgnNvXiIig9CBA2gJbQQqnuBmabhVNTOo/ld/uPJGPN/XlmAVTeGTt/xC1oQHHSCRIiCUSn0+LKvfNrOfKafPa+iTfj7Ubm2lsakYJ/lwzlRqXWkoYI3pCRTJV9VwR+RZwqNt0h6r+JduTikh/4BjgOuBiEREcGfXJbpd7gGuA3wDHutsAjwK3uv2PxUkG+gWwREQWAge4/Raq6mL3XA+6fd/PdrxGalIJAV6ZcHinf9otpggA2k87pkoqmvBu0i3cTHeOMJRaShgjejL5j3kT+ExVnxeRbiKyrap+luV5fw1cxpYaOdsBjaq62X29HEj8xdYCywBUdbOIrHP71wIzPcf07rMsqX243yBE5CzctDt1dXVZXopRiulBoKMI4OTF1zFzw94FH8dhQ/q0badKKpog0889G7FBqd5zIzpCTaOJyJk4XsXv3KZaYFo2JxSR/wVWquqcbPbPJ6p6h6oOU9Vhffr0Sb+D4UvQU7FCWzqZZKa91cDISTM67hQTvJkAnl03gqMW3FIUQwPw4oer2raDFnIC7PvzZxn6i2dDZcitFAmc2gyDpYQxMiWsZ/NjnCmqWQCqukBEsi0xMBL4pogcDWyNU2J6ClAjIl1c76Y/kPiGagAGAMtFpAvQE0cokGhP4N0nqN2IgFS17P3UTmHKPBeTQmcCSIc3rjVu+IDABa5+pYP9yMeiSb97bilhjFSEFQh8oaqbEi/cL/2sIriqermq9lfVnXEC/DNU9RTgRbaUmj4d+Ku7/YT7Gvf9GepI6J4AxorIVq6SbRDwOvAGMEhEBopIV/ccT2QzViMcY4bWtgkB/EhOrJhNTKEQeEUASwssAkhHwhO8f2Y923StzGhEFQLVVRU5eTLJeO95Po9rlC5hPZuXReSnQLWI/A9wDvC3PI/lJ8CDInIt8BZwp9t+J/AnVwCwBsd4oKrvicjDOIH/zcCPVbUFQETOBabjSJ/vUtX38jxWI4nEwryBE570fQrxzuXHcV6/2CKAdHi9iA2bWjos8kxFqwJITqUA/LDFmEYmhP1v+gnwA2Ae8EPgKeAPuZ5cVV8CXnK3F7NFTebt81/g2wH7X4ejaEtuf8odo1FgwizkDOpTDOIiAkiHn/Irk7VIphQzik3aRZ3uupX3VHVIYYZUHGxRZ37wi8cIzpxrbYrFnsXAmwngycaR/LThXNa1bJt+xxiRiYcjwJIME3cmYws5jWTytqjTnZqaLyKmDTbSkhy/SRga8F/sWSxG93iFZwady97VCxi/7AJ+XD+hYIYmbLwlXTG0RJwkETepqa6iV7eqwOP1rA5+Lwy2kNPIhbDpav4BDMUJwG9ItKvqN6MbWmExzyb/BKWigS1ezuylawpaPsBbDuDtjYO4oIDlAMKSrBbLNA3/0F88267cc4Je3ap466ojO7SHJeh+ZpPqxigd8lJiwMPPchyPUYakEgIknoqP37+WCkkEsaMl7iIA2GKEvUYk0zT8jT6GJlV7WGwhp5EL6erZbA38CNgNRxxwp2eVv2G04TeXn04I0NTcUhCvpoIWzurzFy7Z8U+xFgEAgR5CJsqvKKpfRnlcozxIF7O5BxiGY2iOwr88tFHmBM3lHzakT9r69VGTyAQwoe/dRc8EABTk8xg/anCH8+RjwWVUxzXKg3RzCLur6l4AInInTszGKGP8Utf7rVxvam7hxQ9XMfG4vULVvY+C9pkAzi/KAk0RqO5SQVNza5vHd8nDb/tKllOlosmEqKpfWlVNIxfSGZu2bxE3CWbEwzHiTHKg2i8I7eXfjU1t0z+n/P41Xlm0phDDjI0I4NQRdR3qzACBoohxwwd0aMuWqBZc2kJOI1vSGZt9RGS9uy04GQTWu9uqqj0iHZ1RNPxiMJmmmUkk4hw/ajAzF6+NbrAe4iQC8CbQ9JIwQOkKoHmx9S1GZyeU9LkcMOnzFoKkttkuwsxl37AIrfywz+NcsuOfWN1cw0XLLim6CCAfiyghc+mzYRSSfEufjTIiqDBWtqWaozY0O1at5uYBN3NQ93dilQkglUorE0/FCpUZpYAZG6MDQesm4laqGeIhAghi5+38jU2yp+JXhsGLrW8xSoGwJQaMMiLoiTxfaql8UC3/ZWJtcjmAI4mLoQEC41SpPBU/rFCZUQqYsSlzEnVSBk54sq2q5vhRg6mqbP+lXVWZ3RRaFOxVvYC/f/kCTur9HLevPIETFv0ydilnINgTzNRTsfUtRilg02hlTNB0zvH713YsjaewTddKNmwqXqbmZBFAnDMBgLPGxo+ablW+svGagCSatr7FKAXM2JQxQdM5CUmul+ZWZXMRDU1cRQDgTA+0+rRXd/GfOAhyEFWDhQO2vsXo7JixKWMyFQIUaxJtVI9XuaH/1FiKACorhJaALKJNzX4myD/jQqI9E+GAYXQmLGZThiTiNEHGIy5CgIQI4Hc7Xx9bEUBLqwZ+XtkILTIRDhhGZ8I8mzLDb4Ggl+qqSo7fv5bH5jQUtZLmntULmVJ3IwO7/pvbV57A5BWn0Ky5Ff+KihbVDgtXUwXwMxVamMTZKAXMsykzUqWcSVR+vHbMXkWrpOmIAB7l8V0vpZv8l5MXX8cvPz0jtoYGnM/t+P1r2zyWShGO3z84xhL0uWbqIRlGZ8KMTZkR9JQsOLVUEl+QY4bW8sqEwws6YZUoB3B537t5fv0BjF5wa6zVZuB4MIcN6cNjcxraPJYWVR6b0xBYLjlIyjxu+ACTOBslixmbMiPTBYKFeqoe1eNVnhl0LvtUf8T4ZedzTv3lsVGbeREcCbiwxRN88cNVGcVaxgytbfMcvcfxepTedhMHGKWAxWzKjPGjBvsmdQx6ej5sSJ9Iq2lWy3+5qt8djNuuuOUAwuKXWPOih+b69k0VawmSMpvE2ShVzNiUGakWCF45bV7bGptE0a+NAfLdfNCZRACQOqbiVxzOW2LBDIhR7pixKUP8np6vnDavnQejSmSGRmjlrD6Pc8kO9/GfzT05ZfF1vBbz2AwEq8j8vMUEtlbGMBzM2BiAU8irEHgzATzVeBCXN5wXy9iMH0EqMq+36OfhWDkAwzBjY7gUIslmIhNA14pmLlt2Pg/HKBOAl5G79ubN+nWh41qwxVscOOFJ38WynXmtjFUJNfKBqdEMINqsAR0yAXw0lYeLkAmgQqCmOjgmVCnCqSPquP/MA7NWhRVb1ZdvEouAGxqbULZMCwbJug0jCPNsSoB8PHmOGz4gEtVZnEQArepkYvZb7Z9sTLJVhWWq9os7ViXUyBdmbDo5mVZ9DOLaMXsB+GZ8zoa4igDWbmzm1yftG9m0UKmVA7AqoUa+MGPTycnnk+ewnXrz4oerfIPcmbBj1Wpu6n8zI7ctngigUvyLveU6XRjGiyyltTJBsu7OOi1oFA8zNp2cfD15pkvQGZa4iACCvLMW1aw9wXx5kZ2JUpsWNIqHCQQ6OfkKSKdK0BkGrwigftOORRMBgOO9RJHGP5UXWaoEpdYpVeNqRId5Np2cfD155jJ1FicRAGQndgjjCQZ9RrlOO8adUpoWNIpHwT0bERkgIi+KyPsi8p6IXOC29xaR50Rkgfu7l9suIjJVRBaKyDsisp/nWKe7/ReIyOme9v1FZJ67z1SRmFQDi4CwT56JgmkDJzzJyEkz2qSrifZsSC4HcEqRywEkpMvXjgkuj5BLGv+gfeNSbM4w4kwxPJvNwCWq+qaIbAvMEZHngDOAF1R1kohMACYAPwGOAga5P8OB3wDDRaQ3cDUwDCcN1RwReUJV17p9zgRmAU8Bo4GnC3iNBSXdk2dQrGH20jVZF0nboYuTCaCYIgAvCSOTIMjj8ysMF9YTTBUHMgwjNQX3bFT1E1V9093+DPgAqAWOBe5xu90DjHG3jwXuVYeZQI2I9AVGAc+p6hrXwDwHjHbf66GqM1VVgXs9xypLgmIN982sz8rQjOrxKs98+TyGbjOfy2JSDiA53U4UafyDvKViFJkzjM5GUWM2IrIzMBTHA9lBVT9x3/oU2MHdrgW83yTL3bZU7ct92v3OfxZwFkBdXV