ch10/ch10practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#practice 1\n",
    "# This script will create a dataset with 50 rows, where livingroom_size is directly proportional to house_price, and years is indirectly proportional to house_price. The house_price is calculated with a simple linear relationship based on your specifications. The dataset is then plotted using matplotlib to visualize the relationships.\n",
    "# Please note that the actual relationship between these variables in real-world data might be more complex and could require more sophisticated modeling to accurately reflect. The above script is for demonstration purposes and creates a simplified linear relationship between the variables.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(0)\n",
    "\n",
    "# Generate data\n",
    "livingroom_size = np.random.uniform(2, 10, 50)\n",
    "years = np.random.uniform(0, 20, 50)\n",
    "house_price = 100000000 + livingroom_size * (400000000 / 8) - years * (400000000 / 20)\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame({\n",
    "'livingroom_size': livingroom_size,\n",
    "'years': years,\n",
    "'house_price': house_price\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot livingroom_size vs. house_price\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(df['livingroom_size'], df['house_price'], color='blue')\n",
    "plt.title('Living Room Size vs House Price')\n",
    "plt.xlabel('Living Room Size')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "# Plot years vs. house_price\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(df['years'], df['house_price'], color='red')\n",
    "plt.title('Years vs House Price')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#practice 2\n",
    "# This script will create a DataFrame with 50 rows containing the columns livingroom_size, years, and house_price. The house_price is randomly generated within the specified range and then rounded to the nearest million to ensure it's an integer. The dataset is then plotted using matplotlib to visualize the relationships.\n",
    "# Please note that the actual relationship between these variables in real-world data might be more complex and could require more sophisticated modeling to accurately reflect. The above script is for demonstration purposes and creates a simplified relationship between the variables.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(0)\n",
    "\n",
    "# Generate data\n",
    "livingroom_size = np.random.uniform(2, 10, 50)\n",
    "years = np.round(np.random.uniform(0, 20, 50), 1)\n",
    "# Calculate house_price with direct and indirect proportionalities\n",
    "house_price = np.random.randint(100000000, 500000000, 50)\n",
    "house_price = np.round(house_price / 1000000) * 1000000  # Round to nearest million for integer values\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame({\n",
    "'livingroom_size': livingroom_size,\n",
    "'years': years,\n",
    "'house_price': house_price\n",
    "})\n",
    "\n",
    "# Sort by 'years' to make the plot more understandable\n",
    "df.sort_values('years', inplace=True)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Plot livingroom_size vs. house_price\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(df['livingroom_size'], df['house_price'], color='blue')\n",
    "plt.title('Living Room Size vs House Price')\n",
    "plt.xlabel('Living Room Size')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "# Plot years vs. house_price\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(df['years'], df['house_price'], color='red')\n",
    "plt.title('Years vs House Price')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#practice 3 muy bueno\n",
    "# This script will create a dataset with 50 rows, where livingroom_size is directly proportional to house_price, and years is indirectly proportional to house_price. The house_price is calculated with a simple linear relationship based on your specifications. The dataset is then plotted using matplotlib to visualize the relationships.\n",
    "# Please note that the actual relationship between these variables in real-world data might be more complex and could require more sophisticated modeling to accurately reflect. The above script is for demonstration purposes and creates a simplified linear relationship between the variables.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(0)\n",
    "\n",
    "# Generate data\n",
    "#livingroom_size =   np.random.uniform(2, 10, 50)\n",
    "livingroom_size =   np.round(np.random.uniform(2, 10, 50), 2)\n",
    "years =             np.round(np.random.uniform(0, 20, 50), 1)\n",
    "house_price = 100000000 + livingroom_size * (400000000 / 8) - years * (400000000 / 20)\n",
    "\n",
    "# Create DataFrame\n",
    "df3house = pd.DataFrame({\n",
    "'livingroom_size': livingroom_size,\n",
    "'years': years,\n",
    "'house_price': house_price\n",
    "})\n",
    "\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot livingroom_size vs. house_price\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(df3house['livingroom_size'], df3house['house_price'], color='blue')\n",
    "plt.title('Living Room Size vs House Price')\n",
    "plt.xlabel('Living Room Size')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "# Plot years vs. house_price\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(df3house['years'], df3house['house_price'], color='red')\n",
    "plt.title('Years vs House Price')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#practice 4 tambien esta bueno\n",
    "# This script will create a dataset with 50 rows, where livingroom_size is directly proportional to house_price, and years is indirectly proportional to house_price. The house_price is calculated with a simple linear relationship based on your specifications. The dataset is then plotted using matplotlib to visualize the relationships.\n",
    "# Please note that the actual relationship between these variables in real-world data might be more complex and could require more sophisticated modeling to accurately reflect. The above script is for demonstration purposes and creates a simplified linear relationship between the variables.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(0)\n",
    "\n",
    "# Generate data\n",
    "house_price =       np.round(np.random.uniform(100000000, 500000000, 50), 1)\n",
    "\n",
    "# Define the range for X\n",
    "X_min = 2\n",
    "X_max = 10\n",
    "\n",
    "# Normalize Z to a 0-1 range if it isn't already\n",
    "house_price_normalized = (house_price - np.min(house_price)) / (np.max(house_price) - np.min(house_price))\n",
    "\n",
    "# Scale Z to the X range\n",
    "livingroom_size = X_min + (house_price_normalized * (X_max - X_min))\n",
    "livingroom_size  = np.round(livingroom_size, 2)\n",
    "\n",
    "# Invert Z\n",
    "house_price_normalized_inverted = 1 - house_price_normalized\n",
    "\n",
    "# Scale Z_inverted to the N range\n",
    "years = 0 + (house_price_normalized_inverted * (20 - 0))\n",
    "years = np.round(years, 1)\n",
    "\n",
    "# Create DataFrame\n",
    "df4house = pd.DataFrame({\n",
    "'livingroom_size': livingroom_size,\n",
    "'years': years,\n",
    "'house_price': house_price\n",
    "})\n",
    "\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot livingroom_size vs. house_price\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(df4house['livingroom_size'], df4house['house_price'], color='blue')\n",
    "plt.title('Living Room Size vs House Price')\n",
    "plt.xlabel('Living Room Size')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "# Plot years vs. house_price\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(df4house['years'], df4house['house_price'], color='red')\n",
    "plt.title('Years vs House Price')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('House Price')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fhead():\n",
    "    # df4house.head()\n",
    "    print('df3house:\\n', df3house.head())\n",
    "    print('df4house:\\n', df4house.head())\n",
    "    print('Heads')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df3house:\n",
      "    livingroom_size  years  house_price\n",
      "0             6.39   11.4  191500000.0\n",
      "1             7.72    8.8  310000000.0\n",
      "2             6.82   19.8   45000000.0\n",
      "3             6.36    2.0  378000000.0\n",
      "4             5.39    4.2  285500000.0\n",
      "df4house:\n",
      "    livingroom_size  years  house_price\n",
      "0             6.42    9.0  319525401.6\n",
      "1             7.80    5.5  386075746.5\n",
      "2             6.87    7.8  341105350.4\n",
      "3             6.38    9.0  317953273.2\n",
      "4             5.37   11.6  269461919.7\n",
      "Heads\n"
     ]
    }
   ],
   "source": [
    "fhead()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import scatterplotmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2I94nMwxDubm5GjVqlAYOHNjkPrW1taqtrfXfLysrC1Z4AAAAAAA0ElEj3vfcc4927typl19++bT75OfnKyUlxX/LyckJYoQAAAAAADQUMYn3jBkz9Oabb+r999/Xueeee9r98vLy5HK5/LfCwsIgRgkAAAAAQENhX2puGIZmzJih119/XevXr1ePHj3OuL/NZpPNZgtSdAAAAAAAnFnYJ97Tp0/XqlWr9MYbb8jhcKikpESSlJKSooSEhBBHBwAAAADAmYV9qfnSpUvlcrk0evRoZWZm+m+rV68OdWgAAAAAAJxV2I94G4YR6hAAAAAAAGi1sB/xBgAAAAAgkpF4AwAAAABgIhJvAAAAAABMROINAAAAAICJSLwBAAAAADARiTcAAAAAACYi8QYAAAAAwEQk3gAAAAAAmIjEGwAAAAAAE5F4AwAAAABgIhJvAAAAAABMROINAAAAAICJSLwBAAAAADARiTcAAAAAACYi8QYAAAAAwESxoQ4A5nh63Z5QhwAAAAAAECPeAAAAAACYisQbAAAAAAATkXgDAAAAAGAiEm8AAAAAAExE4g0AAAAAgIlIvAEAAAAAMBGJNwAAAAAAJiLxBgAAAADARCTeAAAAAACYiMQbAAAAAAATkXgDAAAAAGAiEm8AAAAAAExE4g0AAAAAgIlIvAEAAAAAMBGJNwAAAAAAJiLxBgAAAADARBGReD/77LPq0aOH7Ha7LrzwQv3jH/8IdUgAAAAAADRL2Cfeq1ev1syZM/Xwww9r+/bt+sEPfqAJEybo66+/DnVoAAAAAACcVdgn3gsXLtQvfvEL3X777Tr//PO1aNEi5eTkaOnSpaEODQAAAACAswrrxNvtdmvr1q0aN25cg+3jxo3TRx99FKKoAAAAAABovthQB3Am3377rbxerzIyMhpsz8jIUElJSZM/U1tbq9raWv99l8slSSorK2v269ZUVrQiWgDhpCWf+fp9DcMwKxwAAABEsbBOvOtZLJYG9w3DaLStXn5+vubMmdNoe05OjimxAQhPv27Fz5SXlyslJSXgsQAAACC6hXXi3alTJ1mt1kaj206ns9EoeL28vDzl5ub67/t8PpWWlqpjx45NJutlZWXKyclRYWGhOnToENgDCHMcO8fOsZ9gGIbKy8uVlZUVwugAAADQXoV14h0fH68LL7xQ69at009/+lP/9nXr1unqq69u8mdsNptsNluDbampqWd9rQ4dOkRdElKPY+fYo01Tx85INwAAAMwS1om3JOXm5urmm2/WsGHDdPHFF2v58uX6+uuvddddd4U6NAAAAAAAzirsE+/rrrtOR48e1dy5c1VcXKyBAwfqrbfeUrdu3UIdGgAAAAAAZxX2ibckTZs2TdOmTTPluW02mx5//PFG5enRgGPn2KNNNB87AAAAQsdi0D8HAAAAAADTxIQ6AAAAAAAA2jMSbwAAAAAATETiDQAAAACAiaIy8c7Pz9fw4cPlcDiUnp6uiRMn6ssvvwx1WCGRn58vi8WimTNnhjqUoDh8+LBuuukmdezYUYmJiRoyZIi2bt0a6rBM5/F49Mgjj6hHjx5KSEhQz549NXfuXPl8vlCHFnAbN27UVVddpaysLFksFv31r39t8LhhGJo9e7aysrKUkJCg0aNHa9euXaEJFgAAAFEhKhPvDRs2aPr06fr444+1bt06eTwejRs3TpWVlaEOLag2b96s5cuXa/DgwaEOJSiOHTumkSNHKi4uTm+//bY+//xzPfXUU0pNTQ11aKZbsGCBli1bpsWLF2v37t168skn9bvf/U7PPPNMqEMLuMrKSl1wwQVavHhxk48/+eSTWrhwoRYvXqzNmzerS5cuGjt2rMrLy4McKQAAAKIFq5pL+uabb5Senq4NGzbokksuCXU4QVFRUaHvfe97evbZZ/XEE09oyJAhWrRoUajDMtVDDz2kDz/8UP/4xz9CHUrQXXnllcrIyNCf//xn/7Zrr71WiYmJ+q//+q8QRmYui8Wi119/XRMnTpR0YrQ7KytLM2fO1KxZsyRJtbW1ysjI0IIFC3TnnXeGMFoAAAC0V1E54n0ql8slSUpLSwtxJMEzffp0XXHFFfrRj34U6lCC5s0339SwYcM0adIkpaena+jQoXruuedCHVZQjBo1Su+++6727NkjSfr000/1wQcf6PLLLw9xZMF14MABlZSUaNy4cf5tNptNl156qT766KMQRgYAAID2LDbUAYSaYRjKzc3VqFGjNHDgwFCHExSvvPKKtm3bps2bN4c6lKD66quvtHTpUuXm5urXv/61PvnkE917772y2Wy65ZZbQh2eqWbNmiWXy6V+/frJarXK6/Vq3rx5uuGGG0IdWlCVlJRIkjIyMhpsz8jI0KFDh0IREgAAAKJA1Cfe99xzj3bu3KkPPvgg1KEERWFhoe677z6tXbtWdrs91OEElc/n07BhwzR//nxJ0tChQ7Vr1y4tXbq03Sfeq1ev1osvvqhVq1ZpwIAB2rFjh2bOnKmsrCxNmTIl1OEFncViaXDfMIxG2wAAAIBAierEe8aMGXrzzTe1ceNGnXvuuaEOJyi2bt0qp9OpCy+80L/N6/Vq48aNWrx4sWpra2W1WkMYoXkyMzPVv3//BtvOP/98vfrqqyGKKHgeeOABPfTQQ7r++uslSYMGDdKhQ4eUn58fVYl3ly5dJJ0Y+c7MzPRvdzqdjUbBAQAAgECJyjnehmHonnvu0Wuvvab33ntPPXr0CHVIQXPZZZfps88+044dO/y3YcOGafLkydqxY0e7TbolaeTIkY3axu3Zs0fdunULUUTBU1VVpZiYhh93q9XaLtuJnUmPHj3UpUsXrVu3zr/N7XZrw4YNGjFiRAgjAwAAQHsWlSPe06dP16pVq/TGG2/I4XD4532mpKQoISEhxNGZy+FwNJrLnpSUpI4dO7b7Oe7333+/RowYofnz5+vnP/+5PvnkEy1fvlzLly8PdWimu+qqqzRv3jx17dpVAwYM0Pbt27Vw4ULddtttoQ4t4CoqKrRv3z7//QMHDmjHjh1KS0tT165dNXPmTM2fP199+vRRnz59NH/+fCUmJurGG28MYdQAAABoz6Kyndjp5nKuWLFCt956a3CDCQOjR4+OinZikvS3v/1NeXl52rt3r3r06KHc3Fz98pe/DHVYpisvL9ejjz6q119/XU6nU1lZWbrhhhv02GOPKT4+PtThBdT69es1ZsyYRtunTJmi559/XoZhaM6cOfrTn/6kY8eO6aKLLtKSJUva/YUnAAAAhE5UJt4AAAAAAARLVM7xBgAAAAAgWEi8AQAAAAAwEYk3AAAAAAAmIvEGAAAAAMBEJN4AAAAAAJiIxBsAAAAAABOReAMAAAAAYCISbwAAAAAATETijWYZPXq0Zs6cKUnq3r27Fi1a1KqfRUMHDx6UxWLRjh07Qh0KAAAAAJPEhjoARJ7NmzcrKSmp2fu/9tpriouLMzGiyJWTk6Pi4mJ16tQp1KEAAAAAMAmJN1qsc+fOLdo/LS3NpEi+YxiGvF6vYmMj60/