Support Vector Machine.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Support Vector Machine\n",
    "A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It is a powerful and versatile method that can handle both linear and non-linear data.\n",
    "\n",
    "The main idea behind SVM is to find a hyperplane in a high-dimensional feature space that separates different classes as much as possible. In the case of linearly separable data, the hyperplane is the line that maximizes the margin between the classes. This hyperplane is referred to as the \"maximum-margin hyperplane.\"\n",
    "\n",
    "SVMs can also handle non-linear data by mapping the input features into a higher-dimensional space through a technique called the \"kernel trick.\" The kernel trick allows SVMs to implicitly compute the dot product of the transformed features without actually performing the transformation explicitly. This way, SVMs can effectively classify data that is not linearly separable in the original feature space.\n",
    "\n",
    "To train an SVM, you need a labeled dataset where each data point is associated with a class label. The algorithm learns the parameters that define the hyperplane by optimizing an objective function that maximizes the margin between classes while minimizing the classification error.\n",
    "\n",
    "Once trained, the SVM can be used for classification tasks by evaluating the position of a new data point in relation to the hyperplane. The point's position determines the predicted class label.\n",
    "\n",
    "**SVMs have several advantages, including:**\n",
    "\n",
    "Effective in high-dimensional spaces: SVMs work well even in cases where the number of features is larger than the number of samples.\n",
    "\n",
    "Robust against overfitting: By maximizing the margin, SVMs tend to have good generalization properties and can avoid overfitting.\n",
    "\n",
    "Versatility through kernel functions: SVMs can handle non-linear data by using different kernel functions, such as polynomial, radial basis function (RBF), or sigmoid.\n",
    "\n",
    "However, SVMs also have some limitations:\n",
    "\n",
    "Computationally intensive: Training an SVM can be time-consuming, especially with large datasets. Additionally, the memory required scales with the number of training samples.\n",
    "\n",
    "Sensitivity to parameter tuning: SVMs have parameters that need to be carefully selected, such as the choice of the kernel function and the regularization parameter. Poor parameter selection can lead to suboptimal performance.\n",
    "\n",
    "Overall, SVMs are powerful machine learning algorithms that can handle both linear and non-linear classification tasks. With appropriate parameter tuning and careful preprocessing of the data, SVMs can achieve excellent performance on a wide range of problems.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f73c9eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d265803d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Iris dataset from scikit-learn\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "# Load the dataset\n",
    "iris_data = load_iris()\n",
    "\n",
    "# Create a DataFrame from the iris dataset\n",
    "iris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\n",
    "\n",
    "# Add the target variable to the DataFrame\n",
    "iris_df['target'] = iris_data.target\n",
    "\n",
    "# Print the first few rows of the DataFrame\n",
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d04de23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13543f4a",
   "metadata": {},
   "source": [
    "## SVM Classification\n",
    "\n",
    "SVM (Support Vector Machine) is a powerful algorithm commonly used for classification tasks. It finds the optimal hyperplane that separates different classes by maximizing the margin between them. \n",
    "\n",
    "We can customize the SVM classification model by specifying different kernel functions (linear, poly, rbf, etc.) and tuning hyperparameters such as C, gamma, and the kernel-specific parameters. Additionally, we can explore techniques like cross-validation and grid search to find the optimal hyperparameters for your specific classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e67db0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cb782e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris_df.drop('target', axis =1)\n",
    "y = iris_df[['target']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7070e356",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf = Pipeline([\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"linear_svc\", SVC(kernel='linear', C=1))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef2bd470",
   "metadata": {},
   "outputs": [
    {
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       "                (&#x27;linear_svc&#x27;, SVC(C=1, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;linear_svc&#x27;, SVC(C=1, kernel=&#x27;linear&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
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       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', SVC(C=1, kernel='linear'))])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90195f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict(X.iloc[[55]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4c882a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict(X.iloc[[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9b29012",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cb1a214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2bb7aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, LinearSVC(C=10, loss=&#x27;hinge&#x27;, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, loss=&#x27;hinge&#x27;, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ac24a54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1263b315",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "prediction = polynomial_svm_clf.predict(X[[0]])\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0d07ed6b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d0c09c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "#plt.savefig(\"moons_polynomial_svc_plot\", format='png', dpi=300)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "261aaebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b719df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "prediction = poly_kernel_svm_clf.predict(X[[3]])\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3310edf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=100, degree=10, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2cba73c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "prediction = poly100_kernel_svm_clf.predict(X[[3]])\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8027b973",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$d=10, r=100, C=5$')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d1f0ce9",
   "metadata": {},
   "source": [
    "## Gaussian RBF Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7be4dbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=5))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=0.001, gamma=5))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.001, gamma=5)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=5))])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bcfc4be7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dea4619",
   "metadata": {},
   "source": [
    "## SVM Regression\n",
    "SVM (Support Vector Machine) can also be used for regression tasks. SVM regression aims to find a function that approximates the relationship between the input features and the target variable. **The goal is to find a hyperplane that has a maximum margin while minimizing the number of instances falling outside the margin.**\n",
    "\n",
    "We can customize the SVM regression model by specifying different kernel functions (linear, poly, rbf, etc.) and tuning hyperparameters such as C, epsilon, and the kernel-specific parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9641f299",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()\n",
    "\n",
    "#Initializing random numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0a88ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e9949c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(epsilon=1.5, 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-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(epsilon=1.5, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(epsilon=1.5, random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR # Importing the SVM Regression class\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42) #Here, epsilon is the margin of tolerance for errors\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5dac8209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg.epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "016ccd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.52640746])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])\n",