32FxJz8rUmwlsO4J2Nu3FB/XiWxKQcgJ93ke80/qbMMozsKZqxEZHuwGPAhaq63htWUVUVkcjnJlT1DuAOgGHDhpXsXEjQWolM8IoAfrPyBG6OeTmAKCi1BZuGUUiKYmxEpArH0Nyvqo+7zStEpK+qfuJOha102xuAAZ7d+7ttDcDXktpfctv7+/QvW1KlwE9HnDIBVACpih4UonaMKbMMIzuKoUYT4E7gA1W92fPWE0BCUXY68FdP+2muKm0EsM6dbpsOHCkivVzl2pHAdPe99SIywj3XaZ5jlSXe+EUm7NBlNfcNvJLL+97N8+sPYPSCW4uaciZddR1LEmkY8aUYns1I4DvAPBFJ1NP9KTAJeFhEvg8sBU5033sKOBpYCGwEvgugqmtE5P+AN9x+v1DVNe72OcDdQDWOCq1klWjQPoVKz+oqRKBxYzM13apQhXVNzW1TPhcGlDBOZlSPV5nU/xa2qtgUq3IAAr4p/BNYkkjDiCeiJtsEnJjN7Nmziz2MjMkkzUxytmPfPvJfftbv95y83fTYiQASpJtOE2DJpGMKNBrDKG9EZI6qDkvXzzIIdHIySTOTrl9nEQGkm06zJJGGET/M2HRy8iFrjpMIIFdMimwY8cSMTScnV1mzNxPA0+sO4vLl59LY0iOPI8yMGjfmtHZjs+/7QaUDwFlcaVJkw4gnZmw6OZkE/ZMphAjArypmUMoY70r+gROe9BUCtKiGqrRpGEa8sBIDnZwxQ2vp1S2zuEq1/Jfra2/hdztfz7JNO0RWDiCRCiablDFBcZegY5qhMYx4Y2o0l86qRoPMFGl7VC9k6oBfMXCrBn636vjIRAB+3kaYKpfevn6pYcywGEa8MDVaGZH48r3mifdobPKPdRRSBOAXO8m0yqWlhjGM0sI8G5fO7NkkGDlphq9YoFAigFSeR9DYamuqeWXC4Xkfi2EYhcE8mzLETwbtFQH8ZPl5PLQmmlLN6ZRgQRLtfGWkNgwj3pixKSG8MuhCZgII450ESbRtAaZhlAemRisRrpw2r81L2KN6IX8fdCFjez/Lb1cez/GLbow05UyYdT7jRw2muqqyXZstwDSM8sE8mxLglN+/xiuL1iC08sN2IoBreW3DPpGfv1LST8tZwN8wyhszNp2caW818MqiNezQZTU3DZjMwdu+XfBMAEEr+pOxWjCGUb6Ysenk3Dh9fuQigEoRxg0fwIsfrgpUlBmGYaTCjE1nZvMGzt3ml4xzRQAXLruUxV/0T79fBgiwaOLRQPBCS4u7GIaRDjM2nZU1b8KrJ3NS74/47crjuWnFqVlnAqiuqmTrqgrf5JdetZjFXQzDyBYzNp0NbYUPboJ3roCt+vDqTg8y5cMeNGvHVDW9ulWxrqmZ1hQhlcT6GCCU12JxF8MwssGMTWdiYwO8djqseAH6fwuG/56Dt9qOidsE5xxL5CNraGxqS8+fagGmeS2GYUSBpatxiX26mmXTYNb3oeW/sP8U2PX7EEJybBiGESWWrqZU2LwB3rwYFt4BvfaDkX+GHhaQNwyjc2HGJs64IgDWfwRfuQz2/j+o7FrsURmGYWSMGZs4kiQC4PDnYUfLjGwYRufFjE3c8BEBsNV2xR6VYRhGTpixiRNeEcABvzcRgGEYJYMZmzhgIgDDMEocMzbFxkQAhmGUAWZsioWJAAzDKCPM2BQDEwEYhlFmmLEpNO1EAHfArj8wEYBhGCWPGZtCYSIAwzDKGDM2hWDNW/DqOBMBGIZRtpixiRJthQ9vhrd/aiIAwzDKGjM2UWEiAMMwjDbM2ESBiQAMwzDaYcYmn5gIwDAMw5eKYg8gKkRktIjMF5GFIjIh8hOueQue2R8W/t4RARz5mhkawzAMl5L0bESkErgN+B9gOfCGiDyhqu/n/WQmAjAMw0hLSRob4ABgoaouBhCRB4FjgfwaG1X45/GwfJqJAAzDMFJQqsamFljmeb0cGJ7cSUTOAs5yX34uIvOzPN+X4C+r4S9Z7t4p+RKwutiDKDB2zeVBuV1zrte7U5hOpWpsQqGqdwB35HocEZmtqsPyMKROg11zeWDXXPoU6npLVSDQAAzwvO7vthmGYRhFoFSNzRvAIBEZKCJdgbHAE0Uek2EYRtlSktNoqrpZRM4FpgOVwF2q+l6Ep8x5Kq4TYtdcHtg1lz4FuV5R1UKcxzAMwyhjSnUazTAMw4gRZmwMwzCMyDFjkyMFT4uTR0RkgIi8KCLvi8h7InKB295bRJ4TkQXu715uu4jIVPda3xGR/TzHOt3tv0BETve07y8i89x9porEIyOpiFSKyFsi8nf39UARmeWO8yFXWIKIbOW+Xui+v7PnGJe77fNFZJSnPXZ/EyJSIyKPisiHIvKBiBxY6vdZRC5y/67fFZEHRGTrUrvPInKXiKwUkXc9bZHf16BzpERV7SfLHxzxwSJgF6Ar8Dawe7HHlcH4+wL7udvbAh8BuwO/BCa47ROAG9zto4GnAQFGALPc9t7AYvd3L3e7l/ve625fcfc9qtjX7Y7rYuDPwN/d1w8DY93t3wJnu9vnAL91t8cCD7nbu7v3eytgoPt3UBnXvwngHuAH7nZXoKaU7zPOwu4lQLXn/p5RavcZOBTYD3jX0xb5fQ06R8qxFvufoDP/AAcC0z2vLwcuL/a4criev+Lkk5sP9HXb+gLz3e3fAeM8/ee7748Dfudp/53b1hf40NPerl8Rr7M/8AJwOPB39x9pNdAl+b7iKBoPdLe7uP0k+V4n+sXxbwLo6X7xSlJ7yd5ntmQR6e3et78Do0rxPgM7097YRH5fg86R6sem0XLDLy1ObZHGkhPutMFQYBawg6p+4r71KbCDux10vanal/u0F5tfA5cBre7r7YBGVd3svvaOs+3a3PfXuf0z/SyKyUBgFfBHd+rwDyKyDSV8n1W1AfgVUA98gnPf5lDa9zlBIe5r0DkCMWNjICLdgceAC1V1vfc9dR5dSkYfLyL/C6xU1TnFHksB6YIz1fIbVR0KbMCZ+mijBO9zL5zkuwOBfsA2wOiiDqoIFOK+hj2HGZvc6PRpcUSkCsfQ3K+qj7vNK0Skr/t+X2Cl2x50vana+/u0F5ORwDdF5GPgQZyptClAjYgkFjl7x9l2be77PYH/kPlnUUyWA8tVdZb7+lEc41PK9/nrwBJVXaWqzcDjOPe+lO9zgkLc16BzBGLGJjc6dVocV1lyJ/CBqt7seesJIKFIOR0nlpNoP81VtYwA1rmu9HTgSBHp5T5RHokzn/0JsF5ERrjnOs1zrKKgqperan9V3Rnnfs1Q1VOAF4ET3G7J15z4LE5w+6vbPtZVMQ0EBuEEU2P3N6GqnwLLRCRRze8InHIbJXufcabPRohIN3dMiWsu2fvsoRD3NegcwRQziFcKPzgKj49wlClXFHs8GY79YBz39x1grvtzNM5c9QvAAuB5oLfbX3CK0i0C5gHDPMf6HrDQ/fmup30Y8K67z60kBamLfP1fY4sabRecL5GFwCPAVm771u7rhe77u3j2v8K9rvl41Fdx/JsA9gVmu/d6Go7qqKTvM/Bz4EN3XH/CUZSV1H0GHsCJSTXjeLDfL8R9DTpHqh9LV2MYhmFEjk2jGYZhGJFjxsYwDMOIHDM2hmEYRuSYsTEMwzAix4yNYRiGETlmbAzDMIzIMWNjGB5EZDsRmev+fCoiDZ7XXfNw/KtFZGJS274i8kGKfa4RkUtzPXeK43/sppEflnzepNeHiFOO4l0MI0PM2BiGB1X9j6ruq6r74qSgn5x4raqbPKlOsuUB4KSktrFuezE5TFVnA4jIwSIyC/iRiLwhIkcAqOo/cRYyGkbGmLExjDSIyN0i8lv3C/iXyZ6GOMW5dna3TxWR111P6HciUuk9lqp+BKwVkeGe5hOBB0TkTPfL/W0ReUxEuvmM5aWEByIiX3JzvCWKwd3o7v+OiPzQbe8rIv9wx/OuiBwS4pJvxsmK/VucHGMLwn5WhhGEGRvDCEd/4CBVvTiog4h8BcdrGel6Ri3AKT5dH8DxZnBzVK1R1QXA46r6VVXdB/gAJ/VIWL6Pk+vqq8BXgTPdXF4n4+S52hfYByclUTo2AX0AVHWdqtZnMA7D8CXXKQHDKBceUdWWNH2OAPYH3nDyFlKNfzbch4BXReQS2k+h7Ski1+JU0eyOkyAxLEcCe4tIIslkT5ykkW8Ad7nZvaepahhjcxZwAzDSTd55uap+nMFYDKMDZmwMIxwbPNubaT8rsLX7W4B7VPXyVAdS1WUisgT4f8DxOFUfAe4Gxqjq2yJyBk6i0GS8597a0y7AearawUCJyKHAMcDdInKzqt6bZnzvA98QketwUsrfiWNIDSNrbBrNMDLnY5x6MIjIfjgFusDJgnuCiGzvvtdbRHYKOMYDwGRgsaomqiFuC3zieiF+02+Jc+/vbp/gaZ8OnO3ui4h8WUS2cc+/QlV/D/whMe5UiMie7mYz8KY7LsPICfNsDCNzHsOpC/IeThntj8DxCETkSuBZEanA+bL+MbDU5xiPAFOB8zxtP3OPt8r97fcl/yvgYRE5C3jS0/4HnFr0b7q1R1YBY3C8o/Ei0gx8jlOTJB3niMi+OAW1xgAXhtjHMFJiJQYMo8xxFW3DVHV1Uvs1qnpNUtvOODWA9sQwMsCm0QzDWAW8kLyoE3jJ+8KVTf8NWI1hZIh5NoZhGEbkmGdjGIZhRI4ZG8MwDCNyzNgYhmEYkWPGxjAMw4ic/w/lFA4SDeIMAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jS3EKTlW9XGf"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVRvJyLcANfy"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "clf = RandomForestRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rb3d6uFZAQH7"
      },
      "source": [
        "rf_model = clf.fit(flatTrain, trainLabels.ravel())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7aZbSiAA02y"
      },
      "source": [
        "rfResults = rf_model.predict(flatTest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dF44xD8uArC6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38f7cc95-3be3-462c-81e9-8b2a8c227ed4"
      },
      "source": [
        "mape(rfResults, testLabels.ravel())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.711440729939648"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(testLabels.ravel())\n",
        "print(rfResults)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTvgKNyTAq0N",
        "outputId": "fc855618-d801-4545-cc35-657b49fa73c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[30998. 13998. 19998. ... 27998. 22998. 33998.]\n",
            "[29128.   15218.15 19748.   ... 28488.   25798.   33288.  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(testLabels.ravel(),rfResults)\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "# plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhHXBrsmIRur",
        "outputId": "02872a23-b487-49b6-f05b-bad121a8aaf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8587819590>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = open(\"rfData.csv\",\"w+\")\n",
        "\n",
        "f.write(\"True, Predicted,\\n\")\n",
        "for i in range(len(y_true)):\n",
        "  f.write(str(testLabels[i][0])+\",\"+str(rfResults[i])+\",\\n\")\n",
        "\n",
        "f.close()"
      ],
      "metadata": {
        "id": "kKs8_SzZ-pRw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SVM MODEL"
      ],
      "metadata": {
        "id": "3cXlD0WoDI4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm"
      ],
      "metadata": {
        "id": "7aiWr8SSDcm1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = svm.SVR()\n",
        "clf.fit(flatTrain, trainLabels.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpVG-HPYEOTO",
        "outputId": "93abe15d-28ea-4961-a2bf-fb0d827f4f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m89j46B1Ffg_"
      },
      "source": [
        "svmResults = clf.predict(flatTest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def mape(actual, pred): \n",
        "    actual, pred = np.array(actual), np.array(pred)\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100\n",
        "\n",
        "\n",
        "mape(svmResults, testLabels.ravel())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lry0ZBnvGBu0",
        "outputId": "89b46ca0-f5a3-417a-e5bf-222326a45604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.29046502677094"
            ]
          },
          "metadata": {},
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(np.asarray(testLabels).astype('float32'),np.asarray(svmResults).astype('float32'))\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "r89E6-SyGZgJ",
        "outputId": "76d7341f-1395-4020-e7e6-3d0f00c4d6d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb15be43f50>]"
