NumberRecognition_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Garamond'; font-weight: 700; font-size: 2em;\"> Analysis and use of different types of classification algorithms for handwritten number recognition </span>\n",
    "</div>\n",
    "\n",
    "<p><b> December 4th, 2022</b></p>  \n",
    "\n",
    "------------------------------------------------------------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 1. Loading data & implementation of functions </span>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random \n",
    "from pathlib import Path\n",
    "\n",
    "# Loading training and testing data as datasets which are in different files\n",
    "\n",
    "train = np.genfromtxt(str(Path.cwd())+'/Data/DigitsTraining.csv', delimiter=',')\n",
    "test = np.genfromtxt(str(Path.cwd())+'/Data/DigitsTesting.csv', delimiter=',')\n",
    "\n",
    "# Segmentation tasks on datasets\n",
    "# Separation of labels and data from each dataset\n",
    "\n",
    "X_train = train[:, 1:]\n",
    "y_train = train[:, 0]\n",
    "X_test = test[:, 1:]\n",
    "y_test = test[:, 0]\n",
    "\n",
    "# Creation of some important functions\n",
    "# Line brings back the regression line y = ax + b\n",
    "# a y b are gotten from the equation w[0]+w[1]x1+w[2]X2=0\n",
    "\n",
    "    \n",
    "def line(w, data=None):\n",
    "    a = - w[1] / w[2]\n",
    "    b = - w[0] / w[2]\n",
    "    x_line = np.linspace(0.4, 1, 100)\n",
    "    if data is not None:\n",
    "        return a*data + b\n",
    "    else:\n",
    "        return a*x_line + b\n",
    "# Function which calculates the mean simmetry of an image \n",
    "\n",
    "def symmetry(X):\n",
    "    X_shapes = (16, 16)\n",
    "    X = X.reshape(X_shapes)\n",
    "   \n",
    "    # We create two new matrixes which are horizontally and vertically flipped versions of the original matrix\n",
    "\n",
    "    image_FlipV=np.flip(X,axis=1)\n",
    "    image_FlipH=np.flip(X,axis=0)\n",
    "    # Following the right equation the horizontal and vertical simmetry is calculated as Sv and Sh\n",
    "    Sv=((np.abs(X-image_FlipV)).sum())/256\n",
    "    Sh=((np.abs(X-image_FlipH)).sum())/256\n",
    "    # The mean simmetry is the mean of the Horizontal and vertical simmetries or Sh and Sv, respectively\n",
    "    symm=(Sv+Sh)/2\n",
    "\n",
    "    return symm\n",
    "\n",
    "# Function to calculate the mean intensity of an image or average intensity\n",
    "\n",
    "def intensity(X):\n",
    "    X_shapes = (16, 16)\n",
    "    X = X.reshape(X_shapes)\n",
    "    AVG_intencity=(X.sum())/256\n",
    "    return AVG_intencity\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de imagenes:7291\n",
      "Número de caracteristicas:256\n",
      "Número de imagenes:2007\n",
      "Número de caracteristicas:256\n"
     ]
    }
   ],
   "source": [
    "# Training data information\n",
    "print(\"Número de imagenes:\"+ str(X_train.shape[0]))\n",
    "print(\"Número de caracteristicas:\"+ str(X_train.shape[1]))\n",
    "\n",
    "# Testing data information\n",
    "print(\"Número de imagenes:\"+ str(X_test.shape[0]))\n",
    "print(\"Número de caracteristicas:\"+ str(X_test.shape[1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 2. Preparing data and building the final dataset for training data </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the data\n",
    "# Extracting images from numbers one and five as Digit1 y Digit2 through a segmentation process\n",
    "\n",
    "Digit1 = X_train[y_train == 5]\n",
    "y_train_Digit1 = np.ones(len(Digit1))\n",
    "Digit2 = X_train[y_train == 1]\n",
    "y_train_Digit2 = -np.ones(len(Digit2))\n",
    "\n",
    "# I calculate the mean simmetry and average intensity for each image\n",
    "\n",
    "symm_Digit2 = [symmetry(im) for im in Digit2]\n",
    "symm_Digit1 = [symmetry(im) for im in Digit1]\n",
    "\n",
    "avg_intensity_Digit1 = [intensity(im) for im in Digit1]\n",
    "avg_intensity_Digit2 = [intensity(im) for im in Digit2]\n",
    "\n",
    "# Intensity and simmetry as new characteristics in the new dataset\n",
    "# the new matrix has the same number of samples or rows\n",
    "# There are just two characteristics or columns intensity(x1) and simmetry(x2)\n",
    "# new_Digit1 and new_Digit2 has data related to each digit\n",
    "\n",
    "new_Digit1 = np.c_[avg_intensity_Digit1, symm_Digit1]\n",
    "new_Digit2 = np.c_[avg_intensity_Digit2, symm_Digit2]\n",
    "\n",
    "# Finally, I combine new_Digit1 and new_Digit2 as one final dataset X_new\n",
    "# We need another dataset with all the labels y_new\n",
    "\n",
    "X_new = np.r_[new_Digit1, new_Digit2]\n",
    "y_new = np.r_[y_train_Digit1, y_train_Digit2]\n",
    "\n",
    "# randomizing both data using random methods as randint \n",
    "# each generated number will be saved as a list and used as indexes in order to have balanced data of each number \n",
    "# The same process has to be made for testing data \n",
    "\n",
    "\n",
    "R_num = np.random.randint(X_new.shape[0], size = (X_new.shape[0],))\n",
    "X_new = X_new[R_num]\n",
    "y_new = y_new[R_num]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 3. Examples </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x207b5828cd0>"
      ]
     },
     "execution_count": 4,
     "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": [
    "# Examples, we have to indicate the max and min values to make a right graph \n",
    "\n",
    "plt.imshow(Digit1[5].reshape(16,16),vmin=0,vmax=1,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x207bf5611c0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "plt.imshow(Digit2[1].reshape(16,16),vmin=0,vmax=1,cmap='gray')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de ejemplos:1561\n",
      "Número de caracteristicas:2\n",
      "Número de ejemplos con 5: 556\n",
      "Número de ejemplos con 1: 1005\n"
     ]
    }
   ],
   "source": [
    "# New Training data information\n",
    "\n",
    "print(\"Número de ejemplos:\"+ str(X_new.shape[0]))\n",
    "print(\"Número de caracteristicas:\"+ str(X_new.shape[1]))\n",
    "\n",
    "print(\"Número de ejemplos con 5: \"+str(len(y_train_Digit1)))\n",