    "eps_y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5352b926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$\\\\epsilon = 0.5$')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+jo5QpYqyY0rF48eP6dq1K4cPHwagb9++LF68mISEBJOcTyQCgsnduHGDLVu2UKRIEQYNGmTu4QjZXWTku6tCyag1cPxGIR6H5aSo8xsaVHyObaL7prHx8YzZuJE9AQFcuHCBPEWLYmNjw2effZZJgxcEyydivmBRMhDzdQIDoVQpk68ZOHHiBF988QXPnj0jd+7crFixgq5duwJyQQpTEImAYFIJCQl069aNS5cuAVC0aFHat29v3kEJ2dudO0nnh77lf7Y4w9a58TA0l26bS4FoFvUKoEOtEG4+eoTnwoUE3LsHwG4fHzxHj87MkQuCxRMxX7A4Rsb8JFQquHsXqlY16VA//PBDYmJiqFKlCn5+flSoUMGk5wOxRkAwsVmzZnHp0iVmzJhBmTJl+Oqrr3j58qW5hyVkVwkJcqUIPW8IHefV4WFo0m6zIaGOdJxXhyG/hFFt9GgC7t2joJMTu0aPxrN8efl4giDoiJgvWBQjY77/2eJJjyNJciJggpgfHR2t+/tHH33EgQMHOHv2bKYkASASAcGErl27xrRp0+jYsSPff/89fn5+vHr1iqFDh5p7aIK1SUiA4GC4eROuX5f/DA5Of1B+/DjFc9QaGLbODfltIukcfwkVEhJL97UkOjae/1auzOU5c2hTvfq7GtSCIAAi5gsKUSreg1ExH8B7nRvq5H3sTBDz9+7dS5kyZTh48KBuW82aNXF0dHzPs5QlpgYJJqFWq+nduzcuLi6sXr0agOrVqzN37lyGDh1K586d+d///mfmUQoWT+l6z9HRKW4RH79RKMmt4ZRsgBL0++9klg8oj63N2+snKhXExBjzqgQhyxExX8gwU9T3NyLmS6gIDs3F8RuFaFTp+bsHFIz5CQkJTJgwgVmzZgEwb948mjRposix00skAoJJ2Nracv78+RTbhwwZwpAhQ8wwIsGqmKres54rSo/Dcho0pP9WboatTXDSjfHxBj1XELI6EfMFo5myvn8GYr7e/RSI+cHBwXh5eXHy5EkAvv76a+bNm5fh4xpLJAKCIFgWbb1nbam39NR7jokBD4/U3xz0tI0v6vzGoGHp3c/e3qDnCoIgCHqYMt6DxcX8P/74gx49evDy5Uvy5s3L6tWr+fLLLzN0zIwSawQEQbAsGa33fO1a6o/nypXijaZBxee4FIgG9L8BqZBwLRBNg4rPkz4gSXJtaUEQBME4poz38N6Yr8rkmH/+/HnatGnDy5cvqV69OpcuXTJ7EgAiERAEwZKkUe/5yPVC+J5w5cj1QikXcmkFBsrH0ado0RRXiBLU8VQtuQA5EUh6UO0bxcJeASlrS9vZyccTBEEQ0s/U8R70xnxbG1jUKwAgRTJgyphfo0YNunXrxtChQzl58iRly5Y1+lhKEomAoIinT5+iUqlQqVTs27fvvfsOGTIElUpF3bp1kTK5bbdg4bT1npPxP1ucUoNb03hKI7osrk3jKY0oNbh1yhJv8K7esz7axWZvzxEYEkLtceP449J4oCN5coYm2d2lQAzbhp/WX1O6TBm9t50FITsQMV/IMFPHe0gR87U61Aph2/DTFC+QdPGv0jH/jz/+IDQ09O0hVKxbt47Fixfj4OCQruOYkngXExRRpEgRypQpw927dzl79iwtWrTQu9/ly5dZvnw5NjY2LFmyBFVaC32E7CONes/JPz5o6z2nCNraes+VKukP2mXLwq1bbDpxgv4rVhAdG0tBJyfWDf6Ylm5HDOsyKUnym4IgZFMi5gsZklnxHnQxP7kOtUJo5xFikpgfFxfHmDFjWLBgAW3btmXnzp2oVCpsbW0NPkZmybQ7AseOHaNt27YUK1YMlUrFjh07kjwuSRITJ06kaNGiODo60rRpU27p+cEJlqtevXoAnD17NtV9hg4dilqtpl+/flSvXj2zhiZYg8yq9+zkBBUq4JQzJ9GxsbreAK2rVcPWBhpVeo5X/WAaVUrlDQHkihUmbjVv7UTMz/pEzBeMlpn1/d/GfH1MEfODgoJo0KABCxYsAOQmYWq12qDnmkOmJQJRUVFUrVqVpUuX6n38xx9/ZPHixSxfvpyzZ8+SO3duWrRowZs3hq3uFsyvbt26QOpvChs2bOD48eM4OzszY8aMzByaYA209Z4TeVfvWf9VxMT1npNIpd5zpHYuaeXKtP3iC/aPH89f48dTLH9+w8dZsqRctk54LxHzsz4R8wWjZUK8T6JyZTl2GyMdMf+3337D3d2dc+fO4ezszM6dO5k/fz52FjyNNNNG1qpVK1q1aqX3MUmSWLhwIePHj6ddu3YA+Pj4UKRIEXbs2IGnp2dmDVPIAO3VodDQUG7fvs1HH32keywyMpJRo0YBMG3aNAoWLGiWMQoWzIT1ntVqNbNnz2bRokVcuHABV1dX8PCgmaNjytrV+mgfN7R2tSBifjYgYr5gtMyu769SyaVGTRTzY2NjGTlyJEuWLAGgdu3abN68mZLGJh96mGp9jUWkKEFBQTx58oSmTZvqtuXLl49atWpx+vTpVN8UYmNjiY2N1X0fERFhkvHVqFGDJ0+emOTY5vDhhx9y4cIFxY9bqVIl8uXLR3h4OGfPnk3ypjBlyhQeP37MJ598wldffaX4uYUswET1nh89ekS3bt04fPgwIF+lHDt2rBzYq1SBUqXkOaZ376bezbJMGflLTAdShDExP7PiPYiYbygR8wWjmaO+vwljfkxMDLt27QJg5MiRzJgxA3sF+8yEh4fTs2dPxY6XmEUkAtqAW6RIkSTbixQp8t5g/MMPPzBlyhSTjg3k8YWEhKS9YzZnY2NDrVq12L9/P2fOnKFr164A3Lhxg8WLFwPw008/WeRiGcECvKfec0ioo26OaGIqJFwKxKRa73nXrl307t2b0NBQcufOzU8//ZQymDo5QdWq8mKzx4/lW8zx8fIbi6Oj3vJzSlCr4fhx+ZRFi0KDBpBdfjWMifmZFe9BxHxDiZgvGM0E8d5gJoj5H3zwAVu2bOH58+e0bt1a7z7GxvwLFy7QuXNn7r6vOlIGWEQiYKyxY8fy3Xff6b6PiIiQb/kr7MMPP1T8mOZkytdTr1499u/fn2TO6DfffEN8fDxdunShQYMGJju3YOW0wTfRLWNtveeO8+qgQkry5vC+es9vNBpGzZ7Nkrfz093d3fH19eXjjz9O/fx2dmCC+KGPvz8MGwYPH77b5uICixZBhw6ZMgSrk1nxHkTMTw8R8wWjKBjvja7vn4GY/+bNG4YPH07VqlUZMGAAADVr1kx1f2NiviRJLFmyhBEjRhCf1tSnDLCIREAbpJ4+fUrRRD/Mp0+f4ubmlurzHBwcMqUWqyluqWZV2sVjly9fJjY2lt27d3PgwAHy5MnDnDlzzDw6waJp6z3fvp3kSpG23vOwdW5vF5LJXArEsLBXgN56z3OPH9clAd9++y0//PCDxdRt9veHjh1TTk8NCZG3b9uW9ZMBY2J+ZsV7EDE/PUTMF4yiYLzP7J4ut27dolOnTgQEBODo6Ej79u0pXLhwqvsbE/PDwsLo27cvv/32m25b9erVuXjxopIvBbCQRKB06dJ8+OGHHDx4UPcmEBERwdmzZxk0aJB5ByekS+3atbG1tSUuLo6TJ08yfPhwAMaPH0+xYsXMPDrB4ilU73n4xIkc+ucfRowYwWeffWb6cRtIrZavCulb8yVJ8nuatze0a5e1pwmJmJ91iJgvGE2p+v4qFQQHm2waZ2KbN2+mf//+vH79moIFC7Jhw4b3JgHGxPxz587RuXNn7t27p9t3+PDhjBkzhkKFCqU8UAZlWiLw+vVrbt++rfs+KCiIgIAA8ufPT4kSJfD29mb69OmUK1eO0qVLM2HCBIoVK0b79u0za4iCAvLkyUOVKlUICAigb9++3L9/n3LlyvHtt9+ae2iCNdDWe9bTdl5b71mfV1FRLN23jzHt22P7n//gWLgwhw4dMvVo0+348aS3hpOTJPn97PhxaNQo04ZlEiLmZw8i5gtGMzLep/Dvv+8W+ZYuLScYChd2iImJwdvbm5UrVwLw6aefsmnTJooX19PtOJH0xPyGDeVqaqNHj9ZNBcqfPz/r1q2jbdu2JiuQkGmJwIULF2jcuLHue+1cz549e7Ju3TpGjRpFVFQUAwYM4NWrV9SvX5+9e/eSM6dh5aQEy1GvXj0CAgJ02eyiRYvIkSOHeQclWI/KleXFW/fvG7T76X//xWvRIu4/f46NszNjO3Uy8QCN976eN8bsZ8lEzM8+RMwXjJbOeK+X9nJ7QoI81ejWLUVLPcfFxVGvXj3+/vtvVCoV48aNY9KkSQb1BjA0lt+69Zr587voKg8B1KlTh82bN1OiRAljh26QTEsEGjVq9N4aqCqViqlTpzJ16tTMGpJgInXr1tU1EWrbtm2qtcQFQS8D6z2rNRpm7djBJD8/1BoNZVxcaNKzp0XX+Dd0PZsx694sjYj52YeI+YLR0lvfPy3a5wYGygmGh0eG3xNy5MjB559/TkhICBs3bkxS9jgthsby8eP78uzZuyRg1KhRTJ8+XdESpKmxiDUCQtbi+LaMl4ODg67FtiCkSxr1nkNCQ+m+ZAmHr18HoMuXX7Js9Wry5s1rxkGnrUEDuVJESIj+9zqVSn5cFFoRrImI+UKGvC/eZyQpuH9fTjCqVEn3U6OjowkNDdVVJvv+++8ZOHDge9cD6JNWzAcJeMizZ9sAKFCgAD4+Ppm6tk3f0gtBMJparWby5MmA3FSjbNmy5h2QYN209Z7btoXateGTTzjw6hVVx4zh8PXr5M6dm19//ZVfN23h0qW8+PrCkSPyAi1LZGsrl4uDlBeptN8vXJi1FwoLWYuI+YJiksf795R7VmvgyPVC+J5w5cj1Qqg1qewYGAiRkekaxj///EPNmjVp06YNMTExANja2qY7CZCfl3rMBw1yIjAM0FC/fn0CAgIyvcCFSAQERS1evJgrV65QqlQpuXurIChBW++5fHkKeXjwOiaGatWqcenSJfLk6UHp0ioaN4YuXaBxY/nCkr+/uQetX4cOcrm45GvMXFyyR+lQIWsRMV9QnDbeq9V6p/X4ny1OqcGtaTylEV0W16bxlEaUGtwa/7N6Fu6qVPIdBgOtW7cODw8Prl+/zrNnzxRp4pVazIeHQEdUqh18//33HD58GBcXlwyfL71EIiAoxtfXl9GjR6NSqVi1ahW5cuVK+0mCYIDw8HDd36tWrcqBAwc4deoU166Vp2PHlFUZtPWZLTkZuHcPDh+GTZvkP4OCRBIgWBcR8wWTSUiQg2Ky+TT+Z4vTcV4dHoYm7SQcEupIx3l1UiYDkvRuqtF7REVF0bNnT3r37k10dDRNmzYlICCASpUqKfJyOnSAu3c19O+/CZWqK9AIKE2hQifYs2cPM2bMMGjxsSmIREDIkD/++INSpUqRL18+unTpQnx8POPHj0/XYhpBSI0kSaxevZqSJUty/vx53fb69etjZ+fw3vrMINdntuRpQo0agZeX/KeYDiRYAxHzhUzx+HGKD+9qDQxb5/a2x3DSOwXaLsTe69xSThNKSHhv+Z5r167h4eGBj48PNjY2TJs2jb1791KkSJGMv463nj9/zv/+15pVq7oiSZuAozRq9CkBAQG0aNFCsfMYQyQCQoacPHmS+/fvk5CQgLu7O6tXrxZVQARFvHr1is6dO9O/f3/Cw8NZtWpVksfTU59ZEARliJgvZIro6BTTgo7fKPS227D+KkASKoJDc3H8RrKmWyqVXEEoFd7e3ty4cYNixYpx6NAhxo8fj62CV2aOHTuGm5sbe/fufTscFRMnTuTAgQMW0XRPVA0SMmTmzJnMnDnT3MMQsphTp07RpUsX7t+/j52dHTNnztR1LNXKTjX5BcFSiJgvZAo9U3kehxnWY0Tvfm8bdOmzZs0aRo8ezeLFixXt3KtWq/nhhx+YNGkSGo18m6JIkSJs3LiRJk2aKHaejBJ3BARBsBhqtZrp06fz6aefcv/+fcqWLcupU6cYOXIkNjZJw1V2qskvCIKQreiZL1/U+Y1BT9W7X6J6/JcvX2b+/Pm670uUKIGvr6+iScDTp09p2bIlEyZM0CUBTZo0ISAgwKKSABCJgCAIFmTr1q1MmDABtVpN165duXTpEh4eHnr31dZnTq1XjEolF54QNfkFQRCsTK5cKRaANaj4HJcC0ajQ31tAhYRrgWgaVHye9AFJAkdHJEli+fLl1KpVi+HDh7Nnzx6TDP3w4cO4ublx4MABAGxsbJg6dSr79u3jww8/NMk5M0IkAoIgWIxOnTrRqVMnfHx82LBhw3sbhIma/Ly3c68gCILVKlo0xV0BWxtY1CsAIEUyoP1+Ya8AbJN/srWzIyJ3bry8vBg0aBCxsbG0bt061YtMxtL21GjSpAlPnjx5+zKKcvDgQSZMmKDougMliURAEASzefPmDVOmTOH169eAfOVky5YtdO/e3aDnZ8Wa/Gq13BQtreZoL1++NPjfSRAEwarY2UHp0imu8nSoFcK24acpXiDp4l+XAjFsG36aDrVCkh5HpeJSbCzVatZky5Yt2NnZMWfOHH7//XcKFiyo2HAfP35M06ZNmTJliu4CTfPmzQkICKBRo0bvfa6hMd9UxGJhQRDM4p9//sHT05OrV69y79491q5da9RxOnSAdu3k6kCPH8sXkho0sM47Af7+MGxY0mpILi7ynY/ESc3x48fp2rUrwcHBmT9IQRCEzFC2LNy6lWJzh1ohtPMI4fiNQjwOy0lR5zc0qPg85Z0AYPWBAwxeu5a4uDhKlCjB5s2bqVOnjqLD/Ouvv+jWrRvPnj0D0JUgHTNmTIq1bckZGvNNSSQCgiBkKkmSWLVqFd7e3sTExFC4cGE6d+6coWNqa/JbM39/uQla8tk+2uZo27bB//6XwIwZM5g6dapuAZogCEKW5OQEFSpAYGCKh2xtoFGl53qelFTu0qWJi4vjf//7H2vXriV//vyKDS8hIYHJkyczc+ZM3V2A4sWL4+vrSwMDFqcZEvO1ycCTJ09M1rBPTA0SBCHThIWF0alTJwYOHEhMTAwtWrTgypUrtGzZ0txDMyu1mjSbow0ZkkDjxk2ZPHmyLgmoV69eJo5SEAQhk1WuDCVLpuspb+Li5L+ULInXqFHs37+fHTt2KJoEhISE8N///pcZM2bokoBWrVoREBBgUBJgSMz39oa4ODVTp06ldOnSXLp0SbHxJyYSAcSCO2shfk7WLSAgADc3N7Zt24a9vT1z587lzz//VLR7o7UypDna48d2nDghf29ra8v06dPZtWtX5gwwixGxxDqIn5OASgUeHvKdAe33qZAkiUV//snH3t48LVQIqUYNJKBZs2ao3vO89Nq7dy9ubm4cf9ut0tbWltmzZ7N7926D1x0Y2hCzdu2RTJo0iTdv3vDHH38oMfwUsnUioJ27pc7slRmCUbQ/p7Tm3AmW6cMPP+TNmze63gDDhw8XP8u3DG96VpQSJUpw9OhRxo0bZ7FVKCyViPnWRcR8AZA//FepAi1bQrly76oJqVS6r7DXr+kwbx7e69bx4MULVp88CSqVoglAfHw8Y8aMoVWrVrx48QIAFxcXjh49yqhRo9L1/9TQmP/334/JlSsXPj4+TJgwwZhhpylbrxGwt7fH3t6e169fkydPHnMPR0hDZGSk7mcmWIewsDCcnZ0BORHYu3cvH330EU5OTmYemWUxtOlZw4Yfs2PHMj744AOTjierEjHfuoiYLyTh5ARVq0KlSvIn6ZgYiI/n7D//0HniRO4/fEiOHDmYO3cuQ4YMUTQJCA4OxtPTk1OnTum2tWnThnXr1lGgQIF0H8/QmF+qVE727LlIhQoViIiISPd5DJGt02yVSoWTkxPh4eHExMSk/QTBbGJiYoiIiMDJyUnRX27BdHbs2MFHH32En5+fbpu7u7tIAvRIqzkaaHB2fs2BA5NEEpABIuZbDxHzhVTZ2YGrK1K5cszft4/6Xbpw/+FDypQpw6lTpxg6dKii/2f++OMP3NzcdEmAnZ0d8+bN4/fffzcqCQDDYn7u3C+5cmUpFbTTokwkW98RAChYsCAxMTE8ePCAvHnz4uTkhK2trQg8FkCSJNRqNZGRkURERODg4KBo3V/BNGJiYhgxYgQ///wzAKtWreLLL78Uv1PvoW2O1rGjBEgkvUajQaVSsXp1nuT9dQQjiJhvuUTMF9Jj4cKFDB8+HIAvv/ySVatWkS9fPsWOHx8fz/fff8/cuXN120qWLMmWLVuoVatWho79LubLyUDS5TAaQIWPT34y47pZtn9bsbW1xdXVlRcvXhAZGcmrV6/MPSQhGXt7ez744AMKFiwo5kRbuOvXr+Pp6cm1a9cAGDFiBDNmzBAfsgxQsOAx8udfT2joRMBVt93FRcWiRSqrbI5miUTMt3wi5guG6NevH+vWreOrr77iq6++UvR95v79+3h6enLmzBndtnbt2rF27VrddNeM6tABtmxR079/NOHh7z7xu7rasHCh6COQqWxtbSlSpAiFCxcmPj5e1Oe2IDY2Ntjb24sPkhZOkiRWrlyJt7c3b968oUiRIvz666+0aNHC3EOzeAkJCUybNo3p06e/jT1ryJu3Df37T6RNm+o0aKCyyuZolkzEfMslYr6QGo1Gw2+//UaHDh100/wuXryIncK3Snfu3Env3r0JCwsD5MR0zpw5fPPNN4r+v3z48CGLF3sRHn6KFi1m0LPnGLM0xBSJQCIqlYocOXKYexiCYHUuXLjAV199BUDLli1Zt26dKAtqgAcPHtC1a1dOaOuCAo0bN2T9+p8pXry4GUeWPYiYLwjW4cWLF/Ts2ZM///yTBQsW4O3tDaBoEhAXF8fo0aNZuHChblupUqXw8/PDw8NDsfMA7Nmzh+7duxMaGoqTkxN9+pShUydFT2EwkQgIgpBhHh4ejBgxgg8//JBvv/1WlPszgL+/P/369dNddbK1tWXKlCmMGTNGTIcQBEF46/jx43h5eRESEkLOnDlNUnAiKCgIT09Pzp07p9vWoUMHfvnlF0ULNMTHxzNu3DjmzJkDyAU0/Pz8+OijjxQ7R3qJREAQhHRLSEhgzpw5dO3alRIlSgDoApvwftHR0Xz33XesWLFCt61kyZL4+vpSp04dM45MEATBcmg0GmbPns2ECRNQq9V8/