aarWqS5cuoQ4DAAAAgIkoNUeLnVxqfsMNN+j6669v8HhdXZ06deqkFStWSGpcat69e3fNnz9ft912mxwOh7p27arly5c3eI6PPvpIQ4YMkd1u17Bhw/TXv/61QUn2+vXrZbFYtGbNGg0bNkw2m03/+Mc/VFtbq3vvvVfp6emy2+0aNWqUNm/e3OC5N2zYoO9///uy2WzKzMzUQw89JI/H43989OjRmjFjhmbOnKlzzjlHGRkZWr58uSorKzV16lQ5HA716tVLb7/9drN+X8eOHdPkyZPVuXNnJSQkqE+fPv7fzaml5rfeeqssFkuj2/r16yVJbrdbDz74oLKzs5WUlKSLLrrI/xgAAACA8ETijTaZPHmy3nzzTVVUVPi3rVmzRpWVlbr22mtP+3NPPfWUhg0bpu3bt2vatGm6++679cUXX0iSysvLddVVV2nQoEHatm2bfvOb32jWrFlNPs+DDz6o/Px87d69W4MHD9aDDz6oV199VStXrtS2bdvUu3dvjR8/XqWlpZKkw4cP6/LLL9fw4cP16aefaunSpfrzn/+sJ554osHzrly5Up06ddInn3yiGTNm6O6779akSZM0YsQIbdu2TePHj9fNN9+sqqqqs/6OHn30UX3++ed6++23tXv3bi1duvS0peV/+MMfVFxc7L/dd999Sk9PV79+/SRJU6dO1YcffqhXXnlFO3fu1KRJk/TjH/9Ye/fuPWscAAAAAELEAJrh0ksvNe677z7DMAyjW7duxtNPP20YhmG43W6jU6dOxgsvvODf94YbbjAmTZrU5M/W//xNN93kv+/z+Yz09HRj6dKlhmEYxtKlS42OHTsa1dXV/n2ee+45Q5Kxfft2wzAM4/333zckGX/961/9+1RUVBhxcXHGSy+95N/mdruNrKws48knnzQMwzB+/etfG+edd57h8/n8+yxZssRITk42vF6vP95Ro0b5H/d4PEZSUpJx8803+7cVFxcbkoxNmzad9Xd31VVXGVOnTm3ysQMHDjQ4rpO9+uqrhs1mM/7xj38YhmEY+/btMywWi3H48OEG+1122WVGXl7eWeMAAAAAEBqMeKNN4uLiNGnSJL300kuSpMrKSr3xxhuaPHnyGX9u8ODB/n9bLBZ16dJFTqdTkvTll19q8ODBstvt/n2+//3vN/k8w4YN8/97//79qqur08iRIxvE9/3vf1+7d++WJO3evVsXX3yxLBaLf5+RI0eqoqJC//znP5uMz2q1qmPHjho0aJB/W0ZGhiT5Yz6Tu+++W6+88oqGDBmiBx98UB999NFZf2b79u265ZZbtGTJEo0aNUqStG3bNhmGob59+yo5Odl/27Bhg/bv33/W5wQAAAAQGpG1EhXC0uTJk3XppZfK6XRq3bp1stvtmjBhwhl/5tRVzi0Wi3w+n6QTC6WdnBjXb2vKyaur1+/T1M/WbzvTc5+8van4Tt5Wv299zGcyYcIEHTp0SP/3f/+nv//977rssss0ffp0/f73v29y/5KSEv3kJz/RL37xC/3iF7/wb/f5fLJardq6dausVmuDn0lOTj5rHAAAAABCgxFvtNmIESOUk5Oj1atX66WXXtKkSZMUHx/f6ufr16+fdu7cqdraWv+2LVu2nPXnevfurfj4eH3wwQf+bXV1ddqyZYvOP/98SVL//v310UcfNUjkP/roIzkcDmVnZ7c65rPp3Lmzbr31Vr344otatGhRo8Xk6tXU1Ojqq69Wv379tHDhwgaPDR06VF6vV06nU717925wY2V0AAAAIHyReKPNLBaLbrzxRi1btkzr1q3TTTfd1Kbnu/HGG+Xz+XTHHXdo9+7dWrNmjX90+NTR6pMlJSXp7rvv1gMPPKB33nlHn3/+uX75y1+qqqrKP3I8bdo0FRYWasaMGfriiy/0xhtv6PHHH1dubq5iYsz5ODz22GN64403tG/fPu3atUt/+9vf/BcCTnXnnXeqsLBQf/zjH/XNN9+opKREJSUlcrvd6tu3ryZPnqxbbrlFr732mg4cOKDNmzdrwYIFeuutt0yJHQAAAEDbkXgjICZPnqzPP/9c2dnZDeZYt0aHDh30v//7v9qxY4eGDBmihx9+WI899pgkNZj33ZTf/va3uvbaa3XzzTfre9/7nvbt26c1a9bonHPOkSRlZ2frrbfe0ieffKILLrhAd911l37xi1/okUceaVPMZxIfH6+8vDwNHjxYl1xyiaxWq1555ZUm992wYYOKi4vVv39/ZWZm+m/188JXrFihW265Rf/+7/+u8847Tz/5yU/0//7f/1NOTo5p8QMAAABoG4txusmzQBh56aWXNHXqVLlcLiUkJIQ6HAAAAABoNhZXQ1h64YUX1LNnT2VnZ+vTTz/VrFmz9POf/5ykGwAAAEDEodQcYamkpEQ33XSTzj//fN1///2aNGnSaRckC7W77rqrQXuvk2933XVXqMMDAAAAEGKUmgNt5HQ6VVZW1uRjHTp0UHp6epAjAgAAABBOSLwBAAAAADARpeYAAAAAAJiIxBsAAAAAABOReAMAAAAAYCISbwAAAAAATETiDQAAAACAiUi8AQAAAAAwEYk3AAAAAAAmIvEGAAAAAMBEJN4AAAAAAJiIxBsAAAAAABOReAMAAAAAYCISbwAAAAAATETiDQAAAACAiWJDHYDZfD6fioqK5HA4ZLFYQh0OgDBkGIbKy8uVlZWlmJjouR7J+RFAc0TrORIAAqndJ95FRUXKyckJdRgAIkBhYaHOPffcUIcRNJwfAbREtJ0jASCQQpp4b9y4Ub/73e+0detWFRcX6/XXX9fEiRP9jxuGoTlz5mj58uU6duyYLrroIi1ZskQDBgxo9ms4HA5JJ/6z6NChQ6APAUA7UFZWppycHP/5IlpwfgTQHNF6jgSAQApp4l1ZWakLLrhAU6dO1bXXXtvo8SeffFILFy7U888/r759++qJJ57Q2LFj9eWXXzb75F9fPtmhQwe+WAI4o2grt+b8CKAlou0cCQCBFNLEe8KECZowYUKTjxmGoUWLFunhhx/WNddcI0lauXKlMjIytGrVKt15553BDBUAAAAAgFYJ2xUyDhw4oJKSEo0bN86/zWaz6dJLL9VHH3102p+rra1VWVlZgxuA9s3nM1RYWqUvSspUWFoln88IdUgAAACAX9gurlZSUiJJysjIaLA9IyNDhw4dOu3P5efna86cOabGBiB87HOWa03BEe3/pkI1Hq/ssVb16pys8QMz1Dud+YgAAAAIvbAd8a536nwiwzDOOMcoLy9PLpfLfyssLDQ7RAAhss9ZrhUfHlRBkUupiXHq2SlZqYlxKihyacWHB7XPWR7qEAEAAIDwHfHu0qWLpBMj35mZmf7tTqez0Sj4yWw2m2w2m+nxITz5fIYOH69WpdujpPhYZacmKCaGxWDaI5/P0JqCIyqtdKtPerL/gpzDHqdkW6z2Oiu0dtcR9eyUzN8AAAAAQipsE+8ePXqoS5cuWrdunYYOHSpJcrvd2rBhgxYsWBDi6BCOKDmOLoePV2v/NxXKTLE3qoKxWCzKTLFrn7NCh49XKyctMURRAgAAACFOvCsqKrRv3z7//QMHDmjHjh1KS0tT165dNXPmTM2fP199+vRRnz59NH/+fCUmJurGG28MYdQIR/Ulx6WVbmWm2JUYn6Aqt0cFRS4Vuao1dWR3ku92ptLtUY3Hq8T4hCYfT4i36khZjSrdniBHBgAAADQU0sR7y5YtGjNmjP9+bm6uJGnKlCl6/vnn9eCDD6q6ulrTpk3TsWPHdNFFF2nt2rXN7uGN6EDJcXRKio+VPdaqKrdHDntco8er3V7ZYq1Kig/bwh6cxtPr9pj6/PeP7Wvq8wMAAJwqpN9IR48eLcM4fdsfi8Wi2bNna/bs2cELKsiYk9x2oSo55r0LrezUBPXqnKyCIpeSbbEN3nvDMFTsqtGg7BRlpzY9Ig4AAAAEC0NBIcSc5MAIRckx713oxcRYNH5ghopc1drrPHHhJSHeqmq3V8WuGqUlxWvcgAwuhgAAACDkSLxDhDnJgRPskmPeu/DRO92hqSO7+y+CHCmrkS3WqkHZKRo3gIsgAAAACA8k3iFwpjnJSfFW7Tzs0osfH9LUkT2Uc04iI3ZnEcySY+aTh5/e6Q71HJ1M2T8AAADCFol3CJxuTnJpZa32Oyt1pLxGe50VKj5eo8HnplK+fBbBLDmmhVV4iomx8PsGAABA2IoJdQDR6Ls5yd9d9yitrNWOwuNyltcoyWZVQpxVCfFWFRS5tOLDg9rnLA9hxOGvvuR4YFaKjlfV6eC3lTpeVadB2SkBLf1u6r07WUK8VbUeLy2sAAAAAPgx4h0Cp85JNgxD+52VqnZ7lZYUL7fXpzhrjM5JjJfDTvlycwWj5JgWVgAAAABaihHvEKifk1zsqpFhGCqv8ai0yq1k+4lkraLGo7SkE0n3qeXLOLP6kuN+XTooJy3w8+NPfe9OVj+fvHd6Mi2sAAAAAPiReIdA/ZzktKR47XVW6FiVW3Ver3yGVFrpVkJ8rHp1/m7hLsqXw8ep7115TZ08Pp/Ka+q011lBCysAAAAAjZB4h8jJc5Kr67yqqfOpstaj9A52DclJVVpSvH9fypfDS7DmkwMAAABoH8jkQqh+TvI/j1XpPz84qANHKzQ4O0UxMd9dDwl0O6ym+HwGrZhaiBZWAAAAAJqLxDvEYmIs6toxSTdd3PXE6uXfVJraDutU+5zlWlNwRPu/qVCNxyt7rFW9OifTwqwZaGEFAAAAoDlIvMNEfflyfRJ8pKxGtlirBmWnaNwAc5Lgfc5yrfjwoEor3cpMsSsxPkFVbo8KilwqclVTNg0AAAAAAUDiHUaCWb7s8xlaU3BEpZVu9Un/biE3hz1OyTZamAEAAABAoJB4B0FL5lAHq3z58PFq7f+mQpkpdn/SXe/UFmaUUwMAAABA65F4myxc51BXuj2q8XiVGN/0gm0J8VYdKauhhRkAAAAAtBHtxExUP4e6oMil1MQ49eyUrNTEOBUUuU4spOYsD1lsSfGxssdaVXWaxJoWZkD483g8euSRR9SjRw8lJCSoZ8+emjt3rnw+X6hDAwAAwEnIqkwS7nOos1MT1KtzsgqKXEq2xTYoNw9GCzMAbbdgwQItW7ZMK1eu1IABA7RlyxZNnTpVKSkpuu+++0IdHgAAAP6FxNsk4T6HOibGovEDM1TkqtZeZ0VQW5gBCIxNmzbp6quv1hVXXCFJ6t69u15++WVt2bIlxJEBAADgZJSam+S7OdRNX9tIiLeq1uMN6Rzq+hZmA7NSdLyqTge/rdTxqjoNyk6hlRgQAUaNGqV3331Xe/bskSR9+umn+uCDD3T55Zc3uX9tba3Kysoa3AAAAGA+RrxNcvIcaoc9rtHj4TKHOpgtzAAE1qxZs+RyudSvXz9ZrVZ5vV7NmzdPN9xwQ5P75+fna86cOW1+3afX7WnzcwAAAEQTRrxNUj+HuthVI5/Pp7LqOn1bUauy6jr5fD4Vu2rUOz05LOZQ17cw69elg3LSEkm6gQixevVqvfjii1q1apW2bdumlStX6ve//71WrlzZ5P55eXlyuVz+W2FhYZAjBgAAiE6MeJukfg717pIyrdl1RF7DkGRIsshqsahvFwdzqAG0yQMPPKCHHnpI119/vSRp0KBBOnTokPLz8zVlypRG+9tsNtlstmCHCQAAEPUY8Q4Gyyn/INcGEABVVVWKiWl4GrdarbQTAwAACDOMeJukvp2Y12dofP8MVdR65fb6FG+NUbLNqn3fVIa0nRiAyHfVVVdp3rx56tq1qwYMGKDt27dr4cKFuu2220IdGgAAAE5C4m2Sk9uJxcTEqENCw1GpULcTAxD5nnnmGT366KOaNm2anE6nsrKydOedd+qxxx4LdWgAAAA4CYm3Sb5rJ9b04mkJ8VYdKasJaTsxAJHN4XBo0aJFWrRoUahDAQAAwBkwx9skJ7cTa0q4tBMDAAAAAJiLxNskJ7cTMwyjwWOGYYRVO7H2yOczVFhapS9KylRYWiWfzzj7DwEAAACACRhuNUl9O7EiV7X2Ok/M9U6It6ra7VWxq0ZpSfHNbifm8xk6fLxalW6PkuJjlZ2awIJsZ7DPWa41BUe0/5sK1Xi8ssda1atzssYPzFDvdEeowwMAAAAQZUi8TdQ73aGpI7v7k8AjZTWyxVo1KDtF4wY0LwkkiWyZfc5yrfjwoEor3cpMsSsxPkFVbo8KilwqclVr6sju/N4AAAAABBWJt8l6pzvUc3Ryq0asSSJbpr6FW2mlW33Sk2WxnPgdO+xxSrbFaq+zIqgt3KhUAAAAACCReAdFTIylxS3Dwi2JjAQnt3Cr/33Vs1gsQW3hFi2VClxcAAAAAM6OxDtMhVMSGUotSezCpYVbtFQqRMvFBQAAAKCtSLzDVLgkkaHU0sTu5BZuDntco8eD0cItWioVouXiAgAAABAItBMLU9HeB7w+sSsocik1MU49OyUrNTFOBUUurfjwoPY5yxv9TDi0cGtJpUKkOvXigsMeJ2uMRQ57nPqkJ6u00q21u47Qwg0AAAD4FxLvMBUOSWSotDaxq2/hlpYUr73OCpXX1Mnj86m8pk57nRUtauHWWt9VKjR9QSQh3qpajzeiKxWi4eICAAAAEEgk3mEqHJLIUGlLYlffwm1gVoqOV9Xp4LeVOl5Vp0HZKUEpf46GSoVouLgAAAAABFLYf/v3eDyaPXu2XnrpJZWUlCgzM1O33nqrHnnkEcXEtO/rBoHoAx6J2jq/vS0t3NqqvlKhoMilZFtsgwsH9ZUKg7JTIrpSIRzm0gMAAACRJOy/GS9YsEDLli3TypUrNWDAAG3ZskVTp05VSkqK7rvvvlCHZ7pQJpGhEojErjUt3AKhvlKhyFWtvc4To/YJ8VZVu70qdtW0i0qFaLi4AAAAAARSqxPv/fv3a8WKFdq/f7/+8Ic/KD09Xe+8845ycnI0YMCAgAW4adMmXX311briiiskSd27d9fLL7+sLVu2BOw1wl2okshQifTErr1XKkTDxQUAAAAgkFqVeG/YsEETJkzQyJEjtXHjRs2bN0/p6enauXOn/uM//kN/+ctfAhbgqFGjtGzZMu3Zs0d9+/bVp59+qg8++ECLFi1qcv/a2lrV1tb675eVlQUsFgRHe0js2nulQnu/uAAAAAAEUqsS74ceekhPPPGEcnNz5XB89wV7zJgx+sMf/hCw4CRp1qxZcrlc6tevn6xWq7xer+bNm6cbbrihyf3z8/M1Z86cgMaA4GsPiV17r1Ro7xcX0H49vW6Pqc9//9i+pj4/AACIPK1KvD/77DOtWrWq0fbOnTvr6NGjbQ7qZKtXr9aLL76oVatWacCAAdqxY4dmzpyprKwsTZkypdH+eXl5ys3N9d8vKytTTk5OQGNCcJDYhb/2fnEBAAAACIRWJd6pqakqLi5Wjx49Gmzfvn27srOzAxJYvQceeEAPPfSQrr/+eknSoEGDdOjQIeXn5zeZeNtsNtlstoDGgNA5XWLn8xkk5AAAAAAiQqsS7xtvvFGzZs3S//zP/8hiscjn8+nDDz/Ur371K91yyy0BDbCqqqpR2zCr1SqfzxfQ10Hk2Ocs95eg13i8ssda1atzssYPjIwSdAAAAADRpVWJ97x583TrrbcqOztbhmGof//+8nq9uvHGG/XII48ENMCrrrpK8+bNU9euXTVgwABt375dCxcu1G233RbQ10Fk2Ocs14oPD6q00q3MFLsS4xNU5faooMilIle1po7sTvINAAAAIKy0KvGOi4vTSy+9pLlz52r79u3y+XwaOnSo+vTpE+j49Mwzz+jRRx/VtGnT5HQ6lZWVpTvvvFOPPfZYwF8rlCidPjufz9CagiMqrXSrT3qyv82Ywx6nZFus9jortHbXEfXslMzvDgAAAEDYaFXivXHjRvXr10+9evVSr169/Nvr6uq0adMmXXLJJQEL0OFwaNGiRadtH9YeUDrdPIePV2v/Nyfai53c21uSLBaLMlPs2ues0OHj1Sz4BQAAACBsxJx9l8ZGjx6tCy64QJs2bWqwvbS0VGPGjAlIYNGivnS6oMil1MQ49eyUrNTEOBUUubTiw4Pa5ywPdYhho9LtUY3Hq8T4pq8XJcRbVevxqtLtCXJkAAAAAHB6rUq8Jen666/XZZddpueff77BdsMw2hpT1Di1dNphj5M1xiKHPU590pNVWunW2l1H5PPxO5WkpPhY2WOtqjpNYl3t9soWa1XSaRJzRBafz1BhaZW+KClTYWkVnwMAAABErFZlKBaLRXl5efrBD36gKVOmaOfOnXrqqaf8j6F5KJ1umezUBPXqnKyCIpeSbbENfmeGYajYVaNB2SnKTk0IYZQIBKZfAAAAoD1p1Yh3/aj2Nddco40bN+ovf/mLJkyYoOPHjwcytnYvEKXT0TQqGBNj0fiBGUpLitdeZ4XKa+rk8flUXlOnvc4KpSXFa9yADBZWi3BMvwAAAEB70+aa3KFDh+qTTz7RxIkTddlllwUipqhxcum0wx7X6PGzlU4Ha1QwnFZc753u0NSR3f3HfaSsRrZYqwZlp2jcAEZDIx0r1wMAAKA9alXiPWXKFCUkfFfO26VLF23YsEF33HGHNm7cGLDg2ru2lE4Hq591OJb89k53qOfo5LC5GIDAYfoFAAAA2qNWJd4rVqxotM1ms2nlypVtDiia1JdOF7mqtdd5ItlIiLeq2u1VsavmtKXTwRoVDFZy3xoxMRYSr3bou+kXTc/TT4i36khZDSvXAwAAIKI0O/HeuXOnBg4cqJiYGO3cufOM+w4ePLjNgUWL1pROB2NUkJJfhEJbp18AAAAA4ajZ316HDBmikpISpaena8iQIbJYLA1ah9Xft1gs8nq9pgTbXp2tdPrUOdblNXWmjwpS8otQYOV6AAAAtEfNTrwPHDigzp07+/8drcxaaOx0pdNNzbHulGyT2+MzdVSQkl+EQmunX0Szw4cPa9asWXr77bdVXV2tvn376s9//rMuvPDCUIcGAACAf2l2ZtatW7cm/x1Ngr3Q2OnmWH9dWqlvymtV6/FpaE6qKaOClPwiVFi5vvmOHTumkSNHasyYMXr77beVnp6u/fv3KzU1NdShAQAA4CStyppWrlypTp066YorrpAkPfjgg1q+fLn69++vl19+uV0m5sFeaOxMc6z7ZsSqstarsuo67TlSoazUwI8KUvKLUGLl+uZZsGCBcnJyGix42b1799AFBAAAgCbFtOaH5s+f728ntmnTJi1evFhPPvmkOnXqpPvvvz+gAYaDU5Nghz1O1hiLHPY49UlPVmmlW2t3HZHPZ5z9yZrpbHOs+2Qkq7PDpq5pCTpeVaeD31bqeFWdBmWnBOQiQH3Jb1pSvPY6K1ReUyePz6fymjrtdVZQ8gvT1U+/6Nelg3LSEvlba8Kbb76pYcOGadKkSUpPT9fQoUP13HPPhTosAAAAnKJVI96FhYXq3bu3JOmvf/2rfvazn+mOO+7QyJEjNXr06EDGFxZCsdBYc+ZY22JjNPF72XLY4kwZFaTkFwhvX331lZYuXarc3Fz9+te/1ieffKJ7771XNptNt9xyS6P9a2trVVtb679fVlYWzHABAACiVqsS7+TkZB09elRdu3bV2rVr/aPcdrtd1dXVAQ0wHLR1obHWLMjW3DnWDltcwJL9puKMppJfsxbOA8zi8/k0bNgwzZ8/X5I0dOhQ7dq1S0uXLm0y8c7Pz9ecOXOCHSYAAEDUa1XiPXbsWN1+++0aOnSo9uzZ45/rvWvXrnY5v7AtC421dkG2YM+xPluc7b1lWLAXzgMCITMzU/3792+w7fzzz9err77a5P55eXnKzc313y8rK1NOTo6pMQIAAKCVc7yXLFmiiy++WN98841effVVdezYUZK0detW3XDDDQENMBzUJ8HFrpoGvcul75Lg3unJjZLg+gXZCopcSk2MU89OyUpNjFNBkUsrPjyofc7y075mMOdYtyXO9iDaj7+98fkMFZZW6YuSMhWWVgV07YVwM3LkSH355ZcNtu3Zs+e0C1zabDZ16NChwQ0AAADma9WId2pqqhYvXtxo+6kljNOmTdPcuXPVqVOn1kUXJlrTW/hMq5In22K111mhtbuOqGen5NMmz8GYYx2IOCNZtB9/exNtlQv333+/RowYofnz5+vnP/+5PvnkEy1fvlzLly8PdWgAAAA4ialNmF988UX96le/ivjEW2p5EhyoBdnMnmMdioXjwkm0H397EuyWf+Fg+PDhev3115WXl6e5c+eqR48eWrRokSZPnhzq0AAAAHASUxPvU8uyI11LkuC2Lsh2svq2SmYIZJyRKNqPv72I5sqFK6+8UldeeWWowwAAAMAZmJp4t0fNTYLbsiBbMEVKnGaJ9uNvL6hcAAAAQDhr1eJqOLvWLsgWbJESp1mi/fjbi+8qF5q+QJIQb1Wtx0vlAgAAAEKCxNskwVyVPBriNEu0H397cXLlQlOoXAAAAEAokXibqH5BtoFZKTpeVaeD31bqeFWdBmWnhNVCT5ESp1nC5fib0wYrmlpltUSkVS707NlTR48ebbT9+PHj6tmzZwgiAgAAgJlMHf656aabor5PrNmrkgdKpMRpllAff3PaYEVbq6yWaE3Lv1A6ePCgvF5vo+21tbU6fPhwCCICAACAmVqdeNfU1Gjnzp1yOp3y+XwNHvvJT34iSVq6dGnbomsnzFyVPJAiJU6zhOr4m9MGS1LUtcpqqWD0vW+rN9980//vNWvWKCUlxX/f6/Xq3XffVffu3UMQGQAAAMzUqsT7nXfe0S233KJvv/220WMWi6XJkRwAjTWnDdaaghIZUlS2ymqpUFcunM3EiRMlnThPTpkypcFjcXFx6t69u5566qkQRAYAAAAztSrxvueeezRp0iQ99thjysjICHRM7ZbPZ4RtQoCWCdR72Zw2WDv/6ZIsJ+Yx0yrr7MK5cqO+OqhHjx7avHmzOnXqFOKIAAAAEAytSrydTqdyc3NJuluA+bntRyDfy+/aYDW96FdCvFVVdSdW6j5Tq6wjZTW0yoogBw4cCHUIAAAACKJWJd4/+9nPtH79evXq1SvQ8bRLzZnDS/IdGQL9Xp7cBsthj2v0eLXbq8S4WMmiM+5Dq6zI8+677+rdd99tcp2M//zP/wxRVAAAADBDq76pL168WJMmTdI//vEPDRo0SHFxDZOBe++9NyDBtQfNmcPL/NzIYMZ7Wd8Gq6DIpWRbbINS8vo2WIPPTZEhaVdR2Wn3GZSdEjatsnB2c+bM0dy5czVs2DBlZmY2mkIAAACA9qVVifeqVau0Zs0aJSQkaP369Q2+NFosFhLvkzQ1h9cwDJXXeOT2+pRss2rvkXLm50aA5szHbulc6+a0wRo/sIskqdhVExGtsnB2y5Yt0/PPP6+bb7451KEAAAAgCFqVeD/yyCOaO3euHnroIcXExAQ6pnbl1Dm8pZVu7XNW6FiVWx6vTzExFlkk7S4pC/vEO9oXh2vOfOzWzLVubhuscG+VheZzu90aMWJEqMMAAABAkLQq8Xa73bruuutIupvh5Dm8dV5DOwqPq9rtUbI9TnH2WFXWenS8qk7/92mxenZKCtsEisXhmjcfu7VzrZvTBivcW2Wh+W6//XatWrVKjz76aKhDAQAAQBC0KvGeMmWKVq9erV//+teBjqfdqZ/D+9nh43JV16na7VFaUrwsFosMw5Db41O3jomq9fjCdq43i8Od0Jz52G2Za92cNljh3CoLzVdTU6Ply5fr73//uwYPHtxonYyFCxeGKDIAAACYoVWJt9fr1ZNPPqk1a9bwpfEs6ufw7nGW69A/XUpNjJMhye3xqqLGo4R4q3qnJyvOGhOWvZhZHO47zZmPzVxrNMfOnTs1ZMgQSVJBQUGDx1hoDeHg6XV7Qh1Cm9w/tm+oQwAAoIFWJd6fffaZhg4dKokvjc3RO92hKwZlandRmbw+Q8er3LLGxCi9g129OicpLckmj88Xlr2YzVhQLJI1dz42cCbvv/9+qEMAAABAELUq8Q72l8bDhw9r1qxZevvtt1VdXa2+ffvqz3/+sy688MKgxtEW52d20ICsDoqzxiguNkbx1hg57N+VK4drL2azFhSLZMy1BgAAANASbc7y/vnPf8pisSg7OzsQ8TRy7NgxjRw5UmPGjNHbb7+t9PR07d+/X6mpqaa8nlmyUxPUO92hgiKX+qQkR0wvZjMXFItkzLVGW4wZM+aM1UHvvfdeEKMBAACA2VqVLfl8Pj3xxBN66qmnVFFRIUlyOBz693//dz388MMBXe18wYIFysnJ0YoVK/zbunfvHrDnD5ZInR9s9oJiQDSqn99dr66uTjt27FBBQYGmTJkSmqAAAABgmlYl3g8//LD+/Oc/67e//a1GjhwpwzD04Ycfavbs2aqpqdG8efMCFuCbb76p8ePHa9KkSdqwYYOys7M1bdo0/fKXv2xy/9raWtXW1vrvl5WVBSyWtorE+cGResEACGdPP/10k9tnz57tv5gJAACA9sNiGIbR0h/KysrSsmXL9JOf/KTB9jfeeEPTpk3T4cOHAxag3W6XJOXm5mrSpEn65JNPNHPmTP3pT3/SLbfc0mj/2bNna86cOY22u1wudejQIWBxtYXPZ0Tc/OCT+3jXek6Ul/dOTw7bCwZAS5SVlSklJSXk54l9+/bp+9//vkpLS4Pyeq097khf8dps7WFF7Uh/j9vDexBOwuUcCQCRrFUj3qWlperXr1+j7f369Qv4F0afz6dhw4Zp/vz5kqShQ4dq165dWrp0aZOJd15ennJzc/33y8rKlJOTE9CY2ioS5wezoBhgvk2bNvkvNgIAAKD9aFXifcEFF2jx4sX64x//2GD74sWLdcEFFwQksHqZmZnq379/g23nn3++Xn311Sb3t9lsstlsAY0BJ0TiBQNEh0irIrnmmmsa3DcMQ8XFxdqyZYseffTREEUFAAAAs7Qq8X7yySd1xRVX6O9//7suvvhiWSwWffTRRyosLNRbb70V0ABHjhypL7/8ssG2PXv2qFu3bgF9nbaKtC/+QHtx8jSIGo9X9lirenVO1viB4TsNIiUlpcH9mJgYnXfeeZo7d67GjRsXoqgAAABgllYl3pdeeqn27NmjJUuW6IsvvpBhGLrmmms0bdo0ZWVlBTTA+++/XyNGjND8+fP185//XJ988omWL1+u5cuXB/R12iISv/gD7cE+Z7lWfHhQpZVuZabYlRifoCq3RwVFLhW5qjV1ZPew/Aye3KUBAAAA7V+rmy9nZWUFdPXy0xk+fLhef/115eXlae7cuerRo4cWLVqkyZMnm/7azRGpX/yBSOfzGVpTcESllW71SU/2t7pz2OOUbIvVXmeF1u46op6dksO2+mTr1q3avXu3LBaL+vfvr6FDh4Y6JAAAAJig1Yn38ePH9ec//7nBl8bbbrutUQllIFx55ZW68sorA/68bdUevvgDkerw8Wrt/+ZEi7uT+8tLksViUWaKXfucFTp8vDrs1iZwOp26/vrrtX79eqWmpsowDLlcLo0ZM0avvPKKOnfuHOoQAQAAEEAxrfmhLVu2qFevXnr66adVWlqqb7/9VgsXLlSvXr20bdu2QMcYtlryxR84G5/PUGFplT4vdumTA0f1eZFLhaVV8vla3PEvKlS6ParxeJUY3/T1w4R4q2o9XlW6PUGO7OxmzJihsrIy7dq1S6WlpTp27JgKCgpUVlame++9N9ThAQAAIMBaNeJ9//336yc/+Ymee+45xcaeeAqPx6Pbb79dM2fO1MaNGwMaZLj67ot/QpOPJ8RbdaSsJiy/+CO81K8TsL3wmL4urVK126uEOKu6dkzU0JxzWC+gCUnxsbLHWlXl9shhj2v0eLX7RL/5pNMk5qH0zjvv6O9//7vOP/98/7b+/ftryZIlLK4GAADQDrXqG+mWLVsaJN2SFBsbqwcffFDDhg0LWHDhLpK/+CN81K8T8PXRKjnLa+T1GnLYY1Vb59U/j1Wp1uNjvYAmZKcmqFfnZBUUuZRsi21QdWIYhopdNRqUnaLs1KYvjIWSz+dTXFzjc0ZcXJx8Pl8IIgIAAICZWlVq3qFDB3399deNthcWFsrhiJ7EoP6Lf7GrRobRsBy4/ot/7/TkkHzxry9b/qKkjHLlMFa/TsDRilp5fD55fYY6JsfLYY9Tx2SbPF5DHq9PRyvcWrvrCO/jSWJiLBo/MENpSfHa66xQeU2dPD6fymvqtNdZobSkeI0bkBGW6yv88Ic/1H333aeioiL/tsOHD+v+++/XZZddFsLIAAAAYIZWJd7XXXedfvGLX2j16tUqLCzUP//5T73yyiu6/fbbdcMNNwQ6xrAViC/+ZiTI+5zlWrp+v55et0d/fHevnl63R0vX79c+Z3mbnxuBVb9OgMMeq2NVdUq2x/lHbi0Wi5L/td1hj2W9gCb0Tndo6sjuGpiVouNVdTr4baWOV9VpUHZKWFcILF68WOXl5erevbt69eql3r17q0ePHiovL9czzzzT6ufNz8+XxWLRzJkzAxcsAAAA2qxVNdC///3vZbFYdMstt8jjOTF/OS4uTnfffbd++9vfBjTAcFf/xb++j/eRshrZYq0alJ2icQPOPC/XjP7ftDeLLPXrBHSwx8nj9SnO3vAjGWeNUWWtR9YYi6rcHtYLaELvdId6jk7W4ePVqnR7lBQfq+zUhLAc6a6Xk5Ojbdu2ad26dfriiy9kGIb69++vH/3oR61+zs2bN2v58uUaPHhwACMFAABAILQ48fZ6vdq0aZMef/xx5efna//+/TIMQ71791ZiYni17AmW1nzxNyNBpr1Z5KlfJ8Dr8ynWGqM6ryFb7HfvTZ3XJ2tMjLw+g/UCziAmxhJ2LcOa8t577+mee+7Rxx9/rA4dOmjs2LEaO3asJMnlcmnAgAFatmyZfvCDH7ToeSsqKjR58mQ999xzeuKJJ8wIHQAAAG3Q4lJzq9Wq8ePHy+VyKTExUYMGDdLgwYOjNumuV//Fv1+XDspJSzxrefnJCbLDHidrjEUOe5z6pCertLJ183lpbxZ56tcJKK/x6JzEOFXU1PnXCzAMQxX/2l5e4wnZegEInEWLFumXv/ylOnTo0OixlJQU3XnnnVq4cGGLn3f69Om64oor2jRiDgAAAPO0ao73oEGD9NVXXwU6lqhhVoIcyX2No1X9OgEdk22KjYmRNcaioxVuldfU6WhFrawxFsVaY9QxOXwXCkPzffrpp/rxj3982sfHjRunrVu3tug5X3nlFW3btk35+fln3be2tlZlZWUNbgAAADBfq+pW582bp1/96lf6zW9+owsvvFBJSUkNHm9qNAffMav/N+3NItPJ6wRsLzymwtIqldd4lBhvVU5aor7X9ZyzrheAyHDkyJEm24jVi42N1TfffNPs5yssLNR9992ntWvXym63n3X//Px8zZkzp9nPj/D09Lo9oQ4BJgvGe3z/2L6mvwYA4DutysDqR2x+8pOfNOqda7FY5PV6AxNdO2VWgtySvsY+nxFRi1G1dyevE1BeW6eKGo+SbbFy2ON4b9qR7OxsffbZZ+rdu3eTj+/cuVOZmZnNfr6tW7fK6XTqwgsv9G/zer3auHGjFi9erNraWlmtVv9jeXl5ys3N9d8vKytTTk5OK44EAAAALdGqxPv9998PdBxRpSUJckvUly0Xuaq113milD0h3qpqt1fFrhp/e7Ovvq0I+GrqaLtIWSAMrXf55Zfrscce04QJExqNUFdXV+vxxx/XlVde2eznu+yyy/TZZ5812DZ16lT169dPs2bNapB0S5LNZpPNZmv9AQAAAKBVWpV4X3rppYGOI6o0N0FuzSjn2dqbSaLdGBAijzzyiF577TX17dtX99xzj8477zxZLBbt3r1bS5Yskdfr1cMPP9zs53M4HBo4cGCDbUlJSerYsWOj7QAAAAidViXeO3fubHK7xWKR3W5X165dGVU5i7b0/27OczfV3kySlq7fT7sxIEQyMjL00Ucf6e6771ZeXp5/BXuLxaLx48fr2WefVUZGRoijBAAAQKC1KvEeMmRIo9W4TxYXF6frrrtOf/rTn5q14E+4CPa859b0/26upsqWC0urmr2aOiXPgDm6deumt956S8eOHdO+fftkGIb69Omjc845JyDPv379+oA8DwAAAAKnVYn366+/rlmzZumBBx7Q97//fRmGoc2bN+upp57S448/Lo/Ho4ceekiPPPKIfv/73wc6ZlPsc5aHZN5zMOf1mrWaOgu1AS13zjnnaPjw4aEOAwAAAEHQ6nZif/jDHzR+/Hj/tsGDB+vcc8/Vo48+qk8++URJSUn693//94hIvPc5y6Ni3rMZq6mH6oIFAAAAAESKmNb80GeffaZu3bo12t6tWzf/CrtDhgxRcXFx26ILAp/P0JqCI/55zw57nKwxFjnsceqTnqzSSrfW7join88IdahtVr+aerGrxj+3tF79auq905ObvZp6/QWLgiKXUhPj1LNTslIT41RQ5NKKDw9qn7PcjMMAAAAAgIjSqsS7X79++u1vfyu32+3fVldXp9/+9rfq16+fJOnw4cMRsUjQ4ePVzZ73HOnqV1NPS4rXXmeFymvq5PH5VF5Tp73Oihatph5NFywAAAAAoC1aVWq+ZMkS/eQnP9G5556rwYMHy2KxaOfOnfJ6vfrb3/4mSfrqq680bdq0gAZrBrPmPYerQK2m3pILFizUBgAAACCatSrxHjFihA4ePKgXX3xRe/bskWEY+tnPfqYbb7xRDseJxO3mm28OaKBmMWPec7gLxGrq0XbBAgAAAABaq9XZZHJysu66665AxhIS9fOeC4pcSrbFNhi9rZ/3PCg7pdnzniNFW1dTj8YLFgAAAADQGq3Oivbs2aP169fL6XTK5/M1eOyxxx5rc2DBUj/vuchVrb3OE6XTCfFWVbu9KnbVtGjeczSJ1gsWAAAAANBSrUq8n3vuOd19993q1KmTunTp0iDpslgsEZV4S4Gb9xxNuGABAAAAAM3TqsT7iSee0Lx58zRr1qxAxxMygZj3HG24YBG9fD6DzwoAAADQTK1KvI8dO6ZJkyYFOpaQa+u852jEBYvos89Z7r/YUuPxyh5rVa/OyRo/kIstAAAAQFNa1cd70qRJWrt2baBjQYSqv2DRr0sH5aQlknS3Y/uc5Vrx4UEVFLmUmhinnp2SlZoYp4Iil1Z8eFD7nOWhDhEAAAAIO60a8e7du7ceffRRffzxxxo0aJDi4hquan3vvfcGJDgA4cPnM7Sm4IhKK93qk57sX9vBYY9Tsi1We50VWrvriHp2SubiCwAAAHCSViXey5cvV3JysjZs2KANGzY0eMxisZB4A+3Q4ePV2v/NiYX0Tl5QUTrxuc9MsWufs0KHj1czZQMAAAA4SasS7wMHDgQ6DgBhrtLtUY3Hq8T4plvEJcRbdaSsRpVuT5AjAwAAAMJbq/t4A4guSfGxssdaVeX2yGGPa/R4tdsrW6xVSfGcVhDdnl63J9QhwGS8xwCAlmr2N+Tc3Fz95je/UVJSknJzc8+478KFC9scGIDwkp2aoF6dk1VQ5FKyLbZBublhGCp21WhQdoqyU5seEQcAAACiVbMT7+3bt6uurs7/79M5de4ngPYhJsai8QMzVOSq1l7nibneCfFWVbu9KnbVKC0pXuMGZLCwGgAAAHCKZife77//fpP/Bnw+gz7eUaJ3ukNTR3b39/E+UlYjW6xVg7JTNG4AfbwBAACAprRqMubKlSv1s5/9TElJSYGOBxFmn7Pcn4TVeLyyx1rVq3Oyxg8kCWuveqc71HN0MhdbAAAAgGaKac0P/epXv1J6erquv/56/e1vf5PHwyrG0Wifs1wrPjyogiKXUhPj1LNTslIT41RQ5NKKDw9qn7M81CHCJDExFuWkJapflw7KSUsk6QYAAADOoFWJd3FxsVavXi2r1arrr79emZmZmjZtmj766KNAx4cw5fMZWlNwRKWVbvVJT5bDHidrjEUOe5z6pCertNKttbuOyOczQh0qAAAAAIRUqxLv2NhYXXnllXrppZfkdDq1aNEiHTp0SGPGjFGvXr0CHaNffn6+LBaLZs6cadproHkOH6/W/m9OLLB16oJ6FotFmSl27XNW6PDx6hBFCAAAAADhoc0NdxMTEzV+/HgdO3ZMhw4d0u7duwMRVyObN2/W8uXLNXjwYFOeHy1T6faoxuNVYnzTraMS4q06UlajSjfTEAAAAABEt1aNeEtSVVWVXnrpJV1++eXKysrS008/rYkTJ6qgoCCQ8UmSKioqNHnyZD333HM655xzAv78aLmk+FjZY62qOk1iXe32yhZrVVJ8m6/tAAAAAEBEa1VWdMMNN+h///d/lZiYqEmTJmn9+vUaMWJEoGPzmz59uq644gr96Ec/0hNPPHHGfWtra1VbW+u/X1ZWZlpc0Sw7NUG9OieroMilZFtsg3JzwzBU7KrRoOwUZac2PSIOAAAAANGiVYm3xWLR6tWrNX78eMXGmjui+corr2jbtm3avHlzs/bPz8/XnDlzTI0JJ1a1Hj8wQ0Wuau11npjrnRBvVbXbq2JXjdKS4jVuQAarXQMAAACIeq3KmletWhXoOJpUWFio++67T2vXrpXdbm/Wz+Tl5Sk3N9d/v6ysTDk5OWaFGNV6pzs0dWR3fx/vI2U1ssVaNSg7ReMG0McbAAAAAKQWJN5//OMfdccdd8hut+uPf/zjGfe999572xyYJG3dulVOp1MXXnihf5vX69XGjRu1ePFi1dbWymq1NvgZm80mm80WkNfH2fVOd6jn6GQdPl6tSrdHSfGxyk5NYKQbAAAAAP6l2Yn3008/rcmTJ8tut+vpp58+7X4WiyVgifdll12mzz77rMG2qVOnql+/fpo1a1ajpBuhERNjUU5aYqjDCCmfz+DiAwAAAIAmNTvxPnDgQJP/NpPD4dDAgQMbbEtKSlLHjh0bbQdCZZ+z3F9uX+Pxyh5rVa/OyRo/kHJ7AAAAAC1IvE+eN30mFotFTz31VKsDAiLJPme5Vnx4UKWVbmWm2JUYn6Aqt0cFRS4Vuao1dWR3km8AAAAgyjU78d6+fXuz9ju5rZQZ1q9fb+rzA83l8xlaU3BEpZVu9UlP9v/tO+xxSrbFaq+zQmt3HVHPTsmUncMU+fn5eu211/TFF18oISFBI0aM0IIFC3TeeeeFOjQAAACcpNmJ9/vvv29mHEDEOXy8Wvu/OdFK7dQLThaLRZkpdu1zVujw8eqonwMPc2zYsEHTp0/X8OHD5fF49PDDD2vcuHH6/PPPlZSUFOrwAAAA8C/mNuEG2rFKt0c1Hq8S4xOafDwh3qojZTWqdHuCHBmixTvvvNPg/ooVK5Senq6tW7fqkksuCVFUAAAAOFVMqAMAIlVSfKzssVZVnSaxrnZ7ZYu1Kime61sIDpfLJUlKS0sLcSQAAAA4GRkB0ErZqQnq1TlZBUUuJdtiG5SbG4ahYleNBmWnKDu16RFxIJAMw1Bubq5GjRp12q4PtbW1qq2t9d8vKysLVngAAABRjcQbaKWYGIvGD8xQkatae50n5nonxFtV7faq2FWjtKR4jRuQwcJqCIp77rlHO3fu1AcffHDaffLz8zVnzpwgRgWExtPr9oQ6BAAAGqDUHGiD3ukOTR3ZXQOzUnS8qk4Hv63U8ao6DcpOoZUYgmbGjBl688039f777+vcc8897X55eXlyuVz+W2FhYRCjBAAAiF6MeANt1DvdoZ6jk3X4eLUq3R4lxccqOzWBkW6YzjAMzZgxQ6+//rrWr1+vHj16nHF/m80mm80WpOgAAABQj8QbCICYGAstwxB006dP16pVq/TGG2/I4XCopKREkpSSkqKEBNYWAAAACBeUmuOMfD5DhaVV+qKkTIWlVfL5jFCHBOBfli5dKpfLpdGjRyszM9N/W716dahDAwAAwEkY8cZp7XOWa03BEe3/pkI1Hq/ssVb16pys8QMzmLsMhAHD4EIYAABAJCDxRpP2Ocu14sODKq10KzPFrsT4BFW5PSoocqnIVc3CYQAAAADQTCTeZ+DzGVG5YJbPZ2hNwRGVVrrVJz3Z35/aYY9Tsi1We50VWrvriHp2So6K3wcAAAAAtAWJ92lEc5n14ePV2v/Nib7U9Ul3PYvFoswUu/Y5K3T4eDULigEAAADAWZB4NyHay6wr3R7VeLxKjG96VeSEeKuOlNWo0u0JcmRoqWit2gAAAADCCYn3KSizlpLiY2WPtarK7ZHDHtfo8Wq3V7ZYq5Li+fMJZ9FctQEAAACEE9qJnaIlZdbtVXZqgnp1Tlaxq6bRqsmGYajYVaPe6cnKTqVPcLiqr9ooKHIpNTFOPTslKzUxTgVFLq348KD2OctDHSIAAAAQNUi8T/FdmXXTo7kJ8VbVerztusw6Jsai8QMzlJYUr73OCpXX1Mnj86m8pk57nRVKS4rXuAEZ7XbEP9KdWrXhsMfJGmORwx6nPunJKq10a+2uI/RkBwAAAIKExPsUJ5dZNyVayqx7pzs0dWR3DcxK0fGqOh38tlLHq+o0KDul3c9xj3RUbQAAAADhpX1nj61QX2ZdUORSsi22QeJSX2Y9KDslKsqse6c71HN0MotzRRgWxwMAAADCC4n3KerLrItc1drrPDFqmBBvVbXbq2JXTdSVWcfEWALeMoyVts3F4ngAAABAeOGbdxPqy6zrV4Q+UlYjW6xVg7JTNG4AK0K3BSttm4+qDQAAACC8kHifBmXWgRft/dGDhaoNAAAAILyQeJ+BGWXWZgn38m36owcXVRsAAABA+CDxbgcioXy7JSttR8rFjnBH1QYAAAAQHki8I1yklG+z0ra5TlfxEElVGwAAAEB7ReIdwSKpfJuVts0TCRUPAAAAQDSLCXUAaL2WlG+HWv1K28WuGhmG0eCx+pW2e6cns9J2C9VXPBQUuZSaGKeenZKVmhingiKXVnx4UPuc5aEOEQAAAIh6JN4R7Lvy7aZHiRPirar1eMOifLt+pe20pHjtdVaovKZOHp9P5TV12uusYKXtVji14sFhj5M1xiKHPU590pNVWunW2l1H5PMZZ38yAAAAAKYh8Y5gJ5dvNyXcyrfrV9oemJWi41V1OvhtpY5X1WlQdkrYzEWPJJFU8QAAAABEs/DIyNAq9eXbBUUuJdtiGyRf9eXbg7JTwqp8m5W2A4cF6wAAAIDIQOIdwerLt4tc1drrPDHymRBvVbXbq2JXTdiWb7PSdmCwYB0AAAAQGSg1j3CUb0cvFqwDAAAAIgNDYe0A5dvBdbqe2cEWioqHcDl2AAAAIJKQeLcTlG8HR7j1zK6veKiP6UhZjWyxVg3KTtG4AYGNKdyOHQAAAIgUJN5AM9X3zC6tdCszxa7E+ARVuT0qKHKpyFUdstL+YFQ8hOuxAwAAAJGAxBtohlN7ZtevIO+wxynZFqu9zgqt3XVEPTslh6zs3KyKh3A/dgAAACDchf3iavn5+Ro+fLgcDofS09M1ceJEffnll6EOC1EmmntmR/OxAwAAAIEQ9on3hg0bNH36dH388cdat26dPB6Pxo0bp8rKylCHhijyXc/spotEEuKtqvV422XP7Gg+dgAAACAQwr7U/J133mlwf8WKFUpPT9fWrVt1ySWXhCgqRJto7pkdzccOAAAABELYj3ifyuVySZLS0tJCHAmiSTT3zI7mYwcAAAACIaKGqAzDUG5urkaNGqWBAwc2uU9tba1qa2v998vKyoIVHtqxUPTMDhfRfOwAAABAIETUiPc999yjnTt36uWXXz7tPvn5+UpJSfHfcnJyghgh2rP6ntkDs1J0vKpOB7+t1PGqOg3KTmn37bSi+dgBAACAtoqYEe8ZM2bozTff1MaNG3Xuueeedr+8vDzl5ub675eVlZF8I2CC0TM7XEXzsYe7Z599Vr/73e9UXFysAQMGaNGiRfrBD34Q6rAAAADwL2GfeBuGoRkzZuj111/X+vXr1aNHjzPub7PZZLPZghQdopGZPbPDXTQfe7havXq1Zs6cqWeffVYjR47Un/70J02YMEGff/65unbtGurwAAAAoAgoNZ8+fbpefPFFrVq1Sg6HQyUlJSopKVF1NT2DAWDhwoX6xS9+odtvv13nn3++Fi1apJycHC1dujTUoQEAAOBfwj7xXrp0qVwul0aPHq3MzEz/bfXq1aEODQBCyu12a+vWrRo3blyD7ePGjdNHH30UoqgAAABwqogoNQ/Ez7O6OYDTqT8/tPV8E2zffvutvF6vMjIyGmzPyMhQSUlJo/1P7fpQ356xpefHmsqKVkQLIJy05HMfqedIAAgnYZ94t1V5ebkkscAagLMqLy9XSkpKqMNoMYul4QJ3hmE02iad6PowZ86cRts5PwLR59et+JlIPUcCQDho94l3VlaWCgsL5XA4mvwierL6FdALCwvVoUOHIEUYWtF4zFJ0Hnc0HrPUvOM2DEPl5eXKysoKcnRt06lTJ1mt1kaj206ns9EouNS464PP51Npaak6duzI+fE0ovG4o/GYpeg87uYec6SeIwEgnLT7xDsmJuaM7cea0qFDh6j5T7deNB6zFJ3HHY3HLJ39uCNxFCc+Pl4XXnih1q1bp5/+9Kf+7evWrdPVV1/daP+muj6kpqa26DX5+4ke0XjMUnQed3OOORLPkQAQTtp94g0A7Vlubq5uvvlmDRs2TBdffLGWL1+ur7/+WnfddVeoQwMAAMC/kHgDQAS77rrrdPToUc2dO1fFxcUaOHCg3nrrLXXr1i3UoQEAAOBfSLxPYrPZ9PjjjzcqxWzPovGYpeg87mg8Zik6jnvatGmaNm2aqa8RDb/HpkTjcUfjMUvRedzReMwAECoWg94QAAAAAACYJibUAQAAAAAA0J6ReAMAAAAAYCISbwAAAAAATETi/S/PPvusevToIbvdrgsvvFD/+Mc/Qh2SqWbPni2LxdLg1qVLl1CHFXAbN27UVVddpaysLFksFv31r39t8LhhGJo9e7aysrKUkJCg0aNHa9euXaEJNkDOdsy33npro/f+3/7t30ITbIDk5+dr+PDhcjgcSk9P18SJE/Xll1822Kc9vtfBFE3nSM6PJ7THzwznR86PABAqJN6SVq9erZkzZ+rhhx/W9u3b9YMf/EATJkzQ119/HerQTDVgwAAVFxf7b5999lmoQwq4yspKXXDBBVq8eHGTjz/55JNauHChFi9erM2bN6tLly4aO3asysvLgxxp4JztmCXpxz/+cYP3/q233gpihIG3YcMGTZ8+XR9//LHWrVsnj8ejcePGqbKy0r9Pe3yvgyUaz5GcH9vnZ4bzI+dHAAgZA8b3v/9946677mqwrV+/fsZDDz0UoojM9/jjjxsXXHBBqMMIKknG66+/7r/v8/mMLl26GL/97W/922pqaoyUlBRj2bJlIYgw8E49ZsMwjClTphhXX311SOIJFqfTaUgyNmzYYBhGdLzXZoq2cyTnx+j4zHB+5PwIAMEU9SPebrdbW7du1bhx4xpsHzdunD766KMQRRUce/fuVVZWlnr06KHrr79eX331VahDCqoDBw6opKSkwXtvs9l06aWXtvv3fv369UpPT1ffvn31y1/+Uk6nM9QhBZTL5ZIkpaWlSYru97qtovUcyfkxej8znB+j570GgGCK+sT722+/ldfrVUZGRoPtGRkZKikpCVFU5rvooov0wgsvaM2aNXruuedUUlKiESNG6OjRo6EOLWjq399oe+8nTJigl156Se+9956eeuopbd68WT/84Q9VW1sb6tACwjAM5ebmatSoURo4cKCk6H2vAyEaz5GcH6P3M8P58Tvt/b0GgGCLDXUA4cJisTS4bxhGo23tyYQJE/z/HjRokC6++GL16tVLK1euVG5ubggjC75oe++vu+46/78HDhyoYcOGqVu3bvq///s/XXPNNSGMLDDuuece7dy5Ux988EGjx6LtvQ6kaPrdcX78TjS97xLnx5O19/caAIIt6ke8O3XqJKvV2uiqrtPpbHT1tz1LSkrSoEGDtHfv3lCHEjT1qxRH+3ufmZmpbt26tYv3fsaMGXrzzTf1/vvv69xzz/Vv571uPc6RnB9PFk3vu8T5MZreawAwW9Qn3vHx8brwwgu1bt26BtvXrVunESNGhCiq4KutrdXu3buVmZkZ6lCCpkePHurSpUuD997tdmvDhg1R9d4fPXpUhYWFEf3eG4ahe+65R6+99pree+899ejRo8HjvNetxzmS82O9aPzMcH6MnvcaAMxGqbmk3Nxc3XzzzRo2bJguvvhiLV++XF9//bXuuuuuUIdmml/96le66qqr1LVrVzmdTj3xxBMqKyvTlClTQh1aQFVUVGjfvn3++wcOHNCOHTuUlpamrl27aubMmZo/f7769OmjPn36aP78+UpMTNSNN94Ywqjb5kzHnJaWptmzZ+vaa69VZmamDh48qF//+tfq1KmTfvrTn4Yw6raZPn26Vq1apTfeeEMOh8M/cpOSkqKEhARZLJZ2+V4HS7SdIzk/cn7k/Bj57zUAhJ1QLacebpYsWWJ069bNiI+PN773ve/522y0V9ddd52RmZlpxMXFGVlZWcY111xj7Nq1K9RhBdz7779vSGp0mzJlimEYJ9qoPP7440aXLl0Mm81mXHLJJcZnn30W2qDb6EzHXFVVZYwbN87o3LmzERcXZ3Tt2tWYMmWK8fXXX4c67DZp6nglGStWrPDv0x7f62CKpnMk58cphmG0z88M50fOjwAQKhbDMAzz03sAAAAAAKJT1M/xBgAAAADATCTeAAAAAACYiMQbAAAAAAATkXgDAAAAAGAiEm8AAAAAAExE4g0AAAAAgIlIvAEAAAAAMBGJNwAAAAAAJiLxBgAAAADARCTeAAAAAACYiMQbOAuv1yufzxfqMAAg7HB+BACgeUi8EVFeeOEFdezYUbW1tQ22X3vttbrlllskSf/7v/+rCy+8UHa7XT179tScOXPk8Xj8+y5cuFCDBg1SUlKScnJyNG3aNFVUVPgff/7555Wamqq//e1v6t+/v2w2mw4dOqT169fr+9//vpKSkpSamqqRI0fq0KFDwTlwADgLzo8AAIQvEm9ElEmTJsnr9erNN9/0b/v222/1t7/9TVOnTtWaNWt000036d5779Xnn3+uP/3pT3r++ec1b948//4xMTH64x//qIKCAq1cuVLvvfeeHnzwwQavU1VVpfz8fP3Hf/yHdu3apbS0NE2cOFGXXnqpdu7cqU2bNumOO+6QxWIJ2rEDwJlwfgQAIIwZQIS5++67jQkTJvjvL1q0yOjZs6fh8/mMH/zgB8b8+fMb7P9f//VfRmZm5mmf77//+7+Njh07+u+vWLHCkGTs2LHDv+3o0aOGJGP9+vUBPBIACCzOjwAAhCeLYRhGaFN/oGW2b9+u4cOH69ChQ8rOztaQIUN07bXX6tFHH1VSUpJ8Pp+sVqt/f6/Xq5qaGlVWVioxMVHvv/++5s+fr88//1xlZWXyeDyqqalRRUWFkpKS9Pzzz+vOO+9UTU1NgxGbqVOn6uWXX9bYsWP1ox/9SD//+c+VmZkZil8BADSJ8yMAAOGJUnNEnKFDh+qCCy7QCy+8oG3btumzzz7TrbfeKkny+XyaM2eOduzY4b999tln2rt3r+x2uw4dOqTLL79cAwcO1KuvvqqtW7dqyZIlkqS6ujr/ayQkJDQqk1yxYoU2bdqkESNGaPXq1erbt68+/vjjoB03AJwN50cAAMJTbKgDAFrj9ttv19NPP63Dhw/rRz/6kXJyciRJ3/ve9/Tll1+qd+/eTf7cli1b5PF49NRTTykm5sR1p//+7/9u9usOHTpUQ4cOVV5eni6++GKtWrVK//Zv/9b2AwKAAOH8CABA+GHEGxFp8uTJOnz4sJ577jnddttt/u2PPfaYXnjhBc2ePVu7du3S7t27tXr1aj3yyCOSpF69esnj8eiZZ57RV199pf/6r//SsmXLzvp6Bw4cUF5enjZt2qRDhw5p7dq12rNnj84//3zTjhEAWoPzIwAA4YfEGxGpQ4cOuvbaa5WcnKyJEyf6t48fP15/+9vftG7dOg0fPlz/9m//poULF6pbt26SpCFDhmjhwoVasGCBBg4cqJdeekn5+flnfb3ExER98cUXuvbaa9W3b1/dcccduueee3TnnXeadYgA0CqcHwEACD8sroaINXbsWJ1//vn64x//GOpQACCscH4EACC8kHgj4pSWlmrt2rWaPHmyPv/8c5133nmhDgkAwgLnRwAAwhOLqyHifO9739OxY8e0YMECvlQCwEk4PwIAEJ4Y8QYAAAAAwEQsrgYAAAAAgIlIvAEAAAAAMBGJNwAAAAAAJiLxBgAAAADARCTeAAAAAACYiMQbAAAAAAATkXgDAAAAAGAiEm8AAAAAAExE4g0AAAAAgIlIvAEAAAAAMBGJNwAAAAAAJiLxBgAAAADARCTeAAAAAACYiMQbAAAAAAATxYY6ALP5fD4VFRXJ4XDIYrGEOhwAYcgwDJWXlysrK0sxMdFzPZLzI4Dm4BzJORJA01pyfmz3iXdRUZFycnJCHQaACFBYWKhzzz031GEEDedHAC3BORIAmtac82NIE++NGzfqd7/7nbZu3ari4mK9/vrrmjhxov9xwzA0Z84cLV++XMeOHdNFF12kJUuWaMCAAc1+DYfDIenEL6NDhw6BPgQA7UBZWZlycnL854towfkRQHNwjuQcCaBpLTk/hjTxrqys1AUXXKCpU6fq2muvbfT4k08+qYULF+r5559X37599cQTT2js2LH68ssvm33yry8N6tChAydNAGcUbaWEnB8BtATnSABoWnPOjyGdqDNhwgQ98cQTuuaaaxo9ZhiGFi1apIcffljXXHONBg4cqJUrV6qqqkqrVq0KQbQAEDz5+fkaPny4HA6H0tPTNXHiRH355ZcN9jEMQ7Nnz1ZWVpYSEhI0evRo7dq1K0QRAwAA4HTCdoWMAwcOqKSkROPGjfNvs9lsuvTSS/XRRx+d9udqa2tVVlbW4AZIks9nqLC0Sl+UlKmwtEo+nxHqkIDT2rBhg6ZPn66PP/5Y69atk8fj0bhx41RZWenfp74qaPHixdq8ebO6dOmisWPHqry8PISRty+cNwAAQCCE7eJqJSUlkqSMjIwG2zMyMnTo0KHT/lx+fr7mzJljamyIPPuc5VpTcET7v6lQjccre6xVvTona/zADPVOj645a4gM77zzToP7K1asUHp6urZu3apLLrmkUVWQJK1cuVIZGRlatWqV7rzzzlCE3a5w3gAAAIEStiPe9U6tlzcM44w19Hl5eXK5XP5bYWGh2SEizO1zlmvFhwdVUORSamKcenZKVmpinAqKXFrx4UHtczI6iPDncrkkSWlpaZJaVxVERVDzcd4AAACBFLaJd5cuXSR9N/Jdz+l0NhoFP5nNZvMvgsFiGPD5DK0pOKLSSrf6pCfLYY+TNcYihz1OfdKTVVrp1tpdRygfPQmlteHHMAzl5uZq1KhRGjhwoKQzVwWdet6sl5+fr5SUFP+tvbbJaevfcDSeN/jcAwBgrrAtNe/Ro4e6dOmidevWaejQoZIkt9utDRs2aMGCBSGODpHi8PFq7f+mQpkp9kaVEhaLRZkpdu1zVujw8WrlpCWGKMrwQWlteLrnnnu0c+dOffDBB40ea0lVUF5ennJzc/3361tgtCeB+BuOtvMGn3sAAMwX0sS7oqJC+/bt898/cOCAduzYobS0NHXt2lUzZ87U/Pnz1adPH/Xp00fz589XYmKibrzxxhBGjUhS6faoxuNVYnxCk48nxFt1pKxGlW5PkCMLP/WltaWVbmWm2JUYn6Aqt0cFRS4Vuao1dWR3voSHwIwZM/Tmm29q48aNOvfcc/3bT64KyszM9G8/U1WQzWaTzWYzN+AQCtTfcDSdN/jcAwAQHCFNvLds2aIxY8b479ePxEyZMkXPP/+8HnzwQVVXV2vatGk6duyYLrroIq1du7bZPbyBpPhY2WOtqnJ75LDHNXq82u2VLdaqpPiwLf4IilNLa+tH+Rz2OCXbYrXXWaG1u46oZ6dkxcREVx/XUDEMQzNmzNDrr7+u9evXq0ePHg0epyqooUD+DUfLeYPPPQBEn6fX7TH1+e8f29fU549kIf3WMHr0aBnG6eeRWSwWzZ49W7Nnzw5eUGhXslMT1KtzsgqKXEq2xTYoGzUMQ8WuGg3KTlF2atMjW5HA5zN0+Hi1Kt0eJcXHKjs1ocVfkttTaW0gfh/hYPr06Vq1apXeeOMNORwO/7ztlJQUJSQkyGKxUBV0kkD+DUfDeUNqX597AADCXWRfrgfOIibGovEDM1TkqtZe54kvmAnxVlW7vSp21SgtKV7jBmREZGImBW5uZnsprW1Pc1WXLl0q6cQFypOtWLFCt956qyRRFXSSQP4Nt/fzRr328rkHACASkHij3eud7tDUkd39CdmRshrZYq0alJ2icQMiLyGrF8i5me2htLa9zVU9UzVQPaqCvhPov+H2et44WXv43AMAECn43xRRoXe6Qz1HJ7eLEmQp8HMzI7G09uSS8oQ4q94pKGGuahQz42+4vZ03ThWJn3sAACIViTeiRkyMpd3MUwz03MxIK609taTc6zVUeKxK/bo4mKsapcz6GzbjvBEu6xBE2uceAIBIRuINRCAz5mZGSmltUyXl/zxWpaOVbn15pFxJtlilJTVsmcVc1egQCX/D4bYOQST8zgAAaA9IvIEIZNbczHAvrT1dif05ifE6JyFOlTUe7f+mUuckxjcY+WauavQI57/hcF2HIJx/ZwAAtBd8CwUikJlzM8O5JP90JfYOe6zOSbKp6Hi1jlbUqrzGow4JJy5IMFc1+oTj33C498wOx98ZAADtSUyoAwDQcvVzM9OS4rXXWaHymjp5fD6V19Rpr7Oi3c7N/K7EvuE1Q4vFot7pyUq2x8pVXadjVe6o+H0gcrRkXQYAAND+kHgDEap+bubArBQdr6rTwW8rdbyqToOyUyKudVZznVxif6q0pHidl5GstCSbquu8UfH7QOQ43UWjegnxVtV6vKxDAABAO0WpORDBImFuZiBXcD5biX11nU9XDOqiqy7IUlWdNyx/H4hO9MwGACC68T88EOHCeW5moFdwbk77o/EDu6hrxyQTjgZoPXpmAwAQ3Sg1B2CK+hWcC4pcSk2MU89OyUpNjFNBkUsrPjyofc7yVj1vNJbYI/JF67oMgFk2btyoq666SllZWbJYLPrrX//a4HHDMDR79mxlZWUpISFBo0eP1q5du0ITLACIEW8AJjB7BedIKLEHTkXPbCBwKisrdcEFF2jq1Km69tprGz3+5JNPauHChXr++efVt29fPfHEExo7dqy+/PJLORx81gAEH4k3gIBryQrOrSmTD+S8cSCYuGgEBMaECRM0YcKEJh8zDEOLFi3Sww8/rGuuuUaStHLlSmVkZGjVqlW68847gxkqAEgi8QZggu9WcG56vmpCvFVHympatYJzoOeNA8EWzusyAO3BgQMHVFJSonHjxvm32Ww2XXrppfroo49Om3jX1taqtrbWf7+srMz0WAFED+Z4Awi4M7X9klq/grNZ88YBAO1HSUmJJCkjI6PB9oyMDP9jTcnPz1dKSor/lpOTY2qcAKILiTeAgKtfwbnYVSPDMBo8Vr+Cc+/05Bat4HzqvHGHPU7WGIsc9jj1SU9WaaVba3cdkc9nnP3JAADt3qlTnQzDaLTtZHl5eXK5XP5bYWGh2SECiCIk3gACzowVnFsybxwAEL26dOkiSY1Gt51OZ6NR8JPZbDZ16NChwQ0AAoXEG4ApAt3267t5402XpyfEW1Xr8bZq3jgAoP3o0aOHunTponXr1vm3ud1ubdiwQSNGjAhhZACiGYurATBNIFdwPnneuMMe1+jx1s4bBwBEnoqKCu3bt89//8CBA9qxY4fS0tLUtWtXzZw5U/Pnz1efPn3Up08fzZ8/X4mJibrxxhtDGDWAaMY3VACmassKzie3DUuIs6pn5yTtKipTsi22Qbl5/bzxQdkpLZo3DkQD2u+hPdqyZYvGjBnjv5+bmytJmjJlip5//nk9+OCDqq6u1rRp03Ts2DFddNFFWrt2LT28AYQMiTeAsNRU27DUhBMLqu11npjrnRBvVbXbq2JXTavmjQPtHe330F6NHj260eKdJ7NYLJo9e7Zmz54dvKAA4AxIvAGEnfq2YaWVbmWm2JUYn6Aqt0fFrhpZY04spHa8qk5Hympki7VqUHaKxg0gkcAJjPCecLrPUUGRS0Wu6lattQAAAFqHxBtAWDm1bVh9SbnDHqdkW6z2OivUMSlet47orqo6b1QnVmiMEd4TmvM5WrvriHp2SuazAwBAEJB4AwgrzWkbtv+bSlksFvXrQquXYAr3kWRGeL/TkvZ7rV2DAQAANB+JN4Cw8l3bsKYXSUuIt+pIWQ1tw4Is3EeSGeFtiM8RAADhhT7eAMLKyW3DmkLbsOCrH0kuKHIpNTFOPTslKzUxTgVFLq348KD2OctDHWKLRnijAZ8jAADCC4k3ECF8PkOFpVX6oqRMhaVV8vlOv5prJMtOTVCvzskqdtU0WrG2vm1Y7/Rk2oYFyakjyQ77iZXlHfY49UlPVmmlW2t3HQn53+N3I7xNJ5IJ8VbVerxRM8LL5wgAgPDCpW4gAoR7me/ptGZOcEyMReMHZqjIVU3bsDAQKXOFTx7hddjjGj0ebSO8fI4AAAgv0fENBIhgkbpgVFsuFvROd2jqyO7+n6dtWOhEylzh+hHegiKXkm2xDS4S1I/wDspOiaoRXj5HAACEDxJvIMACufJzpC4YFYiLBb3THeo5OjmsV9GOBpEykswIb9P4HAEAEB5IvBHxwqnFUaBLws9W5tulg02fFh7Xxr3fqFfn5LD4Qh3IiwUxMRZaHYVYJI0kM8LbND5HAACEHok3Ilo4zX02oyT8TGW+pZVu7TlSpn8eq9afP/hKnZPtYTHvO1LmBKN5Im0kmRFeAAAQjki8EbHCae6zWSXhpyvzLa10a0fhcZVV18keZ1WPjsmKtVrCYt53pMwJRvNF2kgyI7wAACDckHgjIoXb3GezRnmbKvM1DEP7nBWqcnsUGyNldLArNTFOFoslLOZ9R8qcYLQMI8kAAACtRx9vRKSWJLrBYFYP4foy37SkeO11Vqi8pk7HqtxyltfI6/Up0RarXp2T/L+DUBz7qegf3H7VjyT369JBOWmJJN0AAADNROKNiGRWottaJ4/yNqUto7z1Zb4Ds1J0vKpOB49WqsbtVZfUBA3JSVVakq3B/sE+9lM1dbHA4/OpvKZOe50VYTcnGAAAADBb2CfeHo9HjzzyiHr06KGEhAT17NlTc+fOlc/nC3VoCCEzE93WMHuUt3e6Q3eP7qX7x/bVL0b11MBzU3R+lw6Nkm4pPEq5G10s+LZSx6vqNCg7JWz7jgMAAABmCftJlgsWLNCyZcu0cuVKDRgwQFu2bNHUqVOVkpKi++67L9ThIUTCrcVRMFZ+ri/zzU5N0K7DZSoocslhD/2xnw5zgttu48aN+t3vfqetW7equLhYr7/+uiZOnOh//NZbb9XKlSsb/MxFF12kjz/+OMiRAgAA4ExanXjv379fK1as0P79+/WHP/xB6enpeuedd5STk6MBAwYELMBNmzbp6quv1hVXXCFJ6t69u15++WVt2bIlYK+ByBOOLY6CtfJzOB77mWJldenWq6ys1AUXXKCpU6fq2muvbXKfH//4x1qxYoX/fnx8fLDCM43PZ3DBBgAAtCutSrw3bNigCRMmaOTIkdq4caPmzZun9PR07dy5U//xH/+hv/zlLwELcNSoUVq2bJn27Nmjvn376tNPP9UHH3ygRYsWNbl/bW2tamtr/ffLysoCFgvCSzi2OArWKG84HjsCb8KECZowYcIZ97HZbOrSpUuQIjLfPme5/++6xuOVPdYaFv3pAQAA2qJVifdDDz2kJ554Qrm5uXI4vvsiNGbMGP3hD38IWHCSNGvWLLlcLvXr109Wq1Ver1fz5s3TDTfc0OT++fn5mjNnTkBjQPgKx3LmYI3yhuOxI/jWr1+v9PR0paam6tJLL/VfCG1KuF+Y3Ocs14oPD6q00q3MFLsS4xNU5faERX96AACAtmjV4mqfffaZfvrTnzba3rlzZx09erTNQZ1s9erVevHFF7Vq1Spt27ZNK1eu1O9///tG8xrr5eXlyeVy+W+FhYUBjQfhJ5pbHEXzsePEiPhLL72k9957T0899ZQ2b96sH/7whw2S65Pl5+crJSXFf8vJyQlyxKfn8xlaU3BEpZVu9UlPlsMeJ2uMRQ57nPqkJ6u00q21u47I5zPO/mQAAABhplUj3qmpqSouLlaPHj0abN++fbuys7MDEli9Bx54QA899JCuv/56SdKgQYN06NAh5efna8qUKY32t9lsstkar/QMhDvmtaKlrrvuOv+/Bw4cqGHDhqlbt276v//7P11zzTWN9s/Ly1Nubq7/fllZWdgk34ePV2v/NyfWLDh5wUCpcX961g0AAACRplWJ94033qhZs2bpf/7nf2SxWOTz+fThhx/qV7/6lW655ZaABlhVVaWYmIYD81arlXZiaFeY14pAyMzMVLdu3bR3794mHw/HC5P1F5wKilwqrapVZoq9yf0S4q06UlYTsv70AAAAbdGqxHvevHm69dZblZ2dLcMw1L9/f3m9Xt1444165JFHAhrgVVddpXnz5qlr164aMGCAtm/froULF+q2224L6OsAocK8VgTK0aNHVVhYqMzMzFCH0iwnX3AqrXJrv7NS5dUe9c9KUVpSw9XZw6E/PQAAQGu16htMXFycXnrpJc2dO1fbt2+Xz+fT0KFD1adPn0DHp2eeeUaPPvqopk2bJqfTqaysLN1555167LHHAv5aQLCdOq+1vsTWYY9Tsi1We50VWrvriHp2Sg67snNK481XUVGhffv2+e8fOHBAO3bsUFpamtLS0jR79mxde+21yszM1MGDB/XrX/9anTp1anINjnBz6gWnzBS7yqvr9M/j1ar1+DS06zn+5Duc+tMDiAwej0ezZ8/WSy+9pJKSEmVmZurWW2/VI4880qiSEgCCoVWJ98aNG9WvXz/16tVLvXr18m+vq6vTpk2bdMkllwQsQIfDoUWLFp22fRgQySJ1Xiul8cGxZcsWjRkzxn+/fn72lClTtHTpUn322Wd64YUXdPz4cWVmZmrMmDFavXp1g24T4eh0F5z6Z3VQrcerbypq9XmxSxf1SFNNnS/s+tMDCH8LFizQsmXLtHLlSg0YMEBbtmzR1KlTlZKSovvuuy/U4QGIQq1KvEePHq2MjAy99tpruvjii/3bS0tLNWbMGHm93oAFCLRnlW6PajxeJcY3PYoXjvNaKY0PntGjR8swTr+K95o1a4IYTeCc7oJTWpJNQ7ueo8+LyuQsq9Xu4jKdk2ijP/0ZUHkCNG3Tpk26+uqrdcUVV0iSunfvrpdffllbtmwJcWQAolWrJ8tdf/31uuyyy/Tss8/q1ltv9W8/05dERAe+CDZfUnys7LFWVbk9ctjjGj0ebvNaI7k0HuHjTBec0pJsuqhnR+0uLtPPh+doYFYK55DToPIEOL1Ro0Zp2bJl2rNnj/r27atPP/1UH3zwwRkrKGtraxu0YywrKwtCpACiRau+zVssFuXl5ekHP/iBpkyZop07d+qpp57yP4boxRfBlslOTVCvzskqKHIp2Rbb4PMTjvNaI7U0HuHlbBecauq8OicxXgOzUvg7Og0qT4AzmzVrllwul/r16yer1Sqv16t58+bphhtuOO3P5Ofna86cOUGMEmZ4et0eU5///rF9TX1+tF+tWl2iflT7mmuu0caNG/WXv/xFEyZM0PHjxwMZGyJM/RfBgiKXUhPj1LNTslIT41RQ5NKKDw9qn7M81CGGnZgYi8YPzFBaUrz2OitUXlMnj8+n8po67XVWBHVeq89nqLC0Sl+UlKmwtEo+X+Pqle9GKpu+ZpcQb1WtxxtWpfEIP/UXnIpdNY2qpOovOPVOTw6bC07h5tTKE4c9TtYYixz2OPVJT1ZppVtrdx1p8jMMRIvVq1frxRdf1KpVq7Rt2zatXLlSv//977Vy5crT/kxeXp5cLpf/VlhYGMSIAbR3ba5fHTp0qD755BNNnDhRl112WSBiQgSiBLn1eqc7NHVkd3+lwJGyGtlirS2a19rW8v7mVipEWmk8wlP9BaciV7X2Ok9UUCTEW1Xt9rKQWjNQeQKc3QMPPKCHHnpI119/vSRp0KBBOnTokPLz8zVlypQmf8Zms8lmswUzTABRpFXfjqdMmaKEhO9GIrp06aINGzbojjvu0MaNGwMWHCIHXwTbpne6Qz1HJ7cqeW5reX9LSlYjrTQe4SsQF5zag9ZcNIvERRmBYKuqqmrUNsxqtcrn84UoIgDRrlWJ94oVKxpts9lsZyzfQfvGF8G2i4mxtPiiRFvneba0UoGRSgRSWy44tQetvWhG5QlwdldddZXmzZunrl27asCAAdq+fbsWLlyo2267LdShAYhSzf5feefOnRo4cKBiYmK0c+fOM+47ePDgNgeGyMIXweALRHl/ayoVGKlEILXmglN7cPJFsy4d7Er2xaq8pk6fHDyqw8erdduo0180O1vlSdHxGnXtmKDy2joVllZF1cUMoN4zzzyjRx99VNOmTZPT6VRWVpbuvPNOPfbYY6EODUCUanYWNGTIEJWUlCg9PV1DhgyRxWJpsChO/X2LxUIf7yhECXLwBaK8/+RKBcMwVF7jkdvrU7w1Rg577GkrFaJ9pBJoi5MvmnVMitOXJeUqrXLL4/PJarGo+HiN7HExeuSK/k1+ps5UebLXWaGy6hOLNC5+bx+dJRC1HA6HFi1adMb2YQAQTM1OvA8cOKDOnTv7/w2cjBLk4AtEeX99pULR8SqVuGr9X/5jY2KUlhivLim201YqNDVSSQ934OzqL5olxMXo03+6VO32KtkeqzhrrOq8Ph2rdOv9L5z6Yb90jerTucnnaKrypNbjU1l1nTrY49Q1LVGJ8bG0GAMAIEw0O/Hu1q1bk/8G6lGCHFyBKO/PTk1QamKc1n1+RPFWixwJcf4v/0fKqlV4rEpj+2c0q1KBHu5A81S6Paqu8+pYpVvVbq/SkuL9VSu2WKs6O2z657FqvbvbqRG9Op324tXJlSflNXX66/YixcZY1DfDQWcJAADCTKsm3K5cuVKdOnXSFVdcIUl68MEHtXz5cvXv318vv/wyiXkUowQ5eAJW3l8/Y+SUcvUT9w01551r6yJvQDRJio+VzzD0TUWtHPbYRlNFPD5DSbZYFR2vPmsniPrKk8LSKn1bUaus1AQ6SwAAEIZizr5LY/Pnz/e3E9u0aZMWL16sJ598Up06ddL9998f0AAReeq/CPbr0kE5aYkk3SapL+9PS4rXXmeFymtOzOssr6nTXmdFs8r7Dx+v1vHqOg3vfo7SHXbV1Pl0rMqtmjqfMjrYNbz7OTpWVafDx6tP+xynLvLmsMfJGmORwx6nPunJKq10a+2uI/L5jNM+BxBNslMTlJliV2WtR7GnfD4Nw1BFjUedkm2yxqjZnSC+m3rS9PX0hHiraj1eOksAABAirRrxLiwsVO/evSVJf/3rX/Wzn/1Md9xxh0aOHKnRo0cHMj4Ap+HzGbLFWnXpeZ215UCpvimv1ZEyX4vK++u/rPfslKxzz0lstLiax+fT7uIyFRS5JKnJ6gV6uAMtExNj0WXnZ2jDl9/om3K3zkmKU5w1RnVenypqPEqIj1V2ql2SpdmdIOgsAQBAeGvV/8DJyck6evSounbtqrVr1/pHue12u6qrTz8yBkSScF4o7NT51DZrjDo77BrW/Rydn9mh2bGe+mW9Q8J3X9hLK936vMglZ3mtVm8u1LrEI03O2aaHO9ByI3t10pjz0vXB/m9V4/aqwvAoNiZG6R3s6tkpSUcr3S3qBEFnCQAAwlurEu+xY8fq9ttv19ChQ7Vnzx7/XO9du3ape/fugYwPCIlwXijsdPOpC49VqdLtUc/OSc2+QHC6L+ullW5t//qYvqmo1bnnJGhAZgdV13mbnLPNSBvQcjExFt34b11V4/Hq8PFqnZMYL4c9VrExFpWU1ba4EwSdJQAACG+tmuO9ZMkSXXzxxfrmm2/06quvqmPHjpKkrVu36oYbbghogDCfz2eosLRKX5SUqbC0Kurn4tYntgVFLqUmxqlnp2SlJsapoMj1/9u78+im6rQP4N80TdK9pUBLC6UsBVla9mVAEBQtIoqMI8MgIqIOoywCHZVBlE2ligqMIijMDOLLIDjvyLw4hwEq2uLAIFhwWGTfWqElQEu6p1l+7x+1kbRpSdLc3Jvk+zmn5zQ3y32aA8+9z2/F+r0XcVZfKltsnp5P7WieuMliwQ9XDLhWZkTLCC26JUQhWB3U4Dlqi/cCQxWEsD9vbU9bSlwEe9qI6kiJi8RTQ9pjQLvmCFKpUFReDUOlGWmto91akLB2Z4nUxGjcrDDh4vVy3Kwwuf15UuD1hoiIApVbXVAxMTFYtWpVveOLFy+2ezxt2jQsWbIELVq0cC86kpySe3blULewVdqWPFLMp667DVxxhRH60pqe7m4JUYgN1zV6Dva0EbnP0ztBKHlnCV5viIgokEk69nPjxo144YUXWHgrFLeAqk/pC4VJNZ/61pv1Y1cM2HIwH91/6ul25hzcw53IfbU7QSj18zyB1xsiIgp0khbedYedknIovWdXLkpfKEzK+dS33qxnhV1FpcmCSAeFd0PnUHJPGxHJh9cbIiIiN+d4k+9zpWc3kNxa2Doi90Jh3phP3ZRzcA938lecm+w+Xm+IiIgk7vEm5VJ6z65clL4ljzfmU3PONpE9zk1uGl5viIiIWHgHLG4B5ZgvFJ3emE/NOdtENTg3uenkvt5YrYJTYIiISHaBVVWRjdJ7dms5umECIOlNlC8Und6YT8052xTo/H1usrcKUjmvNxytoCwdOnTAwYMHbdvQ1rp58yb69OmD8+fPyxQZEZH0JC28H3/8cURFRUl5CnKTL/TsOrphignTAAK4WWmS9CbKF4pOb6xcrMTVkZUuPz8fKpUKbdq0AQAcOHAAmzZtQrdu3TB16lSZo/M/UhaPSt/loCm8WZDKdb3haAXluXjxIiwWS73jRqMRly9fliEiIiLvcbvwrqqqwpEjR6DX62G1Wu2eGzNmDABgzZo1TYuOJKXknl1HN0xXblYg64erAID+7ZqhQ4sISW+iWHSSOx577DFMnToVkyZNQmFhIe677z50794dGzduRGFhIRYsWCB3iH5D6uLRX+cmy1GQevt64++jFXzNtm3bbL/v3LkT0dHRtscWiwW7d+9Gu3btZIiMiMh73Cq8d+zYgSeeeALXr1+v95xKpXLYmknKpMSeXUc3TEIIFBqM0KpVgEqFwhIj2jQL400UKc6xY8cwYMAAAMBnn32G1NRU7N27F7t27cKzzz7LwttDvFE8yj03WQpyFqTevN7482gFXzR27FgANd/95MmT7Z7TaDRo164d3n33XRkiIyLyHrfuFmbMmIFx48ZhwYIFiI+P93RM5GVK69l1dMNUWmVGUUU1IkNrbn6LyqtRWmVGVKimwZsoLqjjGL8XaZlMJuh0OgDAl19+aRsB1KVLFxQUFMgZmt/wVvHoK2thuELugtRb1xt/Ha3gq2pHRrZv3x4HDx5EixYtZI6IiMj73Cq89Xo9MjIyWHSTJBzdMFVbrDBbrdCoa/7JlhnNqLb8PMWh7k0UF9RxjN+L9Lp3744PP/wQo0ePRlZWFl577TUAwJUrV+otKETu8Vbx6AtrYbgqUApSfxyt4A8uXLggdwhERLJx64rz6KOPIjs7Gx07dvR0PEQOb5i06iAEBwXB9FOxHRwUBK06yPaeW2+iuKCOY/xevOOtt97CL3/5S7z99tuYPHkyevbsCaBmjmPtEHRqGm8Wj0peC8MdgVKQ+uNoBX+xe/du7N692+EaQX/5y19kioqISHpuXVlXrVqFcePG4ZtvvkFaWho0GvuL9/PPP++R4CgwObphigwJRmyYFldLKgGVCvFRIYgMqfnne+tNVEJUCD7ac54L6tTBhYa8QwiB9u3b49KlS7BYLGjWrJntualTpyIsTDlTOnyZt4tHJa6F4a5AKUj9cbSCP1i8eDGWLFmCfv36ISEhod6IFSIif+bWXcmmTZuwc+dOhIaGIjs72y5xqlQqFt7UJA3dMLWK1iG/uAKAQKsoHSxCoNJotruJKiip4oI6dVitAt9dKsKhvGLEhtcvUgL1e5GCEAKdOnXC8ePH0alTJ7vnuGKv58hRPCptLQx3ebIgVfp6Ef42WsEffPjhh/j4448xadIkuUMhIvI6twrvV155BUuWLMEf/vAHBAUF3f4NRC5q6Ibpvm7xtn28L14vr3cTdbKwRNHzF719o1o7p/tQXhGOXylBdKgGPxZXISUuArHhWtvr5P5e/EVQUBA6deqEGzdu1Cu8yXPYm9k0nihIfWW9CH8areAPqqurMXjwYLnDICKShVuFd3V1NcaPH8+imyTV0A0TgAZvopQ8f9HbN6q3zuluHq5DdKgG6iAVrpVWocxoRq+kGFvx7S/zOpVg2bJlePHFF7FmzRqkpqbKHY7fYm9m0zSlIPW19SL8ZbSCP3jmmWewadMmvPrqq1453+XLlzF37lz861//QmVlJTp37ow///nP6Nu3r1fOT0R0K7fusidPnowtW7bg5Zdf9nQ8RHYaumFq6CZKqfMXvX2jWndONwD8WKyDvrQKzcI0KK4w4dy1MjQLq5mD7C/zOpXg8ccfR0VFBXr27AmtVovQUPvvtKioSKbI/A97M5vGnYKU60VQU1RVVWHt2rX48ssv0aNHj3prBC1fvtxj5youLsadd96Ju+++G//6178QFxeHc+fOISYmxmPnICJyhVuFt8ViwbJly7Bz507JEyeRK5Q4BFWOG1VH2y11jAtHqdGE4goTtMFBuF5mRIGhEmVGC4fmetDKlSvlDiGgsDfTu+TeB5x825EjR9CrVy8AwLFjx+ye8/RCa2+99RaSkpKwfv162zGutUFEcnKr8D569Ch69+4NQPrESeQqpQ1BleNG1dF2S7HhOvRKisE5fTmulxtRUmlCUbkJfZObcWiuB02ePNljn7Vnzx68/fbbyM3NRUFBAbZu3YqxY8fanhdCYPHixVi7di2Ki4sxcOBAfPDBB+jevbvHYiC6VaDsA07S+Prrr712rm3btmHkyJEYN24ccnJy0Lp1a0ybNg2//e1vG3yP0WiE0Wi0PS4pKfFGqEQUINwqvL2ZOAHO0SHXKWkIqhw3qg3NdY8N16FZOy0KDFUoKjfid8M6oF9yLHu6JVJZWQmTyWR3LCoqyun3l5eXo2fPnpgyZQp+9atf1Xt+2bJlWL58OT7++GN07twZr7/+Ou677z6cOnUKkZFsSCHPU/I6GkS3On/+PNasWYOMjAy8/PLLOHDgAJ5//nnodDo88cQTDt+TmZmJxYsXezlSInsrsk7LHQJJpMlXxh9//BEqlQqtW7f2RDz1cI4OuUspQ1DluFFtbK47AJQZzeibHMuiWwLl5eWYO3cuPvvsM9y4caPe8xaLxenPGjVqFEaNGuXwOSEEVq5cifnz5+ORRx4BAGzYsAHx8fHYtGkTfve737n3BxA1QqnraJBvuPvuuxsdGfnVV1957FxWqxX9+vXD0qVLAQC9e/fG8ePHsWbNmgYL73nz5iEjI8P2uKSkBElJSR6LiYgCm1vLklutVixZsgTR0dFITk5G27ZtERMTg9deew1Wq9WjAd46R2fAgAFo164dRowYgY4dO3r0PERSqb1RLTBUQQhh91ztjWpKXITH9xwemRqP2HAtzujLUFplgtlqRWmVCWf0ZZzTLaGXXnoJX331FVavXg2dToc//elPWLx4MRITE/HJJ5947DwXLlxAYWEh0tPTbcd0Oh2GDRuGffv2eew8RLdibqGm6NWrF3r27Gn76datG6qrq3Ho0CGkpaV59FwJCQno1q2b3bGuXbsiLy+vwffodDpERUXZ/RAReYpbXWzz58/Hn//8Z7z55pu48847IYTA3r17sWjRIlRVVeGNN97wWICuztHh/BxSGrkWfFPaXPdA8cUXX+CTTz7B8OHD8dRTT2Ho0KFISUlBcnIy/vrXv2LixIkeOU9hYSEAID4+3u54fHw8Ll265PA9zI/kCcwt5K4VK1Y4PL5o0SKUlZV59Fx33nknTp06ZXfs9OnTSE5O9uh5iIic5VbhvWHDBvzpT3/CmDFjbMd69uxpK4o9WXi7OkeH83NIieS6UVXSXPdAUVRUhPbt2wOomc9du33YkCFD8Nxzz3n8fHWHbQohGhzKyfxInqLE3GK1CkXFQ857/PHHMWDAALzzzjse+8w5c+Zg8ODBWLp0KX7961/jwIEDWLt2LdauXeuxcxARucKtwruoqAhdunSpd7xLly4e36PW1Tk6nJ9DSiXXjapS5roHig4dOuDixYtITk5Gt27d8Nlnn2HAgAH44osvPLo2RatWrQDU9HwnJCTYjuv1+nq94LWYH8mTlJRbzupLbQ2bVWYLQoLV6NgyAiNTPduwyeJeGv/5z38QEhLi0c/s378/tm7dinnz5mHJkiVo3749Vq5c6bFRR0RErnKr8O7ZsydWrVqF9957z+74qlWr0LNnT48EVquhOTp///vfHb5ep9NBp9N5NAYl4sXfNynpRpWkMWXKFPz3v//FsGHDMG/ePIwePRrvv/8+zGYzli9f7rHztG/fHq1atUJWVpZte8fq6mrk5OTgrbfecvieQMmPFFjO6kuxfu9FFJVXIyE6BGHaUFRUm3HsigFXDJWYcmc7jxTf3iru/VntQpC1hBAoKCjAd999h1dffdXj53vwwQfx4IMPevxziYjc4VbhvWzZMowePRpffvklBg0aBJVKhX379iE/Px/bt2/3aICco1MfL/7UGDbKyGvOnDm23++++26cPHkS3333HTp27Ohyw2RZWRnOnj1re3zhwgV8//33iI2NRdu2bTF79mwsXboUnTp1QqdOnbB06VKEhYXhscce89jfQ6RkVqvAzmNXUVRejU5xEbZpFpEhGkTognFGX4Zdx6+iQ4uIJuVBbxX3/i46OtrucVBQEO644w4sWbLEbqFIIiJ/5FbhPWzYMJw+fRoffPABTp48CSEEHnnkEUybNg2JiYkeDZBzdOzx4k+NYaOMslRVVaFt27Zo27atW+//7rvvcPfdd9se1w4Tnzx5Mj7++GO89NJLqKysxLRp01BcXIyBAwdi165d3MObAsblm5U4d61m0cq6axuoVCokRIfgrL4Ml29Wuj3ayFvFfSBYv3693CEQEcnG7Y2DExMTPbqIWkM4R+dnvPhTY9goowwWiwVLly7Fhx9+iKtXr+L06dPo0KEDXn31VbRr1w5PP/200581fPjwelvQ3UqlUmHRokVYtGiRByIn8j3l1WZUmS0I0zrejjFUq8bVkiqUV5vdPoc3ivtAk5ubixMnTkClUqFbt2626TJERP7M7cL75s2b+POf/2yXOJ966ql6w4g8gXN0avDiTw1ho4xyvPHGG9iwYQOWLVtmt+1hWloaVqxY4VLhTUQ/czSNJlwbjJBgNSqqzYgM0dR7T2W1BbpgNcK1bt/ueKW4DxR6vR6/+c1vkJ2djZiYGAghYDAYcPfdd2Pz5s1o2bKl3CESEUkmyJ031c5XXLFiBYqKinD9+nUsX74cHTt2xKFDhzwdI/3k54u/4xuIUK0aRrNFcRd/q1Ug70Y5sk/pkX1Kj0s3ymG1NtyLR65zpVGGpPXJJ59g7dq1mDhxItRqte14jx49cPLkSRkjI/JdZ/WlWJN9DiuyTuO93WewIus01mSfQ6XJjI4tI1BgqKo3OkQIgQJDFVLiItA6xnHR7Ixbi3tHPFHcB4qZM2eipKQEx48fR1FREYqLi3Hs2DGUlJTg+eeflzs8IiJJuXWVmDNnDsaMGYN169YhOLjmI8xmM5555hnMnj0be/bs8WiQVMMbLfuedlZfik3f5mH/+RswVJggVEBMqBa/aB+Lx37RlkOfPYQ9Mspx+fJlpKSk1DtutVphMplkiIjIt91uGs09XeJwxVCJM/qaxsdQrRqV1RYUGKoQG65Fevf4Jo30aR0Tio4tI3DsigERumC7xs3a4j6tdXSTivtAsWPHDnz55Zfo2rWr7Vi3bt3wwQcfcHE1IvJ7bvd4z50711Z0A0BwcDBeeuklfPfddx4LjuzVXvylbNn3pLP6Uqz88gyyfriKCqMZzSO1aBmhQ7nRjKwTV7HyyzM4qy+VO0y/wB4Z5ejevTu++eabesf/9re/cR4jkYvqTqOJDNFAHaRCZIgGneIiUFRejVOFpZg8qB1SE6Nxs8KEi9fLcbPChLTW0R5Z2yIoSIWRqfGIDdfijL4MpVUmmK1WlFaZcEZf5pHiPlBYrVZoNPU7DjQaDaxWqwwRERF5j1t34VFRUcjLy0OXLl3sjufn53M1XQnVXvylbNn3FKtVYMexQpwuLIVWrULzCJ2tlyA+Kgg3yqtx+mopdh4rRIfh/j/vWOotvtgjoxwLFy7EpEmTcPnyZVitVnz++ec4deoUPvnkE/zzn/+UOzwin+LsNJqHeibiueEdJcuzKXGRmHJnO9uuEVdLqqALViOtdTTSu3PXCGfdc889mDVrFj799FPbLjiXL1/GnDlzMGLECJmjIyKSlluF9/jx4/H000/jnXfeweDBg6FSqfDvf/8bL774IiZMmODpGOkWvnLxv3yzEkcvG2ARApGhGrsbJpVKhciQYJRWmXHkR4PfLwbnzhZfrhbqvtQo4+8eeughbNmyBUuXLoVKpcKCBQvQp08ffPHFF7jvvvvkDo/Ip7gyjSYoSGV3LbFaBfKLKjxWiKfERaLD8AhJG1H93apVq/Dwww+jXbt2SEpKgkqlQl5eHtLS0rBx40a5wyMikpRbhfc777wDlUqFJ554AmZzzdBWjUaD5557Dm+++aZHA6T6fOHiX15t/mnYs4BGXX9GQ+2xCpPZr+cdu7PFl7t7cftKo4y/e/LJJ/HUU08hJydH7lCIfJ67a5u4m0dvp25xT65JSkrCoUOHkJWVhZMnT0IIgW7duuHee++VOzQiIsm5XHhbLBb85z//wcKFC5GZmYlz585BCIGUlBSEhfFi5C1Kv/iHa4N/Wn1dBZPFCl2w2u55k6VmLleYJthv5x27s8VXU/fi9oVGGX9XWlqK9PR0JCUlYcqUKXjyySdtQyqJyDXuTKNpah4lz/vqq68wY8YM7N+/H1FRUbjvvvtsI4AMBgO6d++ODz/8EEOHDpU5UiIi6bhc8ajVaowcORInTpxAbGws0tLSpIiLfFzrmFCktY7GhWvlKK00QRsRZLthEkKgtMoMdZAKPdr477xjV/dd99Re3EpvlPF3f//733Hjxg1s3LgRH3/8MRYuXIh7770XTz31FMaOHetwYSEicszVaTSeyqPkWStXrsRvf/tbREVF1XsuOjoav/vd77B8+XIW3kR+YEXWacnPMee+zpKfQwpurWqelpaG8+fPezoW8iNBQSrcn9oKnVtFotoicLWkCpUmMypNFlwtMaLabEXn+EiMTG3ltzc/ru67zr24/Ufz5s0xa9YsHD58GAcOHEBKSgqeeOIJJCYmYs6cOThz5ozcIRL5jNppNM6sWs48qkz//e9/cf/99zf4fHp6OnJzc70YERGR97k1xveNN97ACy+8gNdeew19+/ZFeHi43fOOWjQp8KTERWL2vZ1s+3jfKKsGAESHajGoQywmDPTvfbxdnZvIvbj9T0FBAXbt2oVdu3ZBrVbjgQcewPHjx9GtWzcsW7YMc+bMkTtEIp/g7DQa5lFlunr1aqOjfYKDg3Ht2jUvRkRE5H1uFd61rZZjxoypN99KpVLBYrF4JjryeSlxkXhldDf8WFyB89fLAQDtW4QjqVmYz/d0327lcVfnJrq7iBApi8lkwrZt27B+/Xrs2rULPXr0wJw5czBx4kTbdoubN2/Gc889x8KbyAXOTKNhHlWm1q1b4+jRo0hJSXH4/JEjR5CQkODlqIiIvMutK8/XX3/t6TjIjwUFqdC2eTjaNg+//Yt9hDMr5ro6N5F7cfuHhIQEWK1WTJgwAQcOHECvXr3qvWbkyJGIiYnxemxE/o55VJkeeOABLFiwAKNGjUJISIjdc5WVlVi4cCEefPBBmaIjIvIOtwrvYcOGeToOIp/hyoq5rmzxxb24/cOKFSswbty4ejeXt2rWrBkuXLjgxahILrcbGUOexTyqTK+88go+//xzdO7cGTNmzMAdd9wBlUqFEydO4IMPPoDFYsH8+fPlDpOISFJuFd5HjhxxeFylUiEkJARt27aFTqdrUmBESuTOirmubPHFvbh936RJk+QOgRRCqr2kqXHMo8oTHx+Pffv24bnnnsO8efMghABQc984cuRIrF69GvHx8TJHSUQkLbcK7169etVbLfRWGo0G48ePx0cffdRorw+Rr3F1i7Barmzx5Uqhzt40ImXiXtLyciWPknckJydj+/btKC4uxtmzZyGEQKdOndCsWTO5QyMi8gq3Cu+tW7di7ty5ePHFFzFgwAAIIXDw4EG8++67WLhwIcxmM/7whz/glVdewTvvvOPpmIk8xtXC1Vsr5jpTqLM3jUiZuJe0MrjS4Ene06xZM/Tv31/uMIiIvM7t7cT++Mc/YuTIkbZjPXr0QJs2bfDqq6/iwIEDCA8Px+9//3sW3qRY7hSuSlkxl71pRMrl7sgYIiIi8l9B7rzp6NGjSE5Ornc8OTkZR48eBVAzHL2goKBp0RFJpLZwPXbFgJgwDTq0iEBMmAbHrhiwfu9FnNWXOnxf7Yq5BYYq2xy1WrUr5qbERUi6Ym7d3rTIEA3UQSpEhmjQKS4CReXV2HX8KqxWcfsPIyKP+3lkjOMGuFCtGkazhXtJExERBRC3Cu8uXbrgzTffRHV1te2YyWTCm2++iS5dugAALl++zIUySJGaUrjWrpgbG67FGX0ZSqtMMFutKK0y4Yy+zCsr5rrSm0ZE3nfryBhHuJc0ERFR4HHrqv/BBx9gzJgxaNOmDXr06AGVSoUjR47AYrHgn//8JwDg/PnzmDZtmkeDJfKEpg4DlXvFXG/NMyci93AvaSIiIqrLrcJ78ODBuHjxIjZu3IjTp09DCIFHH30Ujz32GCIja4oObqlDSuWJwlXOFXOVMs+ciBzjXtJEypOZmYmXX34Zs2bNwsqVK+UOh4gCkNt35hEREXj22Wc9GQuRV3iqcJVrxVz2phEpn9wjY+pyd+tBbllI/uDgwYNYu3YtevToIXcoRBTA3C68T58+jezsbOj1elitVrvnFixY0OTAiKTi64Ure9OIfINS9pJ2d+tBbllI/qCsrAwTJ07EunXr8Prrr8sdDhEFMLcK73Xr1uG5555DixYt0KpVK7vCRaVSsfAmRfOHwlVpvWlE5Jjce0m7u/UgtywkfzF9+nSMHj0a9957720Lb6PRCKPRaHtcUlIidXhEFEDcKrxff/11vPHGG5g7d66n4yEncfhf0/hD4aqU3jQiUqa6OzjUNpJHhmgQoQvGGX0Zdh2/ig4tIuzyhrvvI1KazZs349ChQzh48KBTr8/MzMTixYsljqppVmSdlvTz59zXWdLPlzp+b/CHv8HX+er/A7cK7+LiYowbN87TsZCTOPzPM/yhcJW7N42IlMvdHRyauvMDkRLk5+dj1qxZ2LVrF0JCQpx6z7x585CRkWF7XFJSgqSkJKlCJKIA49Y+3uPGjcOuXbs8HQs5oXb437ErBsSEadChRQRiwjQ4dsWA9Xsv4qy+VO4QfUpt4dqlVRSSYsN8qugmImrMzzs4OG5jD9WqYTRb6u3g4O77mspqFcgvqsDJwhLkF1XAahUe/XwKLLm5udDr9ejbty+Cg4MRHByMnJwcvPfeewgODobFYqn3Hp1Oh6ioKLsfIiJPcavHOyUlBa+++ir279+PtLQ0aDT2K0M///zzHgmO7HH4HxEROcvdHRzk2LJQ6pFcnJ4VeEaMGIGjR4/aHZsyZQq6dOmCuXPnQq1WyxQZEQUqt66aa9euRUREBHJycpCTk2P3nEqlYuEtEQ7/IyIiZ4tId3dw8PbOD1Iv5MbpWYEpMjISqampdsfCw8PRvHnzeseJiLzBrcL7woULno6DnPDz8D/HNzuhWjWullR5fPgfEREpgytFpLs7OHhz5wepR3JxdXYiIlIKz40TI8nJMfyPiIiUwZ0i0t0dHLy184OUI7k4PYvqys7OljsEIgpgTldoGRkZeO211xAeHm634qMjy5cvb3JgVJ+3h/8REZEyNKWIdHcHB2/s/CDlSC5OzyIiIiVxuvA+fPgwTCaT7feG1L24ked4c/gfEREpR1OLSHe3HpR6y0KpRnJZrQLnrpXhWlkVInTBEELU+944PYuIiLzJ6SvZ119/7fB38i5vDf/zNVyxloj8mb+u8SHFSK7aefBHfryJc9fKceVmFeIjQ9AxLhyx4Trb6zg9i4iIvMmtq82GDRvw6KOPIjw83NPxkBO8MfzPl3DFWiLyd/66xoenR3LdOg8+MSYEhkoTCgxVuFpSiVKjCb2SYhAbrlPM9Cw2GhMRBY4gd970wgsvIC4uDr/5zW/wz3/+E2azb7Ww+4Pa4X9dWkUhKTYsYC/UtTdZx64YEBOmQYcWEYgJ0+DYFQPW772Is/pSuUMkksyiRYugUqnsflq1aiV3WCSB2p7hAkMVhBB2z9UWkSlxEYpZ48NqFcgvqsDJwhLkF1XAahUNvrZ2JFdqYjRuVphw8Xo5blaYkNY62qVVx+vOg48K1aJTfCSiQjWASoWSShPO6MtQUlmNM/oy2adnndWXYk32OazIOo33dp/BiqzTWJN9jtctIiI/5VbTeEFBAXbs2IFPP/0Uv/nNbxAaGopx48bh8ccfx+DBgz0do01mZiZefvllzJo1CytXrpTsPOQbuGItEdC9e3d8+eWXtsdqtVrGaEgqvrTGhzujkDwxksvRPPjYcC16JcXgrL4M+tIq5BdVIDpUg55tYmSdnsVtzoiIAo9bhXdwcDAefPBBPPjgg6ioqMDWrVuxadMm3H333WjTpg3OnTvn6Thx8OBBrF27Fj169PD4Z5Nv4oq1RDX5mL3cgcEX1vhoSkHZ1IXcGpoHHxuuRf92zVBcUY2LN8oxYUBb3NWppWyNFGw0JiIKTE2eDBYWFoaRI0eiuLgYly5dwokTJzwRl52ysjJMnDgR69atw+uvv+7xzyff5K+LDRG54syZM0hMTIROp8PAgQOxdOlSdOjQQe6wSCJKXuND7oKysXnwKpUKGnUQWkaEoGNLeQtaNhoTEQUmt+Z4A0BFRQX++te/4oEHHkBiYiJWrFiBsWPH4tixY56MDwAwffp0jB49Gvfee6/HP5t81603WY746mJDRM4aOHAgPvnkE+zcuRPr1q1DYWEhBg8ejBs3bjh8vdFoRElJid0P+R6lrvHhSkEpBV+ZB/9zo7Hja1OoVg2j2cJGYyIiP+NWRTJhwgR88cUXCAsLw7hx45CdnS3Z3O7Nmzfj0KFDOHjwoFOvNxqNMBqNtse8sfRfUmxDQ+RLRo0aZfs9LS0NgwYNQseOHbFhwwZkZGTUe31mZiYWL17szRApgMg9CslX5sH76wr1RETUOLd6vFUqFbZs2YIrV67ggw8+kKzozs/Px6xZs7Bx40aEhIQ49Z7MzExER0fbfpKSkiSJjeRXe5MVG67FGX0ZSqtMMFutKK0yKWLFWiJvCw8PR1paGs6cOePw+Xnz5sFgMNh+8vPzvRwh+TMljELy1ArpUvKVnnkiIvIst65+mzZt8nQcDuXm5kKv16Nv3762YxaLBXv27MGqVatgNBrrreA7b948u56ekpISFt9+zBcWGyLyFqPRiBMnTmDo0KEOn9fpdNDpdF6OigJFU0YheXI/ayXPgwd8p2eeiIg8y+nC+7333sPUqVMREhKC9957r9HXPv/8800ODABGjBiBo0eP2h2bMmUKunTpgrlz5zrcNoc3loFH6TdZRFJ54YUX8NBDD6Ft27bQ6/V4/fXXUVJSgsmTJ8sdGgUgdwtKd7YfcyYWJS9MxkZjIqLA43ThvWLFCkycOBEhISFYsWJFg69TqVQeK7wjIyORmppqdyw8PBzNmzevd5yazpM9Dt6m9JssIin8+OOPmDBhAq5fv46WLVviF7/4Bfbv34/k5GS5Q6MA5WpBGcj7WbPRmIgosDhdeF+4cMHh7+QfpOhxICJpbd68We4QSEZKbSx1tqCUe/sxJWCjMRFR4HC68Ha0Qq4jKpUK7777rtsB3U52drZknx2oArnHgYjIFym9sdSZgpL7WRMRUSBxuvA+fPiwU6+re/EkZWOPAxGRb/GXxlK5tx9zhVJHFxARke9wuvD++uuvpYyDZMIeByIi3+FPjaW+sp+10kcXEBGRb3BrH2/yHz/3ODi+sQnVqmE0WxTR40BEFOhcaSxVOl/Yz7p2dMGxKwbEhGnQoUUEYsI0OHbFgPV7L+KsvlS22IiIyLew8A5wt/Y4OKKUHgciIvKvxtLa7cdiw7U4oy9DaZUJZqsVpVUmnNGXyb6fdd3RBZEhGqiDVIgM0aBTXASKyqux6/hVWK3i9h9GREQBj4V3gPOFHgciIqrhb42ltduPpSZG42aFCRevl+NmhQlpraNln6t+6+gCACipNOF6mREllSYA8MjoAqtVIL+oAicLS5BfVMEinojIj/nGlZkkU9vjcMVQiTP6mhuMUK0aldUWFBiqZO9xICKin9U2lh67YkCELthuuHltY2la62ifaixV6n7WtaMLqkxBOFlQiqKKapitVgQHBSE2TIt2LcKaNLqAc8eJiAILC2+y9TjU3gBcLamCLliNtNbRSO/OGwAiIqXw18ZSJe5nHa4NRrXZikN5xTBbBCJCgqFRB8NksUJfWoUb5UYkxYa5NbrAX1amJyIi57HwJgDK7XEgIiJ7bCz1joSoEBhNVhRXmNC2WSiCgmpm5+mC1dCEqZBXXIl4sxUJUSEufa4/rUxPRETOY+FNNkrscSAiovrYWCq9gpIq6DRBiAnVoLjC9FOPdxBMFivKqsyICdNCGxyEgpIql66d3trGM9D3Hs/MzMTnn3+OkydPIjQ0FIMHD8Zbb72FO+64Q+7QiChAsfAmxQn0mwUiImewsVRa5dVmaIOD0Dc5Fheul6O4ohplRjOCg4IQFxWC5OZhKKk0uTzH++eV6R3Pww/VqnG1pKpJK9Nz/jiQk5OD6dOno3///jCbzZg/fz7S09Pxww8/IDw8XO7wiCgAsfAmReHNAhERKUHtCvIhmiD0b9cMpVVmVFus0KqDEBkSjDKjGUaT1eEc78YakG9dmT4yRFPvvU1dmZ7zx2vs2LHD7vH69esRFxeH3Nxc3HXXXTJFRUSBjIU3KQZvFoiISCluXUG+U1wEokJ/LpIbW0H+dg3IUq5Mz/njDTMYDACA2NjYBl9jNBphNBptj0tKSiSPi4gCBwtvUgRfuFngEHgiImXwRj52ZwV5ZxuQpVqZ3pn542euluK7S0WICtUEzLVMCIGMjAwMGTIEqampDb4uMzMTixcvbtK5VmSdbtL75ebr8RMpGQtvUgRvLTbjLg6BJyJSBm/mY1dWkHelAVmqlelvN3+80mTBDwUl+CjnHHQadcBcy2bMmIEjR47g3//+d6OvmzdvHjIyMmyPS0pKkJSUJHV4RBQgWHjLhL2n9ryx2Iy7OASeiEgZPJ2PnbkWO7uCvKsNyFKsTN/Y/PGi8moculSM0iozmofrEBcVEhDXspkzZ2Lbtm3Ys2cP2rRp0+hrdToddDqdlyIjokDDwlsG7D2tT+rFZtzlC0PgiYgCgafzsSvXYmdWkHenAdnTK9M3NH9cCIGz+lLcrDShQ4twtPqpccCfr2VCCMycORNbt25FdnY22rdvL3dIRBTgguQOINDUttYfu2JATJgGHVpEICZMg2NXDFi/9yLO6kvlDlEWtTcLBYYqCCHsnqtdbCYlLsKtxWaawpUeDCIiko4n83FD1+Kjlw14f/dZ7PqhEPlFFbBaxW0/q9atDciOeKMBuXZeemy4Fmf0ZSitMsFstaLAUIlLNyrQLEyDlFsaLQD/vZZNnz4dGzduxKZNmxAZGYnCwkIUFhaistJ//kYi8i3s8fYi9p42zJ1FbLxByUPgiYgCiafycUPXYpPFCkNFNS4VVeBkYQm6JUTZFkJraDTarUPVQzVqdGgZjuNXSjy+Wnljf0vdoeodWkRgVGorfPmDHpeLK6EOAoxmgciQYPRp2wyx4fWHUvvjtWzNmjUAgOHDh9sdX79+PZ588knvB0REAY+FtxcpfQExuUm12ExTKHUIPBFRoPFUPnZ0LS4qN+L7/JuorLYgJkwDi1UgWK1qdP6zo6HqMaEaqINUXmlAbuj8UAE3K0yoNFkAFRAXFYIebWLwzelrCNGoHX6WP17L6o6eIyKSm/9kWB/A3tPbk2KxmaaQcr9VIiJynqfycd1rsRAC5/TlqKy2IDZcCwGguKIa2mA1OkWHOhyN1tAibwWGKqiDahrSb1aYJGtAdnT+KzcrkHXiKgCgf7tm6NgywhbTdxeL0CxciwJDFa9lREQyYeHtRf7YeyrF6uyeXmymKZQ6BJ6IKNB4Kh/XvRaXVplRVFGNiJCagrTabEFwUBC06iCHo9GcmTbWPFyLJwe3Q4XJ4vEGZEfnrymejdAGBwFCoLDEiDbNwuxiSoxWo1mYltcyIiKZ+E6F5wf8rfc0UFZnV+IQeCKiQOSJfFz3WlxtscJstUKjDoYQAmVVZsRFhSAypOYWqe5oNGemjZ27Vg6VSoUuraI8/h04On9plRnFFdU/NeoLFJVXo7TKjKhQjS2m4goTftmnNY7kG3gtIyKSAQtvL/Kn3tNA29taaUPgiYgCVVPyce0orU7xETitL8Xpq6WIDNEgSKVCudGMarMVodpgdGz5c0923dFock8bc3T+aosVZosVmpBgAD/9LRZrvZhaRurw3PCOvJYREcmAhbeX+UPvqTdXZ5diKLu7lDQEnogokLmTj+uO0qo2W2E0WWH6qUC9WWFCcvMwpMRFIjZcC8DxaDS5p405Or9WHYRgdRBMFgFAQP3TUHlHMfFaRkQkDxbeMvD13lNvrc4eKEPZiYhIWg2N0rpyswq64CBMHNgSh/JuwmiyQKNWwWy1NjgaTe5pY47OHxkSjGZhWuhLqwAhEB8dahsq74tT2YiI/BELby9oqNfWV1ucvTHMLtCGshMRkTQaG6XVOb5mlFa50YLpd3dE1nH9bUejyT1trKHzJ0Tr8GNxBQCgVZQOFiFQaTT73FQ2IiJ/xcJbYv7Yayv1MDtvDmUnIiL/5uworYd6Jjo9/1nuaWMNnT+9WzwEaobNX7xe7nNT2YiI/BkLbwn5a6+t1MPsvDWUnYiI/J8ro7RcGY3m6rQxT69Z0tD5AfjsVDYiIn/Gwlsi/txrK/UwO7lXjCUiIv8h5SgtZwt1qUa/NXR+NkoTESlP0O1fQu5wpdfWF9UOc0tNjLYNabtZYUJa6+gm9+TfepPkiNQrxhIRkf+oHaVVYKiCEMLuudpRWilxEZItPFY7+u3YFQNiwjTo0CICMWEaHLtiwPq9F3FWXyrJeYmISFlYuUgkEHptpVqdXe4VY4mIyH/IuRiaP49+IyIi17Dwlojc+3x6ixSrs8u9YiwREbnG0/OXPU2uxdC4ZgkREdXy7apPwdhr2zRyrxhLRETO8ZXdO6QapdWYQBj9RkREzmHhfQtPtth7stdW6T0JUpHjJomIiJyn5N07Grp2erNnOVBGvxER0e0x0/9EihZ7T/Ta+kpPglS8fZNERETOUfL8ZaVcOzn6jYiIarHwhrQt9k3ptVVyTwIRKcfq1avx9ttvo6CgAN27d8fKlSsxdOhQucMiP6fU+ctKunZyzRIiIqoV8NuJ1W2xjwzRQB2kQmSIBp3iIlBUXo1dx6/CahW3/7AG1PbadmkVhaTYMKeHl0sdFxH5vi1btmD27NmYP38+Dh8+jKFDh2LUqFHIy8uTOzTycz/PX3bchh+qVcNotnh1/rISr51Sbr9JRES+Q/GFd2ZmJvr374/IyEjExcVh7NixOHXqlMc+X6n7bSs1LiJSluXLl+Ppp5/GM888g65du2LlypVISkrCmjVr5A6N/Nyt85cdkWP+slKvnSlxkXhueEfMua8zZo7ohDn3dcazwzqy6CYiCiCKL7xzcnIwffp07N+/H1lZWTCbzUhPT0d5eblHPl+JLfZKjouIlKO6uhq5ublIT0+3O56eno59+/bVe73RaERJSYndD5G7aucvFxiqIIR9D3Lt/OWUuAivzl9W8rXTndFvRETkPxQ/x3vHjh12j9evX4+4uDjk5ubirrvuavLnK3XFUaXGRUTKcf36dVgsFsTHx9sdj4+PR2FhYb3XZ2ZmYvHixd4Kj/ycEucv89pJRERKpfge77oMBgMAIDY21iOfp8QWeyXHRUTKU3dIrRCi3jEAmDdvHgwGg+0nPz/fWyGSn1La/GVeO4mISKl8qslXCIGMjAwMGTIEqampDl9jNBphNBptj283lFKJLfZKjouIlKNFixZQq9X1erf1en29XnAA0Ol00Ol03gqPAkRTdu/wNF47iYhIqXyqx3vGjBk4cuQIPv300wZfk5mZiejoaNtPUlLSbT9XaS32So+LiJRBq9Wib9++yMrKsjuelZWFwYMHyxQVBSIlzV/mtZOIiJTIZ3q8Z86ciW3btmHPnj1o06ZNg6+bN28eMjIybI9LSkqcLr6V0mLvC3ERkTJkZGRg0qRJ6NevHwYNGoS1a9ciLy8Pzz77rNyhEcmG106qtXr1arz99tsoKChA9+7dsXLlSgwdOlTusIgoACm+8BZCYObMmdi6dSuys7PRvn37Rl/flKGUtS32SqPUuIhIfuPHj8eNGzewZMkSFBQUIDU1Fdu3b0dycrLcoRHJitdO2rJlC2bPno3Vq1fjzjvvxEcffYRRo0bhhx9+QNu2beUOj4gCjOKHmk+fPh0bN27Epk2bEBkZicLCQhQWFqKykvtXExEBwLRp03Dx4kUYjUaP7fhAROTrli9fjqeffhrPPPMMunbtipUrVyIpKQlr1qyROzQiCkCKL7zXrFkDg8GA4cOHIyEhwfazZcsWuUMjIiIiIgWqrq5Gbm4u0tPT7Y6np6dj3759MkVFRIHMJ4aae+L9t1vdnIgCV21+aGq+8TXMj0TkDF/MkdevX4fFYqm3w0N8fHy9nSBq1d0Zp3YLW1dyZFV5mRvREpGSuPJ/3pX8qPjCu6lKS0sBwKkF1ogosJWWliI6OlruMLyG+ZGIXOGLOVKlsl9QTwhR71itzMxMLF68uN5x5kiiwPKyG+9xJj/6feGdmJiI/Px8REZGNphofV3tyu35+fmIioqSOxzF4vfkvED7roQQKC0tRWJiotyheBXzI92K35VzAvF78sUc2aJFC6jV6nq923q9vl4veK26O+NYrVYUFRWhefPmzJEBjt+TcwLxe3IlP/p94R0UFNTo9mP+JCoqKmD+kTcFvyfnBdJ35Wu9OJ7A/EiO8LtyTqB9T76WI7VaLfr27YusrCz88pe/tB3PysrCww8/7PA9jnbGiYmJkTJMxQi0f8/u4vfknED7npzNj35feBMRERFR4MnIyMCkSZPQr18/DBo0CGvXrkVeXh6effZZuUMjogDEwpuIiIiI/M748eNx48YNLFmyBAUFBUhNTcX27duRnJwsd2hEFIBYePsBnU6HhQsX1hseRfb4PTmP3xX5C/5bdh6/K+fwe/It06ZNw7Rp0+QOQ7H479k5/J6cw++pcSrhS3tDEBEREREREfmYILkDICIiIiIiIvJnLLyJiIiIiIiIJMTCm4iIiIiIiEhCLLx93OrVq9G+fXuEhISgb9+++Oabb+QOSXEyMzPRv39/REZGIi4uDmPHjsWpU6fkDkvxMjMzoVKpMHv2bLlDIXIbc2TjmB/dw/xI/oD58faYI93DHOkYC28ftmXLFsyePRvz58/H4cOHMXToUIwaNQp5eXlyh6YoOTk5mD59Ovbv34+srCyYzWakp6ejvLxc7tAU6+DBg1i7di169OghdyhEbmOOvD3mR9cxP5I/YH50DnOk65gjG8ZVzX3YwIED0adPH6xZs8Z2rGvXrhg7diwyMzNljEzZrl27hri4OOTk5OCuu+6SOxzFKSsrQ58+fbB69Wq8/vrr6NWrF1auXCl3WEQuY450HfNj45gfyV8wP7qHObJxzJGNY4+3j6qurkZubi7S09Ptjqenp2Pfvn0yReUbDAYDACA2NlbmSJRp+vTpGD16NO699165QyFyG3Oke5gfG8f8SP6A+dF9zJGNY45sXLDcAZB7rl+/DovFgvj4eLvj8fHxKCwslCkq5RNCICMjA0OGDEFqaqrc4SjO5s2bcejQIRw8eFDuUIiahDnSdcyPjWN+JH/B/Oge5sjGMUfeHgtvH6dSqeweCyHqHaOfzZgxA0eOHMG///1vuUNRnPz8fMyaNQu7du1CSEiI3OEQeQRzpPOYHxvG/Ej+iPnRNcyRDWOOdA4Lbx/VokULqNXqei2Ter2+Xgsm1Zg5cya2bduGPXv2oE2bNnKHozi5ubnQ6/Xo27ev7ZjFYsGePXuwatUqGI1GqNVqGSMkch5zpGuYHxvH/Ej+hPnRdcyRjWOOdA7nePsorVaLvn37Iisry+54VlYWBg8eLFNUyiSEwIwZM/D555/jq6++Qvv27eUOSZFGjBiBo0eP4vvvv7f99OvXDxMnTsT333/PhEk+hTnSOcyPzmF+JH/C/Og85kjnMEc6hz3ePiwjIwOTJk1Cv379MGjQIKxduxZ5eXl49tln5Q5NUaZPn45Nmzbh//7v/xAZGWlr4Y2OjkZoaKjM0SlHZGRkvTlL4eHhaN68OecykU9ijrw95kfnMD+Sv2F+dA5zpHOYI53DwtuHjR8/Hjdu3MCSJUtQUFCA1NRUbN++HcnJyXKHpii1W2UMHz7c7vj69evx5JNPej8gIvIK5sjbY34kCkzMj85hjiRP4j7eRERERERERBLiHG8iIiIiIiIiCbHwJiIiIiIiIpIQC28iIiIiIiIiCbHwJiIiIiIiIpIQC28iIiIiIiIiCbHwJiIiIiIiIpIQC28iIiIiIiIiCbHwJiKfsGfPHjz00ENITEyESqXCP/7xD5c/Y+fOnfjFL36ByMhItGzZEr/61a9w4cIFzwdLRORFzI9ERI4pKT+y8CZJDB8+HLNnz5Y7DK9btGgRevXqJXcYfqm8vBw9e/bEqlWr3Hr/+fPn8fDDD+Oee+7B999/j507d+L69et45JFHPBwpUeOYH8nTmB/JXzA/kqcpKj8KIgkMGzZMzJo1S+4wvK60tFRcv35d7jD8HgCxdetWu2NGo1G8+OKLIjExUYSFhYkBAwaIr7/+2vb83/72NxEcHCwsFovt2LZt24RKpRLV1dVeipyI+ZGkxfxIvoz5kaQkd35kjzeRBwghYDabERERgebNm8sdTkCaMmUK9u7di82bN+PIkSMYN24c7r//fpw5cwYA0K9fP6jVaqxfvx4WiwUGgwH/8z//g/T0dGg0GpmjJ/JfzI/yY34kUibmR/l5Mz+y8CbJWK1WvPTSS4iNjUWrVq2waNEi23N5eXl4+OGHERERgaioKPz617/G1atXbc8/+eSTGDt2rN3nzZ49G8OHD7c9/t///V+kpaUhNDQUzZs3x7333ovy8nLb8+vXr0fXrl0REhKCLl26YPXq1U7FffHiRahUKmzevBmDBw9GSEgIunfvjuzsbNtrsrOzoVKpsHPnTvTr1w86nQ7ffPONw6FCf/nLX9C9e3fodDokJCRgxowZtucMBgOmTp2KuLg4REVF4Z577sF///tfp+Kkn507dw6ffvop/va3v2Ho0KHo2LEjXnjhBQwZMgTr168HALRr1w67du3Cyy+/DJ1Oh5iYGPz444/YvHmzzNFTIGJ+rMH8KD3mR/I1zI81mB+l5+38yMKbJLNhwwaEh4fj22+/xbJly7BkyRJkZWVBCIGxY8eiqKgIOTk5yMrKwrlz5zB+/HinP7ugoAATJkzAU089hRMnTiA7OxuPPPIIakaRAOvWrcP8+fPxxhtv4MSJE1i6dCleffVVbNiwwelzvPjii/j973+Pw4cPY/DgwRgzZgxu3Lhh95qXXnoJmZmZOHHiBHr06FHvM9asWYPp06dj6tSpOHr0KLZt24aUlBQANa2co0ePRmFhIbZv347c3Fz06dMHI0aMQFFRkdNxEnDo0CEIIdC5c2dERETYfnJycnDu3DkAQGFhIZ555hlMnjwZBw8eRE5ODrRaLR599FHbvxsib2F+ZH70FuZH8jXMj8yP3uL1/OjSwHQiJw0bNkwMGTLE7lj//v3F3Llzxa5du4RarRZ5eXm2544fPy4AiAMHDgghhJg8ebJ4+OGH7d4/a9YsMWzYMCGEELm5uQKAuHjxosPzJyUliU2bNtkde+2118SgQYNuG/uFCxcEAPHmm2/ajplMJtGmTRvx1ltvCSGE+PrrrwUA8Y9//MPuvQsXLhQ9e/a0PU5MTBTz5893eJ7du3eLqKgoUVVVZXe8Y8eO4qOPPrptnIEMdebobN68WajVanHy5Elx5swZu5+CggIhhBCvvPKK6Nu3r93n5OfnCwDiP//5jzfDpwDH/FiD+VEazI/ky5gfazA/SkPu/Bjc5KYCogbUbcFLSEiAXq/HiRMnkJSUhKSkJNtz3bp1Q0xMDE6cOIH+/fvf9rN79uyJESNGIC0tDSNHjkR6ejoeffRRNGvWDNeuXUN+fj6efvpp/Pa3v7W9x2w2Izo62un4Bw0aZPs9ODgY/fr1w4kTJ+xe069fvwbfr9frceXKFYwYMcLh87m5uSgrK6s3p6eystLWykbO6d27NywWC/R6PYYOHerwNRUVFVCr1XbHah9brVbJYyS6FfMj86O3MD+Sr2F+ZH70Fm/nRxbeJJm6Cw6oVCpYrVYIIaBSqeq9/tbjQUFB9YZvmEwm2+9qtRpZWVnYt28fdu3ahffffx/z58/Ht99+i7CwMAA1w4UGDhxo9xl1/+O4qm7c4eHhDb42NDS00c+yWq1ISEiwm/tTKyYmxp3w/FpZWRnOnj1re3zhwgV8//33iI2NRefOnTFx4kQ88cQTePfdd9G7d29cv34dX331FdLS0vDAAw9g9OjRWLFiBZYsWYIJEyagtLQUL7/8MpKTk9G7d28Z/zIKRMyPzI+exPxI/oT5kfnRk5SUHznHm7yuW7duyMvLQ35+vu3YDz/8AIPBgK5duwIAWrZsiYKCArv3ff/993aPVSoV7rzzTixevBiHDx+GVqvF1q1bER8fj9atW+P8+fNISUmx+2nfvr3Tce7fv9/2u9lsRm5uLrp06eL0+yMjI9GuXTvs3r3b4fN9+vRBYWEhgoOD68XZokULp88TKL777jv07t3bluQyMjLQu3dvLFiwAEDNYihPPPEEfv/73+OOO+7AmDFj8O2339paxu+55x5s2rQJ//jHP9C7d2/cf//90Ol02LFjx20vckTewvxYg/nRNcyPFAiYH2swP7pGUfnR3THyRI1xtA/jww8/LCZPniysVqvo3bu3GDp0qMjNzRXffvut6Nu3r23+jRBC7NixQ6hUKrFhwwZx+vRpsWDBAhEVFWV7zf79+8Ubb7whDh48KC5duiQ+++wzodVqxfbt24UQQqxbt06EhoaKlStXilOnTokjR46Iv/zlL+Ldd9+9bey1c3Tatm0rPv/8c3HixAkxdepUERERIa5duyaE+HmOTnFxsd17687R+fjjj0VISIj44x//KE6fPi1yc3PFe++9J4QQwmq1iiFDhoiePXuKHTt2iAsXLoi9e/eK+fPni4MHD7r2hRORz2B+rMH8SER1MT/WYH70Tyy8SRKNJU4hhLh06ZIYM2aMCA8PF5GRkWLcuHGisLDQ7vULFiwQ8fHxIjo6WsyZM0fMmDHDljh/+OEHMXLkSNGyZUuh0+lE586dxfvvv2/3/r/+9a+iV69eQqvVimbNmom77rpLfP7557eNvTZxbtq0SQwcOFBotVrRtWtXsXv3bttrnE2cQgjx4YcfijvuuENoNBqRkJAgZs6caXuupKREzJw5UyQmJgqNRiOSkpLExIkT7RYOISL/wvz4M+ZHIroV8+PPmB/9j0oI7hNBdKuLFy+iffv2OHz4cL09FYmIAhnzIxGRY8yPdDuc401EREREREQkIRbeFHCWLl2KiIgIhz+jRo2SOzwiItkwPxIROcb8SE3FoeYUcIqKilBUVOTwudDQULRu3drLERERKQPzIxGRY8yP1FQsvImIiIiIiIgkxKHmRERERERERBJi4U1EREREREQkIRbeRERERERERBJi4U1EREREREQkIRbeRERERERERBJi4U1EREREREQkIRbeRERERERERBJi4U1EREREREQkof8HY+0rLF9w4zgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['livingroom_size', 'years', 'house_price']\n",
    "\n",
    "# scatterplotmatrix(df4house[cols].values, figsize=(10, 8), names=cols, alpha=0.5)\n",
    "scatterplotmatrix(df3house[cols].values, figsize=(10, 8), names=cols, alpha=0.5)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/10_03.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from mlxtend.plotting import heatmap\n",
    "\n",
    "\n",
    "# cm = np.corrcoef(df4house[cols].values.T)\n",
    "cm = np.corrcoef(df3house[cols].values.T)\n",
    "hm = heatmap(cm, row_names=cols, column_names=cols)\n",
    "\n",
    "# plt.savefig('images/10_04.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegressionGD(object):\n",
    "\n",
    "    def __init__(self, eta=0.001, n_iter=20):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "\n",
    "        for i in range(self.n_iter):\n",
    "            output = self.net_input(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.net_input(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = df4house[['livingroom_size']].values\n",
    "# X4 = df4house[['years']].values\n",
    "y4 = df4house['house_price'].values\n",
    "#TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The first five (X4):\n",
      " [[6.42]\n",
      " [7.8 ]\n",
      " [6.87]\n",
      " [6.38]\n",
      " [5.37]]\n",
      "\n",
      "The first five (y4):\n",
      " [3.19525402e+08 3.86075746e+08 3.41105350e+08 3.17953273e+08\n",
      " 2.69461920e+08]\n",
      "\n",
      "The last five (X4):\n",
      " [[7.43]\n",
      " [3.6 ]\n",
      " [2.92]\n",
      " [4.47]\n",
      " [4.87]]\n",
      "\n",
      "The last five (y4):\n",
      " [3.68255148e+08 1.84153024e+08 1.51570519e+08 2.26171340e+08\n",
      " 2.45484308e+08]\n"
     ]
    }
   ],
   "source": [
    "print('\\nThe first five (X4):\\n', X4[:5])\n",
    "print('\\nThe first five (y4):\\n', y4[:5])\n",
    "\n",
    "print('\\nThe last five (X4):\\n', X4[-5:])\n",
    "print('\\nThe last five (y4):\\n', y4[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df3house[['livingroom_size']].values\n",
    "\n",
    "# X = df4house[['years']].values\n",
    "y = df3house['house_price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "sc_x = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X_std = sc_x.fit_transform(X)\n",
    "y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.LinearRegressionGD at 0x7fc54bad0a60>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegressionGD()\n",
    "lr.fit(X_std, y_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, lr.n_iter+1), lr.cost_)\n",
    "plt.ylabel('SSE')\n",
    "plt.xlabel('Epoch')\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('images/10_05.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin_regplot(X, y, model):\n",
    "    plt.scatter(X, y, c='steelblue', edgecolor='white', s=70)\n",
    "    plt.plot(X, model.predict(X), color='black', lw=2)    \n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_regplot(X_std, y_std, lr)\n",
    "plt.xlabel('Years of the house [years] (standardized)')\n",
    "plt.ylabel('Price in $1000s [house_price] (standardized)')\n",
    "\n",
    "#plt.savefig('images/10_06.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.461\n",
      "Intercept: 0.000\n"
     ]
    }
   ],
   "source": [
    "print('Slope: %.3f' % lr.w_[1])\n",
    "print('Intercept: %.3f' % lr.w_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating the coefficient of a regression model via scikit-learn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 54692177.692\n",
      "Intercept: -92657611.297\n"
     ]
    }
   ],
   "source": [
    "slr = LinearRegression()\n",
    "slr.fit(X, y)\n",
    "y_pred = slr.predict(X)\n",
    "print('Slope: %.3f' % slr.coef_[0])\n",
    "print('Intercept: %.3f' % slr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_regplot(X, y, slr)\n",
    "plt.xlabel('Average number of rooms [RM]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "\n",
    "#plt.savefig('images/10_07.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the performance of linear regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df3house.iloc[:, :-1].values\n",
    "y = df3house['house_price'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "slr = LinearRegression()\n",