    "print(\"Número de ejemplos con 1: \"+str(len(y_train_Digit2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forma del nuevo conjunto de entrenamiento en X: (1561, 2)\n",
      "forma del nuevo conjunto de entrenamiento en Y: (1561,)\n"
     ]
    }
   ],
   "source": [
    "print(\"forma del nuevo conjunto de entrenamiento en X:\",X_new.shape)\n",
    "print(\"forma del nuevo conjunto de entrenamiento en Y:\",y_new.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 4. Preparing data and building the final dataset for testing data </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process for testing data\n",
    "\n",
    "X_test_Digit1 = X_test[y_test == 5]\n",
    "y_test_Digit1 = np.ones(len(X_test_Digit1))\n",
    "X_test_Digit2 = X_test[y_test == 1]\n",
    "y_test_Digit2 = -np.ones(len(X_test_Digit2))\n",
    "\n",
    "X_test_new = np.r_[X_test_Digit1, X_test_Digit2]\n",
    "y_test_new = np.r_[y_test_Digit1, y_test_Digit2]\n",
    "\n",
    "R = np.random.randint(X_test_new.shape[0], size = (X_test_new.shape[0],))\n",
    "\n",
    "X_test_new = X_test_new[R]\n",
    "y_test_new = y_test_new[R]\n",
    "\n",
    "avg_int_test = [intensity(im) for im in X_test_new]\n",
    "\n",
    "test_symm = [symmetry(im) for im in X_test_new]\n",
    "\n",
    "X_test_final = np.c_[avg_int_test, test_symm]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x207b7bb3460>"
      ]
     },
     "execution_count": 9,
     "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": [
    "# Examples of the new testing dataset\n",
    "plt.imshow(X_test_Digit1[5].reshape(16,16),vmin=0,vmax=1,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x207be2f21c0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.imshow(X_test_Digit2[1].reshape(16,16),vmin=0,vmax=1,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de ejemplos:424\n",
      "Número de caracteristicas:2\n",
      "Número de ejemplos con 5: 160\n",
      "Número de ejemplos con 1: 264\n"
     ]
    }
   ],
   "source": [
    "# New Testing data information\n",
    "print(\"Número de ejemplos:\"+ str(X_test_final.shape[0]))\n",
    "print(\"Número de caracteristicas:\"+ str(X_test_final.shape[1]))\n",
    "\n",
    "print(\"Número de ejemplos con 5: \"+str(len(y_test_Digit1)))\n",
    "print(\"Número de ejemplos con 1: \"+str(len(y_test_Digit2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "forma del nuevo conjunto de prueba en X: (424, 2)\n",
      "forma del nuevo conjunto de prueba en Y: (424,)\n"
     ]
    }
   ],
   "source": [
    "print(\"forma del nuevo conjunto de prueba en X:\",X_test_final.shape)\n",
    "print(\"forma del nuevo conjunto de prueba en Y:\",y_test_new.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 5. Implementation of the  perceptron algorithm </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################\n",
    "# The perceptron algorithm for N iterations\n",
    "###########################################################\n",
    "\n",
    "# function to execute the algorithm\n",
    "\n",
    "\n",
    "def perceptron(input_data,y,input_data_test,y_test,max_iter):\n",
    "  \n",
    "    data = np.c_[np.ones((input_data.shape[0],1)), input_data]  \n",
    "    data_test = np.c_[np.ones((input_data_test.shape[0],1)), input_data_test]\n",
    "    w = np.random.random(data.shape[1]).reshape(-1,1)   \n",
    "    current = np.sign(data @ w)\n",
    "    f = y.reshape(-1,1)\n",
    "    y_test =y_test.reshape(-1,1)\n",
    "    difference = (f != current)\n",
    "    # We estimate the error counting examples which were correctly classificated \n",
    "    t = 0 \n",
    "    Ein = []\n",
    "    Eout = []\n",
    "      \n",
    "   # itertive process  \n",
    "    for iteration in range(max_iter):\n",
    "        for i in range(len(f)): \n",
    "            \n",
    "            if difference[i]:\n",
    "                \n",
    "                w[0]+=f[i]*data[i][0]\n",
    "                w[1]+=f[i]*data[i][1]\n",
    "                w[2]+=f[i]*data[i][2]\n",
    "                \n",
    "                t+=1\n",
    "          \n",
    "        current = np.sign(data @ w)\n",
    "        difference = (f != current)\n",
    "        Ein.append((t/len(f))*100)\n",
    "        # reset the counter\n",
    "        t=0\n",
    "        prediction_test = np.sign(data_test @ w)    \n",
    "        difference_test=(y_test != prediction_test)\n",
    "        for i in range(len(y_test)):\n",
    "            if difference_test[i]:\n",
    "                t+=1\n",
    "       \n",
    "        Eout.append((t/len(y_test))*100)\n",
    "        t=0\n",
    "      \n",
    "    \n",
    "    return Ein,Eout,w\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 6. Results of the perceptron algorithm </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eU9rqmTFVbWIci9YzEzHTiYiIyIYYZjrJWDQvfLVehAJGIMoxpxRembE2+QKxp2ZHfb529cwwzBARkWrWrFmQJCni4/LLL++y5ygvL8ecOXO67PGOh3szdZKWZaKHCBFWEelwz0x40IndMyNHrDZsGmYKDzpEREQALr/8cixfvtxym8fjSdDVxI+VmU6K2M4gaK6ItNUzE302kxy1ZyZKZSZiSnfYc4nIMMPKDBERmXk8HhQUFFg+evXqBQDYu3cvrr76amRmZiI7OxtTp07FgQMHjPvOmjULkydPtjzenDlzUF5ebhyvrKzEo48+alR9du/e3a2vJ+Fh5ttvv8WPfvQj9OnTB16vF2eddRY2b95sHBdCoKKiAkVFRUhPT0d5eTm2b9+ewCtWtdUz4zDWhbFuNAnEDjPmYabwnhk5Ws+M9phyeM+MedE8wWEmIqITRgjA35SYjy7axVoIgcmTJ+Pw4cOorKzEunXr8PXXX2PatGntfoxHH30Uo0ePxk033YTq6mpUV1ejuLi4S64vloQOM9XV1eGCCy7AxRdfjNdffx39+vXD119/jdzcXOOchx9+GIsWLcKKFSswdOhQPPjggxg/fjx27NiBrKyshF27MZspSl+MrM1YitozE2vRvKjTryODTvjzGf05UcKMg7OZiIhOnNZmYEFRYp77V/sBd0a7T3/11VeRmZlpue2ee+7B+eefj08++QRVVVVGAHn22Wdx+umnY9OmTTj33HOP+9g5OTlwu93wer0oKCjo2OvopISGmd/97ncoLi62jNuVlJQY/xZCYPHixZg/fz6mTJkCAFi5ciXy8/OxatUq3HzzzSf6kg2RezO1Y2E8xO6ZkaFAggIBuc3VgQOKYtkHKmIoKcpqw9zOgIiIzC6++GIsXbrUclvv3r3xl7/8BcXFxZZKSllZGXJzc/HFF1+0K8wkQkLDzCuvvILLLrsM11xzDSorK3HSSSdh9uzZuOmmmwAAVVVVqKmpwYQJE4z7eDwejB07Fhs3bowaZnw+H3w+n/F5Q0NDt1x75HYG5oqIVn0R7emZsd4vYAkzWpNv+FYH5iqQUZnRAoto47mIiKj7uLxqhSRRz90BGRkZOPnkkyNuF0JAkqQ2b5dlObS1jqa1tTXiPidSQntmdu3ahaVLl2LIkCF44403cMstt+COO+7AM888AwCoqakBAOTn51vul5+fbxwLt3DhQuTk5Bgf3TVO52yjZ8YZo/oCxN6byXwsomoTPpspyjRwJXzdmmjPRURE3UeS1KGeRHxECSCdUVZWhr1792Lfvn3GbZ9//jnq6+tx2mmnAQDy8vJQXV1tud+2bdssn7vdbgSDQZwoCQ0ziqJgxIgRWLBgAc4++2zcfPPNuOmmmyJKX+EpMVZyBIB58