            ]
          },
          "metadata": {},
          "execution_count": 207
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN RF Ensemble"
      ],
      "metadata": {
        "id": "hyed8kWcIJIA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ensResults = []\n",
        "count = 0\n",
        "for test in flatTest:\n",
        "  count+=1\n",
        "  test = np.asarray([list(test)])\n",
        "\n",
        "  print(count/len(flatTest))\n",
        "\n",
        "  rf = rf_model.predict(test)[0]\n",
        "  dnn = dnn_model.predict(test)[0][0]\n",
        "\n",
        "  print(rf,dnn)\n",
        "  print((rf+dnn)/2)\n",
        "  ensResults.append((rf+dnn)/2)\n",
        "\n",
        "print(ensResults)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlVgIXT1IQFZ",
        "outputId": "781d6729-2e0b-42c5-b478-0e5ab1626003"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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34060.556640625, 32782.6953125, 41460.76953125, 28593.947265625, 14681.43408203125, 21012.1259765625, 14617.53728515625, 39675.64453125, 25276.7421875, 28221.4638671875, 30211.6591796875, 38858.765625, 29089.98046875, 38191.025390625, 22387.095703125, 29668.8642578125, 39497.115234375, 24623.33203125, 16656.393515625, 21560.958984375, 22526.8154296875, 17128.4970703125, 15704.17998046875, 19140.4169921875, 32538.220703125, 23051.6796875, 25146.6650390625, 19209.6181640625, 33504.75390625, 15818.02958984375, 19221.37109375, 25158.103515625, 28227.8896484375, 23716.6240234375, 28143.1162109375, 38960.029296875, 22730.2470703125, 18914.921875, 34885.60546875, 26884.91015625, 32716.66015625, 53161.80078125, 14740.099824218749, 22245.2607421875, 21230.4697265625, 31497.05859375, 31621.8310546875, 26371.3671875, 18328.36484375, 27693.8486328125, 21241.421875, 25894.240234375, 19434.4892578125, 21233.48046875, 19342.5390625, 36560.46875, 33960.796875, 22324.55078125, 28264.6884765625, 18518.791015625, 20889.09375, 34671.669921875, 42652.3515625, 27478.4482421875, 45202.328125, 30540.0517578125, 19053.3740234375, 25379.59375, 21835.6328125, 13684.30216796875, 27943.35546875, 17010.126093749997, 30848.41015625, 34865.7421875, 24512.9833984375, 33995.919921875, 30936.9814453125, 49843.84765625, 17381.5234375, 18642.2646484375, 28520.6669921875, 22720.0673828125, 22256.7890625, 34129.322265625, 31750.9833984375, 43918.33203125, 22411.623046875, 17854.0791015625, 33169.6171875, 26486.1787109375, 15156.58830078125, 44578.583984375, 20976.595703125, 27898.361328125, 28649.3203125, 36118.158203125, 26827.8310546875, 20830.650390625, 26222.4658203125, 32862.158203125, 17820.4687109375, 34287.408203125, 34343.8515625, 37172.576171875, 28922.130859375, 24702.0595703125, 33032.923828125]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OTMmiCCp954B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testLabelsFilt = []\n",
        "ensResultsFilt = []\n",
        "\n",
        "for i in range(len(ensResults)):\n",
        "  if ensResults[i] < 150000:\n",
        "    testLabelsFilt.append(testLabels.ravel()[i])\n",
        "    ensResultsFilt.append(ensResults[i])"
      ],
      "metadata": {
        "id": "dIqQRY-YKc51"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mape(ensResults, testLabels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWiOgxzCMRqZ",
        "outputId": "c8728ee1-48b6-47cc-ab34-3e0899da818e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.1298615149558"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mape(ensResultsFilt, testLabelsFilt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kzGdCnFUIuQH",
        "outputId": "81a7a36a-ba37-4b3b-a6ac-96358f6b8777"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.9930315180145035"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(testLabelsFilt,ensResultsFilt)\n",
        "plt.xlabel('True Values [$]')\n",
        "plt.ylabel('Predictions [$]')\n",
        "# plt.ylim([0,100000])\n",
