Assignment 6/Ibrahim Mohamed - A6 PCA SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d20d33c-4922-4513-a576-72dfe4c78dfd",
   "metadata": {},
   "source": [
    "---\n",
    "# Cairo University Faculty of Engineering\n",
    "## Machine Learning \n",
    "## Assignment 6\n",
    "\n",
    "---\n",
    "Please write your full name here\n",
    "- **Name** : \"Ibrahim Mohamed\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7f38e9-3316-47a1-9d1e-e0e2d5631988",
   "metadata": {},
   "source": [
    "# Part 1 PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "91704395-9068-4300-8c62-faf1331cba6d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons, make_blobs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "1369176c-24c2-4d62-92f6-fb867011a8ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n",
    "plt.scatter(X[:, 0], X[:, 1])\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a5b94a-b90c-4291-94ab-ba439670be9c",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "Write your own PCA function that will return:\n",
    "* The sorted eigenvectors of all principal components\n",
    "* The corresponding eigenvalues\n",
    "* The transformed data\n",
    "- Function takes in the data only\n",
    "    - You can use np.linalg.eig\n",
    "\n",
    "Note, please make sure that the data is centered before transformation (i.e., the sample\n",
    "mean subtracted out) but not normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "6c58d422-d81f-4d88-8a33-4326b0562cef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#### YOUR FUNCTION\n",
    "def pca(X):\n",
    "    # Center the data\n",
    "    centered_data = X - np.mean(X, axis=0)\n",
    "\n",
    "    # covariance matrix\n",
    "    cov_matrix = np.cov(centered_data.T)\n",
    "\n",
    "    # extarct eigenvectors and eigenvalues of the covariance matrix\n",
    "    sorted_eigenvalue, sorted_eigenvectors = np.linalg.eig(cov_matrix)\n",
    "\n",
    "    # Sort descending order\n",
    "    idx = sorted_eigenvalue.argsort()[::-1]\n",
    "    sorted_eigenvalue = sorted_eigenvalue[idx]\n",
    "    sorted_eigenvectors = sorted_eigenvectors[:, idx]\n",
    "\n",
    "    # Transform the data\n",
    "    X_transformed = np.dot(centered_data, sorted_eigenvectors)\n",
    "\n",
    "    return sorted_eigenvectors, sorted_eigenvalue, X_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c155739-ac8c-447b-982b-08386010d4f5",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "- Apply your PCA function to the data given above\n",
    "- print the pca eigenvalues\n",
    "- plot the transformed data\n",
    "- plot the PCA vectors over the ORIGINAL data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "bda3cfb2-e5ea-49b0-9d9f-36ad3f95809f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Apply your function\n",
    "sorted_eigenvectors, sorted_eigenvalues, X_transformed = pca(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "0c673bfc-4bd7-4d3e-b23f-1d006169597f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues are:  [1.82927343 0.10246373]\n"
     ]
    }
   ],
   "source": [
    "print(\"Eigenvalues are: \", sorted_eigenvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors are:  [[ 0.74306799 -0.66921593]\n",
      " [ 0.66921593  0.74306799]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Eigenvectors are: \", sorted_eigenvectors)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7c9d855b-c8c1-4b43-bd0c-f3d49cf71960",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot transformed data\n",
    "plt.scatter(X_transformed[:, 0], X_transformed[:, 1])\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "a2251e85-9a38-4544-91bc-c8a019c0115d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot eigenvectors over ORIGINAL data\n",
    "\n",
    "# Plot the original data\n",
    "plt.scatter(X[:, 0], X[:, 1])\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "\n",
    "# Plot the eigenvectors\n",
    "origin = np.mean(X, axis=0)\n",
    "\n",
    "for i in range(len(sorted_eigenvectors)):\n",
    "    plt.quiver(origin[0], origin[1], sorted_eigenvectors[0, i], sorted_eigenvectors[1, i], color=['r', 'b'], scale=1/sorted_eigenvalues[i] + 2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea248e0a-671c-4399-bae7-d61b7069e034",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd48ec2b-48b2-474b-8f4f-7f0bc8370f22",
   "metadata": {},
   "source": [
    "- Apply sklearn's PCA function to the same data\n",
    "- print the pca eigenvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "8e351a4d-fcb3-4a21-bf2d-3d6ab65eed36",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.74306799  0.66921593]\n",
      " [-0.66921593  0.74306799]]\n",
      "\n",
      "\n",
      "[1.82927343 0.10246373]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "transformed_data_sklearn = pca.fit_transform(X)\n",
    "\n",
    "# Plot the transformed data from sklearn PCA\n",
    "plt.scatter(transformed_data_sklearn[:, 0], transformed_data_sklearn[:, 1])\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()\n",
    "\n",
    "# Print eigenvectors and eigenvalues of sklearn PCA\n",
    "print(pca.components_)\n",
    "print('\\n')\n",
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b523f4-7624-4352-93ad-8f14dcad8d79",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "- Apply sklearn PCA using 1 component only\n",
    "- print shape of transformed data\n",
    "- plot the transformed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "8f79f203-d4b2-4d0d-8e60-f98177e01237",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 2)\n"
     ]
    }
   ],
   "source": [
    "pca_1_comp = PCA(n_components=1)\n",
    "transformed_data_one_component = pca_1_comp.fit_transform(X)\n",
    "\n",
    "# shape of the transformed data\n",
    "print(transformed_data_sklearn.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "05a6bc1e-a9b3-49aa-9b84-dec4248bc5e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the transformed data\n",
    "plt.scatter(transformed_data_one_component, np.zeros_like(transformed_data_one_component))\n",
    "plt.xlabel('PC1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f68a3b5-431f-49ef-b9ec-5de77eb49125",
   "metadata": {},
   "source": [
    " # Part 2 SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482ab1bb-127e-4c80-9eb0-6da5be9d4b92",
   "metadata": {},
   "source": [
    "### Use the following function to plot the classification regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "a129264e-95ce-42eb-8b9e-e09ccb768c07",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "\n",