+ahvr7e+DC/IV3JXJkRYdWSUK9K5HYGTil20Gn3TKe2KjOmISVWZoiIKBqfz4eamhrLx8GDBzFu3DicccYZmDFjBrZs2YKPPvoIN9xwA8aOHYuRI0cCAC655BJ8/PHHeOaZZ7Bz507cd999+OyzzyyPX1JSgg8//BC7d+/GwYMHoXRzu0NCw0xhYSHKysost5122mnYu3cvABiNQ+FVmNra2ohqjc7j8SA7O9vy0R30MANoYSFKk2+0BmBHxHYG7einMc9mUsLDTGhHbUURlmGmiMcjIiICsHbtWhQWFlo+LrzwQkiShDVr1qBXr1646KKLMG7cOAwaNAjPP/+8cd/LLrsM9957L+bOnYtzzz0XjY2NuOGGGyyPf/fdd8PhcKCsrAx5eXnG7/XuktCemQsuuAA7duyw3Pbll19i4MCBAIDS0lIUFBRg3bp1OPvsswEAfr8flZWV+N3vfnfCr9fMHGYCioAzSihRovbM6A272g2m9Wlc+v3C1q4xV2aCQlj7bMKmbcttzZwiIqKUt2LFCqxYsSLm8QEDBuB///d/23yM+++/H/fff3/M40OHDsX777/f2UvssISGmZ/97GcYM2YMFixYgKlTp+Kjjz7Ck08+iSeffBKAOrw0Z84cLFiwAEOGDMGQIUOwYMECeL1eXHfddYm8dGPRPCCyKVffsiD6UFJblZkYPTNoqzJjPeZqq9mYiIjIhhIaZs4991y8/PLLmDdvHh544AGUlpZi8eLFmDFjhnHO3Llz0dLSgtmzZ6Ourg6jRo3Cm2++mdA1ZoDQdgZAZFNuqPqih5JQl3esoST1ftF7bczVl4iemTaCjkNimCEiIvtL+N5MkyZNwqRJk2IelyQJFRUVqKioOHEX1Q7mykwwGN4A3PFF8wAtfIgo+y+10TPjCJ/pFGVqNntmiIjIzhK+nUGyMrXMRPSxONvomYlYlddyPzXoKBHDTKEu8PDniqzMRFnvhmGGiIhsjGGmkyRJCq01E14tidH7AnRudWBLk6+ixKzMhIcZ9swQEXW/8AXkqP266mvHMBMH2bylQXsDS7uag2M3AB+3Z0ZEVmY4zERE1PUcDgcAdZYtdU5zczMAwOVyxfU4Ce+ZSWZOWYIf0XpmtApLMMqiee2tzIhQMHG01eRrDjphVZuIhmIiIuoyTqcTXq8X3333HVwuF2SZ9YH2EkKgubkZtbW1yM3NNYJhZzHMxMFh3tIgGJqxZPS+6AWRdoYZyzozluqLgAwFCuQo/TmhoKIo4K7ZREQniCRJKCwsRFVVFfbs2ZPoy0lKubm5XbKzNsNMHEI9M4o1RBhbFuiVmbY2moxRmTHdrt/Pr29CGSUcGc9nei4XwwwRUbdyu90YMmQIh5o6weVyxV2R0THMxMERo2cmIkQEzevM6H0s0YagTD0zYWFGDTquiJ4ZZ1s9M+H9OURE1OVkWUZaWlqiLyOlcYAvDkaYidUz09HZTOaKTkRlJnrQsfbMHKcRmYiIyIYYZuKgL5wXsZ2BFjyEiOx/MdZ+ibpvkx4+rLer9wuG7hfludT7ha8zY7oPERGRTTHMxME6zBQZIoDYC+odd0ftGJWZiOeSYg8zRYQqIiIiG2KYiYMjxqJ5kdWSNvZLijIsFK0B2KiyBBXLXk/m52orVLFvhoiI7IphJg7H65kBYged0DBTZGUmfGhKvZ8+ZBR+n7ZWAA4LVURERDbEMBMHfWp2rJ4ZILLK0vbeTObKjLVnxmlUdKIvjGc8Zqygw74ZIiKyKYaZOMSamh3RxxJl7RejZ8ay2J4p6JhuNx8LDzptTc12ma8jyDBDRET2xDATB4dl0by2FrJrqzJjup/UVs+MNjwVsc6MuQoU/bmMY0RERDbEMBOHWD0z5jCjbjEQ2bDb+Z6Z4/XnKKb7mHpmOMxEREQ2xTATh/b1zLRRmVEUAMJ0v9g9M44Yi+Y5pdBjRO6aHRZ0iIiIbIhhJg6y1I51ZsJ7XPQtBoKRa8lYg070ykyHgo45VLFnhoiIbIphJg5OR6x1ZmJvMdB2YDHt29QFQYeVGSIiSgUMM3FwaNsZRK4zE6qIhPe/xOp9Ue8X1O6DyMAitbUJZazKDKdmExGR/THMxEHvmYnYL0lqqzITO5S0VZlxtqMyo/bMhIKUg4vmERFRCmCYiYPeM6OuCxO9MhNznZmooSRguk+soaT2Bx1LqGLPDBER2RTDTBycMRbNa0/PjFrMCVsYT28ObmtvpqgNwO1cUI+IiMiGGGbi4NAbgIOxF6trs48lbJXf9jQHt31MiT01mz0zRERkUwwzcQj1zKDt/ZJiDEEpwfYHFkcbw1OWnbhj7t7NFYCJiMieGGbi4JDM2xlE3606vMfF0scSaKsyE7bRZJuVGXPQMTcAs2eGiIjsj2EmDjE3mozYziBGZSbQvi0LzI8ZvWfm+IvmsWeGiIjsimEmDsaieW3szRRRmTEHnYieGX114CjVlxhDSebHDLS1nQF7ZoiIyKYYZuLgiLHOjEMSkIxdrttoyg0LM+1ZSyb6lO7oG1RGzKoiIiKyIYaZODik6NsZAKahn4gmX1NlJnxqtjmwBGM8Xhvr00RsZ2AJVQwzRERkTwwzcTC2M2gjzIgYM5aAyDCjz0oK34VbvV87e2aEEnY/7RiHmYiIyKYYZuKg98yED+8ApvARYy0ZIPbU7OPuqB1reOp4jcNEREQ2xDATB307g+h9LNpKvzGGkoBoDcCd3FHbsgll+JRu09AVERGRDTHMxMFYNK+NEBHeM6MHD6CNykwbWxa02RwcZRaUw7w6MBERkQ0xzMQhtM5MtGEhLTy00QAc3k+jB5boPTOxqy+hoKNE6ZnRj7XvNRERESUbhpk4hCoziFktCa++tL2dQce3LFDvZ2ryZWWGiIhSDMNMHGTZvJ1B9PAhgn7L7e1ZNE+IyKqNq62g0+Y2COyZISIie2OYiYPTvJ1BjP6XWNUXABBhYcbVyeZgl7lnRoQNQUmczURERPbGMBMHh9zWonlaZSbG8BMQpWfGtAmlUMJnQbWnZyZ20GGYISIiu2KYiYOzjTDjjLFonrlnJnJBvdhr0LRn2rbaM6OE3c8UgoiIiGyIYSYO0SozATgAAOkONTwYoUR2AgBcUhAep3o/vfoijGORQUeR1GMe2Txt2xpm0hyxj7FnhoiI7C6hYaaiogKSJFk+CgoKjONCCFRUVKCoqAjp6ekoLy/H9u3bE3jFVvp2BsFgEIAaFlolNwDA61QDhqQPFznTjPul62FGCyzC4VFPMYUZPZQoDvXx3HLk3kz6c3m04BSM0jPjkVmZISIie0t4Zeb0009HdXW18fHpp58axx5++GEsWrQIS5YswaZNm1BQUIDx48ejsbExgVccog8zmftiWuECAKSpBZpQZcbpMc5J144Z1Rc9zECBtqiwcb+grB5zSVpgMfXMBLRjHlk9Fr1qY7ofERGRDSU8zDidThQUFBgfeXl5ANSqzOLFizF//nxMmTIFw4YNw8qVK9Hc3IxVq1Yl+KpV+jCTeSaTX6uW6EM/RrgwVWa8TjVYCC2UGJUZBI2ApN8vKGuVGSlyKCmgV2a06ou62J61MpOujlJxmImIiGwr4WFm586dKCoqQmlpKaZPn45du3YBAKqqqlBTU4MJEyYY53o8HowdOxYbN25M1OVaOMKCBwD4oIUZrVoiglq4iBZmjMqMeswpKcZj6scCsnrMLZtW+Q0fZjJXZsJWANavg4vmERGRXTkT+eSjRo3CM888g6FDh+LAgQN48MEHMWbMGGzfvh01NTUAgPz8fMt98vPzsWfPnpiP6fP54PP5jM8bGhq65+IRCjPmadQ+ozKjVUKE1hfjSoMWfZDuUADIgHY/RQs6ThGAU5YBKMbO2AGHPswUpWdGVoe0Qv00kYv3eczNwURERDaU0DAzceJE49/Dhw/H6NGjMXjwYKxcuRLnn38+AEDSm0g0QoiI28wWLlyI+++/v3suOIwzyjBTQPuS6iHCaPKV3RBCgiwJpGmVGaPJVxtKckiKUSrT+3CCWjhyRdkZuzVsmEndaFLrp4EDTgS14AQoDDNERGRTCR9mMsvIyMDw4cOxc+dOY1aTXqHR1dbWRlRrzObNm4f6+nrjY9++fd12vRHDTLITrXqYkfXAolVtZAcC2pdbn7atB52guWfGIVseMyBbw4y5Z6YV1n4a8waVeiOyZQiKiIjIhnpUmPH5fPjiiy9QWFiI0tJSFBQUYN26dcZxv9+PyspKjBkzJuZjeDweZGdnWz66SyjMaH0xshMBoX5Jw3tmFMmJoLEGjfYAinXGkgPByJ4ZvTKDyGEmv6Tez21eg0abmu2X9FlVnJpNRET2ltBhprvvvhtXXnklBgwYgNraWjz44INoaGjAzJkzIUkS5syZgwULFmDIkCEYMmQIFixYAK/Xi+uuuy6Rl21whE/Nlp0IKmpScWshQtJ6ZiA5jAX1jH4aLQTpa8k4hGKazWSdfu3U91gyV1+0wOKOMgTlh7U5mGGGiIjsKqFh5ptvvsG1116LgwcPIi8vD+effz4++OADDBw4EAAwd+5ctLS0YPbs2airq8OoUaPw5ptvIisrK5GXbXBqi+ZJRpgJDSV59AXwtEZeRXYgqB8LG4IKmCozsmQdutIrM07zjtpKABJCgcUYgjL1zOjDTG6GGSIisrmEhpnVq1e3eVySJFRUVKCiouLEXFAHyfogXZRhJr0pVz8mJKdRmXGHHTMagBGE02ENM3qTr9O0CaVRfdEqMy7zOjPaMJPP6JnhbCYiIrK3HtUzk2z0yoy5AVgPLKHqizabSQpVZtx6MDGafCN7Zoy+GFmvzJjDjFrRiajMmPtpwhqAWZkhIiK7YpiJgx48QsNMLrRqlRm9WqL3zCimnhk9fEgRYUaBU591HjbM5LCEGWtgMaZtBxVj0Tz9mKWfhoiIyIYYZuKgN+saTb6yA636UJIUNswkuxAQ6jFnjMoMEBl09BlLDq1nRoICSQss+lCSSw86pq0MfNoIYmjlYIYZIiKyJ4aZOIQqM5E9M6G+mGiVGTVY6CFIr74AocX29JWD9aEkvTKjhxogtHWCvtu2MIUZvwjvmeF2BkREZE8MM3GIumieCBtKMg0z6T0z4ZUUfWE8AHDrQcdY5VcNJQ6h9tOYw0yL0TOjVX9M2yocCx+CYmWGiIhsimEmDpHDTKHKTCjMaDOW1PZe9X5hQ0mtpsqMK2wISp+xJCMIhyRZGoH16os+bdsyzCQYZoiIKDUwzMTBqMwIvS/GEWoA1kKJbJrNpK9B4wwLOgEtsAChdWH0Y/owk2xUZkKBRa++6LeZh5mOCes6M5yaTUREdsUwEwc9zMhRpma7JGvPTFAKVWb0YSZjNhOcCAr1sdxhVRu9yVcWQThlKVSFQaj6oocjPVSpx5yWY6zMEBGRXTHMxCGyMuM09cWoIULWj5kagCMqM3AYu227ZesxvcnXIQKQzZUZyWH05zhE2GwmSYY/rD+HlRkiIrIrhpk46IvmyVFW+Q0PLEGEGoD1vhe91yaI2ENQ+jCTFF6ZkZ3GkJbeFCzMQ1rCeh0KwwwREdkUw0wcjGEmaCFCNg0lSdbAopgDRsQwk2zcz2MMM6kzk46ZhpkcsgSH3iAsO+EXejgKaPfRj5mmgbMyQ0RENscwEwc9zDhMQ0mtETOWtMqMpWqjNQcLfVPIUGXGFXbMJ0INwGplJhRmQpUZ7TYROmYMd7FnhoiIbI5hJg56mNHDiZBCIUIPOHooCZp7ZhA2BCVCFZ3InhmX9rneM6MPM4VmTunPYRyT5IjhLlZmiIjIrhhm4qCvM2P0rMihoSRHWGBRRKhnRq+khCozofCh3k8Yx45FzGYKVV98isPyOLJxHc6IIS32zBARkV0xzMTBqMzogUFyGhUW2QgsoanZkZUZ7Zgw76itWFb51deLkSDglERYA7DesxO0XAdMz+Uwema4nQEREdkTw0wcHOGVGdPCePraM7JpxlLMyoyQjUqKS1Isq/we09aLAYA0WYT6Y0wNwOHDTGojsnWmE3tmiIjIrhhm4uCQ2qjMaCHGoe1wHTT3sYStQRMwDTO5Iiozoa0OnLIp6MgOtCqxemZClRmjZ0cwzBARkT0xzMRBliXIUqjSopgrM1qIkGCuzFiHfiyVGf1YRGUmtNWBxxx0ZCf8WpiRwiszkmxUZvRp48EgwwwREdkTw0ycHKamXMW0ZYHex6LPagoI2diE0gHrEFTA3DODoCXMNAvzvk2KMXPKPMwkKa2