        "plt.plot(np.arange(100000),np.arange(100000),c=\"orange\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxTzAw9YIopx",
        "outputId": "cb266fd6-86d7-439c-efda-479e2f66b5d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f858a461bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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arwArx5wR9Fx/ixC9+6d4v8SPG/M5pyS/y/A2r1KkyTyw7kre3XYqmelptXpPk7rM9nMxxoRUnbTwIVs925fzWLObOabRImZu78mdOTewodBZZV9+YL0ubQecqCy4GGNK+Uu9Eu7YRKCZVxvy/oClj8PPd9ElLZnb1t7C+9v6gk9+Yt/gFWw7YNjX5ZWe5kEV8gsKywQgC0zxwYKLMQmuMl+2VUmB4r2+v+60A+pnM77DBPhpMbQ9k+/S7uWTZblA4OAVqAV079RF7CkqKX1t2659M8e8AWjO6q22WDJOBB1zEZGpYVxjq6peGrEaxYiNuZhEFM44SKSvD5BEMVe2nMKtrf9FSVIDFrd9gKGzDmNd3u6ALQ6vQOM+4Qg05bi271Mfz6o65nIIcGWw6wJPV6dixpjoqcrsr8q0dPxd/8D6a/ln1hN0a7iMafm9GbflJnKWZFBQuBtwWhypnmQev6ir3+sGGvcJhy2WjB+hgstdqvpVsANE5L4I1scYE0EhN94qJ9h4h79A4HudZIoZ0vLf3NL6X+wsSeXG1cP4KP94nL9Bww9wgcZ9GniSynSF+WOLJeNH0EWUqvpOqAuEc4wxJjYCfakGKg9ni2F/1zm4/ireP+h2RrR5hRnbj6bfr0/zUf4JEGRT4UABLtB2wKPOqriI0pctlowvIQf0ReQwYJOq5opIc+ARoBFwv6oujnYFjTFVd1KXln639D2pS0u/xwfqjgpUPrzfAaz8ehTXtniTP0rSuG71CD7JP6709WAtjmCtiXAWUabVS2bX3mIUp8VyXo9MHhx4hC2WjBPhzBZ7Hvg/9/FDwAZgAfAS0DtK9TLGRMAXS3MrVR6oWwmgz5iZZb+ot/3CORsvg1Y/MWPniQxfNYSS+i1ITy07WA9UeXpzed6g4+2+89bUm1q/5/7NbE+WOBE0uIjIKOBA4FoREZwg8xLQBcgSkXuAL1X166jX1BgTlL+B+MqOuQQKLLBv/EW0kHM8r8GiB6FeBhz3Hie3P4+5IeoXydZEddPUmOgLGlxU9T4RORt4FWgNHK+qIwFE5FRVvb8G6miMCSHQQHzTVE/ITMK+MkPM1OqYvJxO866D1JWsbTKQdqe8AA1aBDzeq7KtiVAz1iobNE3NC6db7EHge2AvMAj2jcNEsV7GmEoI9Jd8A08SqZ7ksLuk/M3UAvBIITe0eofrWr3DtqImDFl1N98U9GF05h4GdvNfp3CmNPs7BmDYez9TWOy0onLyChj23s/Avhlr1UlTY2qGJa502SJKU5sFSzj5+EVdq7Ti3vvlfXjqCsZlPcEhqat4f9tJ3L/uKvKLG5cenxkk+3GwxZuBjhGUXYUlFeqVkeZh3j39wr6+qRlVWkQpIvup6obqHmOMia5gf8lXtkvKe/yof8+l5aqxXNPqPTYXpXP5ynuYueOYCsf7WwsTzphIoGMC8Z1xVpU0NaZmheoW+wToHoFjyhCRdOBF4HBAgcuBZcBkoAOwCrhQVbe5EwnGA6cDu4BLVfUn9zqDgbvdyz6oqq+65T2AV4BUt35D1ZpoJoGFm3Ay7NX3W35kcP6FHNB6Fe9uPYUH1l/J9uJGAe9fPnCEMyZS3fERmxUW30LtRHmUiGwP8rMDZ6C/ssYDn6lqF+AoYAkwApihqp2AGe5zgNOATu7PVcCzACLSDBgF9AKOAUaJSIZ7zrPAEJ/zBlShjsbUGoEWHvrrqgq6y2Txbpg/Av7bm1R2cOnKexmWfXPQwOLlGyzCWbwZ6BgJsO4yPdUTsg4mfoRaoZ+sqk2C/DRW1Ur96SAiTYHjgUnuPfaqah5wDs6sNNzfA93H5wCvqWMWkC4ibYD+wHRV3aqq24DpwAD3tSaqOsttrbzmcy1jEtbAbpl8O6IvK8ecwbD+nRk3bRkdR3xMnzEzS1ssQVffb54Fn3aDxY/AAZczeONLfLmjQld6QL7BItB2w74tqUALOY89oBmepLIRxpMk3Hv2YWHXxcReqJZLNHQEcoGXRWSeiLwoIg2B1qq63j1mA/taRJnAWp/zs92yYOXZfsorEJGrRGSOiMzJzfW/qMyY2iZQCyXQFOMt+XkwbxhM7wNFO/mu3Rv0+WIQv25LrpC8Rcr99iofOMJpSQVayLlqSwHjLjiqzLnjLjiqdPFknzEzywRNE59isZ9LCs4YzY2qOltExrOvCwwAVVURifoYiapOBCaCM1ss2vczJhJCjZsEaqH4W33fPW0JT+w/HpZkw0FX85+kmxn24SoKCp1ApDiBxPd3+XJ/s8Ug9JhIsHEZf+dWNqmmia1YtFyygWxVne0+fw8n2Gx0u7Rwf3vX0eQA7XzOz3LLgpVn+Sk3ptYLZ9wk2Op7b1dVA9nN3W1e4L0Dh9MiDej7ORzzHKM/X1chMHlzd5X/68sbWL4d0bdKX+7RTqppYius4CIiB4pIfffxiSJykzvjq9LcactrRcTbhj4ZWAxMBQa7ZYOBD93HU4FLxNEbyHe7z6YB/UQkwx3I7wdMc1/bLiK93Zlml/hcy5haLZwv2GALCeunJNG32VI+PfhGrmz5Ie/mn8kx8x6jzyvClHk5lU4LU5UZX96urZy8goDda/66v2xVfu0SbsvlfaBYRA7C6UZqB7xZjfveCPxLRH4BugIPA2OAU0VkOXCK+xycqcS/AyuAF4DrAFR1K/AA8KP7c79bhnvMi+45vwGfVqOuxsSc7xeyP75fsP4G0wFSZTdDM57ixcxhNG2QxODVY7hjzdX8UZJW2gJKT/M/Iys5wBQuhUqNffi2vLzne6/sHZcB/LbOAtXNVuXHp3DHXEpUtUhE/g94UlWfFJF5Vb2pqs4H/E1DOdnPsQpcH+A6L+Ek0ixfPgdnDY0xtV6grYR9+X7B+i4w9H6J9274C49kTWD/+ht4efNZPLZxMDuKG5S5RkFhMfVTKqaLEZyWi++Yi6/KjH34a3n5dq+BE6z8tc781c32aolf4bZcCkVkEE531X/cMpt0bkwN8PeF7EtwvuB9WxDeackNkwq4v+2zvH3gnSjChb+N4b51V1cILF75BYWls7y81y4/iO9PuGMf1Vlc6Vu3QDPQTPwIt+VyGXAN8JCqrhSRjsDr0auWMYmnMnvT+wqWpdj3y79CC2LDDKZ3uZH9kjcyKfccxm34O7vVf1DxKt/F5G8QP5Bwxj7CSTgZyVQ2JnbCarmo6mJVvUlV33Kfr1TVR6JbNWMSR1ir4wMINN4BFb/sCwqLefq/P8EP18DMU2iSlsrfVo/jgfVDQgaWVE8yJ3VpGXRNTDDhjL+Es7gynGNM/Aur5SIifYB7gf3dcwRnOOSA6FXNmMQRapZXsBZNsA28yvtzo58Y0/pJ+G0LHHI7jY64nwsXbGW1z/iLP95tgr9Ymhu0Cy6UnLwChr1bNj2+r3ASTlpSysQQVsp9EVkK3ALMBUr/5anqluhVrWZZyn0TTR1GfBzwNX8D6L6LE8eFCAwAjZN2cmebSQxq/l9WFbanwxmToUXZXciD1cFfPaojPdXD/FH9InItE9+qlHLfR76q2nReY8Lgb2wl2N70/mZPwb6us/N6ZDL5x7Wlm2eVd2LjOTyc+RStPVt5YfMFtO4zmg4tDqx0vQOt4q8Kf7tfmrol3ODyhYiMAz4A9ngLvanvjTGOQClKqvqFXVBYzBuz1vh9rUnSH9zd9kUubPY5v+5uz/Dsf7Bwdxfy313KI9NXV+hKSg+w5bEv7yr+SLVgTN0VbnDp5f72bfoo0Dey1TGmdqtMXi+gyi2Fkxr/yOisJ2mRksdTGy9kwqZBJKc0oKDQCR7+1p7ce/Zh3Dp5PhX3eCwrSQi4piVcSYHnIJg6IqzgoqonRbsixiSCUHm9yi8APK9HJu/PzQm7pdA0eQf3tJ3IeRlfsLRgf65cdQ8LCw5yXgyx8yNAcrJQEqB7zWvn3uq3WkosDWydF25usaYi8pg3Pb2IPOruy2KM8REoFUlmeirn9cgsnVbs/GWv/GvWGuqnJJERILWJr1OazGb6wddxdvrXjN94MWeteGJfYAnAd3HluGnLAo7bVEWqJ5mG9SqmmQHCej8msYW7Qv8lYAdwofuzHXg5WpUypqZEen+QYf0740kut9FVsnBSl5a8PzentAusRKGgsATFGfz23R++vPTk7TzRbhwvdniAzUXpDFz+GI9v/BuFGt4XeKj9XMKVnuohI81TZnW8J9n/V4htKm7CHXM5UFXP83l+n4jMj0aFjKkpUdsfpNwXa2GxBhyUD6V/k+94MPMZ0lN28NiGv/Js7vlhBxVfwcZ9kiR0N5Zv7i9ft0z2/zWQb7PF6rxwWy4FInKc94m7qNLyXJtaLRr7g4ybtozCCAw4NEvO58n2j/B8h4fZWNScs5c/zoRNg6oUWLx893PxSvUkUz8l+NeAv9xlXpXdk8XUHeEGl2uBp0VklYisBp7CyTVmTK0Vjf1BIrG3yOlNv+G/na+jf5PvGbfh7wxc/ihLdlc/GUZGmsdv4sfdhcHnj5Vfd+MbYCxViwkk3Nli84GjRKSJ+3x7VGtlTA0IJ4liMHdPWcBbs9dSrEqyCIN6tQt4zXA0T87j/sxnOSP9W37ZdRB/WfsQv+7pUKVr+aPqf+vhcDIAeJWfgWapWkwgQYOLiPxNVd8QkVvLlQOgqo9FsW7GRNWw/p0r7JMS7l/dd09ZUGYcpVidcZU+BzZj6869lVyEqJzV9Gvuy3yehkm7eGT9YCbmnksx/mdiVVWgcRB//x2CKd86s0zFxp9QLZeG7u/Gfl6z+SCmVqvOX91vzV7rt3zW79t49MKjSlsDoRYjtkzZxgOZzzCg6ffM33Uwt6+9mRV72lfh3YRWvkXm2/JKEkj1JFEQoovM33WM8SdocFHV592Hn6vqt76vuYP6xtRqlf2r27teJNCqet9yAZqmehDBz1Rj5Zz0L7m37UTSknbz8PrLmJQ7MOKtFa/yLbLyLS/v1OhkgWBLYWw8xYQr3KnITwLdwygzJmGFs92wCGWOySsorDDg3SplCw9lPc2pTX5g7s4uDM8eym972kWt3pl+WmSBWl7BAou/6xgTSKgxlz8BxwIty427NIEo/YllTJwKtd0wOIPm/qY3u69yXsZM7mkzkfpJhTyw7gpe3nw2JVH+X8nf+pSq5DPzdx1jAgnVcqkHNHKP8x132Q6cH61KGRNrf33he779bWvpc08ShDEcEVDrlM2MznqKvk3m8MPOQxm+diir9ka/BZCe6n9dTGUTZgbbDdMYf0KNuXwFfCUir6jq6hqqkzExVT6wQHUCi3JBxnT+0XYSHini3pyreHXLmWjYS8yqzpMk3Hv2YX5fG9Srnd+sAZ1aNWT5pp1+jzemMsL9F/6iiKR7n4hIhohMi1KdjImp8oGlqtp4cnm14yjGtZvA4oKO9P/1KV7ZcnbUAkvDesl42xfJIlx0TLuA4yMPDjyCv/VuX9oiSRbhb73bM/3WE/2WPzjwiKjU2SSucLc5nqeq3UKV1Wa2zbHxzgSrboJHUC5uNo272kwiCWXMhkt5Y8vpUW2tNKyX7M74KrtmZ/S5R9gAvImq6m5zXCIi7VV1jXux/bF1LiaBTJmXw7D3fq52SvpMzyZGZz3J8Y3n8d0fRzJ87U1kF+4XoVoG5m8PFn/7uRhTU8INLncB34jIVzjT9/8MXBW1WhlTQyLVWhFK+EuzzxjZxtmJ4q7s63hz64AaGVsJJhK5zoypinBzi30mIt2B3m7Rzaq6OXrVMiayvEFkXV4B6WkeVAm5n3y4sjwbGNtuAsc2+oX/7ejKiOybyClsFZFrV5etpjexEmqdSxdVXeoGFoB17u/2bjfZT9GtnjHVV37xY7CNuSpDKOFvzT9hxH6vUIJwR/aNTN7aD4iPabu2mt7EUqiWy23AEOBRP68pYKuqTNwLZ/FjZbWvt56xWePp3WghX+3ozsjsG1gXwdZKddfV2Gp6E2uh1rkMcX+fVDPVMSZyIjf7ax+hhEubf8TwNq9RqCkMWzuUd7edQqRbK40aeNhdWFKloCjYanoTe6G6xc4N9rqqfhDZ6hgTGeHkAausDvVyGNtuPMc0XMyM7UdzZ/b1bCxqEbHr+9q2q5CMNA/1U5IqPTZk4ywmHoTqFjvL/d0KJ8fYTPf5Sek1gl8AABwuSURBVMB3QJWDi4gkA3OAHFU9U0Q6Am8DzYG5wN9Vda+I1AdeA3oAW4CLVHWVe42RwBVAMXCTqk5zywcA43Hyn72oqmOqWk8TG74D8L6p8P1t0OVvgV8ku8KSKOayFlMZtt/r7CnxcOuaW/ggry/RHlvZtstJehkoFX6aJwlFqrQfjTHRFqpb7DIAEfkvcKiqrneftwFeqea9hwJLcJJgAjwCPK6qb4vIczhB41n39zZVPUhELnaPu0hEDgUuBg4D2gKfi8jB7rWeBk4FsoEfRWSqqi6uZn1NDSnf6vBur/vunDVlVs97N+gCKgSYSHWFHVA/m3FZT9Cj4VKmbz+Gu7KvZ1NR84hcOxwFhcVkpHkoKlYKS/atwfEkCQ+feyRgu0Ca+BTuOpd23sDi2ghUeUcjEckCzgAeAm4VZ2vLvsBf3ENeBe7FCS7nuI8B3gOeco8/B3hbVfcAK0VkBXCMe9wKVf3dvdfb7rEWXGoJf62OgsLigGlZ3pq9NuLpSZIo5sqWU7it9RvsKmnA0DW38WHeicRiJljerkIev6hrwCBiwcTEo3CDyww3l9hb7vOLgM+rcd8ngOHsy7TcHMhT1SL3eTbg/T8mE1gLoKpFIpLvHp8JzPK5pu85a8uV9/JXCRG5CncxaPv20dn9z1ReZRf+FavSYcTHIXd9DNdB9dfwz3ZP0DXtV6bl9+bunOvJLcqIwJWrpm16qm0lbGqdcBdR3iAi/wcc7xZNVNV/V+WGInImsElV54rIiVW5RqSo6kRgIji5xWJZF7NP2/TUKnVrVfcDTKaYq1u+z9DWb7KzJI0bVw/jo/zjieW6FRtDMbVVuC0XgJ+AHar6uYikiUhjVd1RhXv2Ac4WkdOBBjhjLuOBdBFJcVsvWUCOe3wO0A7IFpEUoCnOwL633Mv3nEDlpgYFGpQPZVj/zhGf6RXKwfVXMa7deI5KW87HeX24J+dathSnhzwv0tLdbZHzdhXaGIqp1cIKLiIyBKf7qBlwIE7303PAyZW9oaqOBEa61z0RuF1V/yoi7+JsQPY2MBj40D1lqvv8e/f1maqqIjIVeFNEHsMZ0O8E/IDzZ2Ynd/ZZDs6gv3csx9SQQIPyEHqMwPt6pNeo+JNCEde0eo+bWr3N9pKGXLd6BJ/kHxfVe/pjGYxNogk3q971OC2O7QCquhxnenIk3YEzuL8CZ0xlkls+CWjult8KjHDrsAh4B2eg/jPgelUtdls+NwDTcGajveMea2pQoEH5cdOWhXX+wG6ZfDuiL09c1LXCHvSR0qXBSqZ0uo3b93uDz7YfS79lz8QksGSmp1pgMQkn3G6xPe6aEwDc7qlqj1Go6pfAl+7j39k328v3mN3ABQHOfwhnxln58k+AT6pbP1N1gQblwx2s9+1Sq5cS2czCHinkupbvckPryeQVNebqVXcybfuxEb1HeZXdVtiY2i7c/2u/EpE7gVQRORV4F/goetUytV2gVeLhrB73dqnl5BWgwJ6iaiTZKuewBr/x4UG3cst+b/Jx3nGc+uszUQ8sqZ5kBvVqF7AF5u0ynDLPhgZN4gi35XIHcCWwALgap1XwYrQqZWo/f4Py/mY++Vtx/8XS3IgP5nukkBtaTea6Vu+yragJQ1bdzfTtvUOfWAUZbkr//IKyg/I9928WcBzJNvYyiSZkcHHTtCxS1S7AC9GvkkkEvoPygWaL3T1lQekKeyi74j6SDk9dwbisJzgkdRXvb+3L/euHkF/cOPSJleRvUN7bvXfL5Pml/w1umTzfb5+ybexlEknI4KKqxSKyzHebY2PCEWrh35uzo/vPqZ4UclOrt7im1XtsLkrnspWj+GLH0VG5l78U94FmzDVN9fhNRmkJJ00iCbdbLANYJCI/ADu9hap6dlRqZeqEkiiObx+Vuoxx7cZzcIM1vLP1FB5cdyXbSxpF/D7JScKjFxzlN4gGmjHXwJNEqifZEk6ahBZucPlHVGthTITUl73c0vpfDGn5bzYWNmPw7/fx1R89ona/4hLlvo8W+Q0ugbq5QuUKMyYRhNrPpQFwDXAQzmD+JJ/8X8ZUS6RygXl1T1vC2KzxHNQgm7e29OPh9Vewo6RhBO/gX6BtkwOlsbFcYaYuCDUV+VWgJ05gOQ3/2x0bUyWRCiz1ZQ93tpnEewcOp0HSHv7++/2MzLmpRgJLMMP6d64w/di6v0xdEapb7FBVPQJARCbhpFcxptJ8F0Wmu1N1I6FH2mLGthvPgfVzeGPLaYxZfxl/lKRF5uJhSk/1+C0PZ8acMYkqVHApbe+76e6jXB2TiMrPmgrUjVQZDWQ3w/Z7nctaTCWnsBV/+f1Bvvuja7WvG4onSSps2nXv2YcFPN66v0xdFSq4HCUi293HgrNCf7v7WFW1SeBTTV0RKvtxJLccBjim4ULGZo2nQ/31vLr5DB7ZcCm7SqI/jdc73dhaIsaEFmqb4+hkDDQJI5zsx5HKbJwquxne5lUua/ERq/fsx8W/PcysnUdG5NqheJKkNJBYMDEmtMrs52JMKW9rJZxUJiJUe4zlTw1/4ZGs8bSvv5GXN5/F2PWDKdAG1btoZViPsDGVYsHFVFr51oo/6/IKSvOGVSewNEzaxYj9XuHvLT5h5Z42XLBiDD/uOrzqF6yiwmINO/dXVTdJMyaRWHAxlRbOGIpCtfOEHdtoPmOzJtDWk8uLuefwzw1/Z3dNtlbKCSf3V3U2STMmkVhwMZUW7d0hGyXt4s42L/GX5p/x255Mzv9tLD/tOiSq9wxHOLm/gm2SZsHF1CWR3YXJ1AnJUZyS/udGPzHt4Ou5qNl/eW7TuZz+64SIB5YnLupKZpBAkZHmqfLix+pukmZMorCWi6m0aOyo2DhpJ3e1ncTFzf7Lit1ZnP/bWObt6hLx+ySLlM748jd2lOpJZtRZzrqVqoybBEv5YkxdYsHFVFpmgC/Qqjqx8RweznyK1p6tPLPpfMZv/At7tF7Eru9rUK92pY9DraCvSjdWuJukGZPoLLiYSjupS8uIbOrVJPkP/tHmRS5o9jnLdrfnmhV38kvBwRGoYUXeXS4fHHhEmfJIr1uxlC/GOCy4mLAFW9tSWSc3ns3DWU/TPCWPJzdexJObLmav+s/RVV2Z6al8O6JvVK7tjy20NMaCi/HD3zoNIOTalnA0Td7BPW0ncl7GFywp6MAVq+5hYcFBkai2X9YlZUxsWHAxZQRap1E/JanagaVfk+95MPMZMlK2M37jIJ7adCGFUWqtgP+th40xNcOCiykj0DqN6gSWjOR87m07kXMyvmJxQUcuXXkfi3cfUN2q+lXTXWDGGP8suJgyIr1AckCTb3kg81maJv/BYxv+yjObLqAoiv/sbD2JMfHBgospI1JbDzdLzuf+zOc4M/1/LNh1IH9f+QBLd3eMwJWDa5rqoc+YmTZTy5gYs+BSh/kbuI9EYDm96Tc8kPkMjZN2MXb9JUzMPTeqrRUvT5Kwc28ReQXOZmSW18uY2LHgUkcFS7BYVS1StnF/22c5Pf07ft7ViWFrh/Lrng4RqG1g3pZWZnoqu/YWVdjl0vJ6GRMbFlzqqEAD91WjnJ3+Nfe2fZ6GSbsYs/5SXsj9P4qJ7l5z5WeDdRzxsd/jbBzGmJpnwaWOitQXbsuUbTyY+TT9m85i3s7O3J59M7/taRf6xGoSqDArzPJ6GRM/ajwrsoi0E5EvRGSxiCwSkaFueTMRmS4iy93fGW65iMgEEVkhIr+ISHefaw12j18uIoN9ynuIyAL3nAkiUUzjW0tV/wtXGZj+BdMPvpYTGv/EQ+su57zfxtZIYAH/9R/Wv3OVsxkbYyIrFin3i4DbVPVQoDdwvYgcCowAZqhqJ2CG+xzgNKCT+3MV8Cw4wQgYBfQCjgFGeQOSe8wQn/MG1MD7qhWmzMuhz5iZ5OQVVHnn3lYpW3ixw/080f5RVuxpx+nLJ/DC5nMpqWY3WEaah/RUDwI0rJccsH6BAsbAbpmMPvcIMtNTEZxus9HnHmHjLcbEQI13i6nqemC9+3iHiCwBMoFzgBPdw14FvgTucMtfU1UFZolIuoi0cY+drqpbAURkOjBARL4EmqjqLLf8NWAg8GlNvL94Vn4Qv/Izw5TzMmZyT5uJ1Esq4v51Q3hl85nVDioAf+vdvkJSSa/KbBtc2bxetiWxMdER0zEXEekAdANmA63dwAOwAWjtPs4E1vqclu2WBSvP9lPu7/5X4bSGaN++fdXfSC0RzvbEgezn2czozCc5qclcfth5KMPXDmXV3qp9Cad6kthTVEKJBs5W7CtaiSBtS2JjoidmwUVEGgHvAzer6nbfYRFVVRGJ/I5U5ajqRGAiQM+ePaN+v1ir2iC+cmHGdO5u+yIpUsy9OVfx6pYz0Sr0qArw+EVd4+aL27YkNiZ6YhJcRMSDE1j+paofuMUbRaSNqq53u702ueU5gO8ocZZblsO+bjRv+ZdueZaf4+sk326fykbPtp5NjMl6kuMbz2PWH4czPHsoa/a2qVI94jGJpG1JbEz0xGK2mACTgCWq+pjPS1MB74yvwcCHPuWXuLPGegP5bvfZNKCfiGS4A/n9gGnua9tFpLd7r0t8rlWneLt9ciodWJRBzT5j2sHX0yNtCXfnXMug3x+ucmDp1Koh347oG1eBBQLPmLOpy8ZUXyxaLn2AvwMLRGS+W3YnMAZ4R0SuAFYDF7qvfQKcDqwAdgGXAajqVhF5APjRPe5+7+A+cB3wCpCKM5BfJwfzqzLGkuXZyOisJ/lz4/l8u+NI7si+iezC/apch06tGjL91hOrfH402ZbExkRPLGaLfQMBZ5me7Od4Ba4PcK2XgJf8lM8BDq9GNRNCZTIcCyX8tdlnjGjzMgB3Zl/Pm1sHEPij8i8eu78CsS2JjYkeW6GfIMpPqT2pS8uwz21XbwNjs8bzp0YL+HpHN0Zm30hOYasq1SPWe6lUdmqxbUlsTHRYcEkA/qbUvjFrTcjzhBIuaf4xd7R5hSJN5o7sG5m8tR+Vba14/a13bKdz29RiY+KHBZcEUJWxlf3rrWNs1gR6NVrIl9t7MDLnBtYXht/a8RXOWpWaYFOLjYkfFlxquSnzcio9tnJp848Y3uY1CjWF29fezHvbTqayrZV4CSi+bGqxMfHDgkst5u0GClfHejmMbTeeoxsuZsb2o7kz+3o2FrWo9H2fiKOFkL4sK7Ix8SMWiStNhITbHZZEMVe2+IBPD76Rg+uv5pY1t3LFqnuqFFg8SfGbYNqyIhsTP6zlUouF0x12YP21jMt6gu4NlzE9vxd35lxPblGzkOfVT3Hyf5VXWKJxO4ZhU4uNiR8WXGqxZBGK1f/a+2SKubLlv7m19b/YVdKAm9bcztS8Ewg1tpKe6mH+qH6As7Ojv6vH8xiGTS02Jj5YcKnFAgWWTvVXM67dE3RNW86n+cdyT8615BZl+D22vPyCfXvQ2xiGMaaqbMylFstI85R5nkwx17V8h/90Gkq7ehu5fvUdXLt6ZNiBBcoGjmH9O1cYY/EkiY1hGGNCsuBSi/k2XDo3WMW/D7qN4W1eY/r23vRb9gwf5/+Z8t1gye7WBhlpngqBw+/gd7letMIS5ebJ8+kzZiZT5tXZZNPGmBCsW6wWyy8oJIUirm31Lje2msz2koZcu3oEn+Yf5/f4zPTUMulZQqVKGTdtGYXF