HOD model using VPF, CIC and CIA Keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the autoreload extension\n",
    "%reload_ext autoreload\n",
    "# Set extension to reload modules every time before executing code\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow import keras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_data = np.load('machine_learning_data.npy')\n",
    "np.random.shuffle(full_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = full_data[:,:-5]\n",
    "y_data = full_data[:,-5:]\n",
    "from sklearn.preprocessing import  MinMaxScaler\n",
    "sc= MinMaxScaler()\n",
    "X_data= sc.fit_transform(X_data)\n",
    "y_data= y_data.reshape(-1,5)\n",
    "y_data=sc.fit_transform(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(pred,test):\n",
    "    sns.jointplot(pred.iloc[:,0],test.iloc[:,0],kind = \"kde\")\n",
    "    sns.jointplot(pred.iloc[:,1],test.iloc[:,1],kind = 'kde',color='purple')\n",
    "    sns.jointplot(pred.iloc[:,2],test.iloc[:,2],kind = 'kde',color='blue')\n",
    "    sns.jointplot(pred.iloc[:,3],test.iloc[:,3],kind = 'kde',color='green')\n",
    "    sns.jointplot(pred.iloc[:,4],test.iloc[:,4],kind = 'kde',color='red')\n",
    "    plt.tight_layout\n",
    "    \n",
    "def plot_error(pred,test,dims=(12,8)):\n",
    "    fig,ax = plt.subplots(figsize=dims)\n",
    "    sns.distplot(pred.iloc[:,0]-test.iloc[:,0],color='blue',ax=ax,kde_kws={\"label\": \"M_cut\"})\n",
    "    sns.distplot(pred.iloc[:,1]-test.iloc[:,1],color='cyan',ax=ax,kde_kws={\"label\": \"M_1\"})\n",
    "    sns.distplot(pred.iloc[:,2]-test.iloc[:,2],color='purple',ax=ax,kde_kws={\"label\": \"Sigma\"})\n",
    "    sns.distplot(pred.iloc[:,3]-test.iloc[:,3],color='yellow',ax=ax,kde_kws={\"label\": \"Kappa\"})\n",
    "    sns.distplot(pred.iloc[:,4]-test.iloc[:,4],color='red',ax=ax,kde_kws={\"label\": \"Alpha\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.1,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_dim = X_train.shape[1]\n",
    "out_dim = y_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(128, input_dim=in_dim, activation=\"relu\"))\n",
    "model.add(Dense(64, activation=\"relu\"))\n",
    "model.add(Dense(32, activation=\"relu\"))\n",
    "model.add(Dense(out_dim))\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(in_dim,out_dim):\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(128, activation='relu', input_dim=in_dim),\n",
    "        layers.Dense(128, activation='relu'),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dense(out_dim)\n",
    "      ])\n",
    "\n",
    "    optimizer = tf.keras.optimizers.RMSprop(0.0001)\n",
    "\n",
    "    model.compile(loss='mse',\n",
    "                    optimizer=optimizer,\n",
    "                    metrics=['mae', 'mse'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_16 (Dense)             (None, 128)               7808      \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 5)                 325       \n",
      "=================================================================\n",
      "Total params: 32,901\n",
      "Trainable params: 32,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mod = build_model(in_dim,out_dim)\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#y_rf = pd.DataFrame(y_rf)\n",
    "\n",
    "#y_test.reset_index(drop=True, inplace=True)\n",
    "#y_rf.reset_index(drop=True, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = mod.fit(X_train, y_train, epochs=2500,batch_size = 512,validation_split=0.2, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#pd.concat([y_test,y_rf] ,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mod.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred = pd.DataFrame(y_pred)\n",
    "ytest = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y1 MSE:0.0070\n",
      "y2 MSE:0.0504\n",
      "y3 MSE:0.0154\n",
      "y4 MSE:0.0875\n",
      "y5 MSE:0.0047\n"
     ]
    }
   ],
   "source": [
    "print(\"y1 MSE:%.4f\" % mean_squared_error(ytest.iloc[:,0], y_pred[:,0])) \n",
    "print(\"y2 MSE:%.4f\" % mean_squared_error(ytest.iloc[:,1], y_pred[:,1]))\n",
    "print(\"y3 MSE:%.4f\" % mean_squared_error(ytest.iloc[:,2], y_pred[:,2])) \n",
    "print(\"y4 MSE:%.4f\" % mean_squared_error(ytest.iloc[:,3], y_pred[:,3]))\n",
    "print(\"y5 MSE:%.4f\" % mean_squared_error(ytest.iloc[:,4], y_pred[:,4])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ax = range(len(X_test))\n",
    "plt.scatter(x_ax[:20], ytest.iloc[:20,0],  s=6, label=\"y1-test\")\n",
    "plt.plot(x_ax[:20], ypred.iloc[:20,0], label=\"y1-pred\")\n",
    "plt.scatter(x_ax[:20], ytest.iloc[:20,1],  s=6, label=\"y3-test\")\n",