WfZvMi/cZ1R8OMxERkU0xzMTJYVqVVzGtM6OvL2NMvzYFDL2CogcedZ2Z0Bo0li0LFMl4LldYZcanHZMUdUdt89RsI1Tpe0MxzBARkU0xzMTJKcvGqrxCciIorBURPZgETYFFr9Y4TMNM5j2dzDOWggLGHkwuU8+MebVhSQS01YG1UCU7I4a72DNDRER2xTATJ/MwUxCyESL0adfmJt9YPTN+IVsrM6ahpKAijKDjkcxhxmlUgaCoa9BYFs0TetDRro09M0REZFMMM3FymEKEpWdGhPfMhJqDjTCjhyAhGxUdJxTLwngBRYRmOsmKqS8mrDLjMF8He2aIiCh1MMzEydIAbBpKkiKGmUx9LNp0bYelMhPaNdu8ZYFiqsy4paAxBCVkl3EfGD0zetCRLUEHYM8MERHZF8NMnMzDO0HT3kz6rtfGCryIrMzo/21VrKsDh1dmWqEunKc2AJsqM0IfZgpYKkTCMtzFnhkiIrI3hpk4WXpmpNiVmQDkiOZgfS8lv5BMM50C1gZgU2XGvDqwMD0XlIAlVCnm5mDTbCbBQENERDbEMBOn8GGmUGVG3y9J27VamGczBSAhNJzUato1W63MqAFErcwoxv3ckjBmLJm3LJCUAByyadE8UxVID1UAh5qIiMieGGbiZJ3NZK7MaNsZmCszxqq8od4XQK/M6D0zQUvPTFBRG4T1Y6HKTKjJF9CCTpRGZL0yA7AJmIiI7IlhJk5O0/ouQclhTImGFiKcpuqLPpVaFoHQwngA/Ip12rbRM+NwIWiqzJgXzVMDS+jtC5/pFIgSZhQOMxERkQ0xzMTJIcsxKjNhTb4wzzCybllgmc0ExQhHegNw0BRmzFsnmCszHjk0BKVIslHNgWmYiZUZIiKyI4aZOKm9Kubp1/p0ab0yo6/zYt3J2rwztl8JNQdbKjOyA0FFGEHHPJspPMy4zVUbRK/McOE8IiKyI4aZOKmVGdMwk2lvJlmCUS2x9MyEVWYCink2k2lvJm02U2jadmimkzBNAwfUTSjNQ1DmmU6h52GYISIi+2GYiZPTtFt1wDQrCcFWOGUZLn2YybRlgaUyIzsRELDs2xQ5NdvcMxOqzAhTQDL3zCjmIS0lCIesbkjJnhkiIrIjhpk4mXfNDpqmRIcWsjP1zIjQvk3OsOqLHkrMlRkRtp2B07QJpaLdFlod2FyZkSOuA2BlhoiI7IlhJk4OSTLWhTFvJgmtIqKHltawnhmnFH0oyYGgUc2BZA0sToQ2oVSMY1FmOsE6zOSQ1DDDnhkiIrIjhpk4OR0SnOap2RGVGXPPTKgpN7zJN2gaZjJXZgAY072dYbOZgFCY8UQMM4Wuw6lVZrilARER2RHDTJwcYT0zAaEGED1E6OHDL2LMZpJd2iq/kQvq6WHGqMyYFtTTQ4xiqcyoYSUY1gDscGhhRgnNoCIiIrILhpk4qcNM+pYFkb0qevgwryUTvWfGVJmRQovfAQjrmQmrzEjmnhlTZca0eJ+TPTNERGRjDDNxMldmgqZtBCCCcEiAS9LXmbEummceSgoKgYCIUpmR9MqM3k9jnX6tHlPPCa/amCszstYzE2DPDBER2RDDTJycDsmyZUHA9CVNk82r/EqWNWhC068dCAYFAlooMffM6IFF3wbBhYBltWFADVCANswkhSozxnUIBS5JDTGcmk1ERHbEMBMnhyxHX2cGgFduNf7dKkxNuaa9mULTr82VGeswkzFt2zSUFN4zY95oMmieVQXA5VBDDIeZiIjIjhhm4uSQTFsWIFR9AYB0KRRm/IpszEpSZzNplRnJAUWEembMzcGKrAUWbbjJaRqC0isyevXGaWoAtkzNhtpPAwBBhhkiIrKhHhNmFi5cCEmSMGfOHOM2IQQqKipQVFSE9PR0lJeXY/v27Ym7yCjMG02q9ZbQl9Qr+41/+02VGUkJwCmpa9NEVGZEwFhnRg8xSpTmYCVimCmIqHtEQd3qAGDPDBER2VOPCDObNm3Ck08+iTPOOMNy+8MPP4xFixZhyZIl2LRpEwoKCjB+/Hg0NjYm6EojOWXJ2H/JMpQEIE0K9cy0KrKpKdfc5Kv2zFgrM9owE6zVF4ekGEEnEFaZcZkbgMM2oUxjzwwREdlYwsPM0aNHMWPGDDz11FPo1auXcbsQAosXL8b8+fMxZcoUDBs2DCtXrkRzczNWrVqVwCu2cjhMq/wKGQIyFO3Lqg8zCUhoFZJpdeBoPTORqwMrYYHF3ByshyZjnRmY+mnCQpVLr8xwmImIiGwo4WHmtttuw/e//32MGzfOcntVVRVqamowYcIE4zaPx4OxY8di48aNMR/P5/OhoaHB8tGdHJJp/yVh7XHx6GFGW0tGn5UkKQGjj0Wfmm0MQZl7ZvRhJj3MmHpm9P4bYaramBuAgVB40oeZuGgeERHZkTORT7569Wps2bIFmzZtijhWU1MDAMjPz7fcnp+fjz179sR8zIULF+L+++/v2gttg3mjSb3vRZEcgAhVZhTJupkklIBRLRGSw7I3kyyCEQvj6Y9n3oQyvGrjkoIQljCjHnOIIFwSe2aIiMi+ElaZ2bdvH+6880785S9/QVpaWszzJG3BN50QIuI2s3nz5qG+vt742LdvX5ddczRO06J5/rBqiQetxufmwAIlGKrMSNaNJiURiFgYL9psplBw0o8pMY/pz8WeGSIisqOEVWY2b96M2tpanHPOOcZtwWAQ77zzDpYsWYIdO3YAUCs0hYWFxjm1tbUR1Rozj8cDj8fTfRcexmFaNC+gyAAUI4SkSaEwEzBtWQDTMJMStmWBuTITDGsANjcHB8OGtFxSEEF9CraQLfdzs2eGiIhsLGGVmUsvvRSffvoptm3bZnyMHDkSM2bMwLZt2zBo0CAUFBRg3bp1xn38fj8qKysxZsyYRF12BHPPjF8LEfo2BHplRh1mUowtC8zDTMZKvsa07cihJGFqAA6vvgjZvNWBtTlYmFYHBrjODBER2VPCKjNZWVkYNmyY5baMjAz06dPHuH3OnDlYsGABhgwZgiFDhmDBggXwer247rrrEnHJUTllKTRdWh/ekfXKjLrOjD7MFKrMtMItBwER2igyNMzUajxeMGyYSUZonRn9sYSxaF4QiNa7A4YZIiKyt4Q2AB/P3Llz0dLSgtmzZ6Ourg6jRo3Cm2++iaysrERfmsEhAbK2jotfsVZE0qCGGcXomQkVwjzaonnRKzPaEJQefiyVGW3mFML7aZRQA3BYhUgf0uIwExER2VGPCjMbNmywfC5JEioqKlBRUZGQ62kPvR8FUNeZAUwhwhRmLD0zANK0ISj9tlBlJrTOjNEzI5sqMxGBRV9nJgjFqMxYqzaszBARkZ0lfJ2ZZKdXSgBTmJGts5kUyQHFPJsJQJocVpkx7dsU2rjSGljMzcFGYDF6ZoKQjTVo1Nle+nCXSwtHrMwQEZEdMczESa96AOZhJr0yE2oADoSHGe1YeCiRRGgTytawCot5E8rwKpADSmgWVNgx/RoVhhkiIrIhhpk4OU1hxmdUZrQwI0KVmWBYmNFXBw6G9b7ANJvJmB0luwAADhEwVWasz+VElKAjW8MMKzNERGRHDDNxcpmGmQKKtpifVmXRe2aCUHtmFIQW+zMqM2FDU5ISMG1ZYJ0dJZtWAI6szITCTCBsarbT6JnhdgZERGQ/DDNxMoICHNB3CzCGmUSoAVgd4pGMaonHGGbS3gK9MiMiKzP6Mcm0zow+lATzOjOSNQSxMkNERKmAYSZOenNtUJuxBADQQ4SwzmYCTAvqaWvQ6JtT6pUZBFtD068Va9VGFqF1ZvxGr425MhM2BCVZG4DZM0NERHbEMBMn89YD+tRnPXy4hS/imF5J0degMSozWiiRlKARPvQeHEmOrMwYqwkbPTOhPZ0ij7EyQ0RE9tXpdWYCgQCeeOIJbNiwAcFgEBdccAFuu+22NjeNtCN9mElB7MqM2jOjWI7pQ1CtYfsoQQkYjxm+CJ9smrYdvqmlLMxTs8N7ZrTAxTBDREQ21Okwc8cdd+DLL7/ElClT0NraimeeeQYff/wxnnvuua68vh7PqMwYfTGIOsxk5AjZOm27Naz6AlMDsB5mYKnMWNegkUzDTM6wdWZgmukEsDJDRET21O4w8/LLL+M//uM/jM/ffPNN7NixAw6H+tf/ZZddhvPPP7/rr7CnOlYPHKtH5rFaAOp2BOGVmTSlCUCoL8Z8LEOox4wKi0MPM63wSurwlE8LJfrU7Mh1ZhRTz4xiHPMb97OuAMyeGSIisqN298wsW7YMkydPxrfffgsAGDFiBG655RasXbsWf//73zF37lyce+653XahPc6mZcDi4Thv400A9L4Y61BSYdO/AcCyJxO00FLa+hUAoNWYzq0GFggFp2APAMAXtFZYHM21cEFdOdgISNrjOURoCCqoOCyPycoMERHZWbvDzKuvvorp06ejvLwcf/zjH/Hkk08iOzsb8+fPx7333ovi4mKsWrWqO6+1Z5GdgDMdQUcamoUH73jGGmGhbsBlQFoOWmUPGoQX/5f5PeNuwbIpgMsLn+TBAZGLz70jAQCNUjYw+BLAmY5jcOMrpQi73KcCAL71ngp4cuBs/MbYoFKfth10eAEArkBTZGXGoYYZl74PFMMMERHZUIdmM02fPh2bNm3CJ598gssuuwzXX389Nm/ejG3btuGxxx5DXl5ed11nz3PBHcCva/DONZ+izLccy703GsM4R4ZMAX65F4+PeQ9n+P6M97MmGHdTxt0PzK/GbSWvYZTvcXytBRanwwFc/zLw6xqMdT+Hcf7/QT0yAQDH3L2A61ZDOELN1QGtn8af1hsA4PbXRS6ap52v9+4EuGgeERHZUIenZufm5uKpp57CI488guuvvx6/+MUv0NLS0h3XlhQcsloFCSjCqMzotzkd6n99raFVgp2yrP3Xeky/j/kcXyAYOnfgGDRe9RQCQkaLcOOo8AAAWj29AABu3+FQP402dCWcapjxKNoUcWYZIiKyoXaHmX379mHatGkYPnw4ZsyYgSFDhmDz5s1IT0/HWWedhddff707r7PH0kNJUFGMYRw9jOgBxRcIpQg9szgc1mN68Il2P/1zMWQiJvsfwHX++WgS6hBSwKNVZnx1kMOndDvTAQAufb0bVmaIiMiG2h1mbrjhBkiShEceeQT9+vXDzTffDLfbjQceeABr1qzBwoULMXXq1O681h5JjlKZ0bIMHJJ6zG8KJZJ2W/gxWYoMM/6wMCPLwGdiELaKIcaxVi3MuHx1pk0orZUZPcywAZiIiOyo3VOzP/74Y2zbtg2DBw/GZZddhtLSUuPYaaedhnfeeQdPPvlkt1xkT6ZXZhRFGD0z4ZUZf9AaSsz304855eOHGf1xzceC6eowk6z4ka0f09en0cOM4jeukYiIyG7aHWZGjBiB3/zmN5g5cybeeustDB8+POKcn/zkJ116cclADxr76lqgiOg9M59+U69+HiWw6MeiBZ1Pwu5nPkc/Bncm4EwHAi1I1/Z7MhbNc7EyQ0RE9tfuYaZnnnkGPp8PP/vZz/Dtt9/iiSee6M7rShr9e3nhdsgIKgJCABluB/Kz1ebcQX3V2Uh69WVQXoZxv0F54ccyTccyLMdKtcdxOSQU9063HuuTAXj7WK7pcIvWG+Oy9swca2XPDBER2U+7KzMDBw7ECy+80J3XkpTysjz41z0XY1+dOqNrYB8vstLU5twLh/TF+rvLcbhJrZicVphl3O+WsYNw0dC+ONaqwClLGHZSjnHs0eln46bv1UMRQKbHiaH5apiRJAmv3fE9fHngKACgX5YHxb29QEYfoOEb4/71Pi20aA3AHm1qdpMv0B1fAiIiooTq9N5MFNIvOw39sqNvsFnaNwOlfTMibpckCacX5US5B+ByyDh7QK+ox7LSXDhnYNixsMpMUF8dWBtm0nfvbvS1xnwNREREyarD68xQD+Tta/k0qL2tkjbMpO/QffQYKzNERGQ/DDN2EFaZUcLCjFNbNO8oh5mIiMiGGGbsICzMBMLCjEM5BgBoYGWGiIhsqENhJhAIwOl04rPPPuuu66HOyLCGmQF9s5Cd5kROttpw7AiqlRl/QDG2SCAiIrKLDjUAO51ODBw4EMEgfyH2KGGVmWf+cwxaXRnwNn0FAJACx4xjR48F4Ml0nNDLIyIi6k4dHmb69a9/jXnz5uHw4cPdcT3UGWENwDkZaeib6THWmZECx+B1qwGGfTNERGQ3HZ6a/Yc//AFfffUVioqKMHDgQGRkWKcdb9mypcsujtoprDIDWXtbtXVm0NqCTI8Tzf4gGtk3Q0RENtPhMDN58uRuuAyKS4a1MgPZus4MRBC5HqC2EQwzRERkOx0OM/fdd193XAfFIy0XgARA23tJ0jeaTDdO6e1Rj3GYiYiI7KbTKwBv3rwZX3zxBSRJQllZGc4+++yuvC7qCIcTSM8FWuoAyQFI2kaTTg/0kNPbrTZtNx7jKsBERGQvHQ4ztbW1mD59OjZs2IDc3FwIIVBfX4+LL74Yq1evRl5eXndcJx2Pt68aZmTTTCVJApxpQKAFvbQww8oMERHZTYdnM/30pz9FQ0MDtm/fjsOHD6Ourg6fffYZGhoacMcdd3THNVJ76E3Aclg+1fpmcl1qiGHPDBER2U2HKzNr167FW2+9hdNOO824raysDI899hgmTJjQpRdHHaA3AUtha8g40wHUIcel7qTNMENERHbT4cqMoihwuVwRt7tcLiiK0iUXRZ3g7a3+Vw4LM1plJtuphpij3DmbiIhspsNh5pJLLsGdd96J/fv3G7d9++23+NnPfoZLL720Sy+OOkBfOC88zGgzmnIcaohhZYaIiOymw2FmyZIlaGxsRElJCQYPHoyTTz4ZpaWlaGxsxB//+MfuuEZqj+P0zGRoYeYowwwREdlMh3tmiouLsWXLFqxbtw7//ve/IYRAWVkZxo0b1x3XR+2lh5moPTOhMMPKDBER2U2HwkwgEEBaWhq2bduG8ePHY/z48d11XdRRGTGGmfTKjKzNZuLUbCIispkODTNx1+werM9g9b9ZhdbbnWqYSZf8ANgATERE9sNds+2i9yDgv/4JTHvWeru2c7ZX4jATERHZU4fDzB/+8Ae8++67KCoqwimnnIIRI0ZYPjpi6dKlOOOMM5CdnY3s7GyMHj0ar7/+unFcCIGKigoUFRUhPT0d5eXl2L59e0cvOXX0PwfIKrDeplVm0qBVZo4FIIQ40VdGRETUbRK6a3b//v3x0EMP4eSTTwYArFy5EldffTW2bt2K008/HQ8//DAWLVqEFStWYOjQoXjwwQcxfvx47NixA1lZWV12HbamVWY8WpgJKALHWhWkux1t3YuIiChpdLgBGABuvPFGFBcXx/3kV155peXz3/72t1i6dCk++OADlJWVYfHixZg/fz6mTJkCQA07+fn5WLVqFW6++ea4nz8laJUZl+KDJAFCAI2+VoYZIiKyjQ43AP/P//xPtzQAB4NBrF69Gk1NTRg9ejSqqqpQU1Nj2SLB4/Fg7Nix2LhxY8zH8fl8aGhosHykNK0yIwWOIdOtZleuNUNERHbS4Z6ZSy+9FBs2bOiyC/j000+RmZkJj8eDW265BS+//DLKyspQU1MDAMjPz7ecn5+fbxyLZuHChcjJyTE+uqKClNS0ygwCx5CVpoYZNgETEZGddLhnZuLEiZg3bx4+++wznHPOOcjIyLAcv+qqqzr0eKeccgq2bduGI0eO4MUXX8TMmTNRWVlpHJckyXK+ECLiNrN58+bhrrvuMj5vaGhI7UCjVWbQ2oLMNCdQDxzlWjNERGQjHQ4zt956KwBg0aJFEcckSerwEJTb7TYagEeOHIlNmzbh0UcfxT333AMAqKmpQWFhaO2U2traiGqNmcfjgcfj6dA12JqlMqNuENp4jGvNEBGRfXRq1+xYH13RSyOEgM/nQ2lpKQoKCrBu3TrjmN/vR2VlJcaMGRP386QMc2XGw2EmIiKynw5XZrrSr371K0ycOBHFxcVobGzE6tWrsWHDBqxduxaSJGHOnDlYsGABhgwZgiFDhmDBggXwer247rrrEnnZycVUmcnUemY4zERERHbS7srMFVdcgfr6euPz3/72tzhy5Ijx+aFDh1BWVtahJz9w4ACuv/56nHLKKbj00kvx4YcfYu3atcaeT3PnzsWcOXMwe/ZsjBw5Et9++y3efPNNrjHTEabKTDYbgImIyIYk0c7lYB0OB6qrq9GvXz8AQHZ2NrZt24ZBgwYBUINJUVFRj9u3qaGhATk5Oaivr0d2dnaiL+fE21UJPHMVkHcqfluyHE+9W4WfXDQIv7ritERfGRERUUwd+f3d7spMeObhkvhJwlSZYQMwERHZUYcbgCnJmHtm2ABMREQ21O4wI0lSxPouba33Qj2EUZkJLZrHBmAiIrKTds9mEkJg1qxZxhoux44dwy233GIsmufz+brnCik+RmWmhSsAExGRLbU7zMycOdPy+Y9+9KOIc2644Yb4r4i6lsur/jfoR6ZLLcRxbyYiIrKTdoeZ5cuXd+d1UHdxpRn/zHGpM83YAExERHbCBmC7c6Yb/8x0qiGmkT0zRERkIwwzdifLgMMNAMhyqGHmqC/AqfVERGQbDDOpQKvOZMpqRUYIoMnfsxY3JCIi6iyGmVSg9c144INTVqfTswmYiIjsgmEmFWjTs6WAz9hskk3ARERkFwwzqcC0pYGxCjCbgImIyCYYZlKBaUsDfX8mDjMREZFdMMykAvNmk9yfiYiIbIZhJhVYKjP6/kzsmSEiIntgmEkF5p4Z7s9EREQ2wzCTCqJUZhhmiIjILhhmUoFlNpPWAMzZTEREZBMMM6kgamWGPTNERGQPDDOpwDybyWgAZmWGiIjsgWEmFZgqM5mcmk1ERDbDMJMKtL2Z1MqM2jPDMENERHbBMJMKtF2zzZUZDjMREZFdMMykgvRe6n+P1rIBmIiIbIdhJhX0HqT+t64q1ADMYSYiIrIJhplUoIeZ+m+Q6QgCAJr8QQQVkcCLIiIi6hoMM6kgsx/gzgSEgizffuNm9s0QEZEdMMykAkkCepcCANz1e+B2qm87wwwREdkBw0yq0IeaDn2NbDYBExGRjTDMpAo9zBzeFZqezSZgIiKyAYaZVGEOM9w5m4iIbIRhJlWYwkyWtnN2I3tmiIjIBhhmUkXvwep/j+xFjkf9J3tmiIjIDhhmUkVWgbqtgQhigOMgAPbMEBGRPTDMpApJMoaaipVqAJyaTURE9sAwk0q0tWaKtDDDBmAiIrIDhplUolVm+gXUVYAZZoiIyA4YZlKJFmZ6+74BwAZgIiKyB4aZVNJHndGU07IPAHtmiIjIHhhmUklOfwBAessBAAwzRERkDwwzqSQtFwDgCLbAiQB7ZoiIyBYSGmYWLlyIc889F1lZWejXrx8mT56MHTt2WM4RQqCiogJFRUVIT09HeXk5tm/fnqArTnKebOOfWWhmmCEiIltIaJiprKzEbbfdhg8++ADr1q1DIBDAhAkT0NTUZJzz8MMPY9GiRViyZAk2bdqEgoICjB8/Ho2NjQm88iTlcALuTABAltTCBmAiIrIFZyKffO3atZbPly9fjn79+mHz5s246KKLIITA4sWLMX/+fEyZMgUAsHLlSuTn52PVqlW4+eabE3HZyS0tB/AfRTaasDegwB9Q4HZytJGIiJJXj/otVl9fDwDo3bs3AKCqqgo1NTWYMGGCcY7H48HYsWOxcePGhFxj0tOGmrKlZgBAE5uAiYgoySW0MmMmhMBdd92FCy+8EMOGDQMA1NTUAADy8/Mt5+bn52PPnj1RH8fn88Hn8xmfNzQ0dNMVJ6m0HABAX+cxwK8unNcrw53giyIiIuq8HlOZuf322/HJJ5/gueeeizgmSZLlcyFExG26hQsXIicnx/goLi7ulutNWlqYyXMdAwA0sG+GiIiSXI8IMz/96U/xyiuvYP369ejfv79xe0FBAYBQhUZXW1sbUa3RzZs3D/X19cbHvn37uu/Ck1GaOszUx6GGmWZ/MJFXQ0REFLeEhhkhBG6//Xa89NJL+Oc//4nS0lLL8dLSUhQUFGDdunXGbX6/H5WVlRgzZkzUx/R4PMjOzrZ8kIlWmenlaAHAnhkiIkp+Ce2Zue2227Bq1Sr87//+L7KysowKTE5ODtLT0yFJEubMmYMFCxZgyJAhGDJkCBYsWACv14vrrrsukZeevLQG4FxZbQDmKsBERJTsEhpmli5dCgAoLy+33L58+XLMmjULADB37ly0tLRg9uzZqKurw6hRo/Dmm28iKyvrBF+tTWiVmWyoYabZzzBDRETJLaFhRghx3HMkSUJFRQUqKiq6/4JSgRZmsiS9MsOeGSIiSm49ogGYTiCtAThTqKsss2eGiIiSHcNMqtEqM16FYYaIiOyBYSbVaDtnp+thhj0zRESU5BhmUo02myktqG7U2cSeGSIiSnIMM6lGG2ZyBZogQeHUbCIiSnoMM6lGCzMyFGTgGHtmiIgo6THMpBpXGuBQN5bMRjOauJ0BERElOYaZVGRaa4aVGSIiSnYMM6nItAowwwwRESU7hplUpM1oypaa2ABMRERJj2EmFenDTGhBsz/Yrm0liIiIeiqGmVSkDzNJTQgqAr6AkuALIiIi6jyGmVSk7c+UhRYA4FATERElNYaZVKRVZvo41TDDJmAiIkpmDDOpSAszvWU9zHCtGSIiSl4MM6nIo4aZXIcWZrjZJBERJTGGmVRkWmcGYM8MERElN4aZVGSazQSwZ4aIiJIbw0wq0mYzZQq1MtPMnhkiIkpiDDOpSKvMeIVameEwExERJTOGmVSkhxnlKAAOMxERUXJjmElF2t5MTtEKD/xo8nOYiYiIkhfDTCpyZwKS+tZz52wiIkp2DDOpSJaN6kyWxDBDRETJjWEmVelhBs1sACYioqTGMJOqXOkAgDS0opk9M0RElMQYZlKVKw0AkC75WJkhIqKkxjCTqlxeAFBnMzHMEBFREmOYSVXaMFM6wwwRESU5hplUpVVm0iUf15khIqKkxjCTqsIqM0KIBF8QERFR5zDMpCqn2gCcBh8CioAvoCT4goiIiDqHYSZVacNMaZIfAPdnIiKi5MUwk6q0YaYsuRUAuNYMERElLYaZVKVVZrIcapjhWjNERJSsGGZSlbZoXqYWZjjMREREyYphJlVplZkMrWeGlRkiIkpWDDOpSuuZ8bJnhoiIkhzDTKoyrTMDsDJDRETJi2EmVZlWAAbYM0NERMmLYSZV6YvmCTXMcJiJiIiSFcNMqjJ2zdbDDCszRESUnBIaZt555x1ceeWVKCoqgiRJWLNmjeW4EAIVFRUoKipCeno6ysvLsX379sRcrN1oPTMuRe2ZafFzOwMiIkpOCQ0zTU1NOPPMM7FkyZKoxx9++GEsWrQIS5YswaZNm1BQUIDx48ejsbHxBF+pDWmVGZc4BgBoaWVlhoiIkpMzkU8+ceJETJw4MeoxIQQWL16M+fPnY8qUKQCAlStXIj8/H6tWrcLNN998Ii/VfvTKTFANM+yZISKiZNVje2aqqqpQU1ODCRMmGLd5PB6MHTsWGzdujHk/n8+HhoYGywdFoYUZh2iFA0G0MMwQEVGS6rFhpqamBgCQn59vuT0/P984Fs3ChQuRk5NjfBQXF3frdSYtLcwAQBr8aGllmCEiouTUY8OMTpIky+dCiIjbzObNm4f6+nrjY9++fd19iclJm5oNqGGGw0xERJSsEtoz05aCggIAaoWmsLDQuL22tjaiWmPm8Xjg8Xi6/fqSniSpTcCtzUiX/BxmIiKipNVjKzOlpaUoKCjAunXrjNv8fj8qKysxZsyYBF6ZjWhDTWnwcZiJiIiSVkIrM0ePHsVXX31lfF5VVYVt27ahd+/eGDBgAObMmYMFCxZgyJAhGDJkCBYsWACv14vrrrsugVdtI87Q/ky1XDSPiIiSVELDzMcff4yLL77Y+Pyuu+4CAMycORMrVqzA3Llz0dLSgtmzZ6Ourg6jRo3Cm2++iaysrERdsr0Ym036OMxERERJK6Fhpry8HEKImMclSUJFRQUqKipO3EWlEn2YSeJsJiIiSl49tmeGTgB952z40RoUaA1ySwMiIko+DDOpzKXtnK1tNsnqDBERJSOGmVSmVWa8kr7ZJMMMERElH4aZVKb1zOQ41ZlMDDNERJSMGGZSmRZmMh2tALjZJBERJSeGmVSmDTNlymqYaWnlWjNERJR8GGZSmbY/U6ZD75nhbCYiIko+DDOpTKvMZMj6MBMrM0RElHwYZlKZ1jNjzGbi1GwiIkpCDDOpjFOziYjIBhhmUpmxa7YaZjibiYiIkhHDTCozwgxXACYiouTFMJPKwsMMKzNERJSEGGZSmRZmPEINMxxmIiKiZMQwk8q0BmCX0GczcWo2ERElH4aZVKZVZtzKMQAcZiIiouTEMJPKnGqYcWphhsNMRESUjBhmUplWmXEGtcoMZzMREVESYphJZVrPjEPxQ4bCYSYiIkpKDDOpTKvMAIAHfg4zERFRUmKYSWXartkAkA4/h5mIiCgpMcykMlk2Ak06fBxmIiKipMQwk+r0VYAlP5r9XGeGiIiSD8NMqtOagNPh4zATERElJYaZVGfaObs1KNAaVBJ8QURERB3DMJPqtIXz0iV9SwNWZ4iIKLkwzKQ6rTKToYcZNgETEVGSYZhJdVqYyXaqzb8MM0RElGwYZlKd1gCc7WwFwP2ZiIgo+TgTfQGUYFpl5kpRiQHOr9D3ndeArLTj3ImI4ubJBs77CZBdqH7+2YvAno3d/7yDLwFO/b767yN7gU1/BvxN0c/N6Q+MukX9/0TAD3z0JFBXZT0nswAYdTOQlg0oQeDjp4Hv/m09x9tHfRxvb0AIYOuzQPX/RX9O2QmcOR0oOlv9/JuPgU/+P0CY/tCSZGDYD4AB56uf13wKbP0roLSaHkgCTpsEDCpXP/3uS2DLSiBwLPrz9jkZOPcmwOFUvx4f/glo2G89J3cAcN7NgCsNCPiAD58AjuyxnpNVqL5WTyYQDKhf30M7oz+nwwOcMxPIO0X9vOod4PNXAAjT18MFnHUdUHiG+vneD4HPXgBEjMka/c8DzpgKSBJwtFa9xmNHrOcUDAdGzFTP0SkKsHk5UPu59dz0XurryeirvnfbVgH7t0Q+7+BLgVOviH5NJ4AkhBDHPy15NTQ0ICcnB/X19cjOzk705fQ8r80FPnoi0VdBlJr6nAzc+Abwf6uBN+efuOe96o/qL5+nLwPq97V97tCJwNRngDW3qr9Eoyn5HjDjb8DaeeovxGiKzgZm/h149/fAv/5f28/p8qrnBv3As/8RPYA43OpzpuUCKyYB/sbIcyQZmPos0HcosPxyoPlQ28971gxg0v8DnpsOfP3P6OecOgn44XLgxf8Evngl+jmDLgauXQ289nNg61/afs70Xur3wKGvged/ZA1tOncmMOtVwNcI/OUH6telLZf+Bjjnx8DyK4Dvvoh+zgV3AuMfUP8tBLD2l2qAiyZ/OPDjfwDvPw5UPhT9nAvvAsbd1/Z1dVBHfn8zzKS6xgPAtr/i+fd3oqbhGCadUYjBeZmJvioi+9u2Sg0SuQNDf92feR2QW9x9z3noK7UCJMlAdn+gfi/QezAw/IeR5wb9wAdL1SDRqwSo261WTc6fHdrXTQmqf/n7G0PnQALOvxXwZKnnCAF8vEwNEsY5AM79L7ViE67qXWDvRvWXvKIAvno1LA0cEzpn34fArg3qL3lnGtB8EOh/rlp10lV/Anz5ulr98PYGGqvVisQpUaoH/ib1tYpg6BpdXvV1yNoARsAHfPC4+nXRz3G41XP0rWGUAPDBn4DWptA5kqx+zdwZkc/75Vq1QpVZoFZPAseAoZcDhWeGztm1QX293j5qdczfCJSODVWlzBr2q1UvIPT8WYXA2deHqjDNh4FNT6n/vvQ3wBnT1MC1YaF623k3A+m56r+FADavAJpqre/dObOAzHzrcw8YDQy+OPKa4sAwY8Iw0z4z/vwB3vvqEB6dfhauPuukRF8Okf0d3KlWRvRqwejbgQkPWkv/XU0I4JWfhn7hZRUB//mGOnwSzY7XgdUztGqBBPzgz5HBp+pdrVrgUz+f9P+AkTdaz/l2M7DyKsB/VP18XAVw4c+iP6fvKPDM1cC3H6ufDxgDXP+SZWNcBHzAX68BqirVzwvPBGa+qg516YIB4G8zgX+/qn7eZ4haAcmIEqAANVyuuVX9t+wCrlsNnDzOes7nr6iPKRQ1pFyzAii72nrO1+vVa9OHvK5+DDj7R9Gfs+kQsHwicHCH+vkpV6iVJIepA+RYA7DySqB6m/p5yfeAGS+oQ13RvP2AWv0C1KrVj18H8sus57z3KLDuN5H3vfx3wPm3WG+r/gRY8X3A16B+Xv4roPye6M/dxRhmTBhm2ue/Vm7CW1/U4qEpwzH9vBj/YyOirrV/K/DSzWpF4bIF6n5p3S0YAP5xF/DNJnW4pN+pbZ//6QvAP/9bHUY4Z2b0c/79GvDGr4DzbgJG3xb9nF2VwKs/UysBY+e2HdqaDwP/3w1qVeSaFaFKgZmvEXjhP9VfslOfBTLzIs9pPQa8fLNa+Zr67PGrXh89Bbz/mBq2Tp8c/Zxtz6lDLRfNBc6eEf2cz19Rw8L5s4FRP2n7Oeu/AZ6/Xu1PmvKkNbTpmg6q57gzgB8+bQ1t4YQA3rpPfU8mPw4Unxf9vMpH1FAT9KmVpe/dFTtg7tmohuCyycAlv+7ewG3CMGPCMNM+P31uK/7+f/vxm0lluPHC0kRfDhERpbiO/P7m1GwCAHhdDgBcAZiIiJIPwwwBANLdWpjhOjNERJRkGGYIQCjMcNE8IiJKNgwzBMA8zBRI8JUQERF1DMMMAeAwExERJS9uZ0AAQmHmo6rD+NGfP4QkAYP6ZiArzYXdh5rw7ZEWKIp14ltWmgulfTOQ63Vhz6FmfFPXjGDYORkeJ0r6ZqBvhhv76lqw93AzAkHrMtzpbgdK+2YgL9ODb460YO+hZrSGneNxOVDaJwP5OWmoPtKCPYea4QtYg5fbKWNgnwwU5abjQP0x7D7UhGNhDc0uh4wBfbzon5uO7476sPtgM5r91mqU0yGjuFc6BvT24lCTH1UHm9Dks57jkCX07+XFwD5eHGluRdXBJjQea7WcI8sSinLTUdLHiyZfELsONqG+2bpypyRJKMpNQ2nfDOSkuyzHWvwK9hxqwjd1Leid4UZpXgZ6ea3n+FoV7DncjL2Hm5GT7sKgvhnoneG2zJz0BxTsPdyMPYeakZXmRGnfDPTN9FjOaQ0KfFPXjKqDTfC6nSjpk4H8bOs5AUXgm7oW7D7YBJdDRmnfDBTkpEE2nRNUgG+PNGP3wWbje6gwN91yjiKA6iMt2HWwCUIAJX29OCnXC4dsPaem/hiqDjahNaigpG8G+vdKh9P0QEIAtY0+VB1sQrM/gJI+GSju7YXLYT3n4FGf9v4EMLCPFwN6e+F28u+4eGV41O+lPhke7DnUhH11LQgq4T/bTpT2yUBelgff1Knfg4Gwc9JcDgzsk4EB2nunKMC3R1pQdbAJR31hP1OShJNy0zGwTwaO+gKoOngU9S2R5xTmpKOkrxctfvXn7kj4zx0k5OekYZD2/y8AaGgJoOpgE6rrW6CETfLN1v5fl53uMn4mw8/Rvx69M9zYe6g56tfD61bP6d8rHQ5ZQlD7mdK/h80csvb/oT5eHI7x/yFZktC/VzpK+mYg0+OEEEBds3rukeZWFPdWv1aesO/3I82t2HWwCYeP+tC/lxclfb1I06rz4Y61Kth7uAl7DzWjV4YbpX0z0MvrjpidneVxISfs/08nUlJMzX788cfxyCOPoLq6GqeffjoWL16M733ve+26L6dmt89rn1Zj9l+j7LdBRER0HLPLB2Pu5cdZs6iDOvL7u8dXZp5//nnMmTMHjz/+OC644AI88cQTmDhxIj7//HMMGMDF3brKJaf2w20XDwYAlPTJgBDALq3aoP/F63Za/+LV/1o40tKKgb3Vv3g9LutfAHVNrdh9qAkHj/pR3DsdJX0ykBZ2Tn1LK6oONuO7Rh/690rHwD5eeN3WvxIajwWw+2AzahpaUJSTjoF9M5DpsZ5z1BfEnoNN2F/fgvxstdqRlWb9Fm/2B7UqUgvysjwo7euNqIgca1Ww55Ba7eij/yWSEVkR2Xu4GXsONyM33WX8RWatiKjVjt2HmpDpcaG0rzdGRaQFVQePRjRfu2StitQr3fhaHw37y8wpSyju5cWAPl7Ut7Ri13dNaAirEDmkUBWp0RdA1XdNONJi/UtV/4tX/WtWwa7vjuJwlL9mi3LTUNInA61BBVUHm/DdUV/EOQU5Hsv3UG1j5L46/bLUv4olCdh9qAk19T4IWP+uysv0oLRvBlwOGbsPNWH/kWMR5/T2qhUrr9uB3Qeb1Qpi2N9nuenqOVkeZ6iC2PP/huvx9IrkoaM+DOjjxcDeGRE///XNrag61ISDjbErAOrPtrXaUZCj/vz29rqBsJ8XtcrYpFUZM9E3w3pOIKj/3DUj3aVWfftleSznBBWBb+taLD9TeoW4uJcXTlN1DwJGhbbhmPr/uuLw6p6pIlLX3IoBvdWfyfCKiP712H+kBUKoa88V5aajtE9GRFXDF1CwV/v/UC+vy6iIIKzquu+w+lr1KnRWmsuoOO093Ix9h5sRCKuYZ5qqSN9oFfPwarjO5ZBR3NuL4l7pWkXnKBqPRfZWmqumidDjKzOjRo3CiBEjsHTpUuO20047DZMnT8bChQuPe39WZoiIiJKPbRbN8/v92Lx5MyZMmGC5fcKECdi4cWPU+/h8PjQ0NFg+iIiIyL56dJg5ePAggsEg8vOtu3Pm5+ejpqYm6n0WLlyInJwc46O4uBt3oCUiIqKE69FhRieFtU0LISJu082bNw/19fXGx759+07EJRIREVGC9OgG4L59+8LhcERUYWprayOqNTqPxwOPx3MiLo+IiIh6gB5dmXG73TjnnHOwbt06y+3r1q3DmDFjEnRVRERE1JP06MoMANx11124/vrrMXLkSIwePRpPPvkk9u7di1tuuSXRl0ZEREQ9QI8PM9OmTcOhQ4fwwAMPoLq6GsOGDcNrr72GgQMHJvrSiIiIqAfo8evMxIvrzBARESUf26wzQ0RERHQ8DDNERESU1BhmiIiIKKkxzBAREVFSY5ghIiKipMYwQ0REREmtx68zEy995jl3zyYiIkoe+u/t9qwgY/sw09jYCADcPZuIiCgJNTY2Iicnp81zbL9onqIo2L9/P7KysmLutN1ZDQ0NKC4uxr59+2y5IJ/dXx/A12gHdn99gP1fo91fH8DX2BlCCDQ2NqKoqAiy3HZXjO0rM7Iso3///t36HNnZ2bb95gTs//oAvkY7sPvrA+z/Gu3++gC+xo46XkVGxwZgIiIiSmoMM0RERJTUGGbi4PF4cN9998Hj8ST6UrqF3V8fwNdoB3Z/fYD9X6PdXx/A19jdbN8ATERERPbGygwRERElNYYZIiIiSmoMM0RERJTUGGaIiIgoqTHMdNLjjz+O0tJSpKWl4ZxzzsG7776b6EvqlIULF+Lcc89FVlYW+vXrh8mTJ2PHjh2Wc2bNmgVJkiwf559/foKuuOMqKioirr+goMA4LoRARUUFioqKkJ6ejvLycmzfvj2BV9xxJSUlEa9RkiTcdtttAJLvPXznnXdw5ZVXoqioCJIkYc2aNZbj7XnPfD4ffvrTn6Jv377IyMjAVVddhW+++eYEvoq2tfUaW1tbcc8992D48OHIyMhAUVERbrjhBuzfv9/yGOXl5RHv6/Tp00/wK4nteO9je74ve/L7eLzXF+1nUpIkPPLII8Y5Pfk9bM/vh57ys8gw0wnPP/885syZg/nz52Pr1q343ve+h4kTJ2Lv3r2JvrQOq6ysxG233YYPPvgA69atQyAQwIQJE9DU1GQ57/LLL0d1dbXx8dprryXoijvn9NNPt1z/p59+ahx7+OGHsWjRIixZsgSbNm1CQUEBxo8fb+zrlQw2bdpkeX3r1q0DAFxzzTXGOcn0HjY1NeHMM8/EkiVLoh5vz3s2Z84cvPzyy1i9ejX+9a9/4ejRo5g0aRKCweCJehltaus1Njc3Y8uWLbj33nuxZcsWvPTSS/jyyy9x1VVXRZx70003Wd7XJ5544kRcfrsc730Ejv992ZPfx+O9PvPrqq6uxtNPPw1JkvCDH/zAcl5PfQ/b8/uhx/wsCuqw8847T9xyyy2W20499VTxy1/+MkFX1HVqa2sFAFFZWWncNnPmTHH11Vcn7qLidN9994kzzzwz6jFFUURBQYF46KGHjNuOHTsmcnJyxJ/+9KcTdIVd78477xSDBw8WiqIIIZL7PQQgXn75ZePz9rxnR44cES6XS6xevdo459tvvxWyLIu1a9eesGtvr/DXGM1HH30kAIg9e/YYt40dO1bceeed3XtxXSTaazze92UyvY/teQ+vvvpqcckll1huS6b3MPz3Q0/6WWRlpoP8fj82b96MCRMmWG6fMGECNm7cmKCr6jr19fUAgN69e1tu37BhA/r164ehQ4fipptuQm1tbSIur9N27tyJoqIilJaWYvr06di1axcAoKqqCjU1NZb30+PxYOzYsUn7fvr9fvzlL3/BjTfeaNlcNdnfQ1173rPNmzejtbXVck5RURGGDRuWtO9rfX09JElCbm6u5fa//vWv6Nu3L04//XTcfffdSVVRBNr+vrTT+3jgwAH84x//wH/+539GHEuW9zD890NP+lm0/UaTXe3gwYMIBoPIz8+33J6fn4+ampoEXVXXEELgrrvuwoUXXohhw4YZt0+cOBHXXHMNBg4ciKqqKtx777245JJLsHnz5qRYzXLUqFF45plnMHToUBw4cAAPPvggxowZg+3btxvvWbT3c8+ePYm43LitWbMGR44cwaxZs4zbkv09NGvPe1ZTUwO3241evXpFnJOMP6fHjh3DL3/5S1x33XWWDfxmzJiB0tJSFBQU4LPPPsO8efPwf//3f8YwY093vO9LO72PK1euRFZWFqZMmWK5PVnew2i/H3rSzyLDTCeZ/+IF1Dc6/LZkc/vtt+OTTz7Bv/71L8vt06ZNM/49bNgwjBw5EgMHDsQ//vGPiB/MnmjixInGv4cPH47Ro0dj8ODBWLlypdFsaKf3c9myZZg4cSKKioqM25L9PYymM+9ZMr6vra2tmD59OhRFweOPP245dtNNNxn/HjZsGIYMGYKRI0diy5YtGDFixIm+1A7r7PdlMr6PTz/9NGbMmIG0tDTL7cnyHsb6/QD0jJ9FDjN1UN++feFwOCISZW1tbUQ6TSY//elP8corr2D9+vXo379/m+cWFhZi4MCB2Llz5wm6uq6VkZGB4cOHY+fOncasJru8n3v27MFbb72F//qv/2rzvGR+D9vznhUUFMDv96Ouri7mOcmgtbUVU6dORVVVFdatW2epykQzYsQIuFyupHxfgcjvS7u8j++++y527Nhx3J9LoGe+h7F+P/Skn0WGmQ5yu90455xzIkqA69atw5gxYxJ0VZ0nhMDtt9+Ol156Cf/85z9RWlp63PscOnQI+/btQ2Fh4Qm4wq7n8/nwxRdfoLCw0Cjvmt9Pv9+PysrKpHw/ly9fjn79+uH73/9+m+cl83vYnvfsnHPOgcvlspxTXV2Nzz77LGneVz3I7Ny5E2+99Rb69Olz3Pts374dra2tSfm+ApHfl3Z4HwG1WnrOOefgzDPPPO65Pek9PN7vhx71s9hlrcQpZPXq1cLlcolly5aJzz//XMyZM0dkZGSI3bt3J/rSOuzWW28VOTk5YsOGDaK6utr4aG5uFkII0djYKH7+85+LjRs3iqqqKrF+/XoxevRocdJJJ4mGhoYEX337/PznPxcbNmwQu3btEh988IGYNGmSyMrKMt6vhx56SOTk5IiXXnpJfPrpp+Laa68VhYWFSfP6dMFgUAwYMEDcc889ltuT8T1sbGwUW7duFVu3bhUAxKJFi8TWrVuNmTztec9uueUW0b9/f/HWW2+JLVu2iEsuuUSceeaZIhAIJOplWbT1GltbW8VVV10l+vfvL7Zt22b52fT5fEIIIb766itx//33i02bNomqqirxj3/8Q5x66qni7LPPTorX2N7vy578Ph7v+1QIIerr64XX6xVLly6NuH9Pfw+P9/tBiJ7zs8gw00mPPfaYGDhwoHC73WLEiBGWqczJBEDUj+XLlwshhGhubhYTJkwQeXl5wuVyiQEDBoiZM2eKvXv3JvbCO2DatGmisLBQuFwuUVRUJKZMmSK2b99uHFcURdx3332ioKBAeDwecdFFF4lPP/00gVfcOW+88YYAIHbs2GG5PRnfw/Xr10f9vpw5c6YQon3vWUtLi7j99ttF7969RXp6upg0aVKPes1tvcaqqqqYP5vr168XQgixd+9ecdFFF4nevXsLt9stBg8eLO644w5x6NChxL4wk7ZeY3u/L3vy+3i871MhhHjiiSdEenq6OHLkSMT9e/p7eLzfD0L0nJ9FSbtgIiIioqTEnhkiIiJKagwzRERElNQYZoiIiCipMcwQERFRUmOYISIioqTGMENERERJjWGGiIiIkhrDDBHZTklJCRYvXpzoyyCiE4RhhojiMmvWLEyePBkAUF5ejjlz5pyw516xYgVyc3Mjbt+0aRN+8pOfnLDrIKLEcib6AoiIwvn9frjd7k7fPy8vrwuvhoh6OlZmiKhLzJo1C5WVlXj00UchSRIkScLu3bsBAJ9//jmuuOIKZGZmIj8/H9dffz0OHjxo3Le8vBy333477rrrLvTt2xfjx48HACxatAjDhw9HRkYGiouLMXv2bBw9ehQAsGHDBvz4xz9GfX298XwVFRUAIoeZ9u7di6uvvhqZmZnIzs7G1KlTceDAAeN4RUUFzjrrLDz77LMoKSlBTk4Opk+fjsbGRuOcF154AcOHD0d6ejr69OmDcePGoampqZu+mkTUEQwzRNQlHn30UYwePRo33XQTqqurUV1djeLiYlRXV2Ps2LE466yz8PHHH2Pt2rU4cOAApk6darn/ypUr4XQ68d577+GJJ54AAMiyjD/84Q/47LPPsHLlSvzzn//E3LlzAQBjxozB4sWLkZ2dbTzf3XffHXFdQghMnjwZhw8fRmVlJdatW4evv/4a06ZNs5z39ddfY82aNXj11Vfx6quvorKyEg899BAAoLq6Gtdeey1uvPFGfPHFF9iwYQOmTJkCbm1H1DNwmImIukROTg7cbje8Xi8KCgqM25cuXYoRI0ZgwYIFxm1PP/00iouL8eWXX2Lo0KEAgJNPPhkPP/yw5THN/TelpaX47//+b9x66614/PHH4Xa7kZOTA0mSLM8X7q233sInn3yCqqoqFBcXAwCeffZZnH766di0aRPOPfdcAICiKFixYgWysrIAANdffz3efvtt/Pa3v0V1dTUCgQCmTJmCgQMHAgCGDx8ex1eLiLoSKzNE1K02b96M9evXIzMz0/g49dRTAajVEN3IkSMj7rt+/XqMHz8eJ510ErKysnDDDTfg0KFDHRre+eKLL1BcXGwEGQAoKytDbm4uvvjiC+O2kpISI8gAQGFhIWprawEAZ555Ji699FIMHz4c11xzDZ566inU1dW1/4tARN2KYYaIupWiKLjyyiuxbds2y8fOnTtx0UUXGedlZGRY7rdnzx5cccUVGDZsGF588UVs3rwZjz32GACgtbW13c8vhIAkSce93eVyWY5LkgRFUQAADocD69atw+uvv46ysjL88Y9/xCmnnIKqqqp2XwcRdR+GGSLqMm63G8Fg0HLbiBEjsH37dpSUlODkk0+2fIQHGLOPP/4YgUAAv//973H++edj6NCh2L9//3GfL1xZWRn27t2Lffv2Gbd9/vnnqK+vx2mnndbu1yZJEi644ALcf//92Lp1K9xuN15++eV235+Iug/DDBF1mZKSEnz44YfYvXs3Dh48CEVRcNttt+Hw4cO49tpr8dFHH2HXrl148803ceONN7YZRAYPHoxAIIA//vGP2LVrF5599ln86U9/ini+o0eP4u2338bBgwfR3Nwc8Tjjxo3DGWecgRkzZmDLli346KOPcMMNN2Ds2LFRh7ai+fDDD7FgwQJ8/PHH2Lt3L1566SV89913HQpDRNR9GGaIqMvcfffdcDgcKCsrQ15eHvbu3YuioiK89957CAaDuOyyyzBs2DDceeedyMnJgSzH/l/QWWedhUWLFuF3v/sdhg0bhr/+9a9YuHCh5ZwxY8bglltuwbRp05CXlxfRQAyoFZU1a9agV69euOiiizBu3DgMGjQIzz//fLtfV3Z2Nt555x1cccUVGDp0KH7961/j97//PSZOnNj+Lw4RdRtJcG4hERERJTFWZoiIiCipMcwQERFRUmOYISIioqTGMENERERJjWGGiIiIkhrDDBERESU1hhkiIiJKagwzRERElNQYZoiIiCipMcwQERFRUmOYISIioqTGMENERERJ7f8HXQ1XYKWXtSEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ein,Eout, weight = perceptron(X_new, y_new,X_test_final,y_test_new, 200)\n",
    "# Make a graph which represents a relation between Simmetry and intensity\n",
    "x_line = np.linspace(0.4, 1, 100)\n",
    "plt.scatter(avg_intensity_Digit2, symm_Digit2, marker = 'x', color = 'red')\n",
    "plt.scatter(avg_intensity_Digit1, symm_Digit1, marker='o', color = 'blue')\n",
    "# Drawing a line to show the threshold in based of the w array that we got as a final result \n",
    "plt.plot(x_line, line(weight))\n",
    "plt.title('PLA')\n",
    "plt.xlabel('x1: Average Intensity')\n",
    "plt.ylabel('x2: Symmetry')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# We make a graph which represents the relationship between epochs and error \n",
    "  \n",
    "plt.title('Ein/Eout PLA')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Error %')\n",
    "plt.plot(Ein, label='Ein')\n",
    "plt.plot(Eout, label = 'Eout')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6406149903907752"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minimal error with train data\n",
    "min(Ein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.358490566037736"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minimal error with testing data\n",
    "min(Eout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 283.10872703],\n",
       "       [-731.3301515 ],\n",
       "       [1930.93776293]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 7. Implementation of the  pocket algorithm </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pocket(input_data,y,input_data_test,y_test,max_iter,w=None):    \n",
    "    data = np.c_[np.ones((input_data.shape[0],1)), input_data]  #https://docs.scipy.org/doc/numpy/reference/generated/numpy.c_.html\n",
    "    data_test = np.c_[np.ones((input_data_test.shape[0],1)), input_data_test]\n",
    "    if w is None:        \n",
    "        w = np.random.random(data.shape[1]).reshape(-1,1)    \n",
    "    current = np.sign(data @ w)\n",
    "    f = y.reshape(-1,1)\n",
    "    y_test =y_test.reshape(-1,1)\n",
    "    difference = (f != current)\n",
    "    t = 0\n",
    "    Ein = [2]\n",
    "    Eout = [2]\n",
    "    \n",
    "    e=0\n",
    "    for iteration in range(max_iter):\n",
    "        for i in range(len(f)):\n",
    "            if difference[i]:\n",
    "                w[0]+=f[i]*data[i][0]\n",
    "                w[1]+=f[i]*data[i][1]\n",