/rvebPW7MSYYCy612PEt1zEs/REOT/2NqXnHMyrnarYVNw14fPmB+FCD36EG7m31uzEmEAsutVHxXlj0MC+3eYgtRY25etWdTNt+bMjTKjsQH2hA31c8zxwzxsSOjbnUNlvnwbSjYeF9JHW4mB8O+ZqFSScjON1eT1zUlScu6hqRxYT+FiWWZzPHjDH+WHCpLYr3wM//cALLnlw4/kM49nUKUyrOBBvYLZPR5x5BZnpqadAZfe4Rle6+8r0OVFwhY6vfjTGBiAZYK1HX9OzZU+fMmRPravi3ZQ7MugzyF0LHwdDjcaiXUSHFPDhf+FUJJOGo7F4pxpjEJyJzVbVn+XIbc4lnxbthwf2wZCw02A9O+A9knlH6ck2nmLfV78aYcFlwiVebZzutle1L4IDLofujUC+9zCGWYt4YE69szCXeFBXAvOEw/Vgo2gEnfgq9J1UILBB4MN0G2Y0xsWbBJZ7kfg+fdYMl4+CAK+D0hdB2QMDDLcW8MSZeWbdYPCjaBb/8A5Y+Dmnt4KT/QptTQ55mKeaNMfHKgkusbfoGZl8OO5bDQddAt7HgaRz26TbIboyJRxZcYqVoJ/x8FyybAA33h74zYL++oc8zxphawIJLLGz8CmZfAX/8BgffAEeNBk+jWNfKGGMixoJLTSr8A34eCb8+BY0OgJO/hNYnxLpWxhgTcRZcasqGmTD7Sti5CjoPhaMegpSGsa6VMcZEhQWXaCvcAfPvgOXPQuNOcMrX0Mr/fivGGJMoLLhE04bP3dbKGuhyKxz5AKSkxbpWxhgTdRZcoqFwO/x0O/z2AjTpDKd+Ay1D77dijDGJwoJLpK2bBj8MgYIcOGQYHHEfpFg6FmNM3ZKw6V9EZICILBORFSIyIuo33JsHs66ALwdASiM49TtnQaQFFmNMHZSQLRcRSQaeBk4FsoEfRWSqqi6Oyg1zPoEfroLd6+HQkXDEPZDcICq3MsaY2iAhgwtwDLBCVX8HEJG3gXOAyAeXH66GFROh6eFw/BRoXmHPHGOMqXMStVssE1jr8zzbLStDRK4SkTkiMic3N7dqd2p0EBx2NwyYY4HFGGNcidpyCYuqTgQmgrPNcZUucuiwSFbJGGMSQqK2XHKAdj7Ps9wyY4wxNSBRg8uPQCcR6Sgi9YCLgakxrpMxxtQZCdktpqpFInIDMA1IBl5S1UUxrpYxxtQZCRlcAFT1E+CTWNfDGGPqokTtFjPGGBNDFlyMMcZEnAUXY4wxEWfBxRhjTMSJatXWDiYaEckFVlfx9BbA5ghWJ97Vpfdbl94r2PtNZNF6r/urasvyhRZcIkBE5qhqncn9Upfeb116r2DvN5HV9Hu1bjFjjDERZ8HFGGNMxFlwiYyJsa5ADatL77cuvVew95vIavS92piLMcaYiLOWizHGmIiz4GKMMSbiLLhUk4gMEJFlIrJCREbEuj7hEpF2IvKFiCwWkUUiMtQtbyYi00Vkufs7wy0XEZngvs9fRKS7z7UGu8cvF5HBPuU9RGSBe84EEZGaf6f7iEiyiMwTkf+4zzuKyGy3fpPd7RkQkfru8xXu6x18rjHSLV8mIv19yuPq34GIpIvIeyKyVESWiMifEvyzvcX9d7xQRN4SkQaJ9PmKyEsisklEFvqURf3zDHSPsKiq/VTxByed/2/AAUA94Gfg0FjXK8y6twG6u48bA78ChwJjgRFu+QjgEffx6cCngAC9gdlueTPgd/d3hvs4w33tB/dYcc89Lcbv+VbgTeA/7vN3gIvdx88B17qPrwOecx9fDEx2Hx/qfsb1gY7uZ58cj/8OgFeBK93H9YD0RP1scbYwXwmk+nyulybS5wscD3QHFvqURf3zDHSPsOocy/8BavsP8Cdgms/zkcDIWNeriu/lQ+BUYBnQxi1rAyxzHz8PDPI5fpn7+iDgeZ/y592yNsBSn/Iyx8Xg/WUBM4C+wH/c/4k2AynlP0ucfYD+5D5OcY+T8p+v97h4+3cANHW/bKVceaJ+tpnAWvdLM8X9fPsn2ucLdKBscIn65xnoHuH8WLdY9Xj/UXtlu2W1itst0A2YDbRW1fXuSxuA1u7jQO81WHm2n/JYeQIYDpS4z5sDeapa5D73rV/pe3Jfz3ePr+x/g1jpCOQCL7vdgC+KSEMS9LNV1Rzgn8AaYD3O5zWXxP18vWri8wx0j5AsuNRxItIIeB+4WVW3+76mzp8rtX6uuoicCWxS1bmxrksNScHpQnlWVbsBO3G6NEolymcL4I4DnIMTVNsCDYEBMa1UDauJz7Oy97DgUj05QDuf51luWa0gIh6cwPIvVf3ALd4oIm3c19sAm9zyQO81WHmWn/JY6AOcLSKrgLdxusbGA+ki4t2N1bd+pe/Jfb0psIXK/zeIlWwgW1Vnu8/fwwk2ifjZApwCrFTVXFUtBD7A+cwT9fP1qonPM9A9QrLgUj0/Ap3cWSn1cAYHp8a4TmFxZ4NMApao6mM+L00FvLNIBuOMxXjLL3FnovQG8t3m8jSgn4hkuH9B9sPpn14PbBeR3u69LvG5Vo1S1ZGqmqWqHXA+o5mq+lfgC+B897Dy79X73+B893h1yy92Zxt1BDrhDITG1b8DVd0ArBWRzm7RycBiEvCzda0BeotImlsf7/tNyM/XR018noHuEVqsBuES5QdnZsavOLNJ7op1fSpR7+Nwmri/APPdn9Nx+p5nAMuBz4Fm7vECPO2+zwVAT59rXQ6scH8u8ynvCSx0z3mKcgPMMXrfJ7JvttgBOF8eK4B3gfpueQP3+Qr39QN8zr/LfT/L8JkhFW//DoCuwBz3852CMzsoYT9b4D5gqVun13FmfCXM5wu8hTOeVIjTMr2iJj7PQPcI58fSvxhjjIk46xYzxhgTcRZcjDHGRJwFF2OMMRFnwcUYY0zEWXAxxhgTcRZcjDHGRJwFF2N8iEhzEZnv/mwQkRyf5/UicP1RIjK6XFlXEVkS5Jx7ReT26t47yPVXuenWe5a/b7nnfxZni4aFGBOCBRdjfKjqFlXtqqpdcdK0P+59rqp7fdKJVNVbwEXlyi52y2PpJFWdAyAix4nIbOAaEflRRE4GUNX/4SwmNCYkCy7GhCAir4jIc+4X7tjyLQlxNqjq4D7+m4j84LZ0nheRZN9rqeqvwDYR6eVTfCHwlogMcb/MfxaR90UkzU9dvvS2MESkhZsvzbsR2jj3/F9E5Gq3vI2IfO3WZ6GI/DmMt/wYTgbp53Dydi0P97+VMV4WXIwJTxZwrKreGugAETkEp1XSx235FAN/9XPoWzitFdzcT1tVdTnwgaoerapHAUtwUnyE6wqcHFJHA0cDQ9z8WH/ByR/VFTgKJ81PKHuBlgCqmq+qaypRD2MAJzW3MSa0d1W1OMQxJwM9gB+d/H+k4j+L7GTgOxG5jbJdYoeLyIM4u0Y2wkk0GK5+wJEi4k3U2BQn8eKPwEtuBuwpqhpOcLkKeATo4ya/HKmqqypRF2MsuBgTpp0+j4so2+pv4P4W4FVVHRnsQqq6VkRWAicA5+HsdAjwCjBQVX8WkUtxkmyW53vvBj7lAtyoqhUCkogcD5wBvCIij6nqayHqtxg4S0Qewkm9PgkncBoTNusWM6byVuHsj4KIdMfZpAqc7LHni0gr97VmIrJ/gGu8BTwO/K6q3l0AGwPr3VaGv+407717uI/P9ymfBlzrnouIHCwiDd37b1TVF4AXvfUORkQOdx8WAj+59TKmUqzlYkzlvY+zX8YinK2hfwXnL34RuRv4r4gk4Xw5Xw+s9nONd4EJwI0+Zf9wr5fr/vb3pf5P4B0RuQr42Kf8RZw91n9y9+TIBQbitH6GiUgh8AfOXh2hXCciXXE2lhoI3BzGOcaUYSn3janj3BlnPVV1c7nye1X13nJlHXD2wzkcY4KwbjFjTC4wo/wiSuBL3yfuNOaPgM0YE4K1XIwxxkSctVyMMcZEnAUXY4wxEWfBxRhjTMRZcDHGGBNx/w/JMWX7WfnkQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}