    "def plot_class_regions_for_classifier(clf, X, y, X_test=None, y_test=None, title=None, target_names = None, plot_decision_regions = True):\n",
    "\n",
    "    numClasses = np.amax(y) + 1\n",
    "    color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']\n",
    "    color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']\n",
    "    cmap_light = ListedColormap(color_list_light[0:numClasses])\n",
    "    cmap_bold  = ListedColormap(color_list_bold[0:numClasses])\n",
    "\n",
    "    h = 0.03\n",
    "    k = 0.5\n",
    "    x_plot_adjust = 0.1\n",
    "    y_plot_adjust = 0.1\n",
    "    plot_symbol_size = 50\n",
    "\n",
    "    x_min = X[:, 0].min()\n",
    "    x_max = X[:, 0].max()\n",
    "    y_min = X[:, 1].min()\n",
    "    y_max = X[:, 1].max()\n",
    "    x2, y2 = np.meshgrid(np.arange(x_min-k, x_max+k, h), np.arange(y_min-k, y_max+k, h))\n",
    "    # np.c_ Translates slice objects to concatenation along the second axis\n",
    "    # e.g. np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]\n",
    "    # ravel() Returns a contiguous flattened array.\n",
    "    # x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "    # np.ravel(x) = [1 2 3 4 5 6]\n",
    "    P = clf.predict(np.c_[x2.ravel(), y2.ravel()])\n",
    "    P = P.reshape(x2.shape)\n",
    "    plt.figure()\n",
    "    if plot_decision_regions:\n",
    "        plt.contourf(x2, y2, P, cmap=cmap_light, alpha = 0.8)\n",
    "\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, s=plot_symbol_size, edgecolor = 'black')\n",
    "    plt.xlim(x_min - x_plot_adjust, x_max + x_plot_adjust)\n",
    "    plt.ylim(y_min - y_plot_adjust, y_max + y_plot_adjust)\n",
    "\n",
    "    if (X_test is not None):\n",
    "        plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_bold, s=plot_symbol_size, marker='^', edgecolor = 'black')\n",
    "        train_score = clf.score(X, y)\n",
    "        test_score  = clf.score(X_test, y_test)\n",
    "        title = title + \"\\nTrain score = {:.2f}, Test score = {:.2f}\".format(train_score, test_score)\n",
    "\n",
    "    if (target_names is not None):\n",
    "        legend_handles = []\n",
    "        for i in range(0, len(target_names)):\n",
    "            patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])\n",
    "            legend_handles.append(patch)\n",
    "        plt.legend(loc=0, handles=legend_handles)\n",
    "\n",
    "    if (title is not None):\n",
    "        plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b922960-cffc-477a-ae42-f22b57efe394",
   "metadata": {},
   "source": [
    "### Use the following dataset in questions 1, 2, and 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "1006440e-622e-48cd-8369-0cdb5b73b471",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X, y = make_classification(n_samples = 100, n_features=2,\n",
    "                                n_redundant=0, n_informative=2,\n",
    "                                n_clusters_per_class=1, flip_y = 0.1,\n",
    "                                class_sep = 0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b686a465-4987-44d4-911b-ad09dac8140d",
   "metadata": {},
   "source": [
    "#### Question 1\n",
    "Split the data into training and testing datasets (use random_state=42 and 20% of the data for testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "38eaa5eb-a8eb-4d87-a7f4-2cd222d47005",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b03f201-9b63-4783-819f-cac21f86b644",
   "metadata": {},
   "source": [
    "#### Question 2\n",
    "1. Train an SVM classifier using linear kernel (use SVC)\n",
    "2. Plot the classification regions using the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "7ec9b0cb-9244-473e-ae1a-9e57b3ea0122",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(kernel='linear')",
      "text/html": "<style>#sk-container-id-30 {color: black;background-color: white;}#sk-container-id-30 pre{padding: 0;}#sk-container-id-30 div.sk-toggleable {background-color: white;}#sk-container-id-30 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-30 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-30 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-30 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-30 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-30 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-30 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-30 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-30 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-30 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-30 div.sk-item {position: relative;z-index: 1;}#sk-container-id-30 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-30 div.sk-item::before, #sk-container-id-30 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-30 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-30 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-30 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-30 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-30 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-30 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-30 div.sk-label-container {text-align: center;}#sk-container-id-30 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-30 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" checked><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "# Assuming X_train and y_train are the training data and labels\n",
    "svm_q2 = SVC(kernel='linear')\n",
    "svm_q2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_class_regions_for_classifier(svm_q2, X_train, y_train, title= \"SVM Boundaries\" ,plot_decision_regions=True, target_names=['class 0', 'class 1'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "78139913-e3c0-41d8-916e-0c0919c85ad1",
   "metadata": {},
   "source": [
    "#### Question 3\n",
    "1. Train an SVM classifier using linear kernel with C=0.00002.\n",
    "2. Train another SVM classifier using linear kernel with C=200\n",
    "3. Plot the classification regions for both cases using the training dataset.\n",