    "                w[2]+=f[i]*data[i][2]\n",
    "                t+=1\n",
    "        current = np.sign(data @ w)\n",
    "        difference = (f != current)\n",
    "        # if the current error is lower than the last one, we save the weight array and the error\n",
    "        if t/len(f) < Ein[-1]:\n",
    "            e=t/len(f)\n",
    "            wf=w.copy()\n",
    "        # on the other side, if the current error is higher than the last one, we don´t save anything   \n",
    "          \n",
    "        Ein.append(e)\n",
    "        t=0\n",
    "    \n",
    "    # When we finish all the iterations we make testing process and the error estimation\n",
    "    # but it will be using the best result of w that was saved, and not only taking the last result \n",
    "    \n",
    "        prediction_test = np.sign(data_test @ w)    \n",
    "        difference_test=(y_test != prediction_test )\n",
    "          \n",
    "        for i in range(len(y_test)):\n",
    "            if difference_test[i]:\n",
    "                t+=1\n",
    "        if t/len(y_test) < Eout[-1]:\n",
    "            e=t/len(y_test)\n",
    "            wf=w.copy()     \n",
    "            \n",
    "        Eout.append(e)\n",
    "        t=0    \n",
    "    Ein=[v*100 for v in Ein]\n",
    "    Eout=[v*100 for v in Eout]\n",
    "    return Ein,Eout,wf\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 8. Results of the pocket algorithm </span>\n",
    "</div>"
   ]
  },
  {
   "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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using pocket algorithm\n",
    "Ein, Eout,w = pocket(X_new, y_new,X_test_final,y_test_new, 200)\n",
    "\n",
    "#Ploting\n",
    "plt.scatter(avg_intensity_Digit2, symm_Digit2, marker = 'x', color = 'red')\n",
    "plt.scatter(avg_intensity_Digit1, symm_Digit1, marker='o', color = 'blue')\n",
    "plt.plot(x_line, line(w))\n",
    "plt.xlabel('x1: Average Intensity')\n",
    "plt.ylabel('x2: Symmetry')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Plot Ein and Eout \n",
    "plt.title('Ein/Eout Pocket')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Error %')\n",
    "plt.plot(Ein, label='Ein')\n",
    "plt.plot(Eout, label = 'Eout')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6406149903907752"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minimal error with train data\n",
    "min(Ein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6406149903907752"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minimal error with testing data\n",
    "min(Eout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 9. Implementation of the linear regression model</span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear Regression\n",
    "# We import linear regression model from sklearn module\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def lin(input_data,y,input_data_test,y_test,):\n",
    "    \n",
    "    data = np.c_[np.ones((input_data.shape[0],1)), input_data]  #https://docs.scipy.org/doc/numpy/reference/generated/numpy.c_.html\n",
    "    data_test = np.c_[np.ones((input_data_test.shape[0],1)), input_data_test]\n",
    "    \n",
    "    f = y.reshape(-1,1)\n",
    "    y_test =y_test.reshape(-1,1)\n",
    "    \n",
    "    # We use the model\n",
    "    modeloLinear = LinearRegression()\n",
    "    modeloLinear.fit(X = input_data, y = y)\n",
    "    # We get the wight array parameters\n",
    "    w=np.array([modeloLinear.intercept_,modeloLinear.coef_[0],modeloLinear.coef_[1]]).reshape(-1,1)\n",
    "    # make the current prediction for train and test\n",
    "    prediction_train=np.sign(data @ w)\n",
    "    prediction_test=np.sign(data_test @ w)\n",
    "    # Finally, we estimate the error\n",
    "    NMissClass=0\n",
    "    difference = (f != prediction_train)\n",
    "    for i in range(len(f)):\n",
    "        if difference[i]:\n",
    "            NMissClass+=1\n",
    "    \n",
    "    Ein=(NMissClass/len(f))*100\n",
    "    NMissClass=0\n",
    "    difference_test = (y_test != prediction_test)\n",
    "    for i in range(len(y_test)):\n",
    "        if difference_test[i]:\n",
    "            NMissClass+=1\n",
    "    Eout=(NMissClass/len(y_test))*100\n",
    "    \n",
    "    NMissClass=0\n",
    "    \n",
    "    \n",
    "    return Ein,Eout,w\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style='text-align: center;'>\n",
    "<span style=\"color:#000000; font-family: 'Times new roman'; font-weight: 700; font-size: 2em;\"> 10. Results using linear regression model & comparison vs pocket algorithm </span>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ein LIN: 0.38436899423446513\n",
      "Eout LIN: 2.1226415094339623\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ein Pocket: 0.3203074951953876\n",
      "Eout Pocket: 0.3203074951953876\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Linear Regression VS pocket algorithm Results\n",
    "\n",
    "# LINEAR REGRESSION\n",
    "\n",
    "Ein, Eout,w = lin(X_new, y_new,X_test_final,y_test_new)\n",
    "print('Ein LIN:',Ein)\n",
    "print('Eout LIN:',Eout)\n",
    "\n",
    "plt.scatter(avg_intensity_Digit2, symm_Digit2, marker = 'x', color = 'red')\n",
    "plt.scatter(avg_intensity_Digit1, symm_Digit1, marker='o', color = 'blue')\n",
    "plt.plot(x_line, line(w))\n",
    "plt.xlabel('x1: Average Intensity')\n",
    "plt.ylabel('x2: Symmetry')\n",
    "plt.show()\n",
    "\n",
    "## POCKET ALGORITHM \n",
    "\n",
    "Ein, Eout,w = pocket(X_new, y_new,X_test_final,y_test_new, 200,w)\n",
    "\n",
    "print('Ein Pocket:',min(Ein))\n",
    "print('Eout Pocket:',min(Eout))\n",
    "\n",
    "\n",
    "plt.scatter(avg_intensity_Digit2, symm_Digit2, marker = 'x', color = 'red')\n",
    "plt.scatter(avg_intensity_Digit1, symm_Digit1, marker='o', color = 'blue')\n",
    "plt.plot(x_line, line(w))\n",
    "plt.show()\n",
    "\n",
    "#Plot Ein and Eout \n",
    "plt.title('Ein/Eout Pocket with Linear Regresion Initial Weights')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Error %')\n",
    "plt.plot(Ein, label='Ein')\n",
    "plt.plot(Eout, label = 'Eout')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}