PHH+Pn58cknnyh6nt9++43evXsTHh4OQI4cOZg/fz5ff/214lOBOnXqxOnTpwEYPHgwc+fOJWfOnIqdwxgiERAEIV2Cg4Pp2rUrx48f588//+To0aPiDoCBrl69iqenJ//8849u25dffsnKlStFWVBBEIS3nj17Rvfu3dm/fz8A3bp1Y9myZYr2/4iNjWXEiBH89NNPum1ly5bFz8+PatWqKXYeLRsbG27fvk3evHn55Zdf6Nixo+LnMIZIBARBMNhvv/1G3759CQsLI0+ePAwcOFAkAQaQJIlly5bx3XffERsbC4CjoyNLliyhT58+YmGkIAhCIkFBQRw6dAhHR0d++uknevfurWicvHPnDp07d+bixYu6bZ06dWLVqlXkzZtXsfNoNBrde2SxYsXw9/enWLFilClTRrFzZJR4BxcEIU0xMTF8/fXXdOjQgbCwMDw8PAgICKBbt27mHprFCw0NpUOHDgwePFiXBFStWpWLFy/St29fkQQIgiAkU6tWLVavXs25c+cUv1iydetWqlWrpksCHBwcWLZsGZs3b1Y0Cbh37x5169Zl27Ztum3169e3qCQARCIgCEIaHjx4gIeHB8uWLQNg1KhRnDhxgrJly5p5ZJbv6NGjVK1alR07dui2ffPNN5w5c4aKFSuab2CCIAgW5OnTp/zvf//jypUrum09e/akcuXKip3jzZs3fP3113Tq1ImIiAgAypUrx5kzZxTvQ7Bjxw7c3d05e/YsI0eOJD4+XrFjK82iEoHIyEi8vb0pWbIkjo6O1K1bl/Pnz5t7WIKQrRUqVAgbGxuKFCnC/v37mT17tii5mIaEhAQmTpxI48aNCQkJAaBAgQL8/vvvLFq0yOyLwyyFiPmCIBw6dAg3Nzd27dpFnz59kJK22VXErVu3qFOnju6CFkCXLl24ePEibm5uip0nLi4Ob29vPv/8c169ekWtWrU4fPgw9vb2ip1DaRa1RqBfv35cu3aN9evXU6xYMTZs2EDTpk35559/RE1tQchEr169wsnJCVtbWxwdHfH398fJyUn0BkiFWg3Hj8Pjx6BSPWHx4i85fTpxb4DGrF+/XsSxZETMF4TsS61WM3XqVKZNm4YkSVSqVAkfHx/Fp0v6+voyYMAAXr9+DUDOnDlZsmRJhqZmJo752iZg9+/fpXPnzly4cAGA4cOHM3PmTMu/cCZZiOjoaMnW1lbavXt3ku3VqlWTxo0bZ9AxwsPDJUAKDw83xRAFIVs4duyY5OLiIk2fPt3cQ7EK27dLkouLJEHirwcSfC7Z2tpKM2bMkBISEkxybmuOeRmN+db82gUhuwsJCZEaNWokARIg9e3bV4qKilL0HNHR0VL//v115wCkChUqSFeuXMnQcfXF/KJFE6RcubpJgJQ/f37p999/V+hVvGOqmGcxdwQSEhJQq9Upbpk7Ojom6bqZWGxsrG7xHaCb8yUIQvolJCQwY8YMpk6dikajYePGjYwYMQIHBwdzD81i+ftDx468vZWd+MpScWAb06bdYuzYj800OsuW3pgv4r0gZA23bt2iXr16PH/+nNy5c7NixQq6du2q6DkCAwPp1KkTV69e1W3r3r07P//8c4ZKkL6L+Um3P3liiyT58PHHrvz11yBcXV2NPkemUzStyKA6depIDRs2lEJCQqSEhARp/fr1ko2NjVS+fHm9+0+aNClJpqf9EleIBCF97t+/LzVo0ED3O9SrVy8pMjLS3MOyaAkJ2qtCmmR3A+QvlUojubrK+5mKtV8VT0/MF/FeELKGhIQEqXHjxtInn3wiBQYGKn58Hx8fKXfu3LoY4ejoKK1Zs0bSaDQZOu67mK//S6XSSC4uGpPFfFPFe5UkmWBVhpHu3LlDnz59OHbsGLa2tlSrVo3y5ctz8eJFbty4kWJ/fVeIXF1dCQ8PV7QElCBkZf7+/vTr14+wsDCcnJxYvnw5Xbp0MfewLN7hwxL//W/a80sPH4ZGjUwzhoiICPLly2e1MS89MV/Ee0GwXo8ePSJ//vy6O4AvXrwgd+7cODo6KnaOqKgohg4dytq1a3Xb/vOf/+Dn50elSpUyfPwjR6Bx47T3M1XMN1W8t6iqQWXLluXo0aO8fv2a4OBgzp07R3x8fKo1Vx0cHMibN2+SL0EQDPfo0SO6du1KWFgYNWvWJCAgQCQBBggNDWXEiHkG7fv4sYkHY8XSE/NFvBcE67R3716qVq3Kd999p9tWsGBBRZOAf/75h5o1ayZJAnr37s25c+cUSQLA8FhubTHfohIBrdy5c1O0aFHCwsLYt28f7dq1M/eQBCFLKlasGPPnz2f06NGcOHHC4hqdWKIjR45QtWpVLl3abdD+RYuaeEBZgIj5gpD1xMfHM2bMGFq1asWLFy84c+YMUVFRip9n3bp1eHh48M8//wCQK1cufHx8WLNmDblz51bsPPHxDwzaz9pivkVNDdq3bx+SJPHxxx9z+/ZtRo4cSc6cOTl+/LhBNVit/Ta5IJiaJEksX76catWqUatWLXMPx6okJCQwZcoUZsyY8XZxsA02Ng/QaIqRdKGwTKUCFxcICgJbW9OMydpjXkZivrW/dkHIyoKDg/H09OTUqVMADB48mLlz5yraQyUqKoqvv/4aHx8f3bbKlSvj5+eneMPGTZs2MWDAIKKiriEXg0h5Hd3UMT9bTA0KDw9n8ODBVKhQgR49elC/fn327dtn0Y0YBMFavHz5kg4dOvD111/j5eVFZGSkuYdkNe7fv0/Dhg2ZPn26rtnNf//biBUrHFGpVCQvRa39fuFC0yUBWYGI+YKQ9ezevRs3NzdOnTpF3rx52bp1Kz/99JOiScDVq1epUaNGkiSgf//+nDt3TvEkIC4ujpkzZxIVFUGlSquyXsxXdOmxmVl7BQ1BMJWjR49KLi4uEiDZ29tLCxYsyHAFhexi69atUr58+XQVKGxtbaWZM2fqegPoqynt6ipvN7XsHPOy82sXBEv16tUrydnZWQKk6tWrS7dv31b0+BqNRlq1apWUM2dOXUzOkyePtHHjRkXPk9w///wjTZkyRUpISDBbzM8WVYMyStwqFoSkEhISmDZtGtOnT0ej0VC+fHl8fX2pVq2auYdm8aKiovj2229ZtWqVblvp0qXZtGkTtWvXTrKvvi6TmXFVKDvHvOz82gXBku3cuZNDhw7x448/KtqHJjIykkGDBrFx40bdtqpVq+Ln50f58uUVOw+Aj48PL168SLLAOTFzxHxTxTyRCAhCFhUeHk7r1q05efIkAL169WLJkiUZaqaSXVy5coXOnTsTGBio2+bp6cny5cvJly+fGUeWVHaOedn5tQuCJdm5cycODg60bNnSZOe4fPkynTp14t9//9VtGzRoEPPnz1d83cGQIUNYt24dtra2nD9/Hnd3d8WOnxHZYo2AIAjKyZs3Lx988AFOTk5s2rSJtWvXiiQgDZIksWTJEmrWrKlLAnLlysXatWvZtGmTRSUBgiAI5hQXF4e3tzft27enW7duhISEKH4OSZJYsWIFtWrV0iUBTk5ObNmyhZ9//lnRJOD69evUrFmTdevWYWNjw6RJk/jkk08UO76lsjP3AARBUE50dDQajYY8efKgUqlYu3YtkZGRoiyoAV68eEGfPn3YtWuXbpubmxubN2/m448/NuPIBEEQLEtQUBCdO3fm/PnzgHzHuVChQoqeIyIigoEDB7J582bdtmrVquHn50fZsmUVO48kSaxdu5YhQ4YQExND0aJF2bRpE41M1QnSwog7AoKQRVy9ehUPDw8GDx6s21aoUCGRBBjg0KFDVK1aNUkS4O3tzZkzZ0QSIAiCkIi/vz/u7u6cP3+e/Pnz8/vvvzN37lxy5Mih2Dn+/vtvqlevniQJGDp0KKdOnVI0CQAYMGAAffv2JSYmhubNmxMQEJBtkgAQiYAgWD1Jkli6dKmuocpff/3F06dPzT0sqxAfH8+4ceNo2rQpjx49AuSOl7t372bBggWKLnQTBEGwZhqNhm+++YYvvviC8PBw6tSpw99//03btm0VO4f2/ax27drcvn0bgHz58rFt2zYWL15skpjs5uaGjY0NM2bMYM+ePRQuXFjxc1gyMTVIEKxYaGgoffv2ZefOnQB89tlnrFu3TvFbtFnRvXv36NKlC6dPn9Zta9KkCevXr6eotbWGFARBMDEbGxtiYmIAGDVqFNOnT1e050d4eDj9+vVj27Ztum0eHh5s3rxZ0TvbkiTx4sUL3fvk119/TaNGjahUqZJi57Am4o6AIFipo0ePUrVqVXbu3EmOHDlYuHAhu3fvFkmAAfz8/KhataouCbCzs2PWrFns379fJAGCIAiJxMXF6f6+ePFiDhw4wOzZsxVNAi5cuIC7u3uSJMDb25sTJ04omgRERETQpUsXateuTXh4OAAqlSrbJgEgEgFBsEpv3ryhS5cuhISEUL58ec6cOcOwYcNQJW93KCQRFRVFv3796Ny5MxEREYDcG+DEiROMHj0aGxsREgVBEEB+n/n6669p27YtGo0GAEdHR5o0aaLYOSRJYvHixdStW5egoCAAPvjgA3bs2MGCBQsUXXcQEBBAjRo12Lx5M/fv3+fYsWOKHduaialBgmCFcubMydq1a9myZQuLFy8md+7c5h6SxQsICMDT05ObN2/qtnl5ebFs2TJRFlQQBCGRW7du0alTJwICAgA4duyY4gtow8LC6Nu3L7/99ptuW61atdiyZQslS5Y07qAJCXKXr+ho+e92dkiOjiz//Xe+HTGC2NhYXF1d2bx5M3Xr1lXolVg3kQgIgpXYunUrNjY2fPHFFwA0b96c5s2bm3lUlk/bG2DkyJG6W9y5c+dm6dKl9OjRQ9xFEQRBSMTX15cBAwbw+vVrChUqxPr16xVPAs6ePUvnzp25f/++btvw4cOZOXOmcXcBIiPhzh0ICpITgLdxPTwqiv7Ll7P1zBkA2rRsyboNGyhQoIAiryMrEImAIFi46OhovL29WbVqFXnz5qVGjRrGXy3JZl68eEHv3r3ZvXu3bpu7uzubN29WvCW9IAiCNYuJiWHYsGGsWrUKgIYNG7Jp0yaKFSum2DkkSWL+/PmMGTOGhIQEAJydnfn111+Nqz4kSXDtGgQGyh/+JenddmDUhg1sPXMGO1tbZnftyretW6N69Ajy59clC9mdSAQEwYJduXIFT09Pbty4gUqlYsiQIYoG5azs0KFDdOvWjcePH+u2eXt7M2vWLFEWVBAEIZlu3brh7++PSqVi/PjxTJw4ETs75T4mhoaG0qtXryQXZurWrYuvry8lSpRI/wElCc6fB+1dBW0SkMh0T0+uBwczr0cPapUrJ28MDISYGPDwEMkAYrGwIFgkbS3lmjVrcuPGDYoWLcqBAweYMWOGopUasqL4+Hi+//57mjZtqksCChUqxB9//CF6AwiCIKRi/PjxlChRgv379zN16lRFk4DTp0/j7u6eJAkYPXo0R44cMS4JAPlOQKKpRQCvoqJYvn+/7vtCefNyfOrUd0mA1v378vMFcUdAECyNWq2mY8eO7NixA4A2bdqwdu1aChYsaN6BWYGgoCC6dOnCmbfzQQGaNm3K2rU+3L5dFF9fKFoUGjQAW1szDlQQBMHMoqKiOH36NE2bNgXkaZO3b99W9GKTRqNh7ty5fP/996jVakBu2ujj40OrVq2MP3BkpHxlP5Fzt2/TecEC7j0P5f6LynxSohZFnd/QoOJzbPVd+A8MhFKlwMnJ+HFkASIREAQLY2trS+nSpcmRIwdz5sxh6NChYkGrAbZs2cKAAQN0ZUHt7OyYMWMGZcqMoE4dGx4+fLeviwssWgQdOphpsIIgCGZ0/fp1OnXqxK1btzh58iQeHh4AiiYBL168oEePHuzZs0e3rUGDBvj6+lK8ePGMHfzOHd2aAEmSWPTnn4zasIF49f+wtVnCrB3vju9SIJpFvQLoUCsk6TFUKrh7F6pWzdhYrJyYGiQIFiAhIYEXL17ovv/hhx+4ePEi33zzTZZIAtRqOHIEfH3lP99eGFJEVFQUffv2xdPTU5cElClThpMnT/LRR6Po1ClpEgAQEgIdO4K/v3LjEARBsHSSJLF27Vo8PDz4559/KFCgALGxsYqeQ62GxYuvUL78JPbsiQZsUKlUjBs3jkOHDmU8CUhIkKsDSRIvX7/m8zlz+PbXX4lX/w/YjlqTdB1dSKgjHefVwf9ssvNKkpwIvF20nF2JREAQzOzevXt8+umntG/fXldFwcHBgcqVK5t5ZMrw95fvvjZuDF26yH+WKqXMh/C///6b6tWrs2bNGt22rl27vt1ek2HD9K4f023z9lY2KREEQbBUr1+/pmfPnvTp04eYmBiaNWtGQEAA9evXV+wc27ZpyJ8/gmHDPiEsbClwBBubB0yY8DfTp09XZt3B48eQkMDZW7dwHzWKnRcuYG+bgw9yrX67Q9KLZ9Lb773XuaHWJDuWtu9ANiYSAUEwIz8/P9zc3Dh9+jRXr17ln3/+MfeQFOXvL195V/qKvCRJLFq0iNq1a+sahOXOnZt169axYcMG8ubNy/HjKc+b9BgQHAzHjxs3BkEQsrmEBDmI3LwJ16/LfwYHW+QV5qtXr+Lh4cH69euxsbFhxowZ7N27lyJFiih2jrVrw/nySxUREXmSbJekYkybVlW5O7DR0aBS8SoqigcvXlC2SBF+6rOWV9H5SZ4E6MaAiuDQXBy/USjpAyqVXEEoGxNrBATBDKKiovD29mb1avkKRu3atfH19aVUqVLmHdh7qNXyh+bHjw1bcKtW894r8iqVfEW+Xbv0Ldx9/vw5vXv35o8//tBtq1atGps3b6ZcosoQhl7kyeYXgwRBSK9UmlcBcnCzs4PSpaFsWYtZiLp//34CAwMpVqwYvr6+fPrpp+/dP73x/tCho/TrVw5wIvk1ZklSGR3vk9NoNNi8TbRauLmx2dubVm5u/HGpjEHPfxyWM+XG+HjjB5QFiDsCgpDJLl++TI0aNVi9ejUqlYrvv/+eY8eOWXQSYMz0HlNckT948CBVq1ZNkgR89913nD59OkkSAPKblyEM3U8QhGxOkuDqVdi7F27ffnflX5LefYG8/fZteb+rV/VfDclk3377LVOmTCEgICDNJCA98V6tVjNt2jSaNp2CRlOM1D5WKnEH9uTJk7i5uRH05IluW+e6dcmbKxdFnd8YdAy9+2XzktwiERCETCRJEgMGDNBdmTl48KDF9wYwdnqPklfktb0BmjVrpusNULhwYfbs2cO8efP0tqRv0ECuDpTaWmuVClxd5f0EQRDeS9u8SluyMq0P99rHAwPl52VyMnD58mXat29PVFQUADY2NkycOJFChQq993npifdPnjyhRYsWTJw4EUkybIqRMXdgNRoNs2bNomHDhly9epXvFy9O8e/ZoOJzXApEo0L/v7MKCdcC0TSo+DzpA5IEjo7pH1QWIhIBQchEKpWKdevW0alTJy5fvkzjxo3NPaT3Smt6D6S+4FapK/JBQUE0aNCAH374AentSZs1a8bly5dp2bJlqs+ztZVLhELKZED7/cKFop+AIAgG0NO8ymCZ2LxKkiSWL19OrVq12LlzJxMmTDD4uemJ9wcPHsTNzY2DBw8CoFI9Negc6b0D+/z5c1q3bs3YsWNRq9V06dKFlevWydOvErG1gUW9AuSxJEsGtN8v7BWAbfJPvXZ22f62sEgEBEFhyUtlHjhwmCVLluger1ixIlu2bLGKBmEZmd6jxBX5zZs34+bmxtmzZwG5N8Ds2bPZu3cvH374YZrj79ABtm2D5NXqXFzk7aKPgCAIadLTvEpLrYEj1wvhe8KVI9cLpaxKoxUYKB/HhCIiIvD09GTQoEHExsbSunVrxo0bZ/DzDY33ffqspVmzZjx9Kn/4lzvfT1L8DuyxY8dwc3Nj79695MyZk9WrV7NhwwacnJ3lNRjJTtahVgjbhp+meIGki39dCsSwbfhp/X0EypRJkVRkN9n71QuCwvz95SsqSYPpR6hUS/Hw8KB27drmGppRMjK9R3tFvmNHXd8XnbSuyEdFRTF06FDWrl2r21a2bFl8fX11jW8M1aGDvEAtPQvfBEEQdBI1r0rM/2xxhq1z42FoLt02czWvunTpEp06deLOnTvY2dnxww8/8N1332FjY/j1XkPjvY/Pfnh7lb158+asX7+ewoULGx3v9dm/fz+tWrVCo9FQoUIF/Pz8qFKlyrsdypaFW7dSPK9DrRDaeYRw/EYhHoflfNdZWN8/gyTJiUA2J+4ICIJCUptbCcWRpK3cvetmhlFlTEan9xhzRV7bGyBxEtC1a1cuXbqU7iRAy9YWGjUCLy/5T5EECIJgkETNqxLzP1ucjvPq8DA06fxyczSv2rFjB3Xq1OHOnTuUKFGC48ePM2LEiHQlAZCeGTKPdSVI9+zZQ+HChQFl78A2bNgQd3d3evTowfnz55MmASBXY6pQQe9zbW2gUaXneNUPplGlVJIAkJ9vIVWdzEklSRawnF0hERER5MuXj/DwcPLmzWvu4QjZiFotV1VI7baqSiUHw6Ag03wITW+pt/Qct1QpeaGYvkhh6OsyZHza3gCjR48mLi4OgDx58vDzzz/TvXv3jL+YLCg7x7zs/NqFTBQcDGfOJNmk1kCpwa3fJgEp58KokHApEEPQ0j9SfgitXVueI5NBiWOqvf0LBg2qTN26tVi7di358+c3+pjvi/egAR5SrFh9Nm/eSINU5vkY+350/vx53N3ddU3HIiMjcXrfB3XtAm5j1m6ULAkeHqnPZbJApop5FnNHQK1WM2HCBEqXLo2joyNly5Zl2rRpZKE8RcjCzNm8ypSde5VacJvWFflnz57Rpk0bvv32W10SUL16dS5duiSSgCxKxHzBKrxtXpXY8RuF3k4HMk/zKn9/cHVN0MX8L78siJ3dQ3r02GF0EgDvj/dyEgDVqq3n8uVLqSYB2uOk5w6sWq1mypQp1K5dmylTpui2vzcJ0A7Sw+PdnYG0PtRrH69QweqSAFOymERg9uzZLFu2jJ9++okbN24we/ZsfvzxxySLLAXBUpmredW2bfDFF8p37k3M1AtuDxw4QNWqVfnzzz9120aMGMGpU6dS9AYQsg4R8wWroGcqj96mVHqYonnV1q0SX3wh8fhx0k/XT5/a8eWXqgzHfG28L1AgecLykO7dd3L+/FhFC108efKE5s2bM3nyZDQaDY8fP07fxQCVCqpUgZYtoVy5dwt/Vap3XyBvL1dO3q9KFZEEJGIxi4VPnTpFu3btaN26NQClSpXC19eXc+fOmXlkgpA6SZJ48uQJRQ2cXKlUlTK1GqZNg6lTUxuX8Z179THFgtv4+HgmTJjAjz/+qAv8hQsXxsfHhxYtWmRswNlAQkIC940tZ2gBRMwXrIKeijKZ3bxKO9XG1/cNK1fmQN81XKVifnx8POfOTeDFizlAA6AohQqp8ff/lvr1PzfuoKk4cOAAXbt25dmzZ+TOnZtly5YZfwfYyUleiF2pkvwmFRMjJ1329nKfgKJFs311oNRYzL9K3bp1WblyJf/++y/ly5fn8uXLnDhxgvnz56f6nNjYWGJjY3XfR0REZMZQBQGQ6xv36dOHa9eucfFiAC4u+dKcS69E8yp/fxgwAEJD379f4ulIjRpl/Lza271KuHv3Ll5eXkk+9DVr1gwfHx+DyoJmdw8ePKBbt27cu3fP3EMxWnpjvoj3glnkypVq86qQUEek96wRUKJ5VdJKdO+/E5HRmB8cHIynpyenTp16u+Uobdu2Zd26dRmacpRcQkICU6dOZfr06UiSRJUqVfDz86NCKot/08XOTpE1GNmJxUwNGjNmDJ6enlSoUAF7e3vc3d3x9vama9euqT7nhx9+IF++fLovV/HDFzLJoUOHqFq1Krt37+bx48ecP38mU5pXaSsTpZUEJKb0dKSM2rRpE25ubrokwN7enjlz5hjcGyC78/f3x83NjePHjxMWFmbu4RgtvTFfxHvBLPRcSc6s5lVyvJd4+DB962aMifm7du3Czc1NlwTY2dkxb948du7cqWgSAHKTyLlz5yJJEv379+fs2bPKJAGCcSQL4evrK7m4uEi+vr7SlStXJB8fHyl//vzSunXrUn3OmzdvpPDwcN1XcHCwBEjh4eGZOHIhO4mLi5PGjh0rqVQqCZAqVKggBQQE6B7fvl2SXFwkSb42I3+5usrbMyohIeWxDfk6fDjj51ZCZGSk1LNnTwm5ALUESGXLlpXOnz9v7qFZhbi4OGngwIG6f7uaNWtKAQEBVhvz0hvzRbwXzObvvyVp61ZJ8vNL8rV9+EnJpUBU0nhfIEraPvxkin2lrVslKdF7RVrexXuNSWN+XFycNHz48CRxuWTJktKZM2fS/c+UHuvXr5c2bdpk0nNkNeHh4SaJeRZTPtTV1ZUxY8YwePBg3bbp06ezYcMGAlPp6JecKCcnmFJQUBBdunThzNtScgMGDGDBggXkypUryX6mKuV55IhcESg9XF1NV7I0PS5evIiXlxe3EjWA6d69O0uXLk27MoQAyOtRvvzyS7Zv386oUaOYPn06MTExVhvzMhrzRbwXMk1kJOzdq/chtQbDmleBvFDVwHhnTLyH9MX8+/fv07lzZ13ndoD27duzZs0anJ2d03/yVCQkJDBp0iRatWpF/fr1FTtudmOqmGcxawSio6NTNL+wtbVFo0mtX7cgZK7x48dz5swZ8uXLx6pVq/jyyy/17qfkXPrEjLndq8R0pIzQaDQsXLiQMWPGEP+2WkaePHlYtmwZ3bp1M9/ArIQkScTGxpIzZ05UKhWrVq3i66+/5r///S8AMQqUIjQXEfMFq6FtXqUnQdU2r0qTgc2rNBoNixYtIkeOnkD6p+QYGvN37txJr169ePXqFSBP0Zw7dy5Dhw5FpWBFnYcPH+Ll5cWJEyfw8fEhMDCQ3LlzK3Z8IeMsJhFo27YtM2bMoESJElSqVIm///6b+fPn06dPH3MPTRAAWLRoEXFxccydO5eSJUtm+vnTU3HI1hY2b854ac+MePr0Kb169WJvoitpNWrUwNfXl48++sh8A7MSL1++pG/fvtjb27NlyxZUKhXOzs66JMDaiZgvWJXKleVKNMY2r6pcOc3dnj9/Ts+ePdmzZw9VqgQBiw0+haExPy4ujlGjRrFIu6gNKFOmDFu2bKFGjRoGn88Qf/75Jz169CA0NBQnJyfmzZsnkgBLpOhEowyIiIiQhg0bJpUoUULKmTOnVKZMGWncuHFSbGyswccw1fwpIXu6dOmSNG7cOHMPQ0c7Z1SlSnuOqJ+fece6f/9+qUiRIknmnY4aNSpdv8/Z2dGjRyUXFxcJkOzt7aWrV6/q3c+aY15GY741v3bBSmk0knTlyrs5/8nXASRfE+DnJ++v0aR56GPHjknFixeXAClnzpzS8uWrJBcXjUHx3tCYf+fOHalGjRpJ4nLHjh2lV69eKfCP805cXJw0cuRI3TmqVasm3b59W9FzZEeminkWkwgoQbwxCErQaDTSwoULpRw5ckiARS1o2r5dTgRSe3MoUECZhcnGio2NlUaNGpXkjaZIkSLSvn37zDcoKxIfHy9NnDhRsrGxkQCpfPny0sWLF1PdPzvHvOz82gUzi4iQF/76+7/70K/98vOTtwcEyPulQa1WSzNmzJBsbW0lQPr444+ly5cvS5KUdrxPTzGKbdu2SXnz5tXF5Rw5ckg//fSTpDEgSUmP8PBwqU6dOrrzDB06VHrz5o2i58iuRCJgAPHGIGTUs2fPpNatW+uCWLt27aQXL16Ye1hJ6KtMVKCAJE2ZIt81MJfbt29LHh4eSZKAli1bSk+fPjXfoKzI/fv3pfr16+v+7Xr16iVFRka+9znZOeZl59cuWIj4eEl68ECSbt6UpGvX5D8fPJC3G+DFixdS8+bNdb/z3bt3T/E7ry/eFyokSd7ecnWgtGJ+TEyMNHjw4BTV2t53gSEjNBqN1KFDBylfvnzSdnNelcqCsnzVICWIKhJCRhw8eJBu3brx5MkTHBwcmDdvHl9//bWiC6eUYqrKRMbauHEjgwYNIjIyEpAXns2aNQtvb+8UC0KFlCRJomrVqly9epW8efOyfPlyvLy80nxedo552fm1CxmUkCAHz+ho+e92dnLjsEzuPhsdHU3NmjW5e/cuS5cupVevXnrfb4yN97dv36Zz585cunRJt61z586sXLlS0d+ZuLg44uLiyJMnDwCvXr0iLCyM0qVLK3YOIRtUDRIEc/rxxx8ZM2YMkiTxn//8h82bN1OlShVzDytVpqpMlF6RkZEMGTIEHx8f3baPPvqIzZs3U716dTOOzLqoVCqWLFnC999/z/r16ylTpoy5hyQIWU9kJNy5I9fXTEhI2v1RkuQkoHRpKFvW4DKf6aVWq1GpVNjY2JArVy62bduGWq2mUqVKqT7HmHjv5+dHv379dBdnHBwcWLRoEQMGDFD04ta9e/fo3LkzpUuXxtfXF5VKxQcffMAHH3yg2DkE0xKX6gQBqFatGiD3Bjh//rxFJwGW4uLFi1SvXj1JEtCjRw8uXbokkgADXL16ld9++033fcOGDTlx4oRIAgRBaZIEV6/KvQBu35aTAO127RfI22/flve7evXddoU8efKE5s2b8+OPP+q2VahQ4b1JQHq9efOGQYMG0blzZ10SUL58ec6ePcvAgQMVTQJ27NiBu7s7586dY9++fdw3pqKSYHYiERCyreDgYN3fmzZtytWrV1mxYkWKBmFCUhqNhvnz51OnTh1dg7A8efKwYcMGfv31V9EgLA2SJPHzzz/j4eFB9+7duXnzpu4xS5yGJghWTZLg/Pl3PQDS+nCvfTwwUH6eQsnAwYMHcXNz49ChQ8yaNYuXL18qctzE/v33X2rXrs3y5ct127p27cqFCxeoWrWqYueJjY1l2LBhfP7557x69YratWsTEBBAqVKlFDuHkHlEIiBkO69fv6Z3795UqlSJu3fv6rYreVUmq3r69CmtW7dm+PDhugZhHh4eBAQE0LVrVzOPzvKFhoby+eefM3jwYGJjY2nYsCH586e/aZAgCAa6ds242v8gP+/atQydXq1WM2nSJJo1a8bTp0+pXLkyp0+fVvz33tfXl+rVq3P58mUAcubMyerVq1m/fr2iF2fu3r1LvXr1WLxY7nEwYsQIjh07ZpbeOoIyxBoBIVu5dOkSnp6e3Lp1CxsbG44cOUKZMmUsbvGtJdq/fz89evTg6dOnum2jR49m6tSp5MiRw4wjsw5Hjx6la9euhISEkCNHDn788Ue++eYbcRdAEEwlMlJvN2AAtQaO3yjE47CcFHV+Q4OKz7HVd2k0MBBKlTJqzcCjR4/o2rUrR44cAaB///4sWrQIR0fHdB8rNTExMQwbNoxVq1bptlWoUIGtW7dS2YAmZumh0Who06YNN27cIH/+/Pj4+NC6dWtFzyFkPpEICNmCtm376NGjiY+Pp3jx4mzcuJGGDRvi7w/DhsHDh+/2d3GBRYvM25nXUsTFxTF+/HjmzJmj21akSBHWr19Ps2bNzDgy6zF58mSmTp2KJEmUL1+ezZs34+7ubu5hCULWdueOvCA42fQe/7PFGbbOjYeh76aBuhSIZlGvADrUCkl6DJUK7t6FdE6tefPmDbVr1yY4OJg8efKwYsUKunTpYvRL0ScwMJAvv/ySa4nuWvTs2ZOlS5eapIOvjY0Ny5YtY+LEiWzYsAFXV1fFzyFkPjE1SMjynj17Rps2bfjuu++Ij4+nXbt2XL58WZcEdOyYNAkACAmRt/v7m2fM5qJWw5Ej4Osr/3nz5m3q1auXJAlo2bIlV65cEUlAOsTFxSFJEn369OHSpUsiCRAEU0tIkKsD6UkCOs6rw8PQpFflQ0Id6TivDv5niyc9jiTJiYB2gbGBcubMyejRo6latSoXL15UPAlYv349NWrU0CUBuXLlYu3ataxbt87gJCB5vFerU+5z+/Zt/vjjD933DRs25MiRIyIJyEJEHwEhy5s0aRJTp07FwcGB+fPnM2jQIFQqFWq1fMc3eRKgpVLJdwaCgoybJmRt04303RlRqR4iSd8Av2Fvb8/s2bMZNmyY6A1ggOjoaN3C8/j4eA4cOECrVq0UPUd2jnnZ+bULBggOhjNnkmxSa6DU4NZvk4CUU/JUSLgUiCFo6R8ppwnVrg3v+fCrVoO//3Pu3o2hVq0SNGgANjYScXFxODg4KPCCZFFRUQwdOpS1a9fqtlWqVAk/Pz/+85//GHwcQ+6Eb9myhf79+6PRaLh48SIff/yxUi9DMILoIyAIRho3bhx37txh9OjRScqCHj+eehIA8oWg4GB5v/TWcLa26UbaOyPJLwtIUjFgG0WLfsOuXb1FWVADREdH4+3tTUBAACdOnCBHjhzY29srngQIgvAe0dEppgUdv1EoyXSg5CRUBIfm4viNQjSq9PzdAyoVxMSk+jx/fxg4MIYXLwrptsnxXkWHDsolAdevX6dTp078888/um19+vRhyZIl6ap2l1q8194J37gxlqNHh7FixQoAGjRooGsWJmQ96b6s9/TpU1QqFSqVin379r133yFDhqBSqahbty5Z6MaDYOHu3LnD119/TcLbW7n29vasX78+RW+Ax48NO56h+2lZ23QjtVpOWvT/isohwsZmMW5uIglIy5UrV6hRowarVq3iwoULHDx40NxDEoTsSc9UnsdhOQ16qt793lZJS87PL4EvvpB48SLpB34l470kSaxduxYPDw9dEpA7d27Wr1/PL7/8kq4k4H3xXt4m0bPnS1asWIVKpWLcuHEcOnSI4sWLp3yCkCWkOxEoUqSIruHN2bNnU93v8uXLLF++HBsbG5YsWSIqYwiZYtOmTbi7u7Ns2TJmzpwJoEtckyta1LBjGrofGBJkwdtb/1xMc0nrzgjYEBJiw/HjmTUi6yNJEkuXLqVmzZrcuHGDokWL8tdff4m7AIJgLnYpJzwUdX5j0FP17mdvn2LTvXvBdO8eCkgk/zilVLx//fo1PXv2pE+fPsS8vStRpUoVLly4QLdu3dJ9vLTvhKuIjy9Kvnxt2bdvH9OnT8dOz7+lkHUYNdG3Xr16wPsTgaFDh6JWq+nXr5+YTiCY3OvXr+nVqxddu3YlMjKS+vXr06tXr/c+p0ED+fZtajmqSiVPCW3QwPBxpGe6kaUIDAw3aL/03hnJLkJDQ2nfvj1DhgwhNjaW1q1bc/nyZZo0aWLuoQlC9pUrV4orMg0qPselQDQq9M9QUCHhWiCaBhWfJ31AkiBZyc/du3fzySeDiYsrQmofpTIa769evYqHhwfr16/XbRswYABnz56lQoUKRh3T0Dg+c+ZaURAimzAqEahbty6QeiKwYcMGjh8/jrOzMzNmzDB+dIJggEuXLlGtWjV+/fVXbGxsmDx5MocPH6ZEiRLvfZ6trTxnH1ImA9rvFy5MfYGvvooLpppuZCr79u1j7NheBu2bnjsj2Unfvn35/fffyZEjB4sWLWLXrl0UKlQo7ScKgmA6RYumuCtgawOLegUApEgGtN8v7BWQcqGwnR3qwkV18f7wYYmff15BZKRh1XnSG+8lSWLVqlXUrFmTwLd9EPLkycOmTZtYsWJFhvoQGBrH//MfZ6PPIVgXo+73aO8IhIaGcvv2bT766CPdY5GRkYwaNQqAadOmUbBgQQWGKQj6bdq0iV69ehEfH4+rqysbN26kQTou4XfoANu26V/Yu3Bh6gt7U1sM3L+/Yec194fquLg4xo0bx9y5c5GvBwQDxdF3bUBbPSk9d0ayk3nz5vHo0SNWrlyJm5ub8QdKSJA/MURHy3+3s5Ovaur5QCMIQhrs7KB0abh9O8mdgQ61Qtg2/LSePgIxLEylj4B/kDvDPrJLFO9VFCu2g08/PcKxY2kPJT3xPjIykq+++opNmzbptrm5ueHn50e5cuUMP1AqtHfCQ0L0T2EV8T6TWFC8N6p8qEajIX/+/ISHh7Nhwwa6du2qe2zEiBHMmzePTz75hEuXLmGbifUSRTm57CcwMJDq1avTokULVq9ebXTb9vSU+kyt4oK2QEWBAvDy5fuDrLElSTMybq1bt27h5eXFxYsXddvc3acREDAOUCUZt/bOyLZtllntyBzu3bvHgQMH6Nevn26bJEnGr4OKjJQbHwUFyW8IiY8jSe8+0JQtm6K7aXaOedn5tQsGioyEvXv1PmRoZ2H/s8XpOL/O27j47nfTFPH+8uXLfPnll9y6dUu37euvv2bevHnkzJlTsZLUmza9oWtXB5KvbRDxPhNYYLw3amqQjY0NtWrVAuBMojq9N27cYPHixQD89NNPmZoECNnHvXv3dH+vUKECly5dYvv27UYnASAH00aNwMtL/vN904Hetxg48e+0MdON0sPfX+6D0LgxdOki/1mq1PurVKxfv55q1arpkgB7e3vmz5/PhQvfs22biuSFIVxcxJtCYlu3bsXNzY0BAwYkqQhkVBIgSXD1qvxB5fbtd1VOJOndF8jbb9+W97t6NbXyToIgJOfkBKnMpbe1gUaVnuNVP5hGlfQnAWoNfLO+RookAJSN95IksXz5cmrVqqVLAvLmzYufnx9Lly4lZ86cRsV7fa5du8a0ae7AF0DSux8i3puQBcd7o7sC6Vsw/M033xAfH0+XLl3SNT1DEAwRFxfH6NGjKVeuHMcTrb76+OOPM60qlSGLgUNDYfJkTPqhOr0lSiMiIujevTs9evTg9evXAJQrV44zZ87w7bffYmNjQ4cOcO8eHD4MmzbJfwYFiTcFkJv49O/fn06dOhEeHk6tWrUoW7as8QeUJDh/Ht7O/00z2GsfDwyUnyeSAUEwTOXKULKkUU/d8u+HhDzLgb7mY6BMvI+IiMDLy4tBgwYRGxsLQLVq1bh06RJffvkloExJakmS+OWXX3TrDooWPcPBg3dFvM8MFh7vjZ6IpF0wfPnyZWJjY9m9ezcHDhwgT548zJkzR7EBCgLIvQG8vLw4f/48AAcPHjRLsmnooq9y5eQP1aboLGxIidL+/SFfPvnuxqVL5/Hy8uLOnTu6/Xr16sWSJUtSNInR3hkR3rl8+TKenp4EBgaiUqn4/vvvmTRpEvZ6ygka7No1uH/fuOfevy9XMEnWF0MQBD1UKvDwkH9nAgNTNBnTu78ksT04mH4/7AHSfp8xNt5funSJTp06JYnNQ4cOZc6cObpuxOmN96mdc/r06UycOBGA5s2bs379egoXLpzmaxMUYOHx3uhEoHbt2tja2hIXF8fJkycZPnw4AOPHj6dYsWKKDVAQNm7cyKBBg4iMjMTZ2ZlffvmFzz//3CxjSU/vAVN9qE677r88Z7VpU8iXL4LIyB/RaOQ3GicnJ1asWIGXl5fyA8uCVq5cydChQ4mLi6NYsWJs2LCBxo0bZ+ygkZHvrgwlY+i8ZQID5XkBgiCkTaWSP0iVKgV378pfqc3PLlOGCRs3Mn3OHKChQYdPb7zX9h0ZPnw4cXFxAOTLl49ffvmFL774Ism+6Yn37+te7+XlxYIFCxg5ciSjR4/GxsboCSFCelhBvDc6EciTJw9VqlQhICCAvn37cv/+fcqVK8e3336r5PiEbCwyMpIhQ4bg4+MDyG3ON27ciKurq9nGZAkVF9JTii48PA+wBehIzZoh+Pr66hoCCmnLkSMHcXFxtGnThrVr1ypTBe3OHb1XJf3PFtdTySSaRalUMuHuXXlRmSAIhnFygqpVoVIlOZDGxMgdg+3t5auubyu21Hv8GJt58/juu9r4+ko8eqRSLN6/evWKfv36sX37dt02Dw8PtmzZQmk9v8/piffaqULbtsHnn0tcunRJ18fpo48+4u7du3zwwQeGH1DIOCuI9xlKCbXrBLSLNxctWkSOHDkyPChBANi5cyc+Pj663gCHDh0yaxIAGe89oIT0lR6Vf8WdnNZw9OgJkQQYQLuGAqBnz57s2bOH33//XZkkICFBnoir502h47w6PAxNWh88JNSRjvPq4H822QRkSXp3VVMQsoqEBLkD182bcP26/GdwsPL/z+3s5G6R5cvLSUH58jy2s9OVbWzZsiWBgYHMmTOLxYvlwK5EvD9//jzVqlVLkgR4e3tz4sQJvUkApC/ea8PKN99o8PTsioeHR5KiBiIJyGRWEu8zlAho1wkAtG3bllatWmV4QIKg1bVrV4YOHcrRo0eZNGmSxbQ51/YeMFeFnbQ6IqdkQ2TkB5w5k4E57dlAfHw848ePp1KlSoSGhgJyNaCWLVsqtxj98eMUwVytgWHr3N62M0pWmeTt997r3FBrkh0rIQGePFFmXIJgTpGREBAAu3bBmTNytZQbN+Q/z5yRtwcEyPspLCYmhkGDBlGxYkXu3r2r266t2a9EvJckiUWLFlGvXj2CgoIA+UP5jh07WLBgwXsvoKY33ksShITY4Of3CBsbmySlSIVMZiXxPkOJgLa7nYODAwsWLFBkQEL29fTpU/r06cOrV68A+UPY4sWLqV+/vnkHpoc5K+y8767E+1hKN2NLdO/ePRo2bMiMGTN48OABW7duNc2JoqNT/NCO3yj09vZwKpVJUBEcmovjN5J1K1ap4M0b04xTEDKDmUsq3rx5k9q1a7N8+XIiIiI4cOCA3v0yEu/DwsLo0KED3t7exMfHA/Iay4CAANq1a5fm842N9/nzV+bYsWN89dVXhj9JUJaVxHujL7Gq1WomT54MwMiRIzNWSk/I9vbt20fPnj15+vQp8fHxrF+/3txDSpM5K+y864gs8fChYe8O5u5mbKm2bNnCgAEDdM1aVq5cSadOnUxzMj23dh+H5TToqXr3e/vBQhCsjrakoraaSnpKKsbEyJWAMnCnbtOmTQwcOJDXr19TqFAhNmzYQPPmzVPd35h4f/bsWTp37sz9RBVjRo4cyYwZM9JVdexdvE974bDWmjUzqFs3X/oGLCjLSuK90XcEFi9ezJUrVyhVqhRjx45VckxCNhIXF8fIkSNp2bIlT58+pXLlyowZM8bcw7IKTZtG0KBBD+C/QCiQ/F6iTKWSp8OK1h5JRUVF0bdvXzw9PYmIiKBOnToEBASYLgkAva3jizobdpVH734ZKWEqCOaU0ZKK164Z9dTo6Gj69+9P165def36NQ0bNiQgIOC9SUB6SZLEvHnzqF+/vi4JKFCgALt37+bHH380qvSw9q7EgQPw/t6ZEq6uEm3aiCTA7Kwk3huVCPj6+jJ69GhUKhWrVq0iV65caT9JEJK5ffs29erVY+7cuYDcSv3cuXNUqlQpyX5qNRw5Ar6+8p9qdeaP1dKcO3cOd3d3fH03AIeB/oAKlSrpVbXMWsBsjSZOnMiaNWtQqVSMHz+eY8eOUcrUJTlz5Upx5bNBxee4FIhGhf4roiokXAtE06Di86QPSBLkNOzqkiBYlDRKKh65XgjfE64cuV4o5VxprcBAo9YMLFy4kNWrV6NSqZgwYQIHDhxQtOR5aGgo//vf/xgxYgQJb68I16tXj7///pvWrVtn6Ni2ttCkCaxaJcf2lDdEJFQqFQsXqkS8twRWEu8NTgT++OMPSpUqRb58+ejSpYtuYV3Tpk0VG0ypUqVQqVQpvgYPHqzYOQTLcPjwYdzd3blw4QLOzs789ttvLF26VLfuREuptupZhUaj4ccff6RevXq6hW158+Zl8+bObN+uonjxpO8MomV86iZMmEC9evU4ePAg06ZNy5zF6G/LEyZmawOLegUApHhz0H6/sFdAyvrSdnbw4YemGqnJiXifjWlLKibjf7Y4pQa3pvGURnRZXJvGUxpRanDrlFVU4F1JxXQaPnw4n332Gfv372fq1KmK/t6fOnUKd3d3du/erds2ZswYDh8+rGjFu//+9xXNm6+kaNGkV8VcXVUi3lsSK4n3BicCJ0+e5P79+yQkJODu7s7q1auZOnWqooM5f/48jx8/1n399ddfALo220LW8cknn5AvXz4+/fRTLl++TPv27VPso0Rb9azk8ePHtGjRgtGjR+uuNGkXnXXu3NmsC5itwYsXL5gzZw7S2ys0H3zwAcePH894g7D0sLOTa0En+xDUoVYI24afpniBmCTbXQrEsG34af11pcuU0Xvr2VqIeJ9NZXJJxaioKH788UddzHRwcOCPP/5Q9CKmRqNh9uzZfPrppwQHBwNQsGBB9uzZww8//JCxLuTJaO8G79s3kNq1vUS8t2RWEu9VkqTQ8nsT8Pb2Zvfu3dy6dcug8n3axX7h4eHkzZs3E0YopMedO3coU6aM7md59+5dSpYsia2ee5hqtXzlP7WFUdpGLkFBhk95UavT3wLeUuzZs4eePXvy/Ll8u1ClUjF27FgmT56s6JtMVnXo0CG6devG48ePWbFiBQMGDDDfYCIj5eonehjcaRKgZUsiJCnLxDwR761QQoIcUKOj5b/b2cnTIfRcCdUJDpZLgiai1kCpwa3fJgEpf/YqJFwKxBC09I+Uvw+1a8uLoJJRq8HHJ4gJE34mJOQ848Y1ZPr0KUa+0NQ9f/5c129E69NPP2XTpk0UT15zNAMkSWLhwoWMHj2a+Ph4SpcuzZYtW/Dw8FDsHIIJWEG8t9jLSXFxcWzYsIHvvvsu1TeF2NhYYmNjdd9HRERk1vCEdNBoNMyfP5+xY8eyevVqevbsCfDe5lZptVWXJPn95Phxwyo5+PunrLjwvnbsliI2Npbvv/+e+fPn67YVLVqU9evX06RJEzOOzDrEx8czadIkZs2ahSRJVKxYkdq1a5t3UE5OUKGC3jnStjbQqNJzPU9KpkIF+ThZJOaJeG9lIiPl6T1BQXICkPhnJknvroSWLSv/P01MW1Ix0TXIdyUV9UtcUjHJ74dKJVcQSmb7don+/aMICysNzAFg5co3VKumbLw/fvw4np6ePHr06O1wVIwbN07xvjcvX76kV69e7Nq1C4AvvviC1atXiwZh1sAK4n2G+giY0o4dO3j16hW9evVKdZ8ffviBfPny6b7M3XVWSOnJkye0atWKkSNHkpCQwOHDhw16nqE17w3Zz1qnGP3777/UrVs3SRLQpk0brly5IpIAAwQFBdGgQQN++OEHJEmiX79+nD9/nk8++cTcQ4PKlaFkSeOeW7Kk/PwsRMR7C6ft+BsYCIcOyVc4b90yru6/iUsqbtwYQ8eOEBaWNLF48SKnYvFeo9Ewc+ZMGjVqpEsCChcuzL59+xRfb3Tt2jXc3d3ZtWsXOXLk4KeffmLr1q0iCbAmFh7vLXZqUIsWLciRI4cuA9ZH3xUiV1dXcavYQuzdu5eePXvy7NkzcubMyaJFi+jfv79Bt/2PHJEXBqfl8OH33xEwxRSj9DBmOpIkSfj4+DB48GCioqIAyJEjB3PmzGHo0KHKdbnNwnbu3EmPHj1000dWrVpleXPPJUkugRgYmOIKaQraxytUkN8U3v4fyCrTY0S8t1DJr/wbq2TJd3X/b95MkRwcuV6IxlMapXmYw5OOpLwj8MknUL48AFeuXKd69fwkJBRB33VOJeL9s2fP6Natm25NC0Djxo3ZuHEjRYsWVXwKalhYGO7u7tjb2+Pn54e7u7vxBxPMx4LjvUVODbp//z4HDhzAP43U3cHBAQcHh0walWCouLg4vv/+e+bNmwdAlSpV8PX1TVEW9H20bdVDQvT/vmgDelq18ZWeYpQexkxHioiIYNCgQWzatEm37eOPP2bz5s24ubkpO8AsLH/+/Lx+/Zq6deuyadMmShp7NcaUVCqoUkXOVO/efbfwUd80izJl5K/k0yyyABHvLVB6PrQY4v59cHSU/7+/p6RiSKgj0nvWCOgtqZio0tzFi7lISEi9c2JG4/2RI0fo0qULj9/eilapVEycOJEJEyZga2ur2BTUiIgInJycUKlUODs7s2fPHooXLy4SXmtmwfHeIhOBtWvXUrhw4QzX3BXM4+zZC8ybdxHwpH372qxfP4A8eRzTfF5i2rbqHTumfB9KT218JacYpYd2OlLy90/tdCR9Jd7OnTuHl5eXriwoQJ8+fVi8eDG5c+dWdoBZUEREhO6Nsn79+hw4cIAGDRpkTlnQjHBygqpVoVIl+T9iTIw83cHeXv6Q876Fl1mAiPcWJp0dfw1e8BgYKH8I0v5/TnSHQVtSseO8OqiQkiQDaZVUVBcujPZtIGfO0ga9xPTGe7VazYwZM5gyZQoajdzY4MMPP2Tjxo3897//BYyL+fqcOHECT09PJk6cqCtqULFixfQNWLBcFhjvLW6NgEajYe3atfTs2dPy38CFFPz9oUuXushNrnzZsWMYFSs6GjUvU9tWPXnhhfTUxi+a+sUho/YzhFotXxXS9/6p3ebtDXFx8hSojRs1DBiwibp1GyTrDbCZX375RSQBadBW0yhVqhSBiRZkNW7c2LpiiJ2dXP2kfHn5TaJ8efl7a3oN6STivQVKR8dfo+r+K1hS8e+4OKq4u3Py5EnANPH+yZMnNG/enEmTJumSgKZNmxIQEKBLAtIb8/U1x9RoNMyaNYtGjRoREhLCzz//jFp0z8y6LCjeW9wagf3799OiRQtu3rxJ+bfz/gyVVebLWoPE8yDz5o3C3/9bqlWbwtChRVMEQ228N7bRSUbmXGrXCKQ1xUjJNQKGrm8oVAieJ7nTHQwMo3btx/j6+pq+y20W8Pz5c3r16sWff/4JwMiRI/nxxx/NPKrMY+0xT8R7C5OOUocvInPQaX6dt9frU17B1/vh3c4O2raVr4JmoKSiJEks/+svvl2/ntjYWOrWrcuJEyfQaFSKxvuDBw/StWtXnj59CoCNjQ1Tpkxh7NixScpeGxvztdOGGjR4To8ePdj79t+kW7duLFu2jDx58qR9UCHbMFXMs7hEICPEG0Pm0DcPEoKxs3MiISEfeutAm3hR7vtob9mC/ilGSndi9PWVuyCnTSLpv5UGULFli5pOncTV0bQcPHiQ7t278/jxYxwcHJg/fz6DBg3KVoups3PMy86v3WQCAuRqP3qafQ1b55akxKetjQa1RoXRdf+vXtVbUjEt4dHR9F++nK1vexG0adOGdevWUaBAAXmsCsR7tVrNlClTmD59uq4BYbFixdi0aRMNGzZMsb/hMT8pedqrhLNzf8LCfsHR0ZGffvqJ3r17Z6s4JhjGVDHP4qYGCYloS7bdvAnXr8t/BgdnrHpDBr0rxZk8fyxOQsIH6HtTgKSLtDKbElOM0sPw287J/61sUKlUjBhhh7gjnLr4+HjGjh1Ls2bNePz4Mf/5z384f/48X3/9tXjzFKyXueN9Ojv+qjU2pBrvE9X9TyJx3X8jSipeunuX6qNHs/XMGezs7Jg7dy6///67LgmAjMf7R48e0aRJE6ZNm6ZLAlq0aEFAQIDeJACMn1oqH14iLGwiH3/8H86dO0efPn1EHBMylbjsaIky0qzFhN7Ng0x+JRsMzSmVXpRrqA4doF27zOksnFbFo/cxZRWjrGL16tXMmjULgIEDBzJ//nxy5Uq9GZEgWDRLifePH6dIOtQaGLbOLcX0H4MP+b66/yqVXFLU0dGg6kRXHzygzvjxxCUkULJkSTZv3pxqc0Bj4/2+ffvo3r27roO7ra0t06dPZ9SoUdjYpP4el5GYL793lmDBggtUrpy+ohqCoASRCFiS1Eq2JY8s2mYtt26lqDNrSu9KcRp/LiUX5aaXrW3mfLh+X8UjefpP2kmTuRIma9C/f3/++OMPevXqRUftHABBsDaWFu+N6PiblqLOb1JutLd/9/d0lFSs3KQJbT77DLVKxZo1a8ifP/97z52eeJ+QkMDEiRP54YcfdNtcXFzw9fWlfv36aT7//THfMK9eiSRAMA+RCFiKdJZs0z0eGCjfatU2azGhjHw4NbTuf1ahvT2dci3Fc6BIms83Z8JkaV6/fs28efMYM2YMDg4O2NnZsXv3bnMPSxCMZ4nxPgMdf5MztO6/TiolFS/evEm5ChXIW748Kjs7NmzeTM6cORWdOvPw4UO8vLw4ceKEblvr1q1Zt24dBQsWNPg4qcX8lEUh9LOqmJ+QIP+coqPlv9vZyf0hsnip46xK/MQsRTpKtqWQuFmLCdy9exdXV1eKFrVPe2c90lP3Pyvp0AGKFDnDF18s5OlTFfAYOEnu3E+JjnZGklJfVJ1dEqa0/P3333h6evLvv/8SGRnJ3LlzzT0kQcg4S4z3ej7A6b2in4a06v6/9xPv25KKkiSxaNEiRo0aRYcOHfD19UUFOOpLIjLgzz//pEePHoSGhgLyVKBZs2bx3XffvXcqUGr0TUmqWxdKlVLz+LGK93U7toqYbynT2ARFicXCliAyMtXqCWqN3H7d94QrR64XQq1J5RiBgfJxFCRJEr/++iuffPIJkydP1s2DTO1ijEoFBQrI+yRmqkW5lkyj0fDDDz/QsGF9nj7dAmwmX74A/Pw24eOTH1Cl+HfMrgmTPtreALVr1+bff//FxcWFtm3bmntYgpBxFhrv39fxV/vhPiUJW5ukg3xf3X/KlEnzivHLly/5/PPP+fbbb4mPjyc+Pp64uLj0vpr3io+PZ9SoUbRu3VqXBLi6unL8+HFGjBhhVBKgpZ2S5OUl/3ns2AGiovq9fTTpv5XVxHxJkqs87d0rT1PT3j2SpHdf8G4a29698v5ZpyhllibuCFiCO3f0TizUV7LNpUA0i3oF6A+yd+/Kt1cVEBERwaBBg9i0aRMAZ86cQZISWLTI7r3dfleuzLxFuZbq0aNH9OjRg4MHD+q21alTh02bNul6A+i7heziIr8hZKeESZ9nz57Ru3dvXW+A9u3bs3r16iSVQQTBallgvAeM7vjrO+wMhfLGpd1ZWJLkROA9zpw5Q+fOnXnw4AE5cuRg/vz5ilcDe/DgAZ6enpw+fVq3rW3btqxbty7NdQfpkZCQwJQpU5gxYwaSJFGypDOxsbN58uTdP45VxHxLnMYmKEr0ETC3hATYtSvF/ExtyTajmrVkcI7euXPn8PLy4u7du9ja2jJlyhTGjBmja6Cir4+Aq6sVBLRMoF3E+uLFCwBUKhXjxo1j0qRJKTqnZqRRWlZ16tQpvvjiC548eYKDgwMLFizgq6++EuX0UmGVMU8hVvnaLTDeJ5GOPgKuBaJZqC9JSU2FCqlOZ5Ikifnz5zNmzBgSEhIoW7YsW7ZsoXr16sa+Er12795Njx49CAsLA8DOzo4ff/wRb29vxWNMfHw8DRs25PTp0wwcOJAFCxaQI4ej9cV8I/s96Lzn5y6kj2goZgCrfGMIDoa3jVG01BooNbj127rNRjZrMYJGo2Hu3LmMGzeOhLcl2jZt2kTdunVT7Cs+xCYVGxvL6NGjWbRokW5b8eLF2bBhA42yQR1Qpf4/3LlzB3d3d0qUKMHmzZupXLmy8oPNQqwy5inEKl+7BcV7vdLRWTjVK//6lCz53ivDYWFhVK5cmUePHtGpUydWrVql6M80Li6OsWPHMn/+/ERDKsmWLVuoVauWYucBOanRJhUPHjzg9OnTdO7cWdFzZBql/j+0bCnWDCjAVDFPTA0yNyNKtiVu1tKoUqJyBImbtRghODiYqVOnkpCQwJdffsnKlSv54IMP9O6bWaU4rUFgYCBeXl4EBATotv3vf/9jzZo12WI6i747RC4ucjk9Q+4QvXr1Svf/rGzZsvz1119UqVJF9AYQsh4Livd6OTnJV3D1XAG2tSHp+dOifZ0GlDx1dnbG19eXf/75h4EDByp6df7evXt4enpy9uxZ3bb27duzZs0anJ2dFTtPQkICEyZMQKPRMHv2bABKlChBiRIlFDtHprPUaWyCosRiYXPLQMm29zZrMULJkiVZvnw5q1evZsuWLakmAYJMkiTWrFlD9erVdUmAg4MDP/30Ezt27Mg2SYDcaTrp9pAQebu///ufr1038ddff+m21apVSyQBQtZkQfE+VUZ0/E3Bzg7KlZOvBFepkiIJ0Gg0zJo1i40bN+q2ffrpp4pPA9yxYwfu7u66JMDe3p5Fixbh7++vaBLw8OFDGjduzKxZs/jxxx+5du2aYsc2m3R2mg4JdaTjvDr4n03W0lmS3vWHECySuCNgbhko2ZZms5Y0xMbGMnbsWNq1a6drnd6tWzeDn5+dhYeHM3DgQLZs2aLbVrFiRTZv3swnn3xixpFlnnedplM+Jknye7+3t7x4PPk0ocjISIYMGYKPjw8AK1eupFmzZqYftCCYkxnjvcHS2fFXp0ABKF48zXryz58/p0ePHuzdu5fcuXPTqFEjihcvrndfY8XFxTFq1KgkUzXLlCnDli1bqFGjhqLnSlyCNG/evKxatSprTGlMZ6dpCRUqJLzXudHOIyTpNCFt3wElp7EJihF3BMzNiJJtKiRcC0Qb3qxFj3///Zc6deqwYMECunXrRozSt5izsDNnzuDm5pYkCRgwYAAXLlzINkkAJO40rZ8kyVOijx9Puv3ixYtUr14dHx8fbGxsmDhxIr6+vqYdrCBYAjPF+3TTdvxt2VK+sq/9UK9SvfsCeXv58vJ+//0vfPyx/GEvlSTg2LFjuLm5sXfvXnLmzMmiRYsoVqyYokO/e/cu9erVS5IEdOzYkUuXLimaBCQvQVq9enUuXbpEp06dFDuHWWmnsSXybhqb/rs2iaexJWGKaWyCYsQdAXMzsmSbUc1aeNcbYMiQIURFRVGgQAGWLl2qeKOWrEitVjN79mwmTpyIWq0G4IMPPmDVqlV07NjRzKPLfIZ2mtbup9FoWLhwIWPGjCE+Ph4XFxc2btzIp59+arpBCoIlyeR4n2GpdPzF3l5OQgzsJKvtqzJx4kQ0Gg0VKlRg69atil853759O3369CEiIgLApJXH2rZty759+wAYOnQoc+bMwcHBQdFzmJU1TGMTFCHuCJibthNfsiDVoVYI24afpniBpFl0Rpq1hIeH07VrV3r37k1UVBSNGzfm8uXL/O9//1Ps5WRVjx49onnz5owbN06XBNSrV4+AgIBsmQSA4Z9BtPsdOHCA4cOHEx8fT/v27bl8+bJIAoTsJRPjvaLedvylfHk5KShf/r1X/hNLSEjgs88+Y/z48Wg0Gnr06MH58+cVTQJiY2MZMmQIHTt21CUBH330EadPn2bQoEEmKT/cq1cv8uXLx/bt21m8eHHWSgLAOqaxCYoQdwQsQdmycOtWis0daoXQziPEsBJdaTRrefbsGbVr1yYoKEhvbwAhdbt376ZXr166DpQ2NjaMHz+eCRMmYGdnx4ABA1izZg0rV66kT58+Zh5t5tF2mg4J0T+FWKWSH2/QQP6+efPm9O/fn2rVqileGUQQrEYmxHtLYmdnR9WqVTl27Bg///wzvXr1UvT4t2/fplOnTvz999+6bV5eXqxYsQInBUtWxsXFcffuXSpUqACAp6cnzZo1y7pFId4zjS0k1DHJnSstbanbTJ3GJmSY6CNgKUzctEOSJDp37sy5c+fw9fWlTp06xp8rm3jz5g2jR49m8eLFum3Fixdn48aNusXV4eHhFC5cmLi4OEqUKMG9e/ey1QdcbdUg0NdpWqJTp6389NN/KViwoDmGl+VZdczLIKt+7Vm8SZNarebVq1e6D8nx8fEEBQVRvnx5Rc+zZcsW+vfvT2RkJAA5c+Zk8eLF9OvXT9E4HBQUhKenJw8ePCAgIIAiRYoodmyLlUbzO0DvNLZMa36XDZkq5ompQZYiIyXbXF3l5yfz+PFjXQdFlUrFqlWrCAgIEEmAAQIDA6ldu3aSJKBdu3ZcvnxZlwQALFmyhLi4OEBuHrM3leYrWVWHDrBtm1wsJLEPP4ynTJnRbNnSmT59