    "4. Comment on the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "9daff9c5-2f89-4454-8b21-87cc3f1ab2ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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8zdXZpLUqVYrLpCaWUNKnt976mO+/D8TfP/66OnXc+fXX8VStWjL+SiVNREVFs2XLUQIDH1GqlC916pS3q56U589no1SpIrYOwy6dP3+VUqWCYi0TotExKWWVuNuqWyPFooQQJHRvYDAkXR+pWM7hwxdo3nw4Wm000dF6HB0dyJ07K9u2jadAgaSnWlTSD7scj1xJv1q0qMvvv7vEW37iBAQHO+HnV9QGUWU8T5+G8PbbQ3n8+DkhIeFERGgJDY3g6tV7NGgwGIPBkHQhSrqhErliUX36vMvhw158950j9+9DdDRs2gQffeTKuHG91aBnVrJw4dZ4bZgBDAYDDx8+Y+fO/6wflJJmVCJXLCprVm/27JmFVlufqlWd8fERzJxZkDlzvuWDD962dXgZxtGjlwgP15pdp9NFc+bMDesGpKQpVUeuWJyPT1bmzh3KnDlDMBgMODqqu3BrK1gwF87OTkRFRcdb5+rqRO7c8Tu+KOmXuiNX0owQQiVxG+nR4x2cnMz/eQshaNXKTLMiJd1SiVxR3kCFC+dm2rTeuLm54Oxs/DDVaFzw8NCwbt1I3NxcbRyheT4+xRAik8VePj7FXiuOkSPHMmnSTxY+O6Phw0eRP39pPD3zJL1xMqmqFUV5Q/Xs2Zx69SowZ85Grl27h59fUT79tBm5c2ezdWgJevDAssPOWro8S2jZ8h369OlJsWKWmzBHJXJFeYMVL56PyZM/t3UYdm3x4uVMmjQDIQTly5dhyZK5sdbPm7eQuXMXotNFUbRoIZYsmYu7uzurV6/j++/H4+joSKZM3gQE/MPZs+f5+OPe6HRRGAwG1qxZQrFisTs8Va/+lsXPQSVyRVEyLGsPY5tWVB25oigZlrWHsU0rKpEripJhWXsY27SiErmiKBmWtYexTSsqkSuKYjdy5Up82FlLlxdzGNsKFWoyaNDweNu8GMa2UaM2lCxZ/OXywYO/pVw5f8qWrU6dOjWpUKEcK1eupWzZ6lSsWIsLFy7x0Ucd45U3ZMi35MtXivDwcPLlK8XIkWNTfZ5qGFtFUWxGDWObsJQMY6vuyBVFUdI51fxQUd4AERFaNm06xOPHwfj5FaVq1ZJq7PcMRCVyRUnn/vnnMO+//wMA0dEGHBwEJUrkY/PmseTIkdm2wSVJJqvlSEZjrPJOfrW3SuRW9OxZKGvW7CEo6Dl+fkVp2NAPBwdVu6W8vmvX7tG+/ah4Q9aePn2D1q1HsH//dBtFljwaTTRBQcFky5ZJJXMTKSVBQcFoNPFHrkyISuRWsnLlLnr1mkSdOoJ8+aJYssQFJ6dsbNo0kTx5sts6PCWdmjHjT6Ki4vcejIqK5uTJq5w9e4MyZQpaP7BkypcvhMBAePToMaASuZFEo4kmX76QZO+hErkVnD9/kz59JrF+vZYyZYzLpIxgwoR7dOz4HQEBqgWP8nqOHLlodsxxAEdHR86evWnXidzZWVKo0HNbh5Huqe/1VvDLL3/StWv0yyQOIAR8+aWe69dvcerUNdsFp6RrBQvmwsHB/J2slJI8eex3pEPFclQit4KLF6/x1lvxv/46OYGfnyMXL962QVTKm6BPn9ZoNPEnuwbInNmTGjVKWzkixRZUIrcCX988nD8f/65JSjh/3kCBArlsEJXyJqhevTRDhryPm5srjo7GP2d3d1cyZfJgw4ZR6mF6BqHqyK2gZ882tGq1h3bttOTL92r5okUCd/esvPVWCdsFp6R7I0Z8yLvv1mTevE3cvfuEmjXL8PHHTciSxcvWoSlWohK5FVSpUoKhQ7tRv/5vdOhgwNc3mt27NZw/78rWrT+oZldKqpUvX5gZM/raOgzFRlQit5L+/dvTokUNlizZyp07T3j//dK8/3493N01tg5NUZR0TiVyKypSJA8jR3azdRiKorxhMuyTkOhoPT///Cd+fl3x8WlNgwZfsGHDAVuHZRPBwWEsW7aDOXM2cvbsDVuHoyhKCqX6jlwIkR9YDPgABmCulNKu+wVLKenceSR3757g+++1FC0KBw5cYsCAMVy+/CGDBr1v6xCtZv78TQwePIsaNRzJmlXPyJHg71+epUtHqGofRUknUj0euRAiN5BbSnlcCOEFHAPaSCnPJbSPrccj3779OH36jGDXrkhcXV8tDwyEOnVcuHJlOdmyedssPmvZs+c0HTv+jz//NH6YAeh00Lu3C9my1WHu3KG2DVBRlFjSbDxyKeU9KeVx088hwHkgb2rLTUurV2/ngw9iJ3GAfPmgbl0HNm48aJvArGz69OUMGvQqiQO4uMCECTpWrw7gyRPLdJ0OD4/k8OELnD17A1tMZKIobzqL1pELIQoCfsAhM+t6CiGOCiGOPnoUbMnDpphWq8XDw/w6T09JZKTOugHZyNmz16lePf7yrFmhUCFnrl69l6rypZT8+OMSfH070LPnUJo160v58h+xd++ZVJWrKEpsFkvkQghPYA0wQEoZ71ZOSjlXSllFSlklR45Mljrsa3n7bX/++suNuDeH4eGwZYukYUM/2wRmZXnyZOPKlfjLIyPh9u0ofHyypKr8ceOWsnr1KrZvj+Tff8M5cSKSIUPu8+67/+P8+ZupKltJmZMnr9K790+0bPktY8b8zsOHT20dkmJBFknkQghnjEl8mZRyrSXKTEsdOtQhKCgz333nRLDpy8GtW/Dxx640a1adokXtumbIYnr0aMuUKRpCQ2MvnzXLgUqVipE//+tPhBsZqWPKlFX8+mskBQoYlwkBLVpAz546Jk9enorIlZQYM2YZ/v79mTt3Exs3HmTMmGUULdqVgwcTfIylpDOpTuTC2C3xV+C8lHJK6kNKe66uLuzc+RNBQW9RoYIzfn4aGjRwpVKlFsyf/z9bh2c1nTrVp1q1WtStq2HaNMHixdCpk4blyzMzb97XqSr7/Plb5MrlQKFC8dc1a2Zg797/UlW+kjzHj1/mxx+XExGhRa83ABARoSMkJIJWrUag18cfzE1JfyzRIagm8CFwWgjxn2nZ11LKvy1QdprJkSMzy5ePIjg4jKCg5+TOnRU3N9ekd3yDCCGYM2cI+/Y1Z/nyLdy4EUaHDm/RqVP9VDc99PJy48kTPQYDxB23KSjIuF5Je7NmrScyMsrsushIHTt3/kejRpWtHJViaalO5FLKvaTjqT0yZfIgU6YEnnxmAEIIatUqS61aZS1abtGiecmTJyfr1t2mXbtXy6WE2bNd6dSpmUWPp5h38+ZDDAaD2XVSSu7de2LliJS0kGF7dippb9asIXz9tYYxYxw5eRL+/Rc6d3bl+fN8fP55S1uHlyFUq1YSV1dns+sMBgNlyxa0bkBKmlCJXEkzVauWZP/+X4iMbEzfvjmZOLEA77zTne3bp6teo1bSq1dLnJwc4y13cnKkVClfKlUqZoOoFEtLdc/O12Hrnp2KkpHs2nWSd98dgV4v0ev1CCEoViwvW7aMJWfO1DUxVawroZ6davRDRXnD1atXgQcPVrN58xEePHhGhQqFeeutEmoc/DeISuSKkgG4uDjTqlWNFO1z8eJtfvrpT/777wpFiuShX782VKmiZrOyRyqRK4oSzx9/BNC16wR0umiio/UcPHiBNWv2MGpUV778soOtw1PiUA87FUWJJTg4jK5dJxAeriU62thhyGAwEB6u5dtvF3Llyh0bR6jEpRK5orwh9Ho9GzYcoFu3H+jSZQSLFm1NcAC4yEgdAQGn2LPnNFpt7G3WrdubYP15dLSB337bYvHYldRRVSuK8gbQ6aJo0+Z/3L178eUQzYsWHWfSpKX8++8Msmd/NVDdrFnrGTp0Pg4OxmQtJUyc+CmffdYCgMePn6PTme8NGhUVrToR2SGVyJUE7d9/llmzVnP16m0KFsxLr17tqVOnvK3DUsyYOvUPoqPPs22bDifTX3WXLpEMH/6IQYOms3jxdwCsWPEvgwfPJTxcG2v/QYN+IUeOTLRtWxs/vyK4uroQFRUR7zienhr8/Uun+fkoKaOqVhSzZs78kw4dhlK69H6+++4Wfn4H+eCDr5k0aYWtQ8uwDhw4R7Nmg3B3b0rWrC3o2XM8gYGPAFiw4C8GD36VxME42uRXX0Xz558HCQszJuXhw3+Ll8QBwsO1DB/+GwD161ckb95sODrGTg9CCDQaFzp3rp9GZ6i8LpXIlXju3n3Mt9/O4++/tfTqJaleHT79VPLPP1rGjVvCjRv3bR1ihrNjx3FatRpCkyanOX9eT0CAFg+PndSs2Yu7dx/z4EEIRYrE3y9rVnB3dyA4OAytVpfo/92FC7fR6/U4ODjw77+TqFixCO7urnh7u+Pp6UahQj4EBEzBw0MNeGZvVNWKEs+qVQE0bw6+vrGX58kDbdsa+P33nXz9dWfbBJcBSSn58svpTJ+u5Z13jMu8veG77wxERIQyadIKypbNx75912jRIva+Fy8COJIjR2YcHR1wcnJEp4s2exyNxhkH01CVuXNn4+jRWZw6dY2LF2+TP38OqlUrpToR2Sl1R/4Gk1KyadMh2rQZQtWqH/Pxx2M4ccLMlEBxPHsWSq5c5ls7+PhE8/SpZebyVJLn1q2H3Lv3mCZNXi0LC4OdO6F4cT1r1+5i0KAPGDnSlcDAV9s8fw5ffeXKF1+0xdnZCQcHB9q3r2N27BVnZ0c6dqwfL1GXL1+YDh3qUr16aZXE7ZhK5G+wL7/8mUGDfqBRoxOMHh1IwYK7adp0AMuX70x0v2rVSrJjR/yp8AC2bXOjRg3LDnmrJE6vN+DkJHiRR2fPhuLF4eOP4bvv4NatYK5du0fv3h9Su7YzH3zgxqefavDzc6Z8+Xp8/fUHL8uaPPkzcuXKgpuby8tlbm4u5M6djQkTPrX2qSkWogbNekMdOnSeDh0GExCgJVOMKVLPnoVWrVy5dWs1np7m6zoNBgNVqvSgXr17DBmix9UVdDqYNs2Rv/7KzqlTi8ze1SlpQ0pJyZKdmTTpMQ8fQt++EBGnQYm7uyu//NKf5s2rsXnzUXS6KBo29CNXrizs3PkfISHhVK1akgIFcvHsWShz5mxk+fJ/EULQuXMDevZsnqHH5U8vEho0SyXyN1TfvlPJlOkfvvwy/v9vp05udO06gE6dGiS4/4MHT+nadRTHj1+iVClnLl6MpkyZQixePIK8ebOnZeh2Kzpaz4ED5wgPj6Rq1ZJkyeJltWP/8UcA/ftPQK/X8uCB+W0KFszF9etLX75fv34/H344HjB+GERFRdOiRXWWLBmGRuNivhDFrqnRDzOY4ODnFC9u/kM6Vy4DwcFhie6fK1cWNm+eytWrd7l27R4FC+aiWLF8aRFqurBhwwF6955Itmx6vL3h9OloevZswdixn718QJiW2revg6OjA23bfp/gNrduPUSr1eHq6sLJk1fp1OnHeE0NN206xOefT2PhwiGxlkdG6pg1az3Tp68jODiMypWLM2lST/z8iqbJ+SiWperI31DVq/uxdWv8yRuio2HHDqhevVSyyilSJA+NGlXO0En86NGL9OgxhrlzQ/j333D++iucgwd1BARsYvToRVaLo02bmrHqtuNydnbC2dl4bzZ+/Eqzc3VGROhYsWIXQUGvHlhrtTqqV+/LV1/N5dathwQHh7Fz5wkqV+7NkiXbLH8iisWpRP6G+vDDtzl1ypW5cwXRptZmoaHw5ZfOlC9fgooV1Z1Wck2atJSBA3VUr/5qWc6cMHu2lhkz1hIREb+DTVoQQvDBBw1xcYn/RdrY6qTey28Hhw9fSHCuTo3GhfPnb718/9tvWzh16hpxq1mllHTrNhGdznwLJsV+qET+hvLycmf79mn89Vch/Pxcad3ag4oVXYiKqsKKFaNsHV66cvjwBd5+O341VYECkD274MqVu1aLZdy4T8iXLwfu7q4vl7m7u5I3b3YmTfrs5bIcOTInWEZUVDQ5crx6Aj558h9mWygBGAyS6dPXpTpuJW2pOvI3WLFi+di/fw4XLtzi7t0gSpTIn2EfVKZGliwe3Lv3jKJxvsTodPD4cTRZsnhaLZasWb05eXIOixZtZdmynUgp6dy5Pt26NcHLy/3ldn37tuH06euEhUXG2l8IQaFCPpQokf/lsqdPQxM95qlTNyx6DorlqURuY1JKQkLCcXNzfVm/aWklS/pSsqRv0hsqZn34YUtmzPiNGjW0OMZodblkiaBs2ULky5fDqvF4errxxRet+eKL1glu07FjPdas2cOWLUdfJnONxgWNxoWVK7+JtW3p0gXYs+d0gmWVLVvAMoEraUY1P7QRKSVz525k4sSl3L8fjIODA++9V5tx43rHGnJUsT2tVkfz5oOJjLxG9+6RZM4MGzc6s3mzKzt2TKNUKftMdAaDgX/+OcK8eX/z9GkIjRtXoWfPZvGqXY4cuUjVqn3MluHo6MCdOyvIlUtN0mwPVDtyOzN69CJWrfqDyZMjqVIFHj2CqVMd2bMnO4cPz1MDE9kZnS6KFSt2sWLF34SHR1C3bjV69WqNj09WW4dmEV9//Svjxq2M9cDTycmRn3/uy2efNbdhZEpMKpHbkadPQyhUqCP79unIk+fVcimhSxdXWrX6jF69WtouQCVDOnbsEmPG/M7Fi4FUrFiEwYM7qNZNdkZ1CLIjAQGnqVLFiTx5YjfrEgLef1/LmjW7VCJXrK5y5eKsXTvS1mEoryFdJ3IpJXv2nGbVqh1ERkbSoEE12rWrhaurfXc/dnAQ6PXm10VH83IKrtQ4duwSW7cew9nZiXffrUmRInmS3klRlHQp3bYj1+v1dO36A927Dydbtn8oVWonv/wyjcqVP+Hhw6e2Di9R9epV4ORJPdevx14uJSxbpuHddxu9dtmRkTrefXcYbdsO4s6dRZw/v4Bq1T7lq69mxuvwoSjKm8EiiVwIsUAI8VAIccYS5SXHggVbuHjxMAEBkQwYIOneHf78M4L69R/Sp89ka4XxWry83BkxohvvvefK1q3G9shXrkDv3s6EheWiY8fXn0pr+PC5REef5sgRLaNH65k0KZojR3SsW/cnRYp05K+/9lvwTBRFsQeWuiNfCDS1UFnJMn/+Gr76KhK3GI07jHMU6tm69ThPntj35Af9+7dn7NjBTJqUn7x5BS1buuPr25Lt26e/9sh0Wq2OhQs38+OPOpydXy3PnBnGjgVn5yf06/cjCxb8Y5mTUBTFLlgkkUspA4Anligrue7ceUKJEvGXe3tD9uxOPHoUbM1wXkuHDnU5cmQB0dFbuX//LyZM6IW39+uPCf3oUTAuLpDPzPhWlSvDgwewbJmW4cPnoNPFH1BJsX9SShYv3kbp0j3w8mpJyZLdWbBgM1FR0SxatJWqVb+gaNGudO06gXPnbto6XMVK0u3DztKl83Po0Hny54+9/O5dCArSky9fxuuKni2bNxERkvv3wccn9rozZyB/fihTBnLnlhw+fJFatexnpp+nT0MArDrGd3rUp88MFi3a9rK35sWLt+nb92dGjFjE06ehL5ffuHGfP/4I4K+/RvH225VsGbJiBVZ72CmE6CmEOCqEOGqJu+X+/bswdmzsOQq1Whg61IWuXRtnyA41bm6udOpUn1GjnIk58F1YGIwZY5waDIxVUPby4HPPntPUrNkTX9/38PV9j5o1exIQcMrWYdmlCxdu8dtvW+KNnxIeriUw8HGs5Xq9gfBwLe+//wN//BFAQMCpBEdDVNI/qyVyKeVcKWUVKWWVmCOvva7mzavRp8+H1KnjwiefaBg40IWKFV3w8KjE+PG9LBBx+jRx4hfcu1eIGjWcmDTJmMD9/aFECejaFS5cgMBAqFrVTL2UlR04cI527f5H9+7XuXYtmmvXounR4zrt2n3Nvn1We26ebqxbt4+oqATarSbgyZMQunadQIsW35A3b0d1Xd9QFuvZKYQoCGyUUib5fd2SPTsfPXrGhg0HiYjQUr9+RUqXts9xL6zJYDDw55/76NlzAoULR/K//0GdOrBrFwwe7MqQIT35/PNWtg6Txo3707r1OTp3jr18xQpYs6Yk27fPsE1gdmjfvjN06zYx1UPmenq6cf78r1Yf6EuxjDTtoi+EWA7UA7IDD4ARUspfE9o+o3fRt5Z794IYPnwOq1btISrKQMmSufj66+68/349W4eGXq/H1fUdbt+WaOJMZKTVQv78goiIf9Qkz8DcuZsYOHB2vGnbXoeLizMDBrzL+PGfWiAyxdrStIu+lLKTJcpRLCt37mwsWPA18+bp0emicXNzTXqnBBgMBnbu/I/9+8/i5eVOhw51UnVXJ4TA0VGg08VP5Dqdcb1IfQfXdO/AgXP06fMzUVHRCW7j6upMVJQ+WXXgOl0Ue/ao6pU3Tbrt2akkn6OjY6qSeFDQc/z9P2PAgJEEBy/mv/9+pXz5bkyevPK1y3RwcKBly8osWRI/Wy9dKmjRohKOjpa/G3/2LJR9+85w4cKtpDe2oSdPnlO79kDq1v0y0STu6OhA584NGDXqo2QP7aCGpH3zpNvmh4r1fPLJj1SqFMiYMdGmu+QoBg+G5s2XUKlSCerXr/ha5Y4e/Rn16p1Bp4ukUydjFd+KFYJfftHw77+fWyx+ME5v9tVXP7N48TaKFXPm7t1ofHxy8uuvw6lQoYhFj2UJLVp8y7FjlxJN4mCckLlnz2Y0bDgEgyHpalIPDw29e6sB2d40dpXIQ0MjePjwGT4+WXB3jz8DfEKklGzefITff/+HkJBQatasQo8e75A1q3caRpsx3LnzmICA05w6FR2rqiNvXhgwQMvMmStfO5GXKlWAgICZjBnzGzVqHAagRYuq7N7dzeIzGvXrN5WrV3dz6JCOHDl06PWwatVtmjQZxIkTC8idO5tFj5caJ09e5eTJq+h0iSdxT083mjevyqhRS5NVf+7hoaFTp/opalceFRXNxo0HuXLlLoUK+dCqlT8uLs5J76hYlV0k8ufPwxg06Cf++GMPmTI58vy5gS5dGjJhQu8kE7rBYKBr1x84duwI3btHkj07bN58nunTV7J9+zQ1xVkqXb9+n2LFnHF3jz+TesWKsGhRYPydUqBEifwsXvxdqspIyv37T1ix4l9OnIgik6nlq6MjdOoEx45FMXv2X4wa1T1NY0iJEyeuIJJ4QODo6IC3tzsdO9ZnyJB5iW6r0bhQpUpxvv22C40aVU6y7BdOnrxKo0ZDiYzUERGhw83NBWdnJzZvHstbb9m++aryis3ryA0GA82bf4VWu4fDh6P4779I9u3TcffuDtq3H55kx5UVK3Zx7twRtm+PpEcPaN0aZs/WMnBgKD16jLHSWby5fH1zcuVKFBER8dedOQMFC+a2flApdPToJapUcX6ZxGNq2jSK/fuPWT+oROTIkRlHx4T/NB0dHahWrSQHDvyEm5trksM6REbqOH78Mp6ebslO4pGROho2HMKjR8GEhEQQHa0nJCSCJ09CaNRoKGFhZn4hFJuxeSLftu0YwcF3mDYtihymRhC5c8OcOTouXLjE4cMXEt1/wYK19OsXe/AsgI8+kly7dofLl1N3x5jR+frmpFq1kkya5EjMz9SHD2HqVFd69epgu+CSycvLjcePzd8QPH4MXl6eVo4ocY0aVcLBwfinqdGAszM4OICHBzg7O7JmzQj27ZuOr29OAHr3bpnkw+zwcC2DBv2S7BjWrduLVmt+PJ7oaD0rVuxKdllK2rN5It++/SitWkXgECcSJydo3lzH9u3HE93/4cNnFCwYf7mTE+TLZ5+DZ126FMgPPyzjm28WsGPHcbvpLp+QBQu+YceOXDRt6sbUqfD1147UquXCxx+3p0mTt2wdXpJq1ixLUJAze/fGXq7Twbx5Grp0sa85KV1cnJk9ux9ubtC7t/Gbz61bMH48uLuDp2fs6sYvvmhNhQqFcXdPPJkfOXIJrTZ+FZk558/fJjTU/F13WFgkZ8/eSFY5inXYPJG7uroQHm4+jPBwhySHdPXzK8Hu3fG/LgYFweXLOkqUyG9mL9uQUjJs2C/UqvUZgYFL0OmW06/fSOrW/YLnz8NsHV6CcuXKwrFjCxg+fBgREW3Jk6cLBw7M5dtvu9k6tGRxcnJk3rxh9OjhyuTJDvz3H2zcCK1aaShUqBzvvlvT1iHGs3fvCT75xJFvvoEcOYwJvHNnmDlTz1dfTY/14a/RuLB792Rmz+6XaJlC8PJOPym+vjnw8DD/fMrNzZWCBX3MrlNsw+aTL//33xWaNx/AgQNavGIMfPfkCVSv7sKhQ/MpXDjhetgTJ67QpMkAVqzQ4udnXBYeDp9/7kK+fPWYPXtwWp5KiqxatZvvv5/Ehg2RZDE15TUYYOBAZxwda/Lbb8NtG+Ab7syZ60yfvpIjR86SLVsmunZtTZcuDdKkvXpq+fq2448/nlOsWOzlBgOULOnCiRMLzXbIatbsazZvPmr2W169euX599/kTbry/HkYefJ0jDdAF4C7uyu3bv1OtmyqVZi12e3kyxUrFqVVq3q0abObYcMiKVcOTpyAH3/U0KNH80STOICfX1F++WUonTpNonhxyJ5dsmePnmbNqjFtWn8rnUXyzJy5gqFDXyVxMNZ9jhgRReXK+5g6NZTMme2rvvZNUrZsIebNG/Za+wYFPefPP/fx/Hk4NWuW4a23SiT7weHrMBgk5j5fjHfVJNhmfNKkz9i79wyhoZEvk7kQAnd3V6ZOTf5gct7eHqxbN5J33x2JwWAgIkKHRuOCg4Ng5cpvVBK3Mza/IwdjlcPChVuYPXsVN28+pkiRXPTt24mOHesn+49Fq9WxffsJnj8Pw9+/tF1+9cufvy2bNoXEG0MdoFo1N/76awalSqlBv+zNvHkbGTJkNg0aOJA9ezRbtzpSpEhh1q4dm6qJQBLTs+d4Mmfeyf/+F7vb/c6dMGJELk6fXpLg38b58zcZNuxXNm8+gpTGh6fjxn1CuXKFUhzHo0fPWLhwK2fP3qBkSV+6dWuMj0/W1zonJfXSdNCslMqog2bVrduLHj2u0DJOx7onT6ByZWdu3Fil7sjtzP79Z+nQYSjr12spXNi4TK+Hfv0gKKgsW7ZMTZPjXr16l5o1e9G3bwQffWQcj2bzZuPolfPmDadlS/80Oa5i3xJK5DZ/2JmRfPFFR8aP1/D06atlBgN8/70zbdvWVEncDv300wr69XuVxMHYmWjcONiz5wwbNhxIk+MWKZKHf//9iaNHK1G8uCO+vo7MnFmAhQtHpGkS1+v1nDt3k8uXA+2+NZXyis3ryDOSDh3qcPz4eapV20Dbtnq8vfVs2uRGjhz52bBhgK3DU8y4cOEGn30Wf7mXl3GyjlGjFqRZYi1VqgDr1o1Dq9URHa1P01mvQkMj6N59ImvX7sNgMODo6EiuXFlYuHCwmiouHVB35FYkhGDcuM/Zv38Ovr4f4eraiZ9//p5du35Os7pWJXXy5s3BBTN90rRa40xLJ07cIDo6ZbP2JCU4OIyxY3+ncuVulCvXhWHD5hAUFGLRY8QUEhJOiRLdWL16D3q9ASmNnX7u3HlMy5bfJNkpT7E9dUduA8WK5WP48M5Jb6jYXM+e7fnii9M0aUKs1kYzZ0Lp0nDwoINFx01/+jSE2rW/oGTJIEaP1uHmBuvWbaJq1e3s3PkTpUsXIDQ0gp07TxAdradu3QqpbkEydeoa7t59YnZdZGQUw4f/xrZt41N1DCVtqUSuKIlo1cqf//0vJ9WqPaRrV8iZ0/jQ8dYtaNcOsmXzs2g79AkTllOx4iNmzHg18mHFinry549gwIAptGhRj2HDfsXZ2XhMnS6avn3bMH78J6/dHHL+/L8TXb93r5qIwt6pRK4oiRBCsHr1GGrX7sPhw1ry5YOmTeH5c5g3z40dO8xUoKfCihXbWLo0/vC1H3wgGTXqInv3XiEiQhdrELOZM9fj45OFQYPav9Yxw8MT77bv4qLShDnR0Xr+/HMfq1dvITJSS6NGtejatTFeXu5Wj0XVkStKEsqUKci+fTPJn78mGzc688MPzly7VoNdu2ZQtmzK22YnJixMG6sK5wWNBpycjB1z4goPj2Ts2OXJmurNnHr1KiS6vkuXhq9V7ptMq9XRvPlXjB8/kdq1D9OmzUm2bZtPpUrduXPnsdXjUe3IFcWOvPvuMGrVOkb3OMOjHztmHKI5PNz8fq6uzty+/Ts5cmRO8TFPn75O1apfEBkZf7RDb293Ll9eSM6canq4mCZOXMm2bYtZtkyHU4wvLGPHOnDlSiXWrh2bJsdV7cgVJR0YNqwr48a5snMnL4cNPn8eevd2xdEx8dENPT1fr3liuXKF2Lx5LHnzZsfJyfjwVghBvXoVuHp1MdmzZ+LZs9Akp53LSBYuXM+gQbGTOECfPgZ27DjJs2ehVo1HVX4pih2pVq0US5aMoH//KURFheLm5sCTJ4JvvulKYOATpk1bS2Rk7OoVJydHWrXyT9UE23XrVuD27d+5cOEW4eFaypQpiIuLExMnrmbChJWEhkbg4GCc6HnKlM/JlCljN5d99CiEAmZG0/DygkyZHHn6NMSqHfxUIlfShVu3HjJhwlI2bNiHlNCiRQ2GDv2AAgVy2Tq0ZIuI0LJy5S4OHPiPTJky8cEHjSlfvnC87Zo2fYsLF37n7Nkb6HTRlC1bEBcXZ8LCItiy5SiXLgW+HJXQw0ND9uze/Pxzn1THJ4SINdZPr17TWbx4W6z5QJct28GhQxc4fnxWhp6708+vEHv2nKNDnHlVrlwBrdaBvHmzWzUeVbWi2L2rV+/i7/8Zzs5bWb36OWvWPEej2Yq//2dcuXLH1uEly/Xr9yhb9kN+//1nihbdjsGwjiZN+vHNN+bn2xRCULZsISpVKvYyYXp4uHHw4E/MmTOAxo0rU69eBSZO7MmZM/MtXocdGPiIhQu3xpvUWauN4ubNB/z5536LHi+9+eqrjxg92pVLl14te/oUBgxw5Ysv3rX6h5x62KnYvc6dR1C48AG+/DL27+rUqYJLl6qxYsVoG0WWfLVr96Jx42v06fOqZUlQEDRtqmHmzO/sbqalBQs206/fTLPjkQO0b1+H1au/tXJU9mX+/E0MHTqb8uUdcXeX7N8fTdeuTZg8uU+ajXFvt+ORK0piDAYDa9ce4ty5+Dcc3btLSpY8gsFgSPbMN7Zw8eJtrl69zeefx24emC0b9OkTybx5a+0ukTs6Jt5j1cnJfq+3tXzySXM6dWrAjh0niIyM4tdfy9lsiF+VyBW7JqUkOtqAh5lna+7uoNcb0OvtO5EHBj6iaFEnnJy08daVLAkrVjywQVSJa9r0rQTHkPH0dKNTp/pWjsg+eXi40apVDVuHoerIFfvm6OhIrVpF2bAh/roNG6B69cI