    "\n",
    "slr.fit(X_train, y_train)\n",
    "y_train_pred = slr.predict(X_train)\n",
    "y_test_pred = slr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "ary = np.array(range(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "182 µs ± 68.5 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.linalg.norm(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143 µs ± 39 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sp.linalg.norm(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109 µs ± 18 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.sqrt(np.sum(ary**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train_pred,  y_train_pred - y_train,\n",
    "            c='steelblue', marker='o', edgecolor='white',\n",
    "            label='Training data')\n",
    "plt.scatter(y_test_pred,  y_test_pred - y_test,\n",
    "            c='limegreen', marker='s', edgecolor='white',\n",
    "            label='Test data')\n",
    "plt.xlabel('Predicted values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.legend(loc='upper left')\n",
    "plt.hlines(y=0, xmin=-10, xmax=50, color='black', lw=2)\n",
    "plt.xlim([-10, 50])\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig('images/10_09.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE train: 0.000, test: 0.000\n",
      "R^2 train: 1.000, test: 1.000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print('MSE train: %.3f, test: %.3f' % (\n",
    "        mean_squared_error(y_train, y_train_pred),\n",
    "        mean_squared_error(y_test, y_test_pred)))\n",
    "print('R^2 train: %.3f, test: %.3f' % (\n",
    "        r2_score(y_train, y_train_pred),\n",
    "        r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turning a linear regression model into a curve - polynomial regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([258.0, 270.0, 294.0, \n",
    "              320.0, 342.0, 368.0, \n",
    "              396.0, 446.0, 480.0, 586.0])\\\n",
    "             [:, np.newaxis]\n",
    "\n",
    "y = np.array([236.4, 234.4, 252.8, \n",
    "              298.6, 314.2, 342.2, \n",
    "              360.8, 368.0, 391.2,\n",
    "              390.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[258.],\n",
       "       [270.],\n",
       "       [294.],\n",
       "       [320.],\n",
       "       [342.],\n",
       "       [368.],\n",
       "       [396.],\n",
       "       [446.],\n",
       "       [480.],\n",
       "       [586.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([236.4, 234.4, 252.8, 298.6, 314.2, 342.2, 360.8, 368. , 391.2,\n",
       "       390.8])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc54ba272b0>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rooms_std = sc_x.transform(np.array([[20.0]]))\n",
    "price_std = lr.predict(num_rooms_std)\n",
    "# Asegúrate de que price_std sea un arreglo 2D antes de la transformación inversa\n",
    "price_std_reshaped = price_std.reshape(-1, 1)\n",
    "print(\"Price in $1000s: %.3f\" % sc_y.inverse_transform(price_std_reshaped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_rooms_std = sc_x.transform(np.array([[3.0]]))\n",
    "# price_std = lr.predict(num_rooms_std)\n",
    "# print(\"Price in $1000s: %.3f\" % sc_y.inverse_transform(price_std))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slr = LinearRegression()\n",
    "slr.fit(X, y)\n",
    "y_pred = slr.predict(X)\n",
    "print('Slope: %.3f' % slr.coef_[0])\n",
    "print('Intercept: %.3f' % slr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_regplot(X, y, slr)\n",
    "plt.xlabel('Average number of rooms [RM]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "\n",
    "#plt.savefig('images/10_07.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Python Machine Learning 3rd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2019\n",
    "\n",
    "Code Repository: https://github.com/rasbt/python-machine-learning-book-3rd-edition\n",
    "\n",
    "Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-3rd-edition/blob/master/LICENSE.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Machine Learning - Code Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 10 - Predicting Continuous Target Variables with Regression Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Sebastian Raschka\" -u -d -v -p numpy,pandas,matplotlib,sklearn,mlxtend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The use of `watermark` is optional. You can install this IPython extension via \"`pip install watermark`\". For more information, please see: https://github.com/rasbt/watermark.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mlxtend package (http://rasbt.github.io/mlxtend/), which contains a few useful functions on top of scikit-learn and matplotloib, can be installed via \n",
    "\n",
    "    conda install mlxtend\n",
    "\n",
    "or \n",
    "\n",
    "    pip install mlxtend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Introducing regression](#Introducing-linear-regression)\n",
    "  - [Simple linear regression](#Simple-linear-regression)\n",
    "- [Exploring the Housing Dataset](#Exploring-the-Housing-Dataset)\n",
    "  - [Loading the Housing dataset into a data frame](Loading-the-Housing-dataset-into-a-data-frame)\n",
    "  - [Visualizing the important characteristics of a dataset](#Visualizing-the-important-characteristics-of-a-dataset)\n",
    "- [Implementing an ordinary least squares linear regression model](#Implementing-an-ordinary-least-squares-linear-regression-model)\n",
    "  - [Solving regression for regression parameters with gradient descent](#Solving-regression-for-regression-parameters-with-gradient-descent)\n",
    "  - [Estimating the coefficient of a regression model via scikit-learn](#Estimating-the-coefficient-of-a-regression-model-via-scikit-learn)\n",
    "- [Fitting a robust regression model using RANSAC](#Fitting-a-robust-regression-model-using-RANSAC)\n",
    "- [Evaluating the performance of linear regression models](#Evaluating-the-performance-of-linear-regression-models)\n",
    "- [Using regularized methods for regression](#Using-regularized-methods-for-regression)\n",
    "- [Turning a linear regression model into a curve - polynomial regression](#Turning-a-linear-regression-model-into-a-curve---polynomial-regression)\n",
    "  - [Modeling nonlinear relationships in the Housing Dataset](#Modeling-nonlinear-relationships-in-the-Housing-Dataset)\n",
    "  - [Dealing with nonlinear relationships using random forests](#Dealing-with-nonlinear-relationships-using-random-forests)\n",
    "    - [Decision tree regression](#Decision-tree-regression)\n",
    "    - [Random forest regression](#Random-forest-regression)\n",
    "- [Summary](#Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image(filename='images/10_01.png', width=500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image(filename='images/10_15.png', width=500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Housing dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Housing dataset into a data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description, which was previously available at: [https://archive.ics.uci.edu/ml/datasets/Housing](https://archive.ics.uci.edu/ml/datasets/Housing)\n",
    "\n",
    "Attributes:\n",
    "    \n",
    "<pre>\n",
    "1. CRIM      per capita crime rate by town\n",
    "2. ZN        proportion of residential land zoned for lots over \n",
    "                 25,000 sq.ft.\n",
    "3. INDUS     proportion of non-retail business acres per town\n",
    "4. CHAS      Charles River dummy variable (= 1 if tract bounds \n",
    "                 river; 0 otherwise)\n",
    "5. NOX       nitric oxides concentration (parts per 10 million)\n",
    "6. RM        average number of rooms per dwelling\n",
    "7. AGE       proportion of owner-occupied units built prior to 1940\n",
    "8. DIS       weighted distances to five Boston employment centres\n",
    "9. RAD       index of accessibility to radial highways\n",
    "10. TAX      full-value property-tax rate per $10,000\n",
    "11. PTRATIO  pupil-teacher ratio by town\n",
    "12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks \n",
    "                 by town\n",
    "13. LSTAT    % lower status of the population\n",
    "14. MEDV     Median value of owner-occupied homes in $1000s\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/rasbt/'\n",
    "                 'python-machine-learning-book-3rd-edition/'\n",
    "                 'master/ch10/housing.data.txt',\n",
    "                 header=None,\n",
    "                 sep='\\s+')\n",
    "\n",
    "df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', \n",
    "              'NOX', 'RM', 'AGE', 'DIS', 'RAD', \n",
    "              'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "### Note:\n",
    "\n",
    "\n",
    "You can find a copy of the housing dataset (and all other datasets used in this book) in the code bundle of this book, which you can use if you are working offline or the UCI server at https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data is temporarily unavailable. For instance, to load the housing dataset from a local directory, you can replace the lines\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "                 'machine-learning-databases'\n",
    "                 '/housing/housing.data',\n",
    "                 sep='\\s+')\n",
    "in the following code example by \n",
    "df = pd.read_csv('./housing.data',\n",
    "                 sep='\\s+')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the important characteristics of a dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import scatterplotmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']\n",
    "\n",
    "scatterplotmatrix(df[cols].values, figsize=(10, 8), \n",
    "                  names=cols, alpha=0.5)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/10_03.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cols = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "# cols = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'MEDV']\n",
    "# cols = ['RM', 'AGE', 'DIS', 'RAD', 'TAX', 'MEDV']\n",
    "# cols = ['RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "# cols = ['CRIM', 'ZN', 'RM', 'AGE', 'TAX', , 'MEDV' ]\n",
    "cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']\n",
    "# The target variable is  'MEDV'\n",
    "\n",
    "scatterplotmatrix(df[cols].values, figsize=(10, 8), \n",
    "                  names=cols, alpha=0.5)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/10_03.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mlxtend.plotting import heatmap\n",
    "\n",
    "\n",
    "cm = np.corrcoef(df[cols].values.T)\n",
    "hm = heatmap(cm, row_names=cols, column_names=cols)\n",
    "\n",
    "# plt.savefig('images/10_04.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing an ordinary least squares linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving regression for regression parameters with gradient descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegressionGD(object):\n",
    "\n",
    "    def __init__(self, eta=0.001, n_iter=20):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "\n",
    "        for i in range(self.n_iter):\n",
    "            output = self.net_input(X)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.net_input(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['RM']].values\n",
    "y = df['MEDV'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "sc_x = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X_std = sc_x.fit_transform(X)\n",
    "y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegressionGD()\n",
    "lr.fit(X_std, y_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(range(1, lr.n_iter+1), lr.cost_)\n",
    "plt.ylabel('SSE')\n",
    "plt.xlabel('Epoch')\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('images/10_05.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin_regplot(X, y, model):\n",
    "    plt.scatter(X, y, c='steelblue', edgecolor='white', s=70)\n",
    "    plt.plot(X, model.predict(X), color='black', lw=2)    \n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_regplot(X_std, y_std, lr)\n",
    "plt.xlabel('Average number of rooms [RM] (standardized)')\n",
    "plt.ylabel('Price in $1000s [MEDV] (standardized)')\n",
    "\n",
    "#plt.savefig('images/10_06.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Slope: %.3f' % lr.w_[1])\n",