    "plt.plot(x_ax[:20], ypred.iloc[:20,1], label=\"y3-pred\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'mean_absolute_error', 'mean_squared_error', 'val_loss', 'val_mean_absolute_error', 'val_mean_squared_error'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "# \"Loss\"\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i/AiFebkU5kXIjeSQm2NEcuyN7xEjkjP2eI4ZZox9MXYbxlKopGAONSVzk/ljKMRiUIgBO462c+s/P+vJm8+JGBVF+cwvynv9q768gEUVhSyrKmZ+UZ4n7yMiyRscHuVkZz/NHf2c7hqg/fwQHeeHaO8b+35+aJS+oRH6Bkc9Hdr54OZ6vnjbJcm8VCEWQ8aFmJk9DHgxJ70SaPNgP14KYk2guhIRxJpAdSXCj5ranHNb0vyeoZBxIeYVM9vpnGv0u46JglgTqK5EBLEmUF2JCGJN2UwzCUREJLQUYiIiEloKsdju97uAKQSxJlBdiQhiTaC6EhHEmrKWjomJiEhoqRMTEZHQUoiJiEhoKcRERCS0FGIiIhJaGRdiW7ZscYwtPaUvfelLX5nyFbcM/QyMKeNCrK0taCvUiIikT7Z9BmZciImISPZQiImISGgpxEREJLQUYiIiEloKMRERCS2FmIiIhJZCTEREQkshJiIioaUQExGR0FKIiYhIaCnEREQktBRiIiISWrl+vrmZfRV4H9DinFs/xfO/Dvzp+N1e4Hecc6+kopZzfaOp2K2IyOsqCiMpf4+23sGUv0eQ+N2JfR3YMs3zR4B3O+c2AH8B3J+OokREJBx87cScc0+a2eJpnt824e52oD7VNYmISHj43Ykl4m7goameMLN7zGynme1sbW1Nc1kiIv6a+BnY23ve73LSKhQhZmbXMBZifzrV8865+51zjc65xqqqqvQWJyLis4mfgYVFRX6Xk1a+DifGw8w2AF8BbnDOnfO7HhGRIHPO+V1CWgW6EzOzRcD/AX7DOXfA73pERIIuuyLM/yn23wauBirNrBn4n8AcAOfcPwOfASqAfzIzgBHnXKM/1YqIhECWpZjfsxPvmOH5jwIfTVM5IiKhl2UZFuzhRBERSUxUx8RERCSsFGIiIhJa0ajfFaSXQkxEJIOMRtWJiYhISI1qOFFERMJKnZiIiITWyGiUaBYFmUJMRCSDOODc+SG/y0gbhZiISIY52z3gdwlpoxATEckwp7sUYiIiElJNLb1+l5A2CjERkQySm2PsP9PtdxlpoxATEckgcyI57D/b43cZaaMQExHJIHm5OTS19DI8mh3rTynEREQySH5uDsOjjr2ns2NIUSEmIpJB5s6JALDt0DmfK0kPhZiISAbJzTEaygsUYiIiEk7rFpay40g7QyOZf1xMISYikmHWLSyhf3iUncfa/S4l5RRiIiIZZn1dKfm5Ofzk1dN+l5JyCjERkQwzd06EzReV85NdpzN+qr1CTEQkA12+rILOvmGePtjmdykplet3ASKS2fa3Db7lsVWV+T5Ukl0uqS+jKD/C1ldOcc3qar/LSRmFmIh4ZqrAinc7BZu35kRyuGxJBQ/tPs1n3reW8qI8v0tKCQ0nisis7G8bfP0rCPuRN7x33QIGhqP8+/PH/S4lZRRiIpKUVAaOwswbDfML2VBXyje2Hc3Yc8YUYiKSkHQGjMJs9rasX0BLzyAP7srM6fYKMRGJi5+BojBL3iUNZdSVFfCvTx/GOed3OZ5TiInIjIISIEGpI0xyzHjvugXsOtnNjqMdfpfjOYWYiEwraMERtHrC4KqVlRTn5/LVp4/4XYrnFGIiElNQAyOodQVVfm6Ea1dX89PXznCivc/vcjzla4iZ2VfNrMXMdsd43szs782sycxeNbPN6a5RJFsFPSiCXl/QvHfdAsyMrz1z1O9SPOV3J/Z1YMs0z98ArBj/ugf4chpqEsl6YQmIsNQZBPOL8rhsyXy+u/M4PQPDfpfjGV9X7HDOPWlmi6fZ5Gbgm25sSs12Myszs1rnXGbOFRUJgNkGw97W+F+/pkqrdKTTDRfX8syhc3xvZzN3v2uJ3+V4wu9ObCZ1wIkJ95vHH3sTM7vHzHaa2c7W1ta0FSeSaWYTYHtbBxMKsGRfM5m6sTd/Bna0x76i87KqYlbVzONrzxxhNJoZ0+2DHmI2xWNv+ck75+53zjU65xqrqqrSUJZI5kk2DLwIIi/2kc0mfgaWz6+Ydtv3rquhuaOfJw60pKm61Ap6iDUDDRPu1wOnfKpFJGMlE2CpCJ5k96duLH5vXzyfssI5fGPbMb9L8UTQQ2wr8JvjsxQvA7p0PEzEW8kGWKqoI0ut3EgO166u5skDrRw7d97vcmbN7yn23waeBVaZWbOZ3W1mv21mvz2+yYPAYaAJ+N/Af/OpVJGMFLQAS+d7ZLPrVtdgBt/aHv5uzO/ZiXfM8LwDfjdN5YjIDIIcLvvbBnVNsjjNL8pj86JyfvjSKe69YQ2RnKmmH4RD0IcTRSRFEu3C0h1gQQ7MTHDF8kpaewd57nDs2YxhoBATyUJBDzBJvc2Lypk7J4cfvRzuuXIKMZEso5l8ApCXm0PjRfN5aPdpBkdG/S4naQoxkSwS1IkcQXzvbHD5sgq6B0bYdii8Q4oKMRGJSSGS2dYtLGVOxHjqQJvfpSRNISaSJXQcTCbLy81h1YISnjoY3uX6FGIiWUABJrFsqCvlYEsvZ7sH/C4lKQoxkQyniRwynYvrSwF4+mA4hxR9PdlZRFLLj4kcU72nTkIOrkXzCynMi/DyiU4++LZ6v8tJmDoxkQwVlABLtpZEKSiTk2PGovmF7D7V5XcpSVGIiQiQugCT4FtcUcS+0z2hvMaYQkwkA6V7IkeqAkxXfk6PxZWF9A+PcjSEq9orxEQyTLo7InVg4XdRRREAe051+1xJ4hRiIhkk3cfBghJgOh42O/VlBeTmGK8pxETEL9kaYDJ7uZEc6ssL2BPCyR0KMZEMkIkBpuNh6XVRRRF7TnUzdhnH8FCIiYRcGAIslcN9Gkr0xuKKItrPD9HSE64OWyEmkmWC3oGJPxZXFAKEbkhRISYSYumcSp9sgCXTKWkoMf0WXQixk+Ga3KEQEwmpMARYqmko0TuFebksKMnntdMKMRFJsUwOMHVh/rmooojdJzWcKCIplK4A2982OOsA04SOcFlcUcSJjn66+ob9LiVuCjGREElngEn2WV5dDMBLJzp8riR+CjGRkAhbgGlCR/gsqyomx+Cl451+lxI3XU9MJATSEWBh6r40lJgaBXkRGuYX8uJxdWIi4pFsCTB1YcGwvKqYl090Eg3JZVkUYiIBluoA82LyxlQ0oSO8VteW0DMwEpqLZCrERAIqHQEWFOrCguPiulIAnjzQ6nMl8VGIiQRMMt1RIgGWqu4rWYkEmLqw1CstmMOSyiKePNjmdylxUYiJBEgy4ZVogKWagib8Lq4r5cVjHfQMBP98MYWYSEBk0/DhBerCgumS+lJGoo6nQ9CN+RpiZrbFzPabWZOZ3TvF84vM7DEze8nMXjWzG/2oUyTVsmn4UIJv1YISSgpy+fGu036XMiPfQszMIsB9wA3AWuAOM1s7abNPA99zzm0Cbgf+Kb1ViqReqgMsqNSFBVckx7h0cQW/2NtC39CI3+VMy89O7FKgyTl32Dk3BHwHuHnSNg4oGb9dCpxKY30iKZeqAPOr+1LYZI7Ll1XQPzzKo3tb/C5lWn6GWB1wYsL95vHHJvos8GEzawYeBD451Y7M7B4z22lmO1tbwzEtVLJboiGTyASOIHdfF6gL89bEz8CO9nOe7HPVgnnML8rjv14Jdu/gZ4jZFI9NPkX8DuDrzrl64EbgATN7S83Oufudc43OucaqqqoUlCrinUzrvlJJARafiZ+B5fMrPNlnjhmXL6vgF/taaOkZ8GSfqeBniDUDDRPu1/PW4cK7ge8BOOeeBeYClWmpTiQFUhlgfos3cHRic3hcu7qakajj+zub/S4lJj9DbAewwsyWmFkeYxM3tk7a5jhwHYCZrWEsxDReKKGUiuHDTOy+LlAX5r/a0gIurivl3547xmhA11L0LcSccyPAJ4BHgL2MzULcY2afM7Obxjf7Y+BjZvYK8G3gLudcMH+SItNINMC83mes16c7BNWFhc91a6o51TnAEweCOcHD10uxOOceZGzCxsTHPjPh9mvAFemuS8RLXgdYsqEz3esmPpdMB+R116QuLDjedlE55YVz+NozR7l2dY3f5byFVuwQSaF4AyeR4cNE3z/RbitVnZm6sHDKzclhy7oFPHWwjd0ng7eyvUJMJEUSCbB49pVoEIX1WJm6sOC5fm0NhXkRvvz4Ib9LeQuFmEgKeB1g8b6nV+GVyD7iCZ14uzAFWDAV5uXynrU1PLjrNIdbe/0u500UYiIe8yrA4g2kMHddEh5b1i0gN2L88xPB6sYUYiIe8jLA4nmvMISXurDMUFaYx7Wra/jPF04GqhtTiIl4xIsAiyeYghReMwWPAiyzfGDjQubkGl/86QG/S3mdQkzEA/GEykwzEMMUXpKdygrzuHF9LT/ZdZpXmzv9LgdQiInMWrwBNpt9BDG81IVlp1/eUMu8ubl84eH9fpcCKMREZiXVAabuS4KmMC+XD2ys4+mmNp5p8v/Kz76u2CESZrMNMC+7ryMtM5+EuqS6NO79zZa6sMx2/ZoaHtp9ms8/vI8f/e4VmE11UZL0UCcmkiLJBli83deRlq7Xv+IR73bx8CJ8FGDhlZebw4feVs+rzV08vPuMr7UoxESSMFPIzCbAppNocE31+lTT8lLZ4crlVdSXF/A3j+xnZDTqWx0KMZEEpSLAZuq+ZhNcU+1rJtN1SerCBCAnx7itsYHDbef5jxf8u96YQkwkAbOZZDFdgE0nHd2TV+LpwhRgmaPxonJWVBfzdz8/wMDwqC81KMRE4jSbiRzJBJiX3ZdXpgsgDSNmHzPj9ksXcbZ7kG9sO+pLDQoxkTj4EWCZSF1Y5llbW8IlDaV8+YlD9A6OpP39FWIiM0hngAWh+0omaDSMmN0+tLmezr5hvvns0bS/t0JMZBrpDrBEdJ8+Svfpowm9ZjYUQhLL8up5bGwo4/4nD6e9G1OIicQQtAC7EFqTw2uqx9JJXZgAfHBznS/dmEJMZApBCrBEAmq2QRYrbGYTQgqw7DCxG+sfSt9MRYWYyCRBCTA/u6t4aUaiTPT+DbV09g3zX6+cStt7KsREJvBiQd949zlTgCUr2dcm2oVpGFEmW1NbQn15Ad949ijOubS8p0JMhPjXK0x0NY5EAywd3Ve6FgJWgGUfM+M9a2vYc6qbl06k53pjCjHJel5dkTne/U4XYEEzmy5MstOVy6somBPhgWePpeX9FGKS1RRgY6YKK03mkGQU5EW4YnklD+46nZbp9goxyVqpCrBY0hVgJbWLYz7nxVDiTF2YAkyuWFbB4EiUR/eeTfl7KcQkK6UywKZ6PKgdGCTWhWkYUeKxcsE85hfl8dCu1F9rTFd2lqySSOeUaQE2VRfmddekLkwAcszY2FDG001tDI9GmRNJXb+kTkyyRpACLFWmG0qMV7JdmAJMJtpYX0bv4AgvHU/tLEWFmGQFPwJsOmHrwjSMKIlaXTsPgB1H21P6PhpOlIznxfGv6fYTtpmI00m2m1IXJpPNmzuHurICXkhxiPnaiZnZFjPbb2ZNZnZvjG1uM7PXzGyPmf17umuU8Ir3BGbIjACLNZQYbxemYUTx2tLKInaf6k7pe/gWYmYWAe4DbgDWAneY2dpJ26wA/gy4wjm3DviDtBcqoeTV8OF0+/LqkipemG2AxaIAk9lYVFFIS88gnX1DKXsPPzuxS4Em59xh59wQ8B3g5knbfAy4zznXAeCca0lzjRJCQQ2wTBxGFJlOXVkBAIdae1P2Hn6GWB1wYsL95vHHJloJrDSzZ8xsu5ltSVt1EkqJDB/ONIHDq0kcqaZhRAmqiuKx35FTnQMpew8/J3bYFI9NXvY4F1gBXA3UA0+Z2Xrn3JvmbJrZPcA9AIsWLfK+UgmFVB//muk5P7owBZjAmz8DaxbW+1zNGyqK8gA43dWfsvfwsxNrBhom3K8HJl+Ephn4kXNu2Dl3BNjPWKi9iXPufudco3OusaqqKmUFS3ApwN6QrlXqJTgmfgaWz6/wu5zXFeZFmDsnJ6WdmJ8htgNYYWZLzCwPuB3YOmmbHwLXAJhZJWPDi4fTWqUEXpADLFUSDTB1YeIHM6OyOJ8zXRk4nOicGzGzTwCPABHgq865PWb2OWCnc27r+HO/ZGavAaPAf3fOnfOrZgmedCziO5sAS0UXlgdKyZsAAB3VSURBVOiqHAow8VNRXi5d/cMp27+vJzs75x4EHpz02Gcm3HbAH41/ibyJF1dhTrb7guAFmI6DSRAV5kXoHsjQEBNJlp8BFs/wYVADTCTdCvIitHek7jwxhZiETrYF2EzDh4kEmLowSbeCOZGUXhxTISahMtsAS+XwISjARCYrzIvQM6AQE/EtwOKdfehlgMUzeUMBJmFQkJfL0GiUwZFR8nMjnu9fISYZI1sCLJFp9NM9rgCTdCiYMxZc5wcVYpLFZurCvL4GWCLnfnkVYMl2X5B4gImkS2HeWHD1DAwzf3wFDy8pxCTwghpgQRg+hOQCTF2YpEvB6yGWmuNiCjEJtHQGWFDDCxRgEl5F+WMxk6oTnhViElgKsOnXQVSASRjMGw+xjhRdU0whJhlnqqAK27lfoACTzFA890KIqROTLJJsF5ZIgAVx5Y0L0h1gIqlyoRPrPK9OTLJEEAIs3ZdPmciPAFMXJqmSG8mhKC9Ca29qLiarEJNA8TvAUhVekJoZiDM9pwCTIKgozudUZ2oujOnn9cRE3kQBpgCTzFRZnMfJFIXYjJ2Yma0GbgbqAMfY1Ze3Ouf2pqQiyUqpDjC/wgsUYCKVxflsP5yaS0FO24mZ2Z8C3wEMeJ6xqzEb8G0zuzclFYlMEuYAi4cCTDJdRXE+3QMjKbmu2Eyd2N3AOufcm97ZzP4W2AP8tecVSdaJ9+rMM70mkQBLV3ilax3ECxRgEkQLy+YC0NTSy+ZF5Z7ue6ZjYlFg4RSP144/JzIryQwjKsCmpgCToGooLwTgwJkez/c9Uyf2B8CjZnYQODH+2CJgOfAJz6uRrJLJATYTBZhkk6p5+cydk8O+dIeYc+5hM1sJXMrYxA4DmoEdzrlRz6uRrDGbJaVm2s9sAmzw1P4pH89fuCquei6YrgtL5zqI8exDJNVyzKgvK+DA2fR3YjjnosB2z99ZspZXMxHjDbDZhNfk5xMNs8kUYJKtGuYX8cLxdpxzmJln+9V5YpJWXk6lnyyZABs8tX/GAEtUvCvTX6AAk2yworqY7v4RDred93S/WrFD0iaV54IlGmBeB1c8pltOajIFmGSaFTXFALx4rINlVcWe7VedmKRFtgRYrC4smXPBpqIAk7BaWFZAUV6El050erpfdWLiu3QFmB/dF3h3HEwBJmGWY8ayqmJeONrh7X493ZvIFKbrwjIpwBI5FqYAk2y0praE/Wd7aPNwRXuFmKRUJgXYdDMTExlGTPZcsJkowCTo1teN/Xt49pB36ygqxCRlUrWcVNA6sFjSOZFDASZhsLSyiKK8CM80tXm2Tx0Tk5RI1Woc8QZYOsNrtsOICjDJFjk5xpraEp72MMTUiYnnMjHAEj3JOZFhxFgUYJKJ1teV0tzRz/FzfZ7sTyEmnsrEAJvOVF2YF8fBFGCSqdYvHPv38cwhb7oxhZh4RgGWGAWYZKOFZXOZX5Tn2ZCiryFmZlvMbL+ZNU13kU0z+5CZOTNrTGd9Er9MDrBEhhLj7cIUYJKtzIx1tSVsa2ojGnWz3p9vIWZmEeA+4AZgLXCHma2dYrt5wO8Bz6W3QolXGALswhqJXq2VOJthxNmEkAJMMsH6ulI6+obZe6Z71vvysxO7FGhyzh12zg0B3wFunmK7vwC+AAyksziJT9ADbLahFW8Xlq6JHCKZYN3CEsCb88X8DLE63rjQJoxdp6xu4gZmtglocM79eLodmdk9ZrbTzHa2trZ6X6kkJZUBNlM4xfN8smZzLEzDiJIKEz8DO9q9O5E4VSqK86ktnevJ+WJ+hthUF5R5fYDUzHKAvwP+eKYdOefud841Oucaq6qqPCxRppPMahwz7SPeAJtOKlfoSMUwogJMZmviZ2D5/Aq/y4nLuoWlPHekneHR6Kz242eINQMNE+7XA6cm3J8HrAceN7OjwGXAVk3uCAYvlpPyOsC8vDaY18OICjCRN1u/sIS+oVFebZ7dqvZ+htgOYIWZLTGzPOB2YOuFJ51zXc65SufcYufcYsauLn2Tc26nP+XKBUENsFSLZxjR68BRgEmmWjt+XGxb0+yGP30LMefcCPAJ4BFgL/A959weM/ucmd3kV10yvUwJsOk6rdkMI04l2S5MASaZbN7cOSyuKJz1cTFf1050zj0IPDjpsc/E2PbqdNQksXmxoK+XARa0xX29HkYUyXRrF5bys9fOMDgySn5uJKl9aMUOiYsXU+mDEmBedGGT6TiYSOJW1cxjeNTx2qnkzxdTiMmMUhFgU/G7A0t2GFHHwUSSs7y6GIAXjyc/uUMhJtPy6mTmySZ3YekKsERXo59Mx8FEvDO/KI/K4jxeOt6R9D4UYhJTulbj8LsDg/i6sFQfB1OASTZaVlXMiwox8VqQAsyr879idWFBOQ4mko1WVM/jVOcALd3JrSyoEJO3CFqApVuyx8E0kUMkcStqxo6LvXQiueNiCjF5k0wNsES6sMniPQ6WLAWYZLPFFUXkGOxqfuts5XgoxOR1yQRYPPsJU4DpOJhIeuXl5lBbWsC+JC/LohATIPkAS/RcsKAMISrARIKjYX4B+870JPVahZhkdIBNFVaayCESLA3lhTR39NM7OJLwaxViWS7bAmwqmsgh4q9F8wsBOHA28W5MIZbFMjnAYpkcbLNZ2DdZCjCRN2sYD7H9SQwpKsSyVKYHWLzDiJPpOJhI+lXNy2dubo5CTLyRLQGW7okcIjK1HDPq5xey73TiMxQVYlkoHdcEC1KATcWPAFMXJhJbQ3kh+8724JxL6HUKsSwTpgAbPLmXwZN7Z9xuJjMdB1OAifivYX4BnX3DtPYmdt1ChVgWCUuATQ6veIMsnmFEXRtMJJjqy8cmdxw825vQ6xRiWSJMAZaMZANsNlPp46EAE4lPfXkBkPg0+9xUFCPBEs/1vWZ6TaoDzIthw4mSmYmYaIBpJqKId8oK5lCcn8vBFnViMkEqrsoctACLJ7BmOg7mdYCJSGLMjLryAg4kOM1enVgW8zvA4j7WVbcm9nNJDCPGE2DT0XEwkdSoLytgx7F2nHOYWVyvUSeWwRI9DhbEAEtUMjMRp6KJHCLpV19eQHf/CK098R8CUSeWofwOsHQc/5ppGFFT6UXC5fUZii29VJfMjes16sQyUFADLJnzvmINJSa7rNRECjCRYElmhqJCLMPMNsAm8zLAUsmL42AKMBF/lY7PUDyQwLliCjF5k+lCLd0BFm8XpgATyQyvz1BUJ5adUjmMGNQObCYKMJFwqS8r4GBL/GsoKsQyRNACzKt1DydLtAubiQJMJFjqywsTmqGoEMtwfgXYbE01lOj1MKICTCR43pjcEd9xMYVYBkhkWalEZyJOlK4AS4ZXJzQrwET8VVs6NrX+WPv5uLZXiIVcMgv7xjLdTMR0BlgyXdhEqVzUVwEmklrlhXnk5hgn2vvj2t7XEDOzLWa238yazOzeKZ7/IzN7zcxeNbNHzewiP+oMKi+PgwUlwKYSlONgCjCR1MvJMarm5XOivS++7VNcT0xmFgHuA24A1gJ3mNnaSZu9BDQ65zYA/wF8Ib1VZo6ZhhEnmnwcbKJUB9h06yROJZ3HwUQkParn5YdiOPFSoMk5d9g5NwR8B7h54gbOuceccxfieDtQn+YaA2s2w4jJTuTw4xiYl8OImsghEg4Vxfmc6RqIa1s/Q6wOODHhfvP4Y7HcDTw01RNmdo+Z7TSzna2trR6WGD6JrsqRyESOtzzv9TXAZujCEhlG1EQOySYTPwM72s/5Xc6sFefn0tU/HNe5Yn6G2FTr7E9ZsZl9GGgE/maq551z9zvnGp1zjVVVVR6WGEyxQimdx8HSEWDTdV2Jrk4/VRApwCRTTPwMLJ9f4Xc5s1aYF2F41DEwHJ1xWz9DrBlomHC/Hjg1eSMzux74FHCTcy7xSxRnGC+n00/kZ4BNJR3DiDNRgIn4oyh/7AIr3QPDM27rZ4jtAFaY2RIzywNuB7ZO3MDMNgH/wliAtfhQY2jMZjp9IhM5UrIKxywnc0zk1TCiAkzEP0V5EQC6+gMcYs65EeATwCPAXuB7zrk9ZvY5M7tpfLO/AYqB75vZy2a2NcbusoJXXVi8x8FmOiY20+uTNV0Xlo5hRAWYiL9ycsaONkXjOCbm60UxnXMPAg9OeuwzE25fn/aiAsrL2YgTTTeMONlMXdiF1w+e2h/3tb0md2GpHkbUVHqR4IuOHwrLsammTryZVuwIudnMRvRyGDGZDiyVsxF1HEwkvIZGRwGYmxuZcVuFWAj4PYyYigCbipezEaeiYUSRcOgdGAuxsqI5M26rEAuxdA0jpkLQhhEVYCLB0dk/RF4kh3n5Mx/xUogF3Gy6sInSOYw40/Gw2QwjpmI6vQJMJFjOdA2wqKIQ0zGxzDWbLmyi2QwjTiWZAEtkGHEiL1flEJHgONXVz5LKori2VYgFWKq7MK+nw8c7IzHW6yff13R6kezTOzDCqc4BNjaUxbW9r1PsJTmTu7BkJ3NMNJsuLKzT6RVgIsFz4GwPAJsWxRdi6sQCKpEuLF5+TuZINMBSfRxMRILpxeMdFOZF2LyoPK7tFWIhE7QuLB5BXJ1ewScSPNGo44VjHVyzupq5c2Y+RwwUYoGUSV1YEFenV4CJBNOLxzvo7B/m/Rtq436NQixEEpmRGIQuLJ4AS2Qih46DiWS2n752lpqSfK5fUxP3axRiIRZvx+ZHF+ZHgM1EASYSXIdae9l1sovfuOwiciPxR5NCLGASueBlPK+bTqoCLdEAmyzZANP5YCLh9Z0dxykvnMNdVyxJ6HUKsZDyYnWOVEgmwBKZiRiLhhFFwmvnsXZ2n+zmd69ZTnEcS01NpBDLIrO91MpM0hFgOg4mkln6hkb42jNHWVUzj9985+KEX6+TnQMknUOJXpttgE2mABPJfM45vrHtKJ19Q3ztrreTl5t4X6VOLMMkOytxNrwIME2lF8k+j+1v5cmDbXzimuVcEucyU5MpxEIomVmJ8ZjppOR4XzPbANNMRJHMd6i1l69vO8K7llfy+9evTHo/Gk7MEl7PRIwVeOkKMM1EFAmv0139fOGRfdTMm8uXbt9IJGfmS67Eok5M3iSebizoAaYuTCS4OvuG+PzD+4iY8c27L6WieHb/XtWJBUSykzOSfV3+wlUxu7P8ujVvmamY6PqHCjARmaz9/BB/+ZPX6B4Y4dsfu4ylVcWz3qdCLOBmmpmYKokcH1OAichMzvUOvh5g3/zIpXFfL2wmCrEsMV3nNZt9zvSYAkxEjrf38YWH9zE4EuVbH31H3JdZiYeOiWWxZK7EPN1rFWAiMtnuk138+X/tIZJjfPfjl3kaYKBOLOsl2qHFCj4FmIhM5Jzj53vP8s1nj7G0qoiv/9alLCwr8Px9FGIZrKR28ZvOFYsVWPEEWbzhdeF9J1KAiWSXwZFR/vXpIzx1sI2rV1bxpTs2UVowJyXvpRALuVWV+Z4sO5XM0KICTEQmO9s9wN/+7AAn2vv4w+tX8slrl5Mzi/PAZqIQyzJeTPCIJ7xAASaSbV481sF9jzeRGzG++ltv55pV1Sl/T03sCLhEV6aYHBxThUuyEzryF65SgInIW4xGHd/beYK/+el+llQW8ZNPXpmWAAN1YoHh1bDgVCYfG4M3giyermy60ItnIV8FmEjmau0Z4L7HDrH/bA+3NdbzuZvXM3dOJG3vrxALocmBN/n+kurSt6xmP1WQQfJdWTzd14Xaprt/gQJMJHy2HWrjX58+Qo4ZX7p9IzdvrEt7Db4OJ5rZFjPbb2ZNZnbvFM/nm9l3x59/zswWp7/KcJoqUKa7fle8SmoXxz18qAATyUz9Q6P80+NN/MMvmli9oISHfv9KXwIMfOzEzCwC3Ae8B2gGdpjZVufcaxM2uxvocM4tN7Pbgc8Dv5r+av21pip/xuWn4h2OvBBAiV6mJVYAxtN9xXoMFGAiYdPU0st9jx2kpWeQ379uBZ+8djm5Ef/6IT+HEy8FmpxzhwHM7DvAzcDEELsZ+Oz47f8A/tHMzDnn0lloEMUTWlMNK14wMZSmCrR4ujYFmEj2iEYdW185xX+82MyCkny++/F38vbF8/0uy9cQqwNOTLjfDLwj1jbOuREz6wIqgLaJG5nZPcA9AIsWLUpVvSk328kdU73+QtBMd8XnRIcZY12FWQEm4o+Jn4E1C+s93/+53kHue7yJvad7eP+GWv7ylotTdvJyovwMsanOfpvcYcWzDc65+4H7ARobGzOyS5tqSHGq0IoVhNN1ZfGIFVwX3jPex+M5ZUABJpKYiZ+Bay7e6Oln4I6j7dz/5GGizvHFWy/hVzbXYZa6k5cT5WeINQMNE+7XA6dibNNsZrlAKdCenvL8kWg3lmiQXRBPoE0XXBfeJ5HHFWAi4TEyGuXbzx/nwd1nuLiulH+4YxOLK4v8Lust/AyxHcAKM1sCnARuB35t0jZbgTuBZ4EPAb/I5uNhsSZ4xAoyiH3RzJkCajrTBY2GD0XCr613kL9/9CAHW3q56/LF/NmNq8nPTd+5X4nwLcTGj3F9AngEiABfdc7tMbPPATudc1uBfwUeMLMmxjqw2/2qNygSCbLpHk9GMuEFCjCRMHnxeAf//Pghojju+7XN/PKGWr9LmpavJzs75x4EHpz02Gcm3B4Abk13XX5LNnimC7ILEt1vPAGjABMJvwtLR2195RRra0v4p1/fHMjhw8m0YkdATRdk0503NlMAehkaswkvr2sRkeSdHxzhS48eZNfJLn7tHYv4zPvWpnXpqNlQiIXUTEEGiXdd8ZopfBRgIuFxpmuAv/npPlq6B/nCBzdw29sbZn5RgCjEAmymrmqmlTy8DLN4QifeFfcVYCLB0NTSwxce2U8kx/jWR9/BZUsr/C4pYQqxgIsnyIC4wuyCeEIt0aBRgImEy6vNnfzdzw9QVZzPA3e/IxTHv6aiEMsQ8ayveIGXQaLwEgmfnUfb+dKjB1lWVcwDd19Kdclcv0tKmkIsBOKdrRhPV+aVRC7WqQATCY5Xmzv50qMHWbewhG9+5B2UFgZj+ahkKcRCIpFp96kKs0SvMg0KMJEg2Xemmy/+7AArqoszIsBAIRYqiU7UmBg6yQZaMsEFCi+RoGnpHuBvf3aAurICHvhoZgQYKMRCKZmToZMNo0QpvESCZ2B4lC/+7AA5ZnztrrdTWZw5/059vbKzJC+IYRHEmkQEvvrMEZo7+vjHXwvmIr6zoU4sxFJ9UnOidYhI8Dx/pJ2nDrbxe9et4MoVVX6X4zmFWAbwI8wUXCLB1zs4wr8+c5h1C0v45LXL/S4nJRRiGcTv5aZEJFh+8NJJevpH+MLHNjAnkplHjxRiGSiZFTri2Y+IhMfZ7gF+uucMtzU2sG5h8tcPDDqFWBZQGIlkn5/sOo0Z/NEvrfS7lJTKzP5SRCSL9QwM88SBVm7ZVEdNiJeUiodCTEQkwzzT1MbQSJSPvGuJ36WknEJMRCTDPHPoHOsWlrB6QYnfpaScQkxEJIOMRB1NLb28/5KFfpeSFgoxEZEM0jc0CsC1q6t9riQ9FGIiIhlkYHiUquJ8VlQX+11KWijEREQyyODwKG+7qBwz87uUtFCIiYhkkOGo4+L6zD25eTKFmIhIhllVM8/vEtJGISYikmEy7XIr01GIiYhkmIb5BX6XkDYKMRGRDBIxIz834ncZaaMQExHJIJFIdsxKvEAhJiKSQXJzFGIiIhJSuTnZ9bGeXX9aEZEMl2UZ5k+Imdl8M/uZmR0c/14+xTYbzexZM9tjZq+a2a/6UauISJjkZMlKHRf4ldn3Ao8651YAj47fn6wP+E3n3DpgC/C/zKwsjTWKiIROdkWYfyF2M/CN8dvfAD4weQPn3AHn3MHx26eAFqAqbRWKiIRQtqyZeIFfIVbjnDsNMP592msGmNmlQB5wKMbz95jZTjPb2dra6nmxIiJBNvEzcKC/z+9y0io3VTs2s58DC6Z46lMJ7qcWeAC40zkXnWob59z9wP0AjY2NLsFSRURCbeJn4KJVF2fVZ2DKQsw5d32s58zsrJnVOudOj4dUS4ztSoCfAJ92zm1PUakiIhkjy0YTUxdiM9gK3An89fj3H03ewMzygB8A33TOfT/VBVUUZs8yLSKSuUoL5vhdQlr5dUzsr4H3mNlB4D3j9zGzRjP7yvg2twFXAXeZ2cvjXxv9KVdEJBzyItl1opgvnZhz7hxw3RSP7wQ+On77W8C30lyaiIiESHZFtoiIZBSFmIiIhJZCTEREQkshJiIioaUQExGR0FKIiYhIaCnEREQktBRiIiISWgoxEREJLYWYiIiElkJMRERCy5zLrEvPmFkrcMyDXVUCbR7sx0tBrAlUVyKCWBOorkT4UVObc25LPBua2cPxbpsJMi7EvGJmO51zjX7XMVEQawLVlYgg1gSqKxFBrCmbaThRRERCSyEmIiKhpRCL7X6/C5hCEGsC1ZWIINYEqisRQawpa+mYmIiIhJY6MRERCS2FmIiIhJZCbJyZzTezn5nZwfHv5VNss9HMnjWzPWb2qpn9aopq2WJm+82syczuneL5fDP77vjzz5nZ4lTUkURdf2Rmr43/bB41s4v8rmnCdh8yM2dmaZkaHU9dZnbb+M9rj5n9exDqMrNFZvaYmb00/vd4Yxpq+qqZtZjZ7hjPm5n9/XjNr5rZ5lTXFGddvz5ez6tmts3MLklHXTKJc05fY8cFvwDcO377XuDzU2yzElgxfnshcBoo87iOCHAIWArkAa8Aaydt89+Afx6/fTvw3TT8fOKp6xqgcPz276S6rnhqGt9uHvAksB1oDMjPagXwElA+fr86IHXdD/zO+O21wNE01HUVsBnYHeP5G4GHAAMuA55LdU1x1nX5hL+/G9JVl77e/KVO7A03A98Yv/0N4AOTN3DOHXDOHRy/fQpoAao8ruNSoMk5d9g5NwR8Z7y2WLX+B3CdmZnHdSRcl3PuMedc3/jd7UC93zWN+wvG/pMykOJ6EqnrY8B9zrkOAOdcS0DqckDJ+O1S4FSqi3LOPQm0T7PJzcA33ZjtQJmZ1fpdl3Nu24W/P9Lz+y5TUIi9ocY5dxpg/Hv1dBub2aWM/W/2kMd11AEnJtxvHn9sym2ccyNAF1DhcR3J1DXR3Yz97zmVZqzJzDYBDc65H6e4loTqYqyrX2lmz5jZdjNLxzJB8dT1WeDDZtYMPAh8Mg11zSTR3z0/pOP3XaaQ63cB6WRmPwcWTPHUpxLcTy3wAHCncy7qRW0Tdz/FY5PPg4hnG6/F/Z5m9mGgEXh3SiuaoSYzywH+DrgrxXVMFs/PKpexIcWrGfsf/FNmtt451+lzXXcAX3fOfdHM3gk8MF6X17/nifDj9z1uZnYNYyH2Lr9ryUZZFWLOuetjPWdmZ82s1jl3ejykphzeMbMS4CfAp8eHNrzWDDRMuF/PW4d0LmzTbGa5jA37TDcck666MLPrGftPwbudc4M+1zQPWA88Pj7augDYamY3Oed2+ljXhW22O+eGgSNmtp+xUNvhc113A1sAnHPPmtlcxha8TcdwZyxx/e75wcw2AF8BbnDOnfO7nmyk4cQ3bAXuHL99J/CjyRuYWR7wA8bG57+fojp2ACvMbMn4+90+XlusWj8E/MI5l+r/mc5Y1/jQ3b8AN6XpGM+0NTnnupxzlc65xc65xYwdt0h1gM1Y17gfMjYRBjOrZGx48XAA6joOXDde1xpgLtCa4rpmshX4zfFZipcBXReG/v1kZouA/wP8hnPugN/1ZC2/Z5YE5YuxY0qPAgfHv88ff7wR+Mr47Q8Dw8DLE742pqCWG4EDjB1v+9T4Y59j7AMYxj5Yvg80Ac8DS9P0M5qprp8DZyf8bLb6XdOkbR8nDbMT4/xZGfC3wGvALuD2gNS1FniGsZmLLwO/lIaavs3YTN9hxrquu4HfBn57ws/qvvGad6Xx73Cmur4CdEz4fd+Zjrr09eYvLTslIiKhpeFEEREJLYWYiIiElkJMRERCSyEmIiKhpRATEZHQUoiJJCHe1fNFJLU0xV4kQWYWYexcq/cwdv7QDuAO59xrvhYmkoXUiYkkLt7V80UkxRRiIokLw6rqIllBISaSuECvqi6STRRiIokL7KrqItlGISaSuHhWgxeRNMiq64mJeME5N2JmnwAeASLAV51ze3wuSyQraYq9iIiEloYTRUQktBRiIiISWgoxEREJLYWYiIiElkJMRERCSyEmIiKhpRATEZHQ+r+EM5VSLM0IdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FqZYo0RFWERGJLIWYiIhElkIsfncEXUAHqiW2MNWjWrqmWroWploiQ8fEREQkstSJiYhIZCnEREQkshRiIiISWQoxERGJrIwLsXnz5jnaLj2lhx566JEpj7hl6D4wpowLsX37wnIFGRER/2XbPjDjQkxERLKHQkxERCJLISYiIpGlEBMRkchSiImISGQpxEREJLIUYiIiElkKMRERiSyFmIiIRJZCTEREIkshJiIikaUQExHJJAldLjj68oIuIDSqgi5ARDLeiPS/xZG9R9L/JiGiTkxEJIM4l12tmEJMRCSTtAZdgL8CDTEzu8vMqs1sbYzXbzCzNe2PJWY2xe8aRUSiRJ2Yv+4B5nXz+lbgQufcZOB7wB1+FCUiElnZlWHBTuxwzr1sZqO6eX1Jhw+X4cthURGR6FInFl43AU919YKZ3WxmK8xsRU1Njc9liYgEq+M+8NjRY0GX46tIhJiZzaYtxP6lq9edc3c45yqccxXl5eX+FiciErCO+8CCgoKgy/FV6M8TM7PJwJ3AZc65/UHXIyISZq5Zw4mhYWYnAY8Bn3DOvRV0PSIiYdfanF1z7APtxMzsIWAWMNDMqoBvAfkAzrlfA7cCZcB/mhlAs3OuIphqRUTCr7VFIeYb59z1Pbz+WeCzPpUjIhJ52daJhXo4UUREEtPa0kpjfWPQZfhGISYikmFqN9cGXYJvFGIiIhlm/9vZM5FbISYikmEObDoQdAm+UYiJiGSQnNwc9m3YF3QZvlGIiYhkkLzCPHa9uivoMnyjEBMRySB5hXns27iPY7XZcQ1FhZiISAbJ750PwNbntgZciT8UYiIiGSSvKI9efXrx9lNvB12KLxRiIiIZxDCGnzOcN//0Ji2NLUGXk3YKMRGRDDNmzhiOHTiWFd2YQkxEJMMMqxhGUVkRr//m9aBLSTuFmIhIhsnJzWH81ePZ9NQmatZn9t3uFWIiIhlo/FXjyS3IZdEPFwVdSlopxEREMlBhaSETrplA5YOVvLPqnaDLSRuFmIhIhpr0sUkU9C3g6a88jXMu6HLSQiEmIpKhevXpxdSbprLthW2suntV0OWkhUJMRCSDjbtiHIOnDOaZrz7DoV2Hgi7HcwoxEZEMZjnGzH+aSfPxZv5w/R9obW4NuiRPKcRERDJcyYgSZvzjDHYs2sEL33oh6HI8lRd0ASKhsSyBZWekrYrkdK49bPVJ4E6Zcwp7V+9l8Q8XM+iMQUy6flLQJXlCISbZK5HQ8nJdP8SqT+GW1c75h3M4VHWIxz/9OP1O7sfImSODLillGk6U7LOM8IdQuiwju7/+LJfbK5dZ35lF7/LePHTlQ1SvrQ66pJQpxCR7aOf9Xvp+ZKXC0kLm/NscLMe475L72P/2/qBLSolCTDKfdtbd0/cm6/Qd1pc5t82h5XgL9110Hwc2HQi6pKQpxCSzaQcdHwV91ul3cj/m/GQOxw8f5+7z72bfxn1Bl5QUhZhkLu2UE6fvWVYZcOoALv35pbQ0tnD3hXezZ+WeoEtKmEJMMpN2xsnT9y6r9B/dn0t/filmxt0X3M3bT0brRpoKMck82gmnTt/DrFJ6UimX3345JcNLeOjKh1jx6xVBlxQ3nScmmSXTdr5LPNjGzCTXW4bOK8sivct6c+kvLuXl773Mn7/wZ2q31HLJjy7Bcizo0roVaIiZ2V3AFUC1c+6MLl434N+By4GjwKeccyv9rVKyRqKBkWg4eBFIyej4vonWrCDLKvlF+cz+3mxe++VrLPnJEuq21/Ghez9EXmF4+52gK7sH+BVwX4zXLwPGtj/OAf6r/W+R90umC0slWIIKpVQsQUEm3crJzeGcL59Dn6F9eP03r3No1yGue/w6epf1Drq0LgV6TMw59zLQ3QkKVwP3uTbLgH5mNtSf6iRSEg2wJUQzhLyQzNeeacO00i0z44zrzuDCWy9k9/Ld/Pbc33JgczjPJQv7xI7hwM4OH1e1f+49zOxmM1thZitqamp8K04iKlvDqzMFWcbouA/cX+fdFThGzR7F3NvmUl9dz29n/JaqZVWebdsrYQ+xro4ovu8e2865O5xzFc65ivLych/KklBJZOeqAHsvBVlG6LgPLCst83TbgyYN4vJfXU5uQS73zr6XDY9t8HT7qQp7iFUBHS+zPALYHVAtEnUKsK7p+yI9KBlRwuW/upz+p/RnwfwFLP35Upx7Xz8RiKAndvRkIfBFM3uYtgkddc656J1SLukTb2cQxR21F11PvBMyEpnwoYkeWamwXyFzfzqXxT9czDNfeYbaLbXM+8U8cnKD7YWCnmL/EDALGGhmVcC3gHwA59yvgSdpm16/ibYp9p8OplKJtDAHWLqH505sP57QSWbmomSVvII8LvzWhaz4zQqW/2o5dTvqmP/QfPJ75wdXU2DvDDjnru/hdQf8vU/lSNTEEwBhCbCgjyXFG2bxBpm6saxlOcbZXzibvkP68up/vMr9c+/nY098jMJ+hYHUE/ZjYiLRtKzTIyzCVItE2mkfPo0Lb72QXa/u4p4L7+HI3iOB1KEQk8yV7i6sc1CFMbS60lN98X7fwv51StqNmjWKi35wEfvf2s9dH7iLg9sO+l5D2Cd2iHQtXTvQbNkxazhQPDJ8+nDm3DaH5772HL+d+Vs++ewnKZ/o36lO6sQkMyVz/lMEAuzImiPdPjwTlmOJEgmDTh/EvF/Ma7sv2QV3s3fNXt/eWyEmErLwSiWkEgqzkH3dEm39x/Rn3r/Pw3KM+y65j5oN/lw9SSEm0ePlzjcEO/J0dFNxb6u7rz+ebiwE3z8Jj5LhJcz96Vxcq+Pei+6ldktt2t9TISaZJ+QTE9I2BBjjvUT8VDqylLm3zaX5aDMPXPoAR/cdTev7KcQkOwUQYH6EVqz37Za6KfFYv1H9uOgHF1G3s44Hr3iQpmNNaXsvhZhESwR3uEGFV+cakqIJHpKkQWcM4vxvnM+u13bxxOefSNu1FhVikn18CsIwhFdHYapFssPJ55/MmTeeyZr71/Dar15Ly3soxCSzhKBzSGd4VdZXUllf6f2GI9jhSjRM/sRkRs4cyTNfeYbdK7y/CYlOdpboiMCO1qvw6imounp9UvEkT95bxEuWY3zglg/wP5/9Hx674TE+/8bnPb1gsDoxEQ940X2d6LKS7bTiWU9DihKEgr4FfOD/foD9b+3nuW885+m2FWKSXdLQzaUSDKkGV1fbEwmjodOGMu6qcbz2y9d4Z/U7nm1XISbRENKhxGQDLG3HtlCQSXid9dmzKCgt4Mm/f9Kz2YoKMZEkpRJg6dbde2hIUYJS0LeAqZ+Zys5XdvL2n9/2ZJsKMZEkJBME6ey+RKLi1Hmn0ndEX57/5vO41tS7MYWYiA8UXiJtcvJymPKJKexdvZfNz2xOfXse1CSSXiE7HpZoFxZUgCk4JaxGzR5FUVkRr/7HqylvS+eJiSTA7wBb1inBZ+hOlpIBcvNzGXflOFbfs5qD2w/S7+R+SW9LnZhkjhBcraOjZANsWYc/ibwmEiWnzDkFgHWPrEtpOwoxkTgl0oUlE2CJhlMogkyNoSSp77C+DJw4kLWPrE1pOwoxCbcQ7KcTlWiAqbOSbDXy3JG8s/Id6qvrk96GQkwkDuk6tyrV8Epm/T6T+6T0niJeGTptKABbntuS9DYUYiIeSqQLU/cl2a5sXBl5RXlULa1KehsKMZEexNuFKcBEEpOTm8OAUwew+/Xkb9GiEBPxWSgDLNYEjZlJrCOSgAGnDqC6sjrpaykqxCS8QrivjyXeLszPANP9xSQK+g7rS+PhRo7uO5rU+goxkW7EM5QYxgCLRZM6JGz6DusLwMGtB5NaXyEm4oMwBFhMyQwliniksF8hQDQ7MTObZ2ZvmtkmM7uli9dPMrMXzOwNM1tjZpcHUadILPF0YaEOMJGAFZQWAFBfk9y5YoGFmJnlArcDlwETgevNbGKnxb4JLHDOTQWuA/7T3yolm2Xafbc8HUrUpA7xSF5h2yV8m442JbV+kJ3YdGCTc26Lc64ReBi4utMyDihpf14KJD8PU8RjYe7C4p7UoaFECVhur1wAWo63JLV+kFexHw7s7PBxFXBOp2W+DTxjZl8CioFL/ClNJHUaRhSJQ/vMesuxpFYPshPrquLOJwpcD9zjnBsBXA7cb2bvq9nMbjazFWa2oqamJg2lSrbpaSgxavfq6nIoMdkhQQ0lhk7HfeD+uv1Bl5OQlsa2DuzEsGKiggyxKmBkh49H8P7hwpuABQDOuaVAITCw84acc3c45yqccxXl5eVpKlckfkF2YSmfH6ahxMjpuA8sKy0LupyENB9vBqIZYsuBsWY22sx60TZxY2GnZXYAFwOY2QTaQkytVjZIRwb4lCvJBtiS9j+JiOcmmZ52YSIee7cTK4pYiDnnmoEvAk8DG2ibhbjOzL5rZle1L/ZV4HNmthp4CPiUS/baJCJxCmIosWN4JRpknuqpC1P4iceaG9o6sfyi/KTWD3JiB865J4EnO33u1g7P1wMf8LsuiaCQ3NU5mS7My9CKayhRQSQh0nCwAYDiQcVJra8rdogkwK8JHfEEW9JDibGoC5MAHDtwDIA+Q5I7j1EhJtkhzgYplROcvezCZiYxuyKlLkyTOSQgDbXqxESkC7pCh0TBsQPHKCgtiOTsRBFveHRIKZUJHX4fC4tnKLGLlbqmLkwCdOzAsaSHEkEhJmHk9VT4iF04w4uhRHVhEhUKMREPROlYmLowySQNBxvoM1ghJtnKp6n1fsxKTKYDgxS6sHjeTl2YpNmx/cfoM1QhJtK1OJqkMNxyJd4A87QLS9d6InFqOtZE09EmDSdKBvH5+FU8Aeb1hI7Oku3AuuJpFyaSZqmeIwYBX7FDJCU9DSWGfEJHouHVVRfW47lh6sIkxBoOtJ0jpk5MJAmpdmGp8LL7SuLNu6cAE5940YkpxCSaUuzCvDgO5uftVuLpwt43lNhVGGkYUUJEISaZxatM8CjAonbjS0+oCxMfNdY3AlDYvzDpbSjEJHpSmFYfxQDzrQtTgInPmhuasRwjt1du0ttQiElm8aCbiyfA/BpKTGpKvUhEtBxvIb93PmaW9DYUYhIO8WZCmruwMHVgsagLk0zRfLyZvN6pTZJXiEnm6CYIvQywILuwuG63kvgbiQSipamF3PzkhxJBISZR0l0X5lOA+SXeYUTNSJQoy+2VS0tjS0rb0MnOErw0Njap3F6lK350YbECTF2YZJq8gjyajjaltA11YhINSXRhmR5g6sIk6vIK20LMOZf0NhRiEqxUcyHJ9cM2fJgoT+4Xpi5MApZXmAeubap90tvwsB6R9EhiRmKsLiyZ8Ap6On1cw4iJdmEKMAmBgtICAOr31tNvVL+ktqFOTIKTpi4s0wPM07s2iwSo79C+ANRuqU16G+rEJNwS7MK8CDA/r4kIHpzQrGNhElF9h/01xEZfNDqpbSjEJBg+HgsL2/lfHXUXYGnrwjSUKCHRu7w3OXk51G5VJyaZyIMurKcACyK44hF3gKkLkwjLyc2hz5A+7H9zf/Lb8LAekfikoQtL5tYqQQeYr+eD/fVNRUJlwLgB7HptV9LrK8QknFK4RuIJsbqwZe1/gpTocbC4uzCRiCmfUM6hnYc4tOtQUusrxMRfPnVh3QVY0NJ6HEzT6iViyieWA7Dr1eS6MYWYhE+sLiwDAqw7CQ0jKpAkQww4dQA5+TnsXLozqfUVYuKfADMkLAGW1mFETeiQCMrtlUv5hHK2Pr81qfUDDTEzm2dmb5rZJjO7JcYyHzWz9Wa2zswe9LtG8Uiq9wtLoQuLJ8CWdPqTDoFMp//rm4uE1tCKobyz8h3qq+sTXjewKfZmlgvcDswBqoDlZrbQObe+wzJjga8BH3DO1ZrZoGCqlShINMC6C6uOr830oMVJNMC62VDX1IVJhA0/ezir7lrF5v/dzOQbJie0bpCd2HRgk3Nui3OuEXgYuLrTMp8DbnfO1QI456p9rlG84FMX9v7Vun7jRLutdHVm3fF0GFFdmITcgLEDKCgtYPPTmxNeN8gQGw50PJJX1f65jsYB48zsFTNbZmbzutqQmd1sZivMbEVNTU2aypWk+HQoqnMX1l2AJSOVYcZAhxElK3TcB+6vS/7E4aDk5OYwdL2EPFoAABy9SURBVNpQNj+9Gdea2G1Zggwx6+JznavPA8YCs4DrgTvN7H2XOnbO3eGcq3DOVZSXl3teqPjA4y6s67fwv6Pqji/DiOrCskLHfWBZaVnQ5SRlxPQR1FfX887qdxJaL8gQqwJGdvh4BLC7i2Ued841Oee2Am/SFmoSBakOI8Ypni7MqwBLdDuezEYUyQLDzh4GwKanNiW0XpAhthwYa2ajzawXcB2wsNMyfwJmA5jZQNqGF7f4WqUkx4thRI+6sLB1YKDJHCKdFQ0oYsDYAdEJMedcM/BF4GlgA7DAObfOzL5rZle1L/Y0sN/M1gMvAP/snIvegK/E5kMXFpTALy2loUSJmOFnD2fn0p001DXEvU6gV7F3zj0JPNnpc7d2eO6Ar7Q/JCoi1IV1DL2U7+sVJ08u8KsuTDLQ8HOGU/lgJVuf28qEaybEtY6u2CHeSiTA0tyFdRdgXV0EON4uLp7hSXVhIokrn1hOfnE+bz/1dtzrKMQkGN3lgIczEt+/6e6vYJ/u4UhPptSrC5MMlZOXw5Azh7Dt+W3xr5O+ciTr+Hg4KpUurCc9BVlPV/DwZFhSXZhkqfIJ5dRuqeXYgWNxLa8QE/8F2IUFSV2YSM8GnjYQgN0rOp9x1TWFmHjDi3PC4txGMl2YXwGW1i5MASZZoGx828na8d7tWSEm/klihM+rLixRXoeeb5eX0lCiRFyvPr0oPalUnZj4KE1T6r0S9DBiQtSFiVByUgn734rvlGCFmPgjiWHEeO4X5uWEjlTFGkpUFyaSmL5D+nJw20HaThXunkJMUhPyJsfrLsyLe4vFpC5MBIDe5b1pPtbM8UPHe1xWISbpl6YuLEzUhYl4p1ffXgA01PZ8+SmFmKSXB7MRY6+evqFEvy5B1eENu6YuTLJQfu98AI4fVicm6ZSmocSwdmGxhhLVhYl4y07cbjKO+2MqxCR9PBpGTFaQsxJ1qxWR5LkT6dXVrZM7UYhJcgK8xFTb24djRkkiw47qwkTi01TfBEBBSUGPyyYdYmb26WTXlSzgcxeW7uNhicxKVBcmkprGI40AFJYW9rhsKp3Yd1JYV6IslSYoHA2UJwLvwkQyVH1NPfm98yko7bkT6/ammGa2JtZLwOAkapNs4OHlpfweSoy3C0tkMkc3b9a1eLswDSVKhqrfW0/pyaWY9XxQrKc7Ow8GLgVqO33eSPmWhpJ1fJjMEaRYAaYuTCQxB7ceZNjZw+JatqcQewLo45xb1fkFM3sx8dIk8npqgmL9apNE8+T3tPpUu7AE36xr6sIkyzUeaeRQ1SGm/e20uJbvNsScczd189rHEqxN5H0S7cLSNZSYajCpCxPxxoFNBwAYetbQuJbXFHuJn8ddWHcBlkgXFmtmYqrBFG8XlnCAqQsTienE1euHTYtvOFEhJlkv3rDz/VJUIllo34Z99B3el+JBxXEtrxCT+IS0C0tVrGCK97wwdWEi3nGtjndWvcPo2aPjXkchJpGRzPGwWCE1o/1PV3wfRhQRAGq31NJwsIHRF8cfYj3NThSJfBcW+mFAdWEiAOxZuQcgoRBTJya+SkeAeXmjSnVhIsHZs3IPA04dQOnI0rjXUYhJ93w8LyxoaQswLygEJcO1NrdSXVnNmDljElpPISa+SaULS/dV673s5t6nuwDShX5FgLZZiU1HmxIaSgSFmHQn2dyIYBfWFXVhIv7Zs6rteNioWaMSWk8hJslL4OqZYZlS35VUhxG7pS5MJC7VldWUn1FO77LeCa0XaIiZ2Twze9PMNpnZLd0sN9/MnJlV+FlfVvOpC/MqwJIdDvTi2ojqwkRS09rSSs26Gk6+4OSE1w1sir2Z5QK3A3OAKmC5mS10zq3vtFxf4B+AV/2vUmLyqAuLRzqOhyUaekkNI6oLE4nLgU0HaDraxMnnJx5iQXZi04FNzrktzrlG4GHg6i6W+x7wY6DBz+KyWsS6sBPiDabulvNsGFFE4lZdWQ3ASeeflPC6QYbYcGBnh4+r2j/3LjObCox0zj3R3YbM7GYzW2FmK2pqaryvVN7Lgy4sXcfBuguome1/YvF0GNGLYUANJUqcOu4D99ftD7qchNVuqaV4cDElw0sSXjfIK3Z0dctO9+6LZjnAz4FP9bQh59wdwB0AFRUVrofFpTshm1mYzFCil9Pl0zIbUUOJ4rGO+8DJ4ydHbh9Yt6OO8onlSa0bZCdWBYzs8PEIYHeHj/sCZwAvmtk22n4vXajJHQFL4ORmv7uwVHg6jKgOSiRuzjkObjsYyRBbDow1s9Fm1gu4Dlh44kXnXJ1zbqBzbpRzbhRtu8mrnHMrgik3C4SsC/NLqGYjnqAglCxxdN9Rmuqbohdizrlm4IvA08AGYIFzbp2ZfdfMrgqqrqwVT4D53IWl+yodEDvA0jaZQ0OJIu9xePdhAAaMHZDU+oFexd459yTwZKfP3Rpj2Vl+1CTSnR67MHVQIglpONg28bzPkORGOHTFDlEX1kkgl5bqSEEoWeREiPUemNiVOk5QiElWSss9xhQ+IglrOd4CQK/iXkmtrxDLdj5N5ghTF9ZdgKX1xGYdDxN5n5bGthDLK0zu6JZCTHrmwVBivIIMsO74NpQokmVycttiqLW5Nbn1vSxGIiaVY2EJCON5YV0JxeWlNCQpWSa3MBeApqNNSa2vEJPkpKFhinQXpvARSUpeQdswokJMApfKUGLQARaKLkwkC504FtZY35jU+gqxbOXxtPpYwjCUmJaZiCLiifzifAAaapO7UYlCTDwR1i4sngBTFyYSnOJBxQAcqjqU1PoKsWykCR1x06xEkfQqLm8LsboddUmtrxCTxHjcNAXdhYlIsHr16UV+n3yFmMQpDV1YqueGpYMCTCQ6isuLObRTw4mSbhG5VUsiAabjYSLBKx5UrE5MPOLBsTAI7niYOjCR6CkeVMzBHQeTWlchlk3S0EmFaVZi5AMs4uWLJKt4UDHH9h2j6VjiJzwrxCQ+ERlKFJHoeXeafRLHxRRi2cKnafVBSaYL0/EwkXDoXd52L7FkzhVTiEnPYgSgFze/9ELkhxFFslxR/yIAjuxN/PCEQiwbZHAXFpkA073ERGIq7F8IQP3e+oTXVYhJYFINoMgEmIh0q6BvAZZr6sSkC6lOyAjphI5UA0zHw0TCw3KMov5F6sQkSUkMJXp1lY5kwkgdmEjmyS3MpbmhOeH18tJQi4RFSLuoZCm8RDJXTm4OrU2tCa+nEJPAnQinWCc/BxleR9Yc0ZXsRXyQk5dDS1NLwuspxDKVF12Yz52cX2Gl42Ei4ZOTl0Nrc+KdmI6JZbuITq0XkcziWl1S6ynEMlGGHQvLCjrcJ1mu8XAjRQOKEl5PISYJC+P9w+KloUSRcDp+6DhFZQoxSaQL01CiiIRAS1MLTUeb1ImJdEddmEg4HT90HIDeZb0TXjfQEDOzeWb2ppltMrNbunj9K2a23szWmNlzZnZyEHVGhpfHwnRcTUR8cnjXYQD6je6X8LqBhZiZ5QK3A5cBE4HrzWxip8XeACqcc5OBR4Ef+1ulJCOMHU8YaxKRNge3td3VuXxiecLrBtmJTQc2Oee2OOcagYeBqzsu4Jx7wTl3tP3DZcAIn2uMjkQ7pxSOh0Xt5F8FmEi41e2oI793PqUjSxNeN8gQGw7s7PBxVfvnYrkJeCqtFUVVCIf+FBwiEq+67XWUjS/DcizhdYMMsa6q7fJsNzP7OFAB/CTG6zeb2QozW1FTU+NhiRJ1oQhT3UtM0qzjPnB/3f6gy0mIc44Dmw8weNLgpNYPMsSqgJEdPh4B7O68kJldAnwDuMo5d7yrDTnn7nDOVTjnKsrLEx9TjbQQdmEnBB0gQb9/3HSis6So4z6wrLQs6HIScmjnIRpqGxh53sieF+5CkCG2HBhrZqPNrBdwHbCw4wJmNhX4DW0BVh1AjZkpC84P8yrAonb8TyRq9lbuBeDk85ObfB5YiDnnmoEvAk8DG4AFzrl1ZvZdM7uqfbGfAH2A35vZKjNbGGNzElJ+d0OTiidFpwMTEarXVNN7YG/KxifXQQZ6FXvn3JPAk50+d2uH55f4XlSUBDiU2Gdyn7gvPzWpeBKV9ZVprijA4UMNB4okxTnH3jV7OfmCkzFLfFIH6Iod4pN0d0jqvkSi5+C2gxx55whj5o5Jehu6n1hUJduFBXw87ETYeNWZKbxEomvnK21nWY2/cnzS21CISdISGVLsLJUwU3CJZIadS3YyrGIYfYf1TXobCrEoCtG0+lSCDMIdSJqZKJI+R/cfZd+Gfcz+3uyUtqNjYiLppBOdRbq0/cXtAJz2odNS2o5CTFKW1R2LZiaKJGXTXzYxZOoQBp0xKKXtKMSixq+hxAR3zpkWZJn29YiEyYFNBziw6QBTPzM15W0pxLJJmmcmascvIvHY9JdN5PbK5Yzrz0h5Wwox8ZSCLEEajpQs03y8ma3PbWXcVeOSupNzZwox8VzUgyzq9YuE2aanNtFwsIHpfz/dk+0pxKIkRFPre9Jncp/MDwN1USIJaW1pZd2CdQw/ZzgnX5jcBX87U4hJbB7spKMWZlGqVSRqtj2/jSN7jnD+189P+lqJnelkZ/FFx3BI5eRoEYkm1+qofKiS8onljLtinGfbVScmvgtrd5ZQTfF0qTrRWeRdW1/YysGtBznv6+dhOd50YaBOTHoyg7QdiwtTdxbGUBXJFM3Hm1n53ysZctYQJl3v7aXmFGLSszQG2QmdQyToUOuWJnSIJGTtQ2up31vP/Ifne9qFgUJM4uVDkHXkV5emDkwkvQ5uP0jl7yqZdMMkRs0a5fn2FWLZZCapXbXD5yA7IV1dWlIBpi5MJG6tLa0svW0pBSUFXPqzS9PyHgoxSUxAQdaRF6GmDkwk/Sp/V0n12mqu+d01FA8qTst7KMQkcR27kRCcgN1VIHUONk9CK5EuTDMTJctVr61m9X2rmXTDJCZ9LH33DVSIZZtUhxQ762rHHtJgS4mGEUXidnT/UV78zouUnlTK5bdfntb3UoiJ90IabCKSfi2NLbz07ZdoPtrMjc/fSGFpYVrfTyEm/ugcbFEKtUS7MA0lSpZyzrHkJ0uoXlvN/EfmM3jS4LS/p67YESVeDWmFYSc7o8MjzMJen0iIvHHnG2x5dgsX/eAiTv/o6b68p0IsW4UhyE4Ia6AlU0+Yvq8iPlr70FoqH6zkrJvP4ryvnefb+yrEslkYd7hhCbSg318kQtY+spbX73id0687nQ/e/kHPrlAfDx0Ti5oQnKflmyCm8qcSXmH8pUAkzdYtWMfrv36d0689nWvuv4acPH97I4VYtvN6yn26+DExxO8AU7cnEbf+0fWs+K8VTPzIRK55wP8AA4VYNHndjZ3YAUchzE7wsktLNUzUgUkWWrdgHSv+awUT5k/gmt8FE2CgEJOOotKVdZbMeWmZNNNTxEeu1fH6Ha+z7pF1bR3Y764hNz83sHoUYlGVrmNjUezKupLuobpUw0tDiRJBrc2tvPLjV9jyv1uo+LsKLvvlZeTkBjs/MNAQM7N5wL8DucCdzrkfdXq9ALgPmAbsB651zm3zu87QSuckj2TCLJ4de9TDEdR9SVZqOtbEi996kd3LdzP7+7M5/+vn+zoLMZbAQszMcoHbgTlAFbDczBY659Z3WOwmoNY5d6qZXQf8G3Ct/9WGWLpnK3q9w+68vSiFmsJLslTDwQae+9pz7H9rP1feeSVn3XRW0CW9K8hObDqwyTm3BcDMHgauBjqG2NXAt9ufPwr8yszMOef8LDT0ojztPgqh5nV4aShRIuTwnsM8+3+f5ei+o1z7p2sZf+X4oEt6jyBDbDiws8PHVcA5sZZxzjWbWR1QBuzruJCZ3QzcDHDSSSelq95wO7FjjGqYnRC2UFOASQR03AcOGzzMs+0e2HSAZ295Ftfq+ORzn2TkzJGebdsrQR6R62owtXOHFc8yOOfucM5VOOcqysvLPSkusjJtJzmz08Pv9xWJgI77wLLSMk+2uWflHv7y5b+QV5jHZxZ/JpQBBsF2YlVAx+/KCGB3jGWqzCwPKAUO+FNehGVKV9aVdHZq6Q6tTPsFQzLWthe2sehfF1E2royP/+XjlIwoCbqkmIIMseXAWDMbDewCrgM+1mmZhcCNwFJgPvC8joclIMrHyuIVK3jiDTe/ui0FmETExj9t5NVfvsrID4zk+oXXU9S/KOiSuhVYiLUf4/oi8DRtU+zvcs6tM7PvAiuccwuB3wL3m9km2jqw64KqN7IyuSvrTliGAhVeEiGVD1ay8r9XMu7Kccx/ZD75RflBl9SjQM8Tc849CTzZ6XO3dnjeAHzE77oyUraGWZAUYBIRzjneuOsNKh+o5Izrz+BD934o0KtwJEJX7Mg2CrP0UnBJxLhWx/Lbl7PhsQ1M/exUrvj1FYFfhSMRCrFsFcRtTjKZwksiqLWllWU/W8bbT77NjH+cwdyfzg3FVTgSoRATb7uzeHfmmRCcCi6JMNfqWPrTpWx6ahMX3HoBs749K3IBBgox6SjRq8GnshNP5srzYaDgkgzgXNsQ4qanNnHB/7uA2d+ZHXRJSVOISff83GmHNdgUXJJh1jywhg2PbeCcL5/DrO/MCrqclCjEJNy6C5B0BpyCSzLUlue2sOquVUz++GQu/dmlkRxC7EghJtEVK2iSDTcFl2S4mvU1vPJvr3DSBSdx5Z1XRj7AQCEmmUhhJPI+DXUNvPSdlygZUcK1j11LXkFm7P4z46sQEZGYXKtj8b8upuFgAzc8dQO9y3oHXZJnonNGm4iIJOWtJ95i16u7uPTnlzL0rKFBl+MphZiISAY7svcIr//mdUZfPJqKL1QEXY7nFGIiIhls+e3LweDK/86MiRydKcRERDLU3sq97Fi0g/NuOY/+o/sHXU5aKMRERDKQc46Vd6ykz7A+nPuVc4MuJ20UYiIiGai6sprqtdWc//Xzye8d/vuCJUshJiKSgdY9so6igUVM/fTUoEtJK4WYiEiGObrvKFXLqpj2uWkZ3YWBQkxEJONseW4LrtUx5cYpQZeSdgoxEZEMs/2l7QyrGMbA8QODLiXtFGIiIhmktaWVfRv3Me6qcUGX4guFmIhIBmmqbwIHYy8fG3QpvlCIiYhkkKZjTfTq04shZw4JuhRfKMRERDJIc0Mzw88ZTk5uduzes+OrFBHJEi2NLQyePDjoMnyjEBMRySDOOcrGlQVdhm8UYiIiGUYhJiIikaUQExGRSDIz+g7rG3QZvlGIiYhkkJzcHCwn825+GYtCTEQkg+TkZdduPZCv1swGmNn/mtnb7X+/75ajZnammS01s3VmtsbMrg2iVhGRKFGI+eMW4Dnn3FjgufaPOzsKfNI5dzowD/iFmfXzsUYRkchRiPnjauDe9uf3Ah/qvIBz7i3n3Nvtz3cD1UC5bxWKiERQNh0Pg+BCbLBzbg9A+9+DulvYzKYDvYDNMV6/2cxWmNmKmpoaz4sVEQmzjvvAhsaGoMvxVV66NmxmzwJdXYHyGwluZyhwP3Cjc661q2Wcc3cAdwBUVFS4BEsVEYm0jvvAsYPGZtU+MG0h5py7JNZrZrbXzIY65/a0h1R1jOVKgD8D33TOLUtTqSIiGcNMw4l+WAjc2P78RuDxzguYWS/gj8B9zrnf+1ibiEh0Zde8jsC+3B8Bc8zsbWBO+8eYWYWZ3dm+zEeBC4BPmdmq9seZwZQrIhIN2daJpW04sTvOuf3AxV18fgXw2fbnDwAP+FyaiEikKcSy1YigCxARSV3RgKKgS/BVlo2eiohkttyC3KBL8JVCTEREIkshJiIikaUQExGRyFKIiYhIZCnEREQkshRiIiISWQoxERGJLIWYiIhElkJMREQiSyEmIiKRpRATEZHIUoiJiEhkmXOZdSdrM6sBtqdh0wOBfWnYbjJUS2xhqke1dE21dK27WvY55+bFsxEz+0u8y2aCjAuxdDGzFc65iqDrANXSnTDVo1q6plq6FqZaokTDiSIiElkKMRERiSyFWPzuCLqADlRLbGGqR7V0TbV0LUy1RIaOiYmISGSpExMRkchSiImISGQpxGIwswFm9r9m9nb73/27WOZMM1tqZuvMbI2ZXetxDfPM7E0z22Rmt3TxeoGZPdL++qtmNsrL90+wlq+Y2fr278NzZnZyULV0WG6+mTkzS9u05XhqMbOPtn9v1pnZg0HVYmYnmdkLZvZG+7/T5Wms5S4zqzaztTFeNzP7ZXuta8zsrABruaG9hjVmtsTMpgRVS4flzjazFjObn65aMoZzTo8uHsCPgVvan98C/FsXy4wDxrY/HwbsAfp59P65wGZgDNALWA1M7LTM3wG/bn9+HfBImr4X8dQyG+jd/vwLQdbSvlxf4GVgGVAR4PdlLPAG0L/940EB1nIH8IX25xOBbemopX37FwBnAWtjvH458BRgwAzg1QBrmdnh3+eyIGvp8G/5PPAkMD9dtWTKQ51YbFcD97Y/vxf4UOcFnHNvOefebn++G6gGyj16/+nAJufcFudcI/Bwe02xanwUuNjMzKP3T6gW59wLzrmj7R8uA0akoY64amn3Pdp+EWlIUx3x1vI54HbnXC2Ac646wFocUNL+vBTYnaZacM69DBzoZpGrgftcm2VAPzMbGkQtzrklJ/59SO/PbjzfF4AvAX+gbX8iPVCIxTbYObcHoP3vQd0tbGbTafsNeLNH7z8c2Nnh46r2z3W5jHOuGagDyjx6/0Rr6egm2n7LToceazGzqcBI59wTaaoh7lpo69bHmdkrZrbMzNJ1OaB4avk28HEzq6Ltt/wvpamWeCT6M+WXdP7s9sjMhgMfBn4dVA1Rkxd0AUEys2eBIV289I0EtzMUuB+40TnX6kVttA2zdNb5fIh4lvGrlrYFzT4OVAAXpqGOHmsxsxzg58Cn0vT+cdfSLo+2IcVZtP2Gv8jMznDOHQygluuBe5xzPzWzc4H722vx6mc2EX797MbNzGbTFmLnBVjGL4B/cc61pGdQJfNkdYg55y6J9ZqZ7TWzoc65Pe0h1WVrb2YlwJ+Bb7YPi3ilChjZ4eMRvH/458QyVWaWR9sQUU9DFemqBTO7hLZfAC50zh1PQx3x1NIXOAN4sX0nMARYaGZXOedW+FzLiWWWOeeagK1m9iZtobY8gFpuAuYBOOeWmlkhbRedDWLYKq6fKb+Y2WTgTuAy59z+oOqg7RfAh9t/dgcCl5tZs3PuTwHWFGoaToxtIXBj+/Mbgcc7L2BmvYA/0ja2/3uP3385MNbMRre/z3XtNcWqcT7wvGs/Mux3Le1DeL8BrkrjcZ8ea3HO1TnnBjrnRjnnRtF2jCMdAdZjLe3+RNukF8xsIG3Di1sCqmUHcHF7LROAQqAmDbXEYyHwyfZZijOAuhPD934zs5OAx4BPOOfeCqKGE5xzozv87D4K/J0CrAdBzywJ64O2Y0vPAW+3/z2g/fMVwJ3tzz8ONAGrOjzO9LCGy4G3aDvO9o32z32Xtp0ytO2Efg9sAl4DxqTx+9FTLc8Cezt8HxYGVUunZV8kTbMT4/y+GPAzYD1QCVwXYC0TgVdom7m4Cpibxloeom22bhNtXddNwN8Cf9vh+3J7e62Vaf436qmWO4HaDj+7K4KqpdOy96DZiT0+dNkpERGJLA0niohIZCnEREQkshRiIiISWQoxERGJLIWYiIhElkJMJEHxXolcRNJPISaSuHtov/KFiARLISaSIBfflchFxAcKMRERiSyFmIiIRJZCTEREIkshJiIikaUQE0mQmT0ELAXGm1mVmd0UdE0i2UpXsRcRkchSJyYiIpGlEBMRkchSiImISGQpxEREJLIUYiIiElkKMRERiSyFmIiIRNb/B2MXf2QNkRuWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(ypred,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.037938</td>\n",
       "      <td>0.120769</td>\n",
       "      <td>0.388931</td>\n",
       "      <td>0.716873</td>\n",
       "      <td>0.493704</td>\n",
       "      <td>0.183284</td>\n",
       "      <td>0.510436</td>\n",
       "      <td>0.577620</td>\n",
       "      <td>0.497968</td>\n",
       "      <td>0.650619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.441643</td>\n",
       "      <td>0.445937</td>\n",
       "      <td>0.300748</td>\n",
       "      <td>0.277367</td>\n",
       "      <td>0.188804</td>\n",
       "      <td>0.397744</td>\n",
       "      <td>0.571619</td>\n",
       "      <td>0.406192</td>\n",
       "      <td>0.221614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.068120</td>\n",
       "      <td>0.967466</td>\n",
       "      <td>0.248555</td>\n",
       "      <td>0.733659</td>\n",
       "      <td>0.964694</td>\n",
       "      <td>0.108930</td>\n",