4O9v3/UixYvk4dy7KbBvww4cFJUpYtlORJeTKlYUvv+wQb0JnjcaF8uUL0bx5NRtFppijErli90aM6Mnw4a5s22Ycv11K2L4dvv7alZEje9o6vCT5+uakTp3yjBzpTMzOl5cuwaxZLvTp857tgkvE6NHdmDWrH0WK5MbBQZAtmzdffdWBHTsm2uU8pxmZetippAubNx9hyJCfePDAOEpfzpxZGT++D82apY87w6dPQ3j33WHcvn2Lt9/Wc/++IwEBBqZO7Uu3bk1tHZ6STqTpVG9CiKbAdMARmC+lHJfY9iqRK69DSsnt24+QUuLrmzNNJz9OC1JKDh48z8GD58mc2YM2bWqSJYuXrcNS0pE0a7UihHAEZgKNgEDgiBBivZTyXGrLVpSYhBD4+ua0dRivTQiBv39p/P1L2zoU5Q1jiadEVYErUsprAEKIFUBrQCVyRUkDBoOBbduOcfz4FbJl86Z9+9pkzRp/comwsAguXgwkc2ZPChfObYNIFWuxRCLPC9yO8T4QSB8Vl4pNSSlZu3Yvv/66lvv3n1CuXFH69XufypWL2zo0u3XnzmPq1fuS+/efEh6uRaNxpn//WcyfP+jlcLMGg4FvvvmN6dPX4eTkSFSUniJFcrN06TAqVChi4zNQ0oIlErm5isp4Fe9CiJ5ATyBdfz1WLENKSe/ek9mzZzf9+kVSpAjs23ePZs0OMX36l3TsmHA75f/+u8L8+eu5c+c+ZcoUp2fPVhnmd6pVq++4cePByzbeL7rQ9+w5lYoVi1CmTEGGDJnH7NkbYnWvP3PmBrVrD+L06TlcvnyX/fvPkjmzJx061CF37mw2ORfFclL9sFMI4Q+MlFI2Mb3/H4CUMsEBedXDTmXPntN06/Y1//4biVeM531nzkCbNq7curXa7KzxU6asZOLEJXTvHkWxYgYOHXLmjz8c+f33kTRqVNmKZ5C2goPDePToGXnyZMPdXQPAqVPX8PfvF2/8EzD2xOzevSkTJ/bEx+e9eCMkAjg7O+Lt7YFWG0VYWASuri4AjBvXg/79274c1Ovvvw/j7e1Ot25NqFmzTLp7qPwmS8su+keAYkKIQsAdoCOgZhZWErVs2T989JE2VhIHKFsW/Pwc+OefI7RvXyfWunPnbjJu3GJ27tSRN69xWevWUbRsGUWXLt9z69YfaDQuVjqDtPH4cTD9+k1h06YjZM3qRHCwgY8/bsrYsZ9x+fIdnJzMt9/W6w2cOnWNY8cu4erqbDaRR0XpCQp6/vL9i22+/noBvr456d9/Fk+fhhIaGoEQguXLd1GxYmE6d26Iv38pKlUqljYnraRaqjsESSmjgT7AFuA8sEpKeTa15SpvtufPQ8iZ0/y3wRw5DAQHh8VbvnDh33z4of5lEn/B3x/KlhVs3HgwLUK1Gp0uioYN+5Mp02FOnozi+PEIdu/Wcvz4X1Ss2A0hjAnbHAcHB4oVy4unp1uKp3yLiNDx6adTuXs3iNBQ42SgUkrCwyPZv/8cAwfOpnbtgdSs2d/qEyYoyWORnp1Syr+llMWllEWklGMsUabyZvP3r8TWrZp4y3U6+PdfSY0a8ZvoPXjwiIIFzY//UahQNA8ePLV4nNa0bt0+vLyeMGZMNJkyGZflywe//w737z+kR4+xZMvmbbaqw/jQ812qVCn+siomuaSUPHnyPMEPiaioaMLDtRw9eplOnX5M8XkpaU910VdsomvXxhw75sLcuYJo0wxiz59D377O+PuXizXBwQsVKpRi7974s+BICXv2OKT7Fhnbtx+kdeuIeKMOajTQsiU0bRpF5sxO5MiRCU9PY7J2cXFCo3Hhhx8+pkqVEjg4OLBkyVDc3V1xcBAxynBJtK7b0THpVKDTRbFr10lu3rS/Qb4yOpXIFZvw9vZgx47pbNhQmAoVXGne3IOKFZ1xc/Nn6dIRZvfp1q0Ju3c7sn79q2V6PYwd60iWLD7UrFnGStGnDVdXF0JDzSfb0FCoVg3u33/M7t2TmTmzH59/3oL//a8T587NZ+DAdi+3bdSoMgcO/ET79nXIkycbpUv7MmHCJ1SrVtJswnZ3d0WI5KUCV1dnLl68nfSGNmDsOXuOCRNWMmvWeu7ff2LrkKxGjbWi2NylS4Hcv/+EEiXykytX4jPdHD16kfff/xZv70iKF4dDhyT58uXljz9+tNlY0JYSEHCK7t2HExAQiVuMBjt370LNmnD4MLRo4cbatdMpWzblIybevfuYWrUG8vhxMCEhETg7O+Ho6MCwYR25ceM+K1bsMvuQNCZ3d1cOHpxBuXL2NWJjSEg4bdv+j2vXrtGkSRRPnzqxebOBb7/tyqBB79s6vEQ9fx7GxIkrWLZsM8HBEfj7l2DYsI+pVatsvG3VxBKK3SpePB/Fi+dL1rZVqpTg0qXl7Np1krt3gxgypMAb04Godu1yVKtWmZYtD/DNNwZKlIBDh+CHH2DgQHjyBJ4/F8m+VnHlyZOdS5cW8tdf+9mz5zRZs3rRpUtDihTJQ0SElvv3n7J790kMBolWGxVvfyEEBQv62F0SB+jTZzK5cl1m+fIojAMz6gkMhJYtl1CuXFG7bZoaFhZB/fp9KVr0PosXR5EzJ2zZcor27Ycxa9ZQ2ratnaxy1B25otgRvV7P998v4aeffsfRUVKmDHz2GRQvDt27u/Lxxx+m6R3m2bM32L37FOvXHyAg4BRabTQGgwEPDw1ubi7s3TuNEiXyp9nxX0dQ0HOKFOnIiRNRZM4ce93SpbBtW0XWr59ok9iSMm3aGrZt+40lS7Sxno0cPAiff+7NtWurYg0ZrO7IFSUdcHR0ZNSobjRqVJkvvpjI9etPGDvWkYcPJUOGdGbgwLQdu7xMmYKUKVOQ3r1bcfToRX79dTMPHz6jYUM/Pvzwbby83NP0+K/jxo37FCjgTObM8b9FVKkCP/9sn3X6AGvXbqVvX228B9zVq4OnZxRHj16iWrVSSZajErmi2KHatctx8uQiLl68TXi4ltKlC1i9s1OVKiWoUqWEVY/5OvLmzc7t21GEhRFvSsBz58DXN4dtAksGnS4a9wQ+G93dBTpddLLKUa1WFMVOCSEoWdKXSpWKpfseq2nJxycrdeqUY9IkR2LWFAcHw5QpGj77rIPtgkvC22/7s3atc7zl16/D9esGKldOXm9alciV16LX61Pcg1BJuehoPXfvPiYsLMLWodi1OXOGsWNHTlq0cGPmTPjhBwdq1HClSZPGtGuXvAeGttCnT1u2bNEwY4Z4Oafrf//BBx+4MmxYl2R37lJVK0qKHDlykREj5rBt2xmEEDRrVpHRoz+3y5YM6ZnBYGDixBVMn74KvT6aiAgDrVtXY/LkfuTMmXgTzYwoV64sHD++gL/+2s/u3cfx9PTgn38aWK2TWGSkjj/+CCAg4Bienh507twoWdVSPj5Z2bVrBgMHTmPSpDO4uzui0WgYOvRDPv+8VbKPr1qtKMl2+PAFmjf/iuHDtbz3nrEzzu+/w+TJGnbu/Om12jYr5n355Qz27dvClClaSpWCp09hyhRHdu7MztGj81/eqUVG6pBS4uYWv8erYh137z6mQYP+5MkTwjvvRPDkiQPLljnTpk1Dpk8fkOzRI589Mw5Yljt31gQnt07TOTtTSiXy9KlRo360bn2eLl1iL589W3DixFusXq2G2bGE+/efUKrUBxw7FkWWODffnTppaNeuF35+RejXbxaHD18AoGLFIkyb1ttsJxIlbTVv/iVly55h2LBXVY3Pn0Pz5hpGjPiKDh3qWuxYCSVyVUeuJEtkpI6AgIu0bx9/XZcukg0bjmGLm4I30a5dJ6ld2yleEgdo2zaSlSs3U7fuVxw4cA693oBeb+DYscs0aTKMvXvPvNYxpZTcuHGfqVPX8NZbX1CiRDc++2wqV67cSeXZvNkCAx9x6NAFBgyI/bzI2xsGDoxk7tw1VolD1ZErSiIMBgPbtx9n376zeHu78957dcmfP21nI3JyckSrNf91PCICzpy5Q3h4ZLx14eFaBg6czZEjM1N0vI0bD9Knz8/cuvUw1ofxtWv3WbZsJ1u3jqNGjfQ9jk1auXs3CF9fZzSa+EMblCwJd+48skoc6o5cSRaNxoXatYuzxswNxvLl0KJFpTduJpmgoOf4+3/GV1+NIixsKadP/0bFih8zadKKND1uo0aVOXw4mhs3Yi/X62HJElcePQpJcN8TJ64QERF/BqGEbNlyhPfe+4GbNx/E+0YVHa0nLCySzp3Hqm9bCShcODfXr+sIDo6/7vBhKFky/iieaUElciXZxozpzciRrixZYrwzDAuD+fONbXVHjPjU1uFZ3KefjqVSpUB27Ypg+HCYOjWKgAAdM2YsZefOE2l23EyZPBg1qgft2rmyfj08ewYnTsBHH7mQKVNhHBwS/7NNyQfqoEG/JJn4Hz8O5tSpa8kuMyPJnj0TbdrU4H//c345HDPAzZswZYor/fp1skocKpEryVatWik2bZrE33+XoUABQeHCDuzZU5Ft26a9cc0P79x5zO7dJ/nmm+hY3afz5IEBA7TMnLkqTY/ft29bpk79ml9/LULFii707JmVmjU7sWnTJJo0qZJgsq5evVSyOw+FhkZw6VLSdeBOTo5mZ2xSjGbM+JLg4FJUruzK4MHO9OihoV49Z4YN+4R69SpYJQZVR66kSNWqJdm8eRrR0XqEIMFmUund9ev3KVbMBXf3+ON3+PnBwoWBaR5D69Y18PTUMGHCKi5dCmTHjpP4+RVj4sRPCQg4RWhoZKwqDw8PDdOn9waMkzeHh0eSK1eWBO/gE5r/My6tNirdT9qRljw93fj778kcO3aJvXvP4Onpxrx5NcmWzdtqMahErryW5CaB9KpAgZxcuRJFRASxxgYHOHUKChXKneYxjB+/klGjlhAebqz6uHHjAYcOXaB//zYcOjSDoUPns3nzUaSUNGhQkfHjP8HDQ0ODBoPZt+8MDg4OZMrkwejR3fj002bxyjc+9yjHrl0nE6wDd3d3pUePd8iUycPseuWVypWL22xIZdWOXFES0LLlYIoWPc233+pfVq88eADNmmn45ZcRNG4crzmvxdy585iiRbuanehBo3Hh5Mk58cYlv3cviDJlPuHZs7BYidnd3ZUxY7ozYEDbeGWdPXsDf/9+hIdr483Z6erqTM+ezZky5fM3/oM7vVDtyBUlhebP/5p///WhcWM3Jk+GYcOcqF3bhY8/bp+mSRxg9eqABNdFR+v5/fed8ZZPnvwHYWGR8e6uw8O1fPfdIrTa+B8KZcoU5NixWbz/fj0yZfIgc2ZP2rSpwerV33L//ip++ukLlcTTAVW1oigJyJUrC8eO/cqmTYfYt+80vr6eHDxYnyJF8qT5sUNCws3O0gPGRL5o0TYePHjKZ581p2LFogD89df+BIc9FcLYNLFUqQLMnbuJFSv+RQhBly4N+eSTd1i27H9pdi5K2lOJXFES4ejoSKtWNWjVqoZVj1uzZhk8PDSEhpof9fDGjfvMm/c3ixdv43//68Q333TB2TnhP2eDQRISEkG5cp/y+PHzl00Oz527xYwZf3L48M9kz54pTc5FSXuqakVR7FD9+hUpUiR3oslZrzcQHq5l7NjlHDt2ia5dG+HmZr7pobu7K7/9toV7957EajceEaElMPAxX3/9q8XPQbEelcgVxQ4JIdi5cyJvv+2Hq6szHh4Jj0sdGRnF7Nkb6N27FXnyZMfFJfZEBW5urvzyS3/WrNlDdLQ+3v5RUdEsXbpD9d5Mx1QiVxQ7lTWrN3///SPXry9h4MB2uLubH6rWYDBw69ZDvLzcOXLkZ/r2bUW2bN64ublSt255tm4dR9Omb8VrlRJTRIROJfJ0TCVyRbFzuXNno23bWgmud3V1pmrVkgBkyeLFpEmf8/jxGsLDN7Jr12Rq1SqLRuOS6GBfJUvmT7Lrv2K/1P+coqQDfn5FKV26AM7O8ZsCOjk50qtXy0T3F0IwZszHZu/qje3MP7ZYrIr1qUSuKOnEpk0/ULZsITw8NLi7a/DycidTJg8GDGhLu3bfU6jQh7z33miOH7+MwWBgyZJtVKrUi9y536dx46HkzZuNH3/sjqenBm9vD7y93fH0dGPy5M9p29Z+57VUkqZ6dipKOiKl5OjRS5w8eZWcOTOzaNE2tmw5SliYcXxyBweBRuOCn19R/vvv6svlYLzznjLlcz76qBEHDpxDCIG/f+lkD7Kl2F6aTPUmhOgAjARKAVWllEeTs59K5Io9u3btHgsWbCIw8B4lSxahe/d37HLC482bj9C+/ahYyTopbm4u3Lu3Klljp0gpWblyFzNnruT69QcULpyLL77oyHvv1X3jxp5PL9Kqi/4ZoC2QcH9iRUlHFi3aQtWqn/DkyRoqVQrg7NlllCnzEbt2nbR1aPHMm/d3ipI4GDs4bdx4MFnbDhkyix9+mMJnn13ln39C6dnzKqNHT2bYsF9eJ1wlDaWqZ6eU8jykbCB7RbFXN28+YNCgn/j7bx3FTYPYffihjoAAeP/977hxY5VdzVb/7FloivcxGAwJ9haN6cKFWyxa9DeHDunInNm4LF8+qFEjkmrVNtKjR8t