    "print('Intercept: %.3f' % lr.w_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_rooms_std = sc_x.transform(np.array([[5.0]]))\n",
    "price_std = lr.predict(num_rooms_std)\n",
    "print(\"Price in $1000s: %.3f\" % sc_y.inverse_transform(price_std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating the coefficient of a regression model via scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slr = LinearRegression()\n",
    "slr.fit(X, y)\n",
    "y_pred = slr.predict(X)\n",
    "print('Slope: %.3f' % slr.coef_[0])\n",
    "print('Intercept: %.3f' % slr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_regplot(X, y, slr)\n",
    "plt.xlabel('Average number of rooms [RM]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "\n",
    "#plt.savefig('images/10_07.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normal Equations** alternative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a column vector of \"ones\"\n",
    "Xb = np.hstack((np.ones((X.shape[0], 1)), X))\n",
    "w = np.zeros(X.shape[1])\n",
    "z = np.linalg.inv(np.dot(Xb.T, Xb))\n",
    "w = np.dot(z, np.dot(Xb.T, y))\n",
    "\n",
    "print('Slope: %.3f' % w[1])\n",
    "print('Intercept: %.3f' % w[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting a robust regression model using RANSAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RANSACRegressor\n",
    "\n",
    "ransac = RANSACRegressor(LinearRegression(), \n",
    "                         max_trials=100, \n",
    "                         min_samples=50, \n",
    "                         loss='absolute_loss', \n",
    "                         residual_threshold=5.0, \n",
    "                         random_state=0)\n",
    "\n",
    "\n",
    "ransac.fit(X, y)\n",
    "\n",
    "inlier_mask = ransac.inlier_mask_\n",
    "outlier_mask = np.logical_not(inlier_mask)\n",
    "\n",
    "line_X = np.arange(3, 10, 1)\n",
    "line_y_ransac = ransac.predict(line_X[:, np.newaxis])\n",
    "plt.scatter(X[inlier_mask], y[inlier_mask],\n",
    "            c='steelblue', edgecolor='white', \n",
    "            marker='o', label='Inliers')\n",
    "plt.scatter(X[outlier_mask], y[outlier_mask],\n",
    "            c='limegreen', edgecolor='white', \n",
    "            marker='s', label='Outliers')\n",
    "plt.plot(line_X, line_y_ransac, color='black', lw=2)   \n",
    "plt.xlabel('Average number of rooms [RM]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "#plt.savefig('images/10_08.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Slope: %.3f' % ransac.estimator_.coef_[0])\n",
    "print('Intercept: %.3f' % ransac.estimator_.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the performance of linear regression models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.iloc[:, :-1].values\n",
    "y = df['MEDV'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slr = LinearRegression()\n",
    "\n",
    "slr.fit(X_train, y_train)\n",
    "y_train_pred = slr.predict(X_train)\n",
    "y_test_pred = slr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "ary = np.array(range(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit np.linalg.norm(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit sp.linalg.norm(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit np.sqrt(np.sum(ary**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(y_train_pred,  y_train_pred - y_train,\n",
    "            c='steelblue', marker='o', edgecolor='white',\n",
    "            label='Training data')\n",
    "plt.scatter(y_test_pred,  y_test_pred - y_test,\n",
    "            c='limegreen', marker='s', edgecolor='white',\n",
    "            label='Test data')\n",
    "plt.xlabel('Predicted values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.legend(loc='upper left')\n",
    "plt.hlines(y=0, xmin=-10, xmax=50, color='black', lw=2)\n",
    "plt.xlim([-10, 50])\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig('images/10_09.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print('MSE train: %.3f, test: %.3f' % (\n",
    "        mean_squared_error(y_train, y_train_pred),\n",
    "        mean_squared_error(y_test, y_test_pred)))\n",
    "print('R^2 train: %.3f, test: %.3f' % (\n",
    "        r2_score(y_train, y_train_pred),\n",
    "        r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using regularized methods for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso = Lasso(alpha=0.1)\n",
    "lasso.fit(X_train, y_train)\n",
    "y_train_pred = lasso.predict(X_train)\n",
    "y_test_pred = lasso.predict(X_test)\n",
    "print(lasso.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('MSE train: %.3f, test: %.3f' % (\n",
    "        mean_squared_error(y_train, y_train_pred),\n",
    "        mean_squared_error(y_test, y_test_pred)))\n",
    "print('R^2 train: %.3f, test: %.3f' % (\n",
    "        r2_score(y_train, y_train_pred),\n",
    "        r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LASSO regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(alpha=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elastic Net regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "elanet = ElasticNet(alpha=1.0, l1_ratio=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turning a linear regression model into a curve - polynomial regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([258.0, 270.0, 294.0, \n",
    "              320.0, 342.0, 368.0, \n",
    "              396.0, 446.0, 480.0, 586.0])\\\n",
    "             [:, np.newaxis]\n",
    "\n",
    "y = np.array([236.4, 234.4, 252.8, \n",
    "              298.6, 314.2, 342.2, \n",
    "              360.8, 368.0, 391.2,\n",
    "              390.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "lr = LinearRegression()\n",
    "pr = LinearRegression()\n",
    "quadratic = PolynomialFeatures(degree=2)\n",
    "X_quad = quadratic.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit linear features\n",
    "lr.fit(X, y)\n",
    "X_fit = np.arange(250, 600, 10)[:, np.newaxis]\n",
    "y_lin_fit = lr.predict(X_fit)\n",
    "\n",
    "# fit quadratic features\n",
    "pr.fit(X_quad, y)\n",
    "y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))\n",
    "\n",
    "# plot results\n",
    "plt.scatter(X, y, label='Training points')\n",
    "plt.plot(X_fit, y_lin_fit, label='Linear fit', linestyle='--')\n",
    "plt.plot(X_fit, y_quad_fit, label='Quadratic fit')\n",
    "plt.xlabel('Explanatory variable')\n",
    "plt.ylabel('Predicted or known target values')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/10_11.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_lin_pred = lr.predict(X)\n",
    "y_quad_pred = pr.predict(X_quad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Training MSE linear: %.3f, quadratic: %.3f' % (\n",
    "        mean_squared_error(y, y_lin_pred),\n",
    "        mean_squared_error(y, y_quad_pred)))\n",
    "print('Training R^2 linear: %.3f, quadratic: %.3f' % (\n",
    "        r2_score(y, y_lin_pred),\n",
    "        r2_score(y, y_quad_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling nonlinear relationships in the Housing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['LSTAT']].values\n",
    "y = df['MEDV'].values\n",
    "\n",
    "regr = LinearRegression()\n",
    "\n",
    "# create quadratic features\n",
    "quadratic = PolynomialFeatures(degree=2)\n",
    "cubic = PolynomialFeatures(degree=3)\n",
    "X_quad = quadratic.fit_transform(X)\n",
    "X_cubic = cubic.fit_transform(X)\n",
    "\n",
    "# fit features\n",
    "X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]\n",
    "\n",
    "regr = regr.fit(X, y)\n",
    "y_lin_fit = regr.predict(X_fit)\n",
    "linear_r2 = r2_score(y, regr.predict(X))\n",
    "\n",
    "regr = regr.fit(X_quad, y)\n",
    "y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))\n",
    "quadratic_r2 = r2_score(y, regr.predict(X_quad))\n",
    "\n",
    "regr = regr.fit(X_cubic, y)\n",
    "y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))\n",
    "cubic_r2 = r2_score(y, regr.predict(X_cubic))\n",
    "\n",
    "\n",
    "# plot results\n",
    "plt.scatter(X, y, label='Training points', color='lightgray')\n",
    "\n",
    "plt.plot(X_fit, y_lin_fit, \n",
    "         label='Linear (d=1), $R^2=%.2f$' % linear_r2, \n",
    "         color='blue', \n",
    "         lw=2, \n",
    "         linestyle=':')\n",
    "\n",
    "plt.plot(X_fit, y_quad_fit, \n",
    "         label='Quadratic (d=2), $R^2=%.2f$' % quadratic_r2,\n",
    "         color='red', \n",
    "         lw=2,\n",
    "         linestyle='-')\n",
    "\n",
    "plt.plot(X_fit, y_cubic_fit, \n",
    "         label='Cubic (d=3), $R^2=%.2f$' % cubic_r2,\n",
    "         color='green', \n",
    "         lw=2, \n",
    "         linestyle='--')\n",
    "\n",
    "plt.xlabel('% lower status of the population [LSTAT]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "#plt.savefig('images/10_12.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['LSTAT']].values\n",
    "y = df['MEDV'].values\n",
    "\n",
    "# transform features\n",
    "X_log = np.log(X)\n",
    "y_sqrt = np.sqrt(y)\n",
    "\n",
    "# fit features\n",
    "X_fit = np.arange(X_log.min()-1, X_log.max()+1, 1)[:, np.newaxis]\n",
    "\n",
    "regr = regr.fit(X_log, y_sqrt)\n",
    "y_lin_fit = regr.predict(X_fit)\n",
    "linear_r2 = r2_score(y_sqrt, regr.predict(X_log))\n",
    "\n",
    "# plot results\n",
    "plt.scatter(X_log, y_sqrt, label='Training points', color='lightgray')\n",
    "\n",
    "plt.plot(X_fit, y_lin_fit, \n",
    "         label='Linear (d=1), $R^2=%.2f$' % linear_r2, \n",
    "         color='blue', \n",
    "         lw=2)\n",
    "\n",
    "plt.xlabel('log(% lower status of the population [LSTAT])')\n",
    "plt.ylabel('$\\sqrt{Price \\; in \\; \\$1000s \\; [MEDV]}$')\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig('images/10_13.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with nonlinear relationships using random forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "X = df[['LSTAT']].values\n",
    "y = df['MEDV'].values\n",
    "\n",
    "tree = DecisionTreeRegressor(max_depth=3)\n",
    "tree.fit(X, y)\n",
    "\n",
    "sort_idx = X.flatten().argsort()\n",
    "\n",
    "lin_regplot(X[sort_idx], y[sort_idx], tree)\n",
    "plt.xlabel('% lower status of the population [LSTAT]')\n",
    "plt.ylabel('Price in $1000s [MEDV]')\n",
    "#plt.savefig('images/10_14.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1].values\n",
    "y = df['MEDV'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.4, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest = RandomForestRegressor(n_estimators=1000, \n",
    "                               criterion='mse', \n",
    "                               random_state=1, \n",
    "                               n_jobs=-1)\n",
    "forest.fit(X_train, y_train)\n",
    "y_train_pred = forest.predict(X_train)\n",
    "y_test_pred = forest.predict(X_test)\n",
    "\n",
    "print('MSE train: %.3f, test: %.3f' % (\n",
    "        mean_squared_error(y_train, y_train_pred),\n",
    "        mean_squared_error(y_test, y_test_pred)))\n",
    "print('R^2 train: %.3f, test: %.3f' % (\n",
    "        r2_score(y_train, y_train_pred),\n",
    "        r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(y_train_pred,  \n",
    "            y_train_pred - y_train, \n",
    "            c='steelblue',\n",
    "            edgecolor='white',\n",
    "            marker='o', \n",
    "            s=35,\n",
    "            alpha=0.9,\n",
    "            label='Training data')\n",
    "plt.scatter(y_test_pred,  \n",
    "            y_test_pred - y_test, \n",
    "            c='limegreen',\n",
    "            edgecolor='white',\n",
    "            marker='s', \n",
    "            s=35,\n",
    "            alpha=0.9,\n",
    "            label='Test data')\n",
    "\n",
    "plt.xlabel('Predicted values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.legend(loc='upper left')\n",
    "plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='black')\n",
    "plt.xlim([-10, 50])\n",
    "plt.tight_layout()\n",
    "\n",
    "#plt.savefig('images/10_15.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Readers may ignore the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../.convert_notebook_to_script.py --input ch10.ipynb --output ch10.py"
   ]
  }
 ],
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