       "      <td>0.492061</td>\n",
       "      <td>0.284796</td>\n",
       "      <td>0.468924</td>\n",
       "      <td>0.966790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.293352</td>\n",
       "      <td>0.055517</td>\n",
       "      <td>0.302236</td>\n",
       "      <td>0.215699</td>\n",
       "      <td>0.588926</td>\n",
       "      <td>0.328303</td>\n",
       "      <td>0.402682</td>\n",
       "      <td>0.331904</td>\n",
       "      <td>0.421316</td>\n",
       "      <td>0.576892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.044627</td>\n",
       "      <td>0.187002</td>\n",
       "      <td>0.958735</td>\n",
       "      <td>0.342727</td>\n",
       "      <td>0.091207</td>\n",
       "      <td>0.065293</td>\n",
       "      <td>0.153030</td>\n",
       "      <td>0.969680</td>\n",
       "      <td>0.665252</td>\n",
       "      <td>0.113021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0.096681</td>\n",
       "      <td>0.813483</td>\n",
       "      <td>0.160894</td>\n",
       "      <td>0.874632</td>\n",
       "      <td>0.509081</td>\n",
       "      <td>0.232622</td>\n",
       "      <td>0.750933</td>\n",
       "      <td>0.393615</td>\n",
       "      <td>0.391120</td>\n",
       "      <td>0.532508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.368325</td>\n",
       "      <td>0.063253</td>\n",
       "      <td>0.830251</td>\n",
       "      <td>0.659393</td>\n",
       "      <td>0.336618</td>\n",
       "      <td>0.375574</td>\n",
       "      <td>-0.003858</td>\n",
       "      <td>0.882506</td>\n",
       "      <td>0.519184</td>\n",
       "      <td>0.374276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.463700</td>\n",
       "      <td>0.404914</td>\n",
       "      <td>0.105439</td>\n",
       "      <td>0.521489</td>\n",
       "      <td>0.403149</td>\n",
       "      <td>0.720699</td>\n",
       "      <td>0.270012</td>\n",
       "      <td>0.470520</td>\n",
       "      <td>0.434556</td>\n",
       "      <td>0.416402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.541586</td>\n",
       "      <td>0.814673</td>\n",
       "      <td>0.788282</td>\n",
       "      <td>0.020293</td>\n",
       "      <td>0.266992</td>\n",
       "      <td>0.579980</td>\n",
       "      <td>0.552079</td>\n",
       "      <td>0.877703</td>\n",
       "      <td>0.359861</td>\n",
       "      <td>0.195636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.260573</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.399903</td>\n",
       "      <td>0.906449</td>\n",
       "      <td>0.663120</td>\n",
       "      <td>0.301082</td>\n",
       "      <td>0.650930</td>\n",
       "      <td>0.478478</td>\n",
       "      <td>0.576453</td>\n",
       "      <td>0.679861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         0         1  \\\n",
       "0    0.037938  0.120769  0.388931  0.716873  0.493704  0.183284  0.510436   \n",
       "1    0.112667  0.441643  0.445937  0.300748  0.277367  0.188804  0.397744   \n",
       "2    0.068120  0.967466  0.248555  0.733659  0.964694  0.108930  0.492061   \n",
       "3    0.293352  0.055517  0.302236  0.215699  0.588926  0.328303  0.402682   \n",
       "4    0.044627  0.187002  0.958735  0.342727  0.091207  0.065293  0.153030   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995  0.096681  0.813483  0.160894  0.874632  0.509081  0.232622  0.750933   \n",
       "996  0.368325  0.063253  0.830251  0.659393  0.336618  0.375574 -0.003858   \n",
       "997  0.463700  0.404914  0.105439  0.521489  0.403149  0.720699  0.270012   \n",
       "998  0.541586  0.814673  0.788282  0.020293  0.266992  0.579980  0.552079   \n",
       "999  0.260573  0.333285  0.399903  0.906449  0.663120  0.301082  0.650930   \n",
       "\n",
       "            2         3         4  \n",
       "0    0.577620  0.497968  0.650619  \n",
       "1    0.571619  0.406192  0.221614  \n",
       "2    0.284796  0.468924  0.966790  \n",
       "3    0.331904  0.421316  0.576892  \n",
       "4    0.969680  0.665252  0.113021  \n",
       "..        ...       ...       ...  \n",
       "995  0.393615  0.391120  0.532508  \n",
       "996  0.882506  0.519184  0.374276  \n",
       "997  0.470520  0.434556  0.416402  \n",
       "998  0.877703  0.359861  0.195636  \n",
       "999  0.478478  0.576453  0.679861  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.reset_index(drop=True, inplace=True)\n",
    "ypred.reset_index(drop=True, inplace=True)\n",
    "pd.concat([ytest,ypred] ,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_rf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-dd7165d366df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'kde'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'y_rf' is not defined"
     ]
    }
   ],
   "source": [
    "sns.jointplot(y_rf.iloc[:,0],y_rf.iloc[:,0]-y_test.iloc[:,0],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_rf.iloc[:,1],y_rf.iloc[:,1]-y_test.iloc[:,1],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_rf.iloc[:,2],y_rf.iloc[:,2]-y_test.iloc[:,2],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_rf.iloc[:,3],y_rf.iloc[:,3]-y_test.iloc[:,3],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.jointplot(y_rf.iloc[:,4],y_rf.iloc[:,4]-y_test.iloc[:,4],kind = 'kde',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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