4g3YptqMediqKycKF/9Chg+FlEn+hTh0oW1ayfv0B2wSWgCZNqiTYkzMhUkKdOuWS3G716gDatdO/TOIvZMkCbdvqWbNmT4qOq6StJBO5EGK7EOKMmVfrlBxICNFTCHFUCHH00SMzE9wpio3duXOfYsXMDzpVrFgUd+8GWTmixH3yyTu4ubnG+0as0big0Tjj6OgQb3mDBhUpVSrpeSQjI7V4ecXvBQrg5aU3O/mzYjtJJnIp5dtSyrJmXn+l5EBSyrlSyipSyio5cqjBeRT7U7p0MQ4dil91IiUcOuRMqVK+NogqYVmzerNvn3GaPTc3VzJl8sDV1Zn336/LqVNzqVmzDBqNM97e7mg0znTsWI/Vq79NVtkNG1Zi0yY3DHE6gxoMsHGjhgYN/NLgjJTXpUY/VBSTjz5qxNixi9m6FRo3Ni6TEubPF0RGetGoUSXbBmhGyZK+nDw5h8uXA3n0KJgSJfK/nGJs9+4p3L37mAcPnlGokA+ZM3smu9z69SuSPXt+Bgy4zogRUWTLBkFBMHKkMz4+Baw2F6WSPKltfvguMAPIATwD/pNSNklqP9X8ULFXBw6co337byhcOJoSJfQcOeJIdLQ369dPsMo45PYkJCScAQOmsWbNXrJnd+Lx42jat6/N1Kn98fJyt3V4GVKatCN/XSqRK/ZMp4vi778PExj4iJIlfWnQoGKGHockODiMe/eCyJMnG97eau5OW0ookauqFUWJw8XFmTZtato6DLuRKZOHmnzZzmXc2wxFUZQ3hErkiqIo6ZxK5IqiKOmcqiNXFCVNhYSEc+jQBVxcnPD3L53oPKTK61FXVFGUNCGl5PvvFzNhwmpT8pY4OAhmz+5Px471bR3eG0UlckVR0sS0aWuZOPEPIiK0RERoXy7v0WMyPj5ZVaciC1J15IqiWJxer+eHH5aZHZMlPFzLd98ttH5QbzCVyBVFsbi7d4OIjNQluP7YsctWjObNpxK5oigW5+XlTnS0+dETX6xXLEclckVRLC5zZk9q1y5ndmgDjcaFTz9tZoOo3lwqkSuKkibmzRtI1qyesSZ3dnd3pWjRPAwd+r4NI3vzqFYriqKkiUKFcnP+/AJmzVrPunX7cHV1plu3xnTt2tiupsx7E6jRDxVFUdIJNfqhoihpzmAw8M8/R5g//2+ePQuladO3+OSTZi8nu1DShkrkipJBPXz4lIULt3D16k0KFy5At25NyJUrS4rLOXz4AiNHLubAgXNotVHo9QZ0OuPcp4cOXWTChFXs2zeNkiXta6q8N4l62KkoGdDmzUcoXfpDTp9eQvHi2zl3bgmlS3/Ipk2HUlTOpk2HqF//KzZvPsqzZ2FEROheJnGAiAgtT5+G0rHjGEufghKDuiNXlAwmODiMLl2+Z9kyLdWqvViq44MPoGPH0Vy+/DtZsyZdFaLX6/n444mEh2sT3U5KyaVLd7h69W6Gmy7PWtQduaJkMCtX7qJOHREjiRtVqQINGsDy5f8mq5xjxy4TEZFw782YXFycePToWQojVZJLJXJFyWBu3XpIiRLxx0ABKFlSy61b95NVTkSEFgcHkaxttVodJUrkT3aMSsqoRK4oGUypUr4cPepmdt2RIxpKlSqYrHIqVy5OVFTC3fBfcHNzoUuXhmTJ4pWSMJUUUIlcUTKYdu1qc+GCE+vWxV6+YQOcOuXEe+/VTVY5np5uDB7cAXd38517PD01aDTOtGrlz8yZfVMbtpII9bBTUTIYjcaFjRsn0Lr1MBYs0FG+fBRnzjhz/bozGzaMxd1dk+yyRo78CHd3DWPHLkev1xMVpad8+UJ0796ULFm88Pcvja9vzjQ8GwVUz05FybCioqLZuPEgV6/eo3BhH1q0qI6Li/Nrl3Xjxn28vNzx8clq4UiVF1TPTkVRYnF2duLdd2tZrKxixfJZpCwl5VQduaIoSjqnErmiKEo6pxK5oihKOqcSuaIoSjqnErmiKEo6l6pELoSYKIS4IIQ4JYRYJ4TIbKG4FEVRlGRK7R35NqCslLI8cAn4X+pDUhRFUVIiVYlcSrlVSvli8OGDgGpIqiiKYmWWrCPvDvxjwfIURVGUZEiyZ6cQYjvgY2bVcCnlX6ZthgPRwLJEyukJ9ATU2AuKoigWlGQil1K+ndh6IURXoAXQUCYycIuUci4wF4xjraQwTkVRFCUBqRprRQjRFBgK1JVShlsmJEVRbO3Bg6cYDAZ8fLIiRPImj1BsJ7V15D8DXsA2IcR/QohfLBCToig2snv3SapW7UGpUl0oV+5D/Py68s8/h20dlpKEVN2RSymLWioQRVFs6+DBc7RvP5yJE7W0aAFCwPbt9+jWbRRLl35Po0aVbR2ikgDVs1NRFABGjZrPt99qad0aHB3BwQEaN4YJE7SMGDHH1uEpiVCJXFEUpJRs336Wtm3jr2vWDI4fv0lYWIT1A1OSRSVyRVEQQuDi4kCEmVyt1Rr/dXJytG5QSrKpRK4oCgBt2/qzYEH8lLB0qaBJkwq4urrYIColOdRUb4qiAPD9959Sq9YJdLpwunQx4OQEK1c6MH++Kzt39rZ1eEoi1B25oigAFCqUm/37fyEs7G1atPCgcWN37t2ry549syhTpqCtw1MSIRLpjJl2BxXiEXDT6gdOXHbgsa2DMMMe47LHmMA+47LHmEDFlRL2FFMBKWWOuAttksjtkRDiqJSyiq3jiMse47LHmMA+47LHmEDFlRL2GFNcqmpFURQlnVOJXFEUJZ1TifyVubYOIAH2GJc9xgT2GZc9xgQqrpSwx5hiUXXkiqIo6Zy6I1cURUnnVCJXFEVJ5zJsIhdCTBRCXBBCnBJCrBNCZE5gu6ZCiItCiCtCiGFpHFMHIcRZIYRBCJFgcychxA0hxGnTGPBH0zKmFMZltWtlOl5WIcQ2IcRl079ZEtguza9XUucujH4yrT8lhKiUFnG8Rlz1hBDBpmvznxDiOyvEtEAI8VAIcSaB9Va/VsmIyerXKUWklBnyBTQGnEw/jwfGm9nGEbgKFAZcgJNA6TSMqRRQAtgFVElkuxtAditeqyTjsva1Mh1zAjDM9PMwc/+H1rheyTl3oBnGyckFUB04ZIX/t+TEVQ/YaK3fJdMx6wCVgDMJrLfFtUoqJqtfp5S8MuwduZRyq5Qy2vT2IJDPzGZVgStSymtSSh2wAmidhjGdl1JeTKvyX1cy47LqtTJpDSwy/bwIaJPGx0tIcs69NbBYGh0EMgshcttBXFYnpQwAniSyidWvVTJismsZNpHH0R3jHUBceYHbMd4HmpbZmgS2CiGOCSF62joYE1tcq1xSynsApn9zJrBdWl+v5Jy7La5Pco/pL4Q4KYT4RwhRJo1jSg57/buzt+v00hs9+qEQYjvgY2bVcCnlX6ZthgPRwDJzRZhZlqr2msmJKRlqSinvCiFyYpwv9YLpjsKWcVn8WkHicaWgGItfrziSc+5pcn2SkJxjHsc4fkeoEKIZ8CdQLI3jSootrlVS7PE6vfRGJ3Ip5duJrRdCdAVaAA2lqSIsjkAgf4z3+YC7aRlTMsu4a/r3oRBiHcav0KlKTBaIy+LXChKPSwjxQAiRW0p5z/TV+2ECZVj8esWRnHNPk+uT2riklM9j/Py3EGKWECK7lNKWg0TZ4lolyk6v00sZtmpFCNEUGAq0klKGJ7DZEaCYEKKQEMIF6Aist1aM5gghPIQQXi9+xvjQ1uyTdiuzxbVaD3Q1/dwViPfNwUrXKznnvh74yNQiozoQ/KJaKA0lGZcQwkcIIUw/V8WYE4LSOK6k2OJaJcpOr9Mrtn7aaqsXcAVjPdx/ptcvpuV5gL9jbNcMuITx6f/wNI7pXYx3I1rgAbAlbkwYWyCcNL3OpnVMyY3L2tfKdLxswA7gsunfrLa6XubOHfgc+Nz0swBmmtafJpFWSVaOq4/pupzE+NC/hhViWg7cA6JMv1c9bH2tkhGT1a9TSl6qi76iKEo6l2GrVhRFUd4UKpEriqKkcyqRK4qipHMqkSuKoqRzKpEriqKkcyqRK4qipHMqkSuKoqRz/wdrpcSyf/C4GwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_q3_c1 = SVC(kernel='linear', C=0.00002)\n",
    "svm_q3_c1.fit(X_train, y_train)\n",
    "plot_class_regions_for_classifier(svm_q3_c1, X_train, y_train, title= \"SVM with C=0.00002 Boundaries\" ,plot_decision_regions=True, target_names=['class 0', 'class 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "300096d9-68d8-4f68-bf6d-4b07a0ddab8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_q3_c2 = SVC(kernel='linear', C=200)\n",
    "svm_q3_c2.fit(X_train, y_train)\n",
    "plot_class_regions_for_classifier(svm_q3_c2, X_train, y_train, title= \"SVM with C=200 Boundaries\" ,plot_decision_regions=True, target_names=['class 0', 'class 1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comment\n",
    "When C was bigger the model had better results and was able to classify the data better. this are shown in the plots above. as C is the penalty parameter of the error term. It controls the trade off between smooth decision boundary and classifying the training points correctly. so when the C is bigger the model will try to classify the data correctly even if the decision boundary is not smooth. and when C is smaller the model will try to make the decision boundary smooth even if it will not classify the data correctly."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "af24125e-ee59-4fd0-843e-dd6a4f016816",
   "metadata": {},
   "source": [
    "### Use the fruits dataset in questions 4, 5, 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "1eca04a9-ba10-4bf4-9841-07ccf1249dcb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "fruits = pd.read_table('./data/Ibrahim Mohamed - fruit_data_with_colors.txt')\n",
    "feature_names_fruits = ['height', 'width', 'mass', 'color_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "outputs": [
    {
     "data": {
      "text/plain": "   fruit_label fruit_name fruit_subtype  mass  width  height  color_score\n0            1      apple  granny_smith   192    8.4     7.3         0.55\n1            1      apple  granny_smith   180    8.0     6.8         0.59\n2            1      apple  granny_smith   176    7.4     7.2         0.60\n3            2   mandarin      mandarin    86    6.2     4.7         0.80\n4            2   mandarin      mandarin    84    6.0     4.6         0.79",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fruit_label</th>\n      <th>fruit_name</th>\n      <th>fruit_subtype</th>\n      <th>mass</th>\n      <th>width</th>\n      <th>height</th>\n      <th>color_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>apple</td>\n      <td>granny_smith</td>\n      <td>192</td>\n      <td>8.4</td>\n      <td>7.3</td>\n      <td>0.55</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>apple</td>\n      <td>granny_smith</td>\n      <td>180</td>\n      <td>8.0</td>\n      <td>6.8</td>\n      <td>0.59</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>apple</td>\n      <td>granny_smith</td>\n      <td>176</td>\n      <td>7.4</td>\n      <td>7.2</td>\n      <td>0.60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>mandarin</td>\n      <td>mandarin</td>\n      <td>86</td>\n      <td>6.2</td>\n      <td>4.7</td>\n      <td>0.80</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>mandarin</td>\n      <td>mandarin</td>\n      <td>84</td>\n      <td>6.0</td>\n      <td>4.6</td>\n      <td>0.79</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "203d0e0f-f858-4125-b973-c30287168337",
   "metadata": {},
   "source": [
    "#### Question 4\n",
    "1. Split the data by columns into X_fruits and y_fruits where X_fruits is the data records and y_fruits is the labels.\n",
    "2. Using only the **'height'** and **'width'** features, split the data into training and testing using random_state=42, and 20% of the data for testing.\n",
    "3. Using **sklearn LinearSVC**, train a linear svm using random_state=42, C=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "b9d29253-4327-4dcc-9b43-bcf47f19d4e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_fruits = fruits.drop(['fruit_label', 'fruit_name'], axis=1)\n",
    "y_fruits = fruits['fruit_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_fruits_2d = fruits[['height', 'width']]\n",
    "y_fruits_2d = fruits['fruit_label']\n",
    "\n",
    "X_fruits_2d_train, X_fruits_2d_test, y_fruits_2d_train, y_fruits_2d_test = train_test_split(X_fruits_2d, y_fruits_2d, random_state=42, test_size=0.2)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\I1bra\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "LinearSVC(C=10, random_state=42)",
      "text/html": "<style>#sk-container-id-31 {color: black;background-color: white;}#sk-container-id-31 pre{padding: 0;}#sk-container-id-31 div.sk-toggleable {background-color: white;}#sk-container-id-31 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-31 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-31 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-31 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-31 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-31 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-31 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-31 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-31 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-31 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-31 div.sk-item {position: relative;z-index: 1;}#sk-container-id-31 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-31 div.sk-item::before, #sk-container-id-31 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-31 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-31 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-31 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-31 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-31 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-31 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-31 div.sk-label-container {text-align: center;}#sk-container-id-31 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-31 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-31\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(C=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, random_state=42)</pre></div></div></div></div></div>"
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svm_q4 = LinearSVC(random_state=42, C=10)\n",
    "svm_q4.fit(X_fruits_2d_train, y_fruits_2d_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "b72d70f3-9e4d-4244-b8e2-dc991c3c7902",
   "metadata": {},
   "source": [
    "#### Question 5\n",
    "For the previous model from question 4. \n",
    "1. How many SVM models have been trained? and Why?\n",
    "2. Print the models' coefficients and intercepts."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Four models has been trained because LinearSVC is a one-vs-rest classifier\n",
    "So we have 4 classes and for each class we have a model.\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "387e4b9b-d4c5-4df8-86fc-864422def90f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:\n",
      " [[-0.22459715  0.76048347]\n",
      " [-1.70338354  1.18717003]\n",
      " [-0.0062549   0.3183943 ]\n",
      " [ 1.14746624 -1.71656282]]\n",
      "Intercepts:\n",
      " [-3.42751628  1.46849592 -3.01719598  1.66954999]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients:\\n', svm_q4.coef_)\n",
    "print('Intercepts:\\n', svm_q4.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6af4b33-dcb2-466d-a796-397486843806",
   "metadata": {},
   "source": [
    "#### Question 6\n",
    "Plot the classification boundary of each svm on a scatter plot of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "403fcb17-1202-4c98-8cd7-64a2b7641448",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\I1bra\\anaconda3\\lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearSVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_class_regions_for_classifier(svm_q4, X_fruits_2d_train.values, y_fruits_2d_train.values, title= \"SVM Fruits\" ,plot_decision_regions=True, target_names=['class 1', 'class 2', 'class 3', 'class 4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "c1d0a342-8e9f-4b17-a91c-c8fca1c81aee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "(X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b10a88e-17f0-4d29-8681-e9012fb1c9da",
   "metadata": {},
   "source": [
    "#### Question 7\n",
    "1. Split the dataset into training and testing datasets, use random_state = 42, and 20% of the data for testing.\n",
    "2. Use GridSearchCV to find the best C value for and SVM  (SVC) classifier with rbf kernel and print the best C value. *use:*  \n",
    "```python\n",
    "parameters = {'kernel' : ('poly', 'rbf'), 'C':[0.01, 0.1, 0.5, 1, 10]}\n",
    "```\n",
    "3. Print the best parameters resulted from your grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "X_breast_train, X__breast_test, y_breast_train, y_breast_test = train_test_split(X_cancer, y_cancer, random_state=42, test_size=0.2)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters are :\n",
      "{'C': 10, 'kernel': 'poly'}\n"
     ]
    }
   ],
   "source": [
    "parameters = {'kernel' : ('poly', 'rbf'), 'C':[0.01, 0.1, 0.5, 1, 10]}\n",
    "svc_q7 = SVC()\n",
    "clf = GridSearchCV(svc_q7, parameters)\n",
    "clf.fit(X_breast_train, y_breast_train)\n",
    "print(\"Best parameters are :\")\n",
    "print(clf.best_params_)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "27bbcbe6-a2b5-4f30-92a7-c177ae7a8a67",
   "metadata": {},
   "source": [
    "#### Question 8\n",
    "Train an SVM model using the best parameters resulted from question 7, then calculate and print the training and testing accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=10, kernel='poly')",
      "text/html": "<style>#sk-container-id-32 {color: black;background-color: white;}#sk-container-id-32 pre{padding: 0;}#sk-container-id-32 div.sk-toggleable {background-color: white;}#sk-container-id-32 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-32 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-32 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-32 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-32 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-32 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-32 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-32 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-32 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-32 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-32 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-32 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-32 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-32 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-32 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-32 div.sk-item {position: relative;z-index: 1;}#sk-container-id-32 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-32 div.sk-item::before, #sk-container-id-32 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-32 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-32 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-32 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-32 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-32 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-32 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-32 div.sk-label-container {text-align: center;}#sk-container-id-32 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-32 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-32\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" checked><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_q8 = SVC(kernel='poly', C=10)\n",
    "svc_q8.fit(X_breast_train, y_breast_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVC on training set: 0.92\n",
      "Accuracy of SVC on test set: 0.93\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of SVC on training set: {:.2f}'\n",
    "     .format(svc_q8.score(X_breast_train, y_breast_train)))\n",
    "\n",
    "print('Accuracy of SVC on test set: {:.2f}'\n",
    "        .format(svc_q8.score(X__breast_test, y_breast_test)))\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "03a774eb-4174-4e05-8ad5-e6e26a96beaa",
   "metadata": {},
   "source": [
    "#### Question 9\n",
    "Improve the previous results from question 8.\n",
    "\n",
    "**Hint:** Don't change the model, instead, make some preprocessing on the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "ed324fbb-7e52-47da-b987-fc983a3f165b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Made Data Scaling as preprocessing\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_cancer)\n",
    "\n",
    "X_preproccssed_train, X_preproccssed_test, y_preproccssed_train, y_preproccssed_test = train_test_split(X_scaled, y_cancer, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=10, kernel='poly')",
      "text/html": "<style>#sk-container-id-33 {color: black;background-color: white;}#sk-container-id-33 pre{padding: 0;}#sk-container-id-33 div.sk-toggleable {background-color: white;}#sk-container-id-33 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-33 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-33 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-33 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-33 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-33 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-33 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-33 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-33 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-33 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-33 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-33 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-33 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-33 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-33 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-33 div.sk-item {position: relative;z-index: 1;}#sk-container-id-33 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-33 div.sk-item::before, #sk-container-id-33 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-33 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-33 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-33 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-33 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-33 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-33 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-33 div.sk-label-container {text-align: center;}#sk-container-id-33 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-33 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-33\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=10, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" checked><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=10, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_q9 = SVC(kernel='poly', C=10)\n",
    "svc_q9.fit(X_preproccssed_train, y_preproccssed_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "6aa31c32-4bcd-4279-9598-1a199b218d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVC on training set: 0.97\n",
      "Accuracy of SVC on test set: 0.96\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of SVC on training set: {:.2f}'\n",
    "     .format(svc_q9.score(X_preproccssed_train, y_preproccssed_train)))\n",
    "print('Accuracy of SVC on test set: {:.2f}'\n",
    "     .format(svc_q9.score(X_preproccssed_test, y_preproccssed_test)))"
   ]
  }
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