+pCFrjcIQsaZIN5biidPntCiRQs+++wzXWy0t7dXNAmIiYnhq6++wtPTU5cElC9fnrNnz9K/f39Fk4DffvsNd3d3zp07R2xsLP/++69ix7Zo1jqNTUg3cUfAkkgSXLuWvpJtINdmLFNGvuX89lbon3/+Sc+ePWnYsCFbt27NVlepM0LbG+Cbb74hOjoakBeczZ8/P8Vc0/DwcEqXLq1LtgBq1qzJmTNnst2/d+LOwteu/cXixV/y+nU4zs7O/PLLL3z++efmHmKWZPUxLwOs/rUrGO8txcGDB+natStPnz4ld+7cHD58GA8PD0XPcfPmTTp16sSVK1d027p168ayZcvIkyePYueJjY1l5MiRLFmyBIDatWuzefNmSma0x4I1EZ2FLYq4I5AdpFayLS1qNdy+DXv3EnvxIt96e9O6dWtevHjBnTt3ePXqlUmHnVW8evUKLy8v+vXrp0sC/vOf/3D+/Hm+/vrrFB/ulyxZQlhYmO4X0t7ennPnzmW7uwIgfzapXj2SvXt7MnNmc16/DqdBgwYEBASIJEAQ9FEg3nP1quEJhAmp1WomTpxIs2bNePr0KVWqVOHChQuKJwEbN26kevXquiTA0dGR1atX4+Pjo2gScOfOHerVq6dLAkaMGMGxY8eyVxIA7zpN66HtNO1VP5hGld6TBFSoIJIACycSAUvk5ASffJK+0nCSxM1Hj6jdsSML39ZPHjZsGGfOnFG0g2JWdfr0adzd3VP0Bjh//jxV9MzFDQ8PZ/78+QC6x+vVqwfA5MmTs91UmJs3bybpDTBlyhQOHz5MiRIlzD00QbBsRsZ7QL6bcP68WZOBR48e0aRJE6ZNm4YkSfTv35+zZ8/qFtUqITo6mv79+9OtWzeioqIAuYHjuXPn6Nu3r+J3YGfNmsXFixcpUKAAu3fvZs6cOdhn16o3GZnGVrKkRU9jE2QiEbBU167J3ZgMIEkSaw8fptro0QTcu0dBJyd2LVnCwoULs15JM4Wp1WpmzJhBgwYNuHfvHiD3Bti6dSsrVqwgV65cep/3119/ERYWRsWKFXUfdhs1aoSjoyPnzp0jKCgos16CRfjwww+Jj4/H1dWVo0ePMnHiRFGRShAMlY54n8L9+/LzzaRPnz4cPXqUPHnysHHjRlauXKloX5obN25Qq1YtVq9erdtmihKkic2fP5/u3bsTEBBA69atTXIOq6HtNK1N7NJKurSPV6ggPy+bTZO1RmL1hiWKjEy1ooS+eXkxcbFM9PMjOjaW/1auzPohQyiWP798HHFLLlUhISF069aNI0eO6LbVq1ePjRs3pnkLuFmzZnh7e9O7d29+/PFHAJycnPDz8+Pvv/+mVKlSJhy5ZXj58iXOzs6oVCry5cvH7t27KVasmLgDJQjpkc54r3cKRmAglCpllnj/008/0bdvX1atWqV4VSAfHx8GDRqkm6qZK1cufv75Z3r27KnoeW7dusXq1auZNWsWKpUKJycnfHx8FD2HVdNOYytVCu7elb8SEpJ+yJckeXpbmTLyl/jsYTVEImCJ7tzRu3jM/2xxhq1ze9viW+ZSIJpFvQLYOHQoJ2/eZFS7dtja2MjPv3tX7goppPD777/Tu3dvXr58CaTsDZCWfPnysWDBghTb27RpQ5s2bRQfr6XZt28fPXr0YMqUKXz11VcAVKpUycyjEgQrdOeO3s3vi/cpKrNApsX7hw8fcvDgQd2H8Y8++oijR48qeo6oqCiGDBnCunXrdNsqVaqEn58f//nPfxQ91+bNm+nfvz+vX7/G1dWVIUOGKHr8LEWhTtOCZRFTgyxNQgIEBelNAjrOq8PD0KS3XENCHek4rw4vIpsx9vPP5SQA5Odrs3ZB582bNwwdOpR27drpkoDixYtz6NAhpkyZYlASkJ3FxcUxcuRIWrZsybNnz1izZg0ajcbcwxIE66SN98mkFe/9zxZP8ZzMiPd//vknbm5u9O7dm4MHD5rkHNevX6dmzZpJkoC+ffty7tw5RZOAmJgYBg4ciJeXF69fv+bTTz8VhQ0MlYFO04LlEYmApXn8OEUwV2tg2Dq3t+06ks630zb08F7nhjr557GEBPl4AvBurulPP/2k29a+ffsUvQEE/W7fvk29evWYO3cuAF9//TVHjx7FxkaEEUEwipXE+/j4eEaNGkXr1q0JDQ3F3d1d8emP2tLNHh4e/PPPPwDkzp2bDRs2sHr16lTXaxnj5s2b1K5dm5UrV6JSqRg/fjwHDx6kePKGKIKQDYh3cEsTHZ1icc3xG4Xe3h7Wv+hGQkVwaC6O3yiU9AGVSr51l81JksSqVauSlJ1zcHDg559/xt/fP+u2iFfQhg0bcHd358KFCzg7O/Pbb7+xdOlSRRcFCkK283bue2JGx3uQ7y7cvCkvPFbo7sCDBw9o2LAhc+bMAWDIkCGcOnWKsmXLKnJ8gNevX9OzZ0/69u1LzNv3rE8++YSLFy/StWtXxc4D4O/vr3svKFy4MPv27WPatGnibrCQbVlUIqBdvFmgQAEcHR11tYizFT3B+8ELw8qWPQ7LmXJjfHxGR2TVXr16RefOnRkwYIDuDaZSpUqcP38+RYMwQb9///2Xnj176m6fX758mfbt25t7WEIWkO1j/tvOu4npjeN66N3v6VO5t8CZM7BrFwQEyIuRjbR7927c3Nw4ffo0+fLlY/v27SxZskTRanRXr17Fw8OD9evX67YNHDiQM2fO8PHHHyt2Hq1ixYoRGxtL48aNCQgIoFmzZoqfQxCsicWkwGFhYdSrV4/GjRuzZ88eChUqxK1bt7JfBZJkVyXiEhKYvv1noF6aTy2c703Kjdm19jFw6tQpvLy8ePDggW7bV199xbx58xS9zZzVlS9fnmnTppGQkMC4ceNEWVBBESLmo/eOgN44rkeq+2nXlyUkyI3Hbt2SSzlWrpzuUo5Pnz4lLCwMDw8PtmzZQunSpdP1/PeRJInVq1fzzTff8OaN/FqcnJxYuXIlnp6eip0H5MXHuXPnBuQOwUePHqVWrVoilgkCFpQIzJ49G1dXV9auXavbpmTQsRq5ciVZKJzDzg6Pjz7i1hMjjiVJ8kr+bEatVvPDDz8wefJk1Go1AM7OzqxevZoOHTqYeXSWT6PRsGDBAlq1aqVbnPf999+beVRCViNifiZI3HgsJsaguu4ajUa37qdPnz44ODjQqVMncuTIodiwIiMjGThwIL6+vrptbm5u+Pn5Ua5cOcXOA/Drr78yfPhwDh8+rGv+WLduXUXPIQjWzGKmBv3+++/UqFGDL7/8ksKFC+Pu7s6qVave+5zY2FgiIiKSfFm9okV5FRvLgxcvdJtaurUw6KnPwpPdKrazS1+3ygxQq+HIEfD1lf98+/k70z18+JAmTZowYcIEXRLQoEEDAgICRBJggCdPntCqVStGjBiBl5cXsbGx5h6SkEWlN+ZnyXiv585kijieCkP30zGg8diOHTuoVq2arqKaSqWiW7duepMAY2N+QEAA1atXT5IEDB48mNOnTyuaBERFRdGzZ0969epFaGgoP//8s2LHFoSsxGISgbt377Js2TLKlSvHvn37GDRoEN988w2//vprqs/54YcfyJcvn+7L1dU1E0dsGqfPn8d99Gg6zJ1L3Nv1Aq4FUs4j1aeos3x7Va2BI/8UxvdmNY6csDP5h3J/f7nPSOPG0KWL/GepUvL2zLRz506qVq2qq2ltY2PD5MmTOXTokK77r7Fu3LjBt99+i7u7O/ny5cPe3p5ChQpRuXJlTpw4ocTwzW7fvn1UrVqV/fv34+joyODBgxW9CigIiaU35mfFeI+e3y9tHE9L4v3UGjhyvRC+J1w5cr1QyopCWoGBetcMxMXF4e3tzeeff87ly5d1C4NTY0zMlySJZcuWUbt2bW7dugVA3rx58fPz46effiJnznQmNu9x7do1atSogY+PDzY2NkybNi1JtThBEBKRLIS9vb1Up06dJNuGDh0q1a5dO9XnvHnzRgoPD9d9BQcHS4AUHh5u6uEqLiEhQZoxY4Zka2srAVLpwoWlO0uWSJKfn5Sw2U9yKRAlqdBI8r3epF8qNJJrgSgpYbOftH34ScmlQFSSx11cJGn7dtOMe/t2SVKp9IxJJX+Z6ryJRUdHS4MHD5YA3Zerq6t07NgxRY4/depUycbGJsnx9X3NmzdPkfNlttjYWGn48OG611GlShXp+vXr5h6WYIDw8HCrjXnpjflZKd7rPHggSX5+Sb7SE+8lv1RifoEoafvwkymOLW3dKkkBAUmGcOfOHalGjRq63//hw4dLsbGxqQ7ZmJj/6tUrqVOnTkniZfXq1aXbt28r+s+p0Wik1atXS46OjhIgFStWTDpy5Iii5xAEczFVvLeYOwJFixZN0SykYsWKSRZ6Jufg4EDevHmTfKUpIUEurXbzJly/rnipNWOEhITQrFkzxo0bh1qtxsvLi79/+40yRYoAYGsDi3oFAKAiaaMx7fcLewWw83wqTWhCoGNH5a/Qq9UwbFiK3mfAu23e3qadJvTPP/9Qq1Ytli5dqtv2+eefExAQQIMGDTJ8/GXLljFx4kQ0Gg3u7u4sWbKEI0eOcPHiRfbu3cvKlSsVLaOX2Z49e0bdunWZN28eIN+iP3v2rOLdOwUhufTG/KwS75PQ04nV0Hhva2NE47FkjSa3b9+uKwucP39+du3axdy5c1O9E2hMzL948SLVq1fHz89Pt+2bb77h5MmTisdOf39/+vXrR0xMDC1atCAgIED0iBGENFjMYuF69epx8+bNJNv+/fdfSpYsqcwJIiPlVu5BQXIQTLxgSpLkYFy6NJQtK7fRziS7du2id+/ehIaGkjt3bn766Sd69uwpV5A+f16e1wl0qBXCtuGn9bScj2FhrwDaeYRQanBr/U1oJPnlentDu3agVKGE48fh4cPUH5ck+T33+HFo1EiZc747tlxxYtiwYbqyoDlz5mT+/Pl89dVXipUFnTlzJgAdOnRg69ateptnHT16lDt37ihyvsyWP39+cubMSf78+VmzZg3t2rUz95CEbMKkMd9C430K2nHcvp3k03Va8b5DrZA0G4+pkPBe50Y7jxBsE4ett43H1vz1F3379gXkxbObN29Oc7pVemJ+w4YSS5cuZfjw4cS9LZP6wQcfsGbNGpN18G3fvj3NmzencePGjBo1SjQ7FAQDWEwi8O2331K3bl1mzpxJp06dOHfuHCtXrmTlypUZO7AkyQukAgPlNwNtsE1+SUOBUmvppdFomDFjhq5To6+vb9K6yR4ectWft2PvUCuEdh4hHL9RiMdhOSnq/IYGFZ9jayOvCUj8hpGcKT6UG9rEUulml2FhYQwYMIBt27bptlWuXBlfX18qV66s2HkiIiJ4+PZdr1WrVlnmTSUiIgIHBwccHByws7PD19cXlUqFi4uLuYcmZCMmifkWHO9TVbasPI5k3hfvIXHjMf0SNx5rVOn5uwfeNpr8/PPPmT59Op06dWLatGnYG1Bq2tBYfvt2FEuW9MA/0W3omjVrsmXLFkU7EkuSxJYtW2jfvj05c+bE1taWPXv2ZJlYLQiZwWJ+Wzw8PPjtt990H+amTZvGwoULM9ZVUJLkq+qBge++T2t/kPc/fz7t/TPIxsaGjRs3MmrUKE6fPp2yeYpKBVWqQMuWUK4c2NlhawONKr/Aq8FDGlV+Ib8p2Nnx2KGUQedU8kO5oQWJlCxcdPLkSdzc3JIkAYMGDeLcuXOKJgEAefLkoWDBggBMnToVf39/Xrx4gWTi/xemdP78eapVq8bYsWN121xdXUUSIGQ6xWO+hcf7VDk5ycmIHrY20KjSc7zqB9Oo0vMkV/aNaTx2MjBQjl/x8Tg7O3PlyhVmzZplUBIAhsfyCRP6JUkCvvvuO44fP65oEhAREYGXlxdeXl4MHz5ct10kAYKQPhZzRwCgTZs2tGnTRrkDXrumm1qTbvfvy1fj39YdVoIkSfzyyy+EhIQwadIkAMqWLcvs2bPf/0QnJ6haFSpVkj/Jx8TIHYPt7eUxFi1KUWfDfpRKfihv0ABcXOQ1CPreQ1Uq+XEFpuqjVquZOXMmkydPRqORS2I4Ozvzyy+/mOw2s42NDb/++iudOnUiODiYL774Isnj69evp1u3biY5t9I0Gg1z585l3LhxJCQk8NtvvzF58mTD5lkLgokoGvMtLN6nS+XKclxPx/jTU10oJi6Ob9etY8WBA6wYMIABVasC8sWO9Egr5svrgB/y5Im8HsDZ2Zlff/2Vtm3bpus8afn777/p1KkTt2/fxs7OjlKlSiFJkugULwhGyLqpc2TkuytDyWS01JoxXr16RefOnenfvz9Tpkzh3Llz6T+InR24ukL58nJSUL68/L2dnS5ApxYHVSp5VyU+lGvZ2sKiRe+On/x8AAsXZnxNgrY3gHbRLsi9AS5fvmyyJECradOm/PzzzxQoUCDFY5988olJz62UJ0+e0LJlS0aPHk1CQgIdO3bk0qVLIgkQsg4Li/fpplLJU0GT3xV+jwYVn+NSIDrFgmLdIZFwLRBN4XxXqD1uHCsOHEClUvH01SujG02+L+aDBjkRGAZoqFOnDgEBAYomAZIkrzuoXbs2t2/fpkSJEhw7doyRI0eKJEAQjGRRdwQUdedO0jmib/mfLa5nAVY0i94uwEpCpZIrLLy9emKsU6dO0aVLF+7fv4+dnR3Tp0+nRo0aGTpmctoA3bFjypet5Ify5Dp0gG3b5EoSiReRubjI58toD6+dO3fSp08fXYMbGxsbJk2axLhx40zeHv7ChQt0796dwMBAWrZsSdeuXSldujT58uUD5Aonlm7Pnj307NmT58+f4+joyKJFi+jXr5940xSyFguK9xnyxrCr/PCuulDHeXVQISElWjCsTQ461NxEzbGjiIqNpVDevGz85huaVauWoVvDqcV8eAh4A78xatQopk+fbvCUI0OEh4fTr18/3bTQtm3bsm7dOvLnz6/YOQQhO8qadwQSEuRqEXreFDJSai291Go1M2bM4NNPP+X+/fuUKVOGkydPMnr0aJPMY9QG6OLJXoaLi7zdVI11O3SAe/fg8GHYtEn+MygoY+eLiYlh8ODBtG/fXpcEuLq6cvToUSZOnGjyJODu3bs0adKEwMBAli9fzp49e+jWrRv16tWjcuXKVK5c2eRjyKhXr17h5eXF8+fPqVKlChcuXKB///4iCRCyFguJ9xlmxNQmbXWh4gVikmwvnj+a/1aezKI9/YmKjaVRpUpcnjOHZlWrQpkyKUqWpleHDhAUJPHVV1uwsekGNAJKU6DAMf744w9mz56taBIAcjw7ePAgdnZ2zJ8/n507d4okQBAUkDXvCDx5kiKYZ7TUGunsYilJEp9//jm7du0CoEuXLixbtizD0zHUarnyz+PH8kWdBg2SXuXv0EEuEfq+fUzB1la5akT//PMPnp6eXL16Vbftiy++YNWqVTg7OytzkjTMnDmTiIgI6tSpw8CBAzPlnEr74IMPWLFiBSdOnGDOnDmKdu4UBIthAfE+w9KY2pRa5SDQX13I1uYkjSZPQ6VSMfGLL5jQsSO2NjZyslOmTIaHGxoaSq9evdi9e7duW/369fH19TVZ4YGSJUvi6+uLs7MzNWvWNMk5BCE7ypqJQExMitvEGS21ll4qlYqOHTty6NAhfv75Z7p3757hK7H+/vqn4CxalPTqu5IfyjOTJEmsWrUKb2/vJL0BFi1alOlXso8ePQpAGQXeNDOLJEn4+PhQrFgxmjVrBkDnzp3p3LmzmUcmCCZkAfE+wzI4tUlbXeid8izo2ZNKrq40SbwAukKFDPdNOHnyJJ6enrrSygBjx45l6tSp2GXwTkNiYWFh9OvXjz59+tC6dWsAWrRoodjxBUGQZd2pQckYU2pNJz7eoOe+efOGGzdu6L7v3r07t27dokePHookAR07pmzmYqquwZktLCyML7/8koEDB+qSgMqVK3PhwgUGDBiQ6dNZtGM4fPgwoaGh791PbcrWyQaKiIigW7du9OrVi+7du/P8+fO0nyQIWYGZ4r1iFJjaFPXmDYNWreLfR49027757LOkSUDJknJ1IiNpNBpmz55Nw4YNdUlAoUKF2Lt3LzNnzlQ0CTh37hzu7u74+/vTv39/3qRj7YQgCOmTNRMBPQEpPaXWUjBgruONGzeoVasWTZs25cWLF4B8V6CoAvU6jWnrbk2OHz9O1apV2b59u27b119/zblz56hUqZJZxlSvXj0AHj16RO3atVmyZAknT57k8uXLnDp1ig0bNtCzZ0+KFStm9r4C2jfNTZs2YWtry9ChQ8XcWSH7MEO8V9Tjx+me2gTgvc4NtQauBwdT8/vvWf7XX3gtWqSrrCY/9e1zK1SQqxIZeUHl+fPntG7dmjFjxugufDRs2JCAgABFr9JLksT8+fOpV6+ebl3d77//LqY1CoIJZc2pQY6OKT41a0uthYQ6JqmuoKVCwqVADA0qJruSKknvLbUmSRKrV69m2LBhxMTEUKhQIe7cuaNrRKWE9LR1t6YpQWq1munTpzN16tQkvQHWrFlD+/btzTq2H3/8kXPnznHv3j1u377NN998o3e/ihUrKnolLD2S9wYoWbIkmzZtom7dumYZjyCYRSbGe5OIjjZ6atPYjcH8tG8sMXFxFHV2Zl6PHu8KUdjZyesBypTJ0HSgY8eO4eXlxaO3dxtUKhXjx49n4v/bu/PwmK43DuDfm0kQEUGEhJBYGrFUYglSuxJaVYpqNlstrT2lKC2xLz8UtVVpRSyxxt7apWIrFUEQtSYRsQVJJEFm5vz+uGZkkplkljv7+3meeSJ37r1zZp5477z3nPOeqVMFjX3Pnz/HgAED5PPqevfujbVr18qrtBFC9MMyEwFXV+DmTYW7LOqUWlsyIF5x4hjAB1MVd/VfvnyJoUOHYvv27QCATp06ITIyEq6uroK+HXVXAxZy1WB9S0lJQUhICGJjY+Xb2rRpg02bNpnEKrceHh64evUqVq9ejf379+P69et48eIFSpQogcqVK6NWrVpo1aoVPvnkEyyVFdY2oNevX6Nbt244evQoAKBPnz5YvXo1ypUrZ/C2EGJUBor3eqPD0KYF+y4AeIsAHx9sGDkSlZycgMqVgRo1+Pehwxd1iUSCuXPnIjw8XH6jpnLlyti4cSM6duyo9XmVef78ORo1aoTk5GSUKFECixcvxrBhw6jCGSEGYJmJgK0tHwhv31a4yyIrtVZ48lUulqiqK62i1NqZM2cQFBSE5ORk2NraYu7cuRg7dqxeyoKqe10y9PVLW7t27cKgQYPw4sULAPzaANOmTcPkyZNNqiRnmTJlMG7cOIXl601FqVKlUKVKFdjb22PZsmX4+uuv6aJJrJMB4r1e6TC0icMjzAoMxA89evDXHo7jEyMdqx49fvwYoaGh8hsNANChQwds2rRJ8BtdAFChQgUEBAQgJiYG27ZtQ6NGjQR/DUKIcpaZCABArVrArVuFNisrtVawHJtcEaXWVqxYgeTkZNSqVQtRUVHw8/MT+A28V9yy7hzHPy/kqsH6kJubi3HjxmHVqlXybdWqVcPmzZvRqlUrI7bMPLx58wa5ubnyu/7Lly/HpEmT4O3tbdyGEWJseo73elW6tMZDmwApRNxDHJ3aAe3q5/v/L8DQphMnTiA4OBiPHj0CwA8FCg8Px08//STojZpnz56BMQYXFxcAwC+//IK8vDxa8ZwQA7PMycIAPyZSxRckWam1oFYpaFdfxUUBKLLU2sqVKzF27FhcunRJr0kAUPSy7vpcNVhICQkJ8PPzU0gCevXqhcuXL1MSoIb//vsP/v7+CA0NlU9OdnR0pCSAEEDv8V6vlAzhkQ1tAt4PZZLh3q2EsPbbRMUkANBpaJNEIsH06dPx8ccfy5MAV1dXHDt2DOHh4YImAadOnYKvry9CQkLkw47s7e0pCSDECCw3EQD4UmkeHtodW6DU2t69ezFkyBD5lzAnJycsWrQIjga6cBhr1WBdMcbw66+/ws/PD9euXQPAB/zVq1dj+/btBlsgzFwxxhAREYHGjRvj0qVLOHfuHO7du2fsZhFiegSM9wYlG9pU4C6PbGiTi1OWwnZ351zsGHcWA9q/UDyPDkObHj16hICAAEybNk1+jevYsSPi4+PRvn17jc+nilQqxdy5c9GuXTukpqYiOTkZjx8/Fuz8hBDNWe7QIIAPjH5+fFdpYqLSBVsK7c8Yf2eoQQOA45Cbm4vx48djxYoVAICPP/4YgYGBBnoDioy1arC2nj9/jiFDhiA63yIHH374IbZs2YJ69eoZsWXmITMzE99++y2ioqIAAO3bt8eGDRtQtWA2SAgRJN4bjZKhTYwxPHr5B1682gDAH86OXlg/vCe6NMoSdGjT0aNHERISgidPngDg52zNmDEDkyZNEnTO25MnT9CvXz8cOnQIABAaGopVq1ahTJkygr0GIURzlp0IAHxw//BDwNMTuHuXf4jFikGfMaWl1q5fv47AwEBcvXoVADBu3Dj0NPKtd3NZNTg2NhYhISFISUmRbxsxYgQWLlxINaHVcP78eQQGBuLevXsQiUSYOXMmJkyYYFKTqQkxOTrEe6OSDW1KTAQAZOTkYPCvv2LHuXMAgM8aZyFiRAs4O2apPoeGQ5tkQ4FmzZol7wWoUqUKoqKi0KZNG+3fixIxMTEIDg5GWloa7O3tsXz5cgwcOJAKHBBiAiw/EZBxdAR8fID69fnb6bm5/AqSdnb8HaR84zQZY1izZg3CwsKQm5uLSpUqITIykpY3V4NYLMasWbMwc+ZM+djPChUqYN26dfj888+N3DrzIJFI0K9fP9y7dw+enp6IiopCixYtjN0sQsyHBvHeZDRoAOTm4t/jx/HVkiW4+/gxbEUizA8JwXdduxb9pVnDoU0PHz5EcHAw/v77b/m2Ll26IDIyUj55VyhisRjDhg1DWloa6tati+3btxttoUhCSGEmFgkNwNa22NJqYWFh+OWXXwAAAQEBWL9+vV5Kplma5ORkhIaGKqwN0K5dO2zcuJGGs2hAJBJh/fr1WLZsGVasWEEL6hCiLTXivcngOGTXq4cunTsj/eVLeLi4YGtYGJp/8IHK/bUZ2nTo0CGEhobi2bNnAPh4M3v2bIwfP14v5a9tbW2xefNmrFy5EkuWLIGDg4Pgr0EI0Z5lTxbW0pdffolSpUphwYIF+OuvvygJUEN0dDR8fX3lSYBsOMvRo0cpCVDDn3/+iYiICPnvzZs3x8aNGykJIMSKOJQpg2UrV6LHZ5/h0s6daF63Lv8Ex71/AHyC88EHQJcu/FAoNZIAsViMyZMno0uXLvIkwN3dHX///TcmTpwoaBJw/Phx/P777/LfGzVqhDVr1lASQIgJsr4eASUkEgmuXLkiX8SkVatWSEpKQqVKlYzcMtOXk5ODsWPHYvXq1fJtHh4e2Lx5Mz766CMjtsw8vHnzBhMnTsTSpUtRsmRJNGvWjCZSE2JFzp8/jzdv3qD1u4VggoKCEBgYyA8FEosFGdr04MEDBAUF4dSpU/JtXbt2xfr16+Hs7CzYe5FIJJgxYwZmzpwJW1tbNGrUCI0bNxbs/IQQ4Vl9j8CDBw/QoUMHtGzZEjdu3JBvpySgeAkJCWjWrJlCEvDll18iPj7e4pOAK1euwM3NTV5NSmbo0KGoV68eXr58Wew5bt68iRYtWmDpu0Uivv32W9SqVUsfzSWEmBjGGBYvXoxWrVqhT58+CmU0ufx3/qtVA7y8+PkOXl787xokAX/++Sd8fX3lSYCtrS0WLFiAvXv3CpoEpKWloWPHjpgxYwYYY+jXrx+tc0KIGbDqRGDPnj3w8fHByZMnIRKJcPv2bWM3ySwwxrBq1apCawOsXbsWW7dula98a8mePXuGR48eYdKkSXjz5g0AICkpCWvWrMF///0HsVis8ljGGNatW4fGjRsjPj4eFStWxL59+7BkyRKULFnSUG+BEGIkz58/R48ePTB27Fjk5eWhVatWgldTy8vLw4QJE9C1a1ekp6cDAKpXr47Y2Fh8//33gg4FOnLkCHx9fRETEwMHBwds3LgRa9euRenSpQV7DUKInjALkpGRwQCwjIyMIvfLyclhw4cPZwAYANa0aVN269YtA7XSvKWnp7MePXrIPzsArGHDhuz69evGbppBSaVS5uvrywCw+vXrMwDM29ubAWBff/11kcf17dtX/tl16NCBpaamGrDlxJKoG/Mskbm+9zNnzrDq1aszAKxEiRJsxYoVTCqVCvoaSUlJzN/fXyFOf/755yw9PV3Q12GMsWnTpjGO4+TXgsTERMFfgxCiv5hndT0C165dQ7NmzbBy5UoAwPjx43H69GnUrl3byC3TnkQCxMQAUVH8T4lEP69z8uRJ+Pj4YPfu3fJto0aNwj///IO6skltVoLjOISHhwPgh/gAQGJiIkQiEX788ccij6tTpw5EIhHmzp2Lw4cPo0qVKgZpMyHEeBhjWLhwIdq0aYPk5GTUrl0b586dw/DhwzWup19UzN+3bx98fX1x9uxZAICdnR0WL16M3bt3o0KFCsK8GbEYSEkBbt6EfWYmGGP4JiQE506dQp06dbQ+F65d43+mpPDbCSF6Z3WThbdv346EhARUqlQJGzZsQEBAgLGbpJPoaGDMGODBg/fb3N2BpUv5lYiFIBaLMXPmTMyaNUu+NoCzszPWrVuHbt26CfMiZqh79+7w9fVFfHy8fFv//v1Rs8DqnlKpFE+fPkXlypUBAD/88AO6deuGhg0bGrK5hBAju3TpEsRiMQIDA7F69WqULVtW43OoivmLFuXh3LmJWLx4sXy7p6cntm3bBj8/PyGaD2RlAXfuIDcxEfYiEcBx+L55czSdOhUdGjQAjh4FatTgV0oubnGzd+fCvXuqF31T91yEEK1xjBW1Brt5yczMhJOTEzIyMlQGWLFYjB9//BFjx46VfzEzV9HRQO/efMzMTxZPd+zQPRlITk5GSEiIQrUJWhvgvd27d+OLL74AANjY2ODWrVsKicDDhw/Rt29fPH36FP/88w/s7e2N1VRigdSJeZbKXN47Y0x+xz8rKwu7d+9GaGioVqvqqo757N22XgB2AQC++OIL/PHHH8LM2WIMSEhAXkICpmzdiv1xcfhn9mw4KJvXUNz6Bu/OhcTE9/uqouVaCYRYIn3FPIsfGnTq1Cn06NFDPqHT1tYW8+fPN/skQCLh7wopi6GybWFhug0T2rlzJ3x8fORJgEgkwqxZs2htgHy6d+8u725v3ry5QhKwf/9+NGzYEMePH8edO3dw6dIlYzWTEGJgUqkU8+bNw5dffgnZ/TZHR0f07dtXqySg6JjPgZ8KsAR2dqXwyy+/YOfOncIlARcuIOXUKbSfPh3z9+zBtZQU7L5wQfX+AP9F/8IFxQa/OxcSExX3Leq1VZ2LECIIi00EZPWM27Ztiz179mDhwoXGbpKgYmMVu4YLYowfZplvkV+15eTk4Ntvv0Xv3r3lZTA9PDxw8uRJ/PjjjxCJRNo12gJxHIeIiAj4+flh/fr1APi1AcLCwtCtWzekp6fD19cXcXFxFl9SlRDCe/r0Kbp27YpJkyZh586d+Ouvv3Q+Z3Exn7+cV8eyZfEYNWqUVsmGUgkJ2B8dDd8JE3D65k2UtbfH9rFjEfJu3YMiJSXxd//znQtJSdq1o+C5CCGCsMg5Ag8ePMCwYcNw8uRJAPy47dGjRxu5VcJKSxN2P5mrV68iMDAQ169fl2/78ssv8dtvvxV5d0ki4S9UaWn8WjetWwO65AtCn0+funXrJp8rkZiYiKCgIPm8gbCwMMybN4/KghJiJU6ePImgoCA8fPgQpUqVwvLly/HJJ5/ofF51Y3nZshpO1i1C3vPnmPzjj1i4bx8AoEnNmtgaFoZarq6QSIHYGy5Ie1EKbuVfo3XdpxApu7WYmAh4er7/txIan4vmDBAiGJNJBKZNm4bp06crbKtTpw4SVQSOorRs2RIvX75EmTJlsGrVKoSGhgrVTJPh5ibsfowxrFy5EuPGjZMPoypdujSWLl2KQYMGFXl3SegJy4aYAK0v3333HeLj4+Hi4oKIiAh8+umnxm4SISZJyJhvCiQSCebOnYvw8HBIpVJ4e3tj+/btaNCggSDnFzrmq2P8qFFY+i4JGP3JJ/hfaChK2tkh+p+qGBPhiwfp79cJcHfOwdIB8ejZPFXxJBwH3L3Ld1MrmROg1bl8fIR7k4RYOZMaGlS/fn2kpaXJH/knqGri5cuX8PPzQ3x8vEUmAQB/h9zdXfXcKY7jF6BUp/c2PT0dX3zxBUaOHClPAnx8fPDvv/9i8ODBxSYBvXsX7rJOTeW3R0er+470cz5D++2339CrVy9cvnyZkgBCiiFUzDcFQ4cOxZQpUyCVStG/f3/8+++/giUBAODn9xoODi8ASJU+r0nMV4tYjAkdOsDLzQ3R33+PpQMHypOA3ov88SBdsfBBaro9ei/yR/Q/BeaPMfa+OpCSJEDjc929S6VFCRGQSSUCtra2cHV1lT8qVqyo1XnGjBmDU6dOoVatWgK30HSIRPwdcqBwMiD7fcmS4ofT/P333/Dx8cGePXvk20aNGoVz584VuzaA0BOWDTEBWmhnz57F3Llz5b9Xq1YNO3bsgJuQt+UIsVBCxXxTMGTIEDg5OWHdunWIiIiAg4ODYOe+desWWrf+CNnZg95tUUwGNIn5RXn79i2iZXdb0tJQpWxZXPv5Z3zRrBkAfgjPmAhf8OFY8cLD3v0eFuELScFcRSIp9OVd63OJxZqPeSWEqGRSicCtW7dQpUoV1KxZEyEhIUhOTi5y/zdv3iAzM1PhAQAzZsxAiRIlDNFko+rZky8RWrCAj7t78aVDxWIxpk6divbt2yM1le9+dXZ2xt69e/HLL7+otdy90BOW9TkBWmgSiQRz5sxB69atMXnyZBw6dMjYTSLE7GgS81XFe2ORSCSIi4uT/96iRQskJSVhwIABgr7O1q1b0aRJk3dVx3bBzi4Y5cvnKOyjTswvzr1799CqVSv06tULO3bsAHJyAI6Dbb7MIvaGy7shPMp7iRk4pKSXRuwNl2JfT+tzcRyQm6vu2yKEFMNk5gg0b94cERERqFOnDtLS0jB9+nS0bt0aCQkJcFQxMWju3LmFxpham549ge7dNZtYm5SUhODgYJw5c0a+rX379tiwYYNGZUGFnrCsrwnQQktNTUXfvn1x4sQJAEBQUBBatGhh3EYRYmY0jfmmFO/T0tIQEhKCs2fP4vz58/jwww8BAE5OToK9Rm5uLr777jusXr1avq1OnTrYtm0y6tcvo3sxBdmd9Zwc7Dp4EAOnTEFGVhbKly/Pr3eiZPhN2ovibxCpu59O58rLU+tYQkjxTCYRyF9VoWHDhmjevDk8PDywbds2DBo0SOkxkyZNwtixY+W/Z2Zmolq1anpvq6kRiYB27dTbd8eOHRg8eDAyMjLeHSvCjBkzMHHiRI3Lggo9ec0Yk+E0tX//fgwYMADp6elwcHDAihUr0K9fP+FK9RFiJTSN+aYS748cOYLQ0FA8efIEDg4OuHv3rjwREMrNmzfRp08fXLlyRb4tNDQUq1atQpkyZQCoH/MLybei75vcXHy/YQOWHzwIAGjxwQfY+v33qF61qtIxmG7lX6v1Eursp9O57OzUOpYQUjyTSQQKKleuHLy8vHD79m2V+5QsWZLKMqopJycHYWFhWLNmjXybp6cnoqKitL6bLZuwnJqqfFw/x/HPqzt5TejzCe2nn37C7NmzAQCNGjXCli1b4OXlZZzGEGJhiov5xo73YrEY06dPx+zZs8EYQ8OGDbFt2zbUqSNcuU4A2LRpE7755htkZ2cDAOzt7bF8+XIMHDhQtxsOBVb0vZOWhq+WLMHFu3cBABM+/xyzAgNhZ2sL3L6tNAi3rvsU7s45SE23l4/jz48Dg7tzLlrXfVpsc7Q+F2MArdBOiGBMao5Afq9evcKdO3do0qUArly5gqZNmyokAV999RXi4+N1GtIi1IRlfZ1PKbGYn2hw8yZw7Rr/MyVFrSoUPu9K1n333Xc4e/YsJQGECMiUY/7Dhw/x8ccfY9asWWCM4ZtvvsG5c+cETQJycnIwePBghIaGypOAunXr4sKFC/j66691TwIKrOh7NTkZF+/ehbOjIw788APmh4bySYBsfyVENsDSAfEA+C/q+cl+XzIgvvAaACIRYKt431Hrc9naatYtrEPMJ8QacIyZxprd33//Pbp16wYPDw88fPgQ4eHhiI+Px/Xr1+HiUvzEI4DvKnZyckJGRgbKli2r5xabPlVrAyxfvhwDBgwQbDiLsrr/1arxX9qFWkdAl/MBUOgOh1ismGkwxl9catQAatWSL1bDGENqairc3d3lu169elXwYQCEaMucY56uMd+Q733evHmYNGkSypQpgzVr1iAwMFDQ89+4cQN9+vRBQr6VcwcMGIDly5cLU33o6lWli3mtPHQInzdtCndnZ41Op6z2fzXnHCxRVfv/gw/4OKukp0Grc6mzjoAWMZ8QU6avmGcyiUBgYCBOnjyJ9PR0uLi4oFWrVpg9e7ZGJUDN+aIotPT0dHz99dfYu3evfJuvry+2bNlS5F0sbVf0NdmVhQt0h6u60wXg/fPe3njp7o5vvv0WMTExuHz5MlxdXbV+L4ToiznHPF1jviHfu0QiQVhYGEaPHo0PPvhA0HOvX78ew4cPR04OXwmodOnSWLVqFfr16yfMC2RlAQcP4lZaGoavXYt1w4fLv/irvaKvEhod26UL//PdXASdz1XUF3ctYz4aNFC9MA8hJsDiEwEhmPNFUUgxMTEIDQ2VlwUF+LUV5s+fX+QYW3Ne0VcpWXd4UpJGh5397z8Er1iB+2lpsLW1RVRUFHr37q2nRhKiPWuOefp87ykpKZg5c6bapZS1kZ2djREjRmD9+vXybfXr18f27duLXcNFI/HxiIqIwNDVq/Hq9Wt80awZor//XrMVfXXh7Q3IelFV9ExodS5ltIz5AAAPD8DPj5IBYrL0FfNMdo4A0ZxYLMaUKVPQoUMHeRJQsWJF7Nu3D0uWLCk2CTDnFX2VSkjQ6IIgkUoxOzoaradOxf20NNR0d8fp06cpCSDEivz5559o1KgR1qxZg8mTJ+vlNRISEuDn56eQBAwaNAjnz58XNAnIzcrC0AkTELx0KV69fo229eph+ddfa76ir7Y8PPg77TINGvDbhDiXMhrGfAVJSfzxhFgZk60aZOmEHkqjbG2ADh06YMOGDahSpUqxbSlqRV+O41f07d5dsY1CvwdBZWWpvPOkrBv60cvnCF22DDHXrgEAglu1wqrBg1FWyDtzhBCTlZeXhx9//BELFiwAADRu3BgjRowQ7PwSCXDyJENUVAzWr5+Ht29vAgAcHBywevVqhISECPZaAJCYmIg+X3yBq4mJ4DgOP/Xsiam9e4PjREWu6MuBISzCF939UhWG52g8jEjZcBuO4++629sLP3RHw5ivtO2JiYCnJ80ZIFaFEgEjEHoIzvbt2zFkyBCFtQFmzZqF8ePHq7U2gCYr+spqV5v8MKI7d5ReZFR1h39YbRFirl2DQ8mSWD5oEPq3bQvOxga4e1e9iWmEELOVnJyMwMBAnD17FgAwatQoLFiwQLBypdHRwOjRUqSm2gBo/+6RAg+PxTh06BvBS5CeOXMGAQEByM7ORiUnJ2waNQodGzYEAMRcc1GIfwXlX9G3XX2+dKfGw4g8PVUP4eE4/jlPTz6+3r2rejJvzZr8Q50v5hrGfKVt5ziK+cTqUCJgYLIhOAVvgsiG4GiyTHx2djbCwsKwdu1a+bYaNWpg8+bNGpUF1XRFXyHfg16IxXylCCUXhN6L/FHw/lNquj1S03/CR145WDfcFV6yHhTG+ItC/fqFSt8RQixDTEwMevbsiRcvXsDJyQl//PEHegoYwPh4yZTc+HZHcvIiXLvGQeA8AL6+vvD09EQlBwdsGjwYbuXKyZ/TdEXfouJm70X+2DHubOEv1A8eAI0aFR03HR35L9z16/MXl9xcfsVgOzu+x8DNTf24q0XMV9p2ivnECtEcAQMqbggOwA/BUbKgYyGXL19G06ZNFZKAwMBAXLp0SeO1ATRZ0VfI96A3aWmFakRLpCiyOxwAUtKnoJZrgWFUYrH6mRIhxOx4enqCMQY/Pz9cunRJ0CRALGYYPPgV+JocBS+3HABOsHh59+5dSKVSAHzloaNHj+LIunVwK19eYT9NVvRVJ26GRfhCIi1wsCZx09aWrw/t5cV/Affy4n/X5Iu4ljFf57YTYgEoETAgTYbgqN6HYdmyZWjWrBkS342HLF26NP744w9s3rwZTk5OGrdLtqKvquGXHMfH5dathXkPepeTU+jNxN6QdYcrf5P5u8MVcBx/p4oQYjFkwygBPhGIiYnBqVOnUKNGDcFeIzMzEwEBM/HiRRmoutQKFS8jIiLQoEEDzJ8/X77N1dUVIkfHQndtZCv6FlzES4YDQzXnHLSu+9R84ibFfEK0RomAAUgkQEwMsHOnevuruhnx7NkzdO/eHaNHj8bbt28B8F3AcXFxOi0/r8mKvpoOIzIKJStGatodriAvT9cWEUJMxK5du1CjRg0cOHBAvs3HxwclSpQQ5PwSCbBmzS14eYXjxAn1jtE2Xr569Qr9+/fHwIEDkZubi5MnT8p7BQAoHV6jyYq+ZhM3KeYTojVKBPQsOpqfE9W+PbB8uXrHKBuqc+LECfj4+GDfvn3ybWFhYYItc9+zJz+2v2qBqnHu7opj/jUZRmQ0SrqU01/dUutQpd3mdna6togQYmRv3rzBmDFj5PMBVq1aJfhr7NzJULHiKwwd+gEeP14MYKpax2kTL69evQo/Pz9ERkbCxsYGs2bNwoEDB2Bjk++yLls9t8Adnp7NU7Fj3FlUdVa88+3unKswbl6TYUSFGDJuKon5ZtN2QoyMZsPokapJtapwHP/Fu3Xr99vy8vIwffp0zJkzB7K13ypWrIiIiAh07dpV0Pb27MmXCC2qJKhsGFFqqvL3pew9GFzp0vLGMcaw+sgRjFkXAaAdgKpQlv9yYHB3zkXruk8Vn2CMn7hGCDFbd+7cwVdffYWLFy8CAMaPH4/Zs2cL+hobNmSjX7/SAApW5GFQNTxFm3jJGMPvv/+OUaNG4fXr16hSpQqioqLQpk0b5QfUqgXcKnwjpGfzVHT3Sy2yrKZsGFFqur18XL1C+00lbuaL+TJm03ZCjMziEwFj1bovalKtMgWH4ADA/fv3ERwcLC9pBwAff/wxNmzYADc93XIXid6XCFX1/NKlfIJTsFKbsvdgFLLu8HfdxU1q1oSNDUPLD37FqcRZAJjChaFgd7gCW1sjd28QQjRRMOY/frwDQ4cOQmZmJpydnbF+/XrBb6KcP38RAwdWAWAP5ZOCC9M2Xt6/fx8jRozA27dv0aVLF0RGRsLFxUX1AY6OfB1+JTX2RTaQlwhVRjaMqPcif3CmHDcLxHzAjNpOiJFZ9NCg/MNygoP5n56ehlklt7hJtQUVHIKzbds2+Pr6ypMAW1tbzJs3D4cPH9ZbEqAudYcRGY2tLZJLlQI4DhzHwa92bVxesAB/T/dRqztcjuP4GtZURo4Qs6As5gcGNkdm5sdo2bIl4uPjBU0CZMUbWrb8ARKJGzS5pGobL2vUqIElS5Zg3rx5OHDgQNFJgIwOK/qqO4xIzhhxU8chUHIU84kV4hhT95616cvMzISTkxMyMjJw9GhZpcNyZHFC319Yo6L4C1FxRo4EevV631ORnZ2NMWPG4Pfff5fvU6NGDURFRaF58+b6a7AWTHFl4dzcXIwfPx5r167FPzNnwsfTs9A+Gq2Q2aULrTJJTFb+mFe2bFljN8egCr531UMx+Q3btknx5ZfCBaiXL19i0KBBiI6OBhAIIKrYY376CahXT7N4yRjDb7/9hiZNmqBp06baN5gxICFBvRV9lTD5uJmVBRw8qPQpk287IWrQV7y3yLS3uFr3HMfXuu/eXX9fXNW9ad+r1/uhOPHx8QgMDMTNmzflzwcFBWHVqlValQXVt+KGERna9evXERgYiKtXrwIAjj99qjQRKK47XM7bmy4IhJiBoodicuA4YNw4EXr2FCbmnz9/Hl999RXu37//bot6ZX8+/lizmJmZmYmhQ4di69atqFmzJuLj4+GobUxSd0VfGxtAWrC4vhnETR2GQMlRzCdWyCITgTNn1K91X1RQ1uWOtyaTamXdy+PHj5eXBXVwcMDy5cvRv39/rcuCWgvGGNasWYOwsDDk5uaiUqVKiIyMROeAAODCBSApSfOTenjw3emEEJOnyfomqmK+OvGeMYYlS5Zg4sSJyHtXYrJChQr4/fdxGDVKwyIKsoWrcnL4f9va8pNe3413j4uLQ58+fXDnzh3Y2tpi2LBhcHBw0OhzUaq4FX1dXYFLl8wzbjZowL8fc2w7IUZikYnAo0fq7VdU7eboaP4OU/6Li7s7P1FWnSFF6k6qff78KQYOHKhQ07pRo0aIiooSpCyopXvx4gWGDh2KHTt2AAACAgIQGRmJypUr8zv4+fEXN3W6w2XPe3vzFwRKwAgxCxcvPgRQpdj9VMV8deL98+fPMXDgQOzdu1e+j7+/P7Zs2YLq1atDKlWziEJWFnDnDnDvntI78kwkwsoLFzD255/x9u1bVK9eHVu2bIG/v79an4XaZCv6KmOucZPjzLfthBiJRSYCrq7q7adq+I6qsaapqfz2gvMLVN1Jkk2qVXaBWbIEKFfuOHx8QpGW7+oUFhaGefPmoWTJkuq9CSsXGRmJHTt2wM7ODnPnzsV3332nWEdb3e5wW1t+kljNmtQ1TIgZ2bp1K376KRrAX8XuqyzmqxPv3dzOIjAwEMnJyeAnBLfGZ58NxZgxfVC1Kn8ZLS7e9/yCAVeVjNHP98I5b96g3/Ll2PnPPwCAz9u1w7odO1DB2Vn9D0QI5hw3zbnthBiBRU4Wfv48Aw0bli22m/bevcJdvxIJHz9UdTMXPFadO0kFE4UWLfIwY0Y45s2bJ18bwMXFBREREfj00091/yCsiEQiwTfffINvv/1WvYl0su74gt3hSlbgJMTU0WRh2dwpG5QsmYa3b13AmJKa8SpifvHxnsHJKQtZWS6QSN4C+AI2Nssglb4vmVZcvG/dGhDZMLWGKUqlUnw2fz6OXrmC+SEhCOvaFZynJ3+X25h3q805bppz2wnJR1/x3iITgfxVgwDl3bSqqgbFxPAl54pz4gTw/LnyO0lFvca9e/cQHByMc+fOybd17NgRkZGRRi8Lag4ePHiA6dOn45dffoE9LfpCrBwlAnwiEB4ejgYNpqBPH/5bvroxX914zy9GWAHATtlZ1Tq/3NWrSiex8m1lyJNIUOLdl9KnmZm4/+QJ/GrXfr+Ttzd/l5sQYrX0Fe8tdh0BbWvdFzVvIL/U1KIrEwF8ZSKJ5P32LVu2wNfXV54E2NraYv78+Th06BDc3NwgkfAXpqgo/mf+Ywmwe/du+Pj4YO3atZg0aZKxm0MIMQF79+7FtGnT0Lu3SOOYr268B6rC0XHdu38r3plXFe/lsrJUJgHPMrPReupxBMy8jZhrLpBIAZeyZRWTAIA/PitL3cYSQojaLLpfrGdPvkSoJpV/1L0p//Sp+lUq/PyyMWrUKKxbt07+fK1atbB582Y0a9YMgO6Tky1Zbm4uvv/+e6xcuRIA0LRpU4wcOdLIrSKEmIK2bdvK/61pzFc33n/zzRSsXq26hHORVYnu3FE6aXXebhv8tKUTJNIBAID20wF35xwsHRCvfKGru3f5aj+EECIgi+0RkJHVug8K4n8WV/5TVvZT1XBMjuMLLaizmCMAnD17H02aNFFIAkJCQhAXF6eQBPTuXTixkE1WM8RKyKbq2rVraNasmTwJGD9+PE6fPo3aBe+YEUIINIv5xcV7QIoqVcRo3dpbrdcu1MMgFvMTE/IlAYwxDFiRhkmbv4BEqpiJpKbbo/cif0T/U6Bbg7H3k14JIURAFp8IaEpW9hMofHHIXwauYPezKlOnDpEvEObg4ID169dj48aN8vFdxS1+BhTR5Wzh9u3bBz8/PyQkJKBSpUo4ePAg/ve//6FEiRLGbhohxALI4j0/Va7gIloMHMdh2TJbteN9oR6GtDSFL+/pWVnoNm8B1v/9xbstipdg9m7YUViELyQFmyOb9EoIIQKiREAJdeYXqHMnCUiGWHwcANC4cWPExcWhX79+CntpshCOtWnUqBHs7e3RuXNnXLlyBZ07dzZ2kwghFkQikeD69VnguC8BKA7HqVaNw44dnFrxXtZTrLBgGMAvFvbuIMYYOs+ejQOXHABUg6rLLwOHlPTSiL1RoNuZ4/jKN4QQIiBKBFTo2RO4f5+vDrR5M//z3r334/WL6jl4f2cpDIAU48aNw9mzZ+Hl5VXoddS9wWMtN4Lu3r0r/7e7uzvOnTuHP//88/0CYYQQIoDHjx+jS5cumDJlChjbCcATjRqNxcqVLzWK94UWDMsvX28Ax3GYFRgI13L11Gpf2otShTe+W82YEEKEQolAEYoba6qq5wB4AKA3KlU6jb/++gsLFy5UOZxF3clqll5ZVCwWY/r06fDy8sK+ffvk2z/44APFBcIIIURHx48fh6+vL44ePQoAsLGxwYwZ03DhwgIMG1ZOo3hfVFWiZ1lZiL1xQ/57F19fbBjZS602upV/XXijnZ1axxJCiLroG5aOevYEjh+/C2/vYQCCwNebroFOnV7h8uXL6NKlS5HHa93lbEFSUlLQoUMHTJs2DRKJBMeOHTN2kwghFkgikSA8PBwdO3bEo0ePAABubm44duwYpkyZAlEx1SSK6ynOLzY2Fr5ffIFu8+bh3pMn8u3tGzyHu3MOOChfwocDQzXnHLSu+1TxCcb4hbAIIURAlAjoKCoqCk2a+CIx8VcAW2Brexr/+988HDx4EK6ursUer3WXs4XYtWsXfHx8EBsbizJlymDDhg1YsmSJsZtFCLEwaWlp6NSpE2bMmCFf0T0gIADx8fFoV6jmp2rF9RRLpVLMmTMH7du3R+qjR3AtXx65b9++P94GWDogHgAKJQOy35cMiIeo4NXZ1tbyu4YJIQZnsonAvHnzwHEcwsLCjN0UpV69eoWvv/4awcHByHq30EutWrVw5swZjB8/XqPhLNoufmbOcnNzMXz4cPTs2RMvXryAn58f4uPjERoaauymEUKMQJ8x/8iRI/D19cWJEycA8EOBZs+ejb/++guVKlUS7HWePHmCTz75BD/++CMkEglCQ0Px77ZtqFetmsJ+PZunYse4s6jqrDj51905FzvGnVW+jkDNmnwyQAghAjLJqHLhwgWsXr0aDRs2NHZTlLp06RICAwPx33//ybeFhoZi5cqVcHR01Oqc2ix+Zs6OHz+OVatWAeDXBpg1axaVBSXESukr5ovFYkybNg1z5syR9wJUrVoVUVFRaC3weMuYmBgEBwcjLS0N9vb2WLFiBQYMGADu1St+UZgCejZPRXe/VMTecEHai1JwK/8ares+LdwTAPDDgmrWFLS9hBACmGAi8OrVK4SEhGDNmjWYNWuWsZujgDGGpUuXYuLEiXj7rqu3TJkyWLlyJfr27avz+WVdztaga9eumDx5Mtq2bYuAgABjN4cQYiT6ivmpqakICgpCbL7ay5988gkiIyNRsWJFwV5HZteuXUhLS0O9evWwbds21K9fn3/C0RHw9gYSEwsdI7IB2tV/Wmh7Id7e/HkIIURgJjc0aMSIEejatSs6duxY7L5v3rxBZmamwkNfnj59is8++wzfffedPAlo0qQJ4uLiBEkCLN3z588xaNAgpOWrgzp79mxKAgixcurGfE3i/cGDB+Hr6ytPAkQiEebNm4f9+/frJQkAgP/973+YPn06zp8//z4JkGnQAPDw0O7EHh788YQQogcm1SOwZcsWxMXF4cKFC2rtP3fuXEyfPl3QNkgkhYfnxMQcQ2hoqLzKBACMGzcOc+bMoeEsaoiNjUVwcDAePHiAx48fY//+/cZuEiHEBGgS89WJ92KxGFOmTMG8efPk29zd3bFlyxa0bNlS6THKYr46QzKPHTuG3377DZs2bYKtrS1KliyJqVOnKt+Z4wA/P77qT2Ii/7uy5eTz788Y3xPQoEFRK1cSQohOTCYRSElJwZgxY3DkyBGUKqVkIRUlJk2ahLFjx8p/z8zMRLUCk7I0ER0NjBmjuNKvo+NLZGWtBMAnAZUqVUJkZCStcqsGsViMWbNmYebMmZBKpahdu7bgiRshxDxpGvOLi/cpKSkICgrC6dOn5ds+++wzREREwNnZWek5lcV8d3e+kpuqIg0SiQQzZszAzJkzwRjDRx99hDFjxhTbfnAc8OGHgKcncPcu/xCLFb/kM8ZPCK5Zk3/QcCBCiL4xE7Fr1y4GgIlEIvkDAOM4jolEIiYWi4s9R0ZGBgPAMjIyNH79nTsZ4zjG+Eic/yF59/iCBQQEsLS0NG3entVJSkpirVq1YgAYANavXz+WmZlp7GYRYlF0iXnGpmvMz//e9+/fzypUqCCPN7a2tmzhwoVMKpWqPF5VzOc4/rFzZ+FjUlNTWbt27eSvM3jwYJadna3dB5CXx1hyMmM3bzKWkMD/TE7mtxNCSAH6ivccY0X1TxpOVlYWkpKSFLYNHDgQ3t7emDhxIhqoMUYyMzMTTk5OyMjIQNmyZdV+bYmEv0mT/66QIinKlXuFJ0/KwM7O5KZVmJwLFy4gICAAL1++hKOjI3799VcEBwcbu1mEWBxtY54p0DXmy977qFGjsGzZMvl2Dw8PbNmyBS1atFB5bHExn+P4noF7994PEzp8+DBCQ0Px9OlTlClTBqtXr6a4RggxGH3Fe5MZGuTo6Fgo8Ds4OMDZ2VmtJEAXsbFFJQEAYIOXL8vi9Gnrqeqji3r16qFy5crw8vJCVFQUalLZO0JIAULF/PxJQPfu3bFu3TqUL19e+c5iMZCWhtgTDA8eVFd5TsaAlBT+2tCuHbBy5UqMHDkSjDH4+Phg27Zt8PLyUruNhBBiqkwmETCmfIVsBNnPGt2+fRs1a9aEjY0NHBwccPjwYbi5ucHOzs7YTSOEWDg7OzssWLAAo0ePBqdsYm1WFnDnDn+LXyxGWlx1AKoTARlZzG/bti1KlSqF/v37Y/HixWrPYyOEEFNn0olATEyM3l9DKpUiNnYHgD7F7kuruxfGGMOqVaswduxYzJkzRz6Zr3r14i+yhBCSnzYx38PDA9u3b4efn1/hJxkDEhIKVepxK5dbeF8lZDG/fv36uHHjBjy0LQFKCCEmyqoHvD9+/Bhdu3bFqlVBAFIASJXux3FAtWp8WTny3vPnz9GrVy+MGDECb968QWxsLExkygkhxEqcPHlSdRJw4cL7hbzyxabWdZ/C3TkHHFTFKymAFNjYvK9AREkAIcQSWW0icOTIEfj4+ODgwYPgg/4YABw4TvHCIOtlXrJEvdrS1uLkyZPw8fHBrl27YGdnh8WLF2Pnzp3Ku+UJIURPypUrp/yJhASgwGRkGZENsHRAPAAoSQZkN4TG4MSJo0I0kRBCTJbVJQJv377FxIkTERAQgMePHwMAKlVyw/z5PyAsjIOzs+IXWXd3YMcO1TWlrY1YLEZ4eDjat2+PBw8ewMvLC+fOnUNYWBhsbKzuz4kQYoqyst73BBQgkQIx11zwJs8G0/pcQ5UKBYcJPYB9iRBsH1sD4fnWLSCEEEtk0nMEhHbnzh0EBQUprGLp6zsDjx9PxsSJ72/3u7gAISFA9+7qrzJpLRITEzF37lxIpVIMHDgQv/zyC8qUKWPsZhFCyHt37ihdvTf6n6oYE+GLB+ml5duqVsjBR14bcea/AwDS0KTmQ2z7bjRqurryi375+Bi48YQQYjhWkwhs2rQJw4YNQ1ZWFgC+ykRw8A5ERnYDY4q9AM+e8StLUhJQWIMGDbBkyRKUL18eQUFBxm4OIYQoEov56kBKkoDei/wLDQRKfW6P1OfBAKIx5lMHzA+ZhpJ2dvzxd+8C9evzq/3qo51paUBODv9vW1ugdGl+hrI+Xo8QQpSw+GiTlZWFkSNHIjIyUr6tdu3a2LRpC3r1alLwWgGAj/8cB4SF8b0C1pwM5OTkYPz48RgyZAh8fX0BAMOHDzduowghRJW0NP6LdT4SKTAmwvddElBwHhMHgKGi4+9Y1O8oRPlHOMq+rFerJlz7CpQyRf55VYzxSUCNGkCtWoCjo3CvSwghSlj0oO6LFy+icePGCklA//79ERcXh5ycJkUuIpZ/QRlrdeXKFfj5+WHlypUIDg6GuMDFlRBCTE5OjuKXawCxN1zeDQdSVczABs+yyiP2hoviZo4DctUrNVosxoCrV4GDB4Hbt98nK4y9fwD89tu3+f2uXi3Us0EIIUKyyERAKpVi0aJF8Pf3x+3btwHwq1hu3LgRERERcHR0pEXEisAYw4oVK9CsWTNcv34dbm5uWLZsGWypu5oQYuqU3LBIe6HeAmBK98vL07VFRZYyVbk/wO9/4QIlA4QQvbHIb3a9e/fGsWPH5L/7+fkhKioKtWrVkm9Td3Ewa1tELD09HYMGDcKePXsAAJ9++ikiIiLg4uJSzJGEEGIClNywcCv/Wq1Dle4nxOroRZQyLVZSEmBvD3z4oe7tIISQAiyyRyB/EjBhwgScOnVKIQkA+InA7u6FepDljLGImEQCxMQAUVH8T4nEcK8NAPfv34ePjw/27NmDEiVKYPHixdi/fz8lAYQQ81G6tMId9Dd5edh+bgGKXDQSDNWcc9C67lPFJxjjv4TrQo1SplGnqiHmmgskypvHH/+u0AUhhAjJInsEAKBy5crYsGEDOnXqpPR5kYivDNS7d+Eqc8ZYRCw6GhgzBgrzFtzd+TYaag2D6tWro27dunBwcMCWLVvQqFEjw7wwIYQIRVZ1RyzG/SdP0GvRIsTduwd+0cid4MDA8s0VkC0otmRAvOJEYYA/j67dwhqUMnV3zsHSAfHo2TxV8RwcR6VMCSF6YZE9Ah07dsSVK1dUJgEyPXvyi4VVraq43dCLiEVH8wlJwcnLqan89uho/b12UlIScnJyAAA2NjbYvHkzLl68SEkAIcQ8yarucBxKlyyJtJcv4ezoiAM/1MHOcWdR1Vlx8q+7cy52jDur/Mt3zZq6lfIsppTpg3TF3obUdHv0XuSP6H8KXJRkpUypYAMhRGAcY5YzCykzMxNOTk548eKF6mXnlZBI+OpAaWn8zR9Drh8gkQCenoWTABmO4xOTe/eEb9P27dsxZMgQBAYG4tdffxX25IQQvZPFvIyMDJQtW9bYzTEoVe89Ly8Pdq9f81V3AFy4fRtu5cvD3dkZAD8cJ/aGC9JelIJb+ddoXfdp4Z4AmS5ddCvhmZICnDunsEkiBTxHdH2XBBQem8qBwd05F/dWHCjcrhYthC1lSggxG/qK9xbZI2Bjo9nbEomAdu2AoCD+pyHXDYiNVZ0EAPopY5qTk4OhQ4eiT58+yMjIQHx8PHKFKpFHCCFGcvPmTfj5+WHT3r2AtzcAwK92bXkSAAAiG6Bd/acIapWCdvWLSAK8vXWv469FKVMGDinppfVbypQQQt6xyETAnBi6jOmVK1fQtGlTrFmzBhzHYfLkyYiNjYW9rhPiCCHEiDZt2oQmTZrg8uXLmDp1KvLq1AE8PLQ7mYcH0KCB7o0yxVKmhBCSDyUCRmaoMqaMMSxfvhzNmjXDjRs34ObmhiNHjmD27NmwE6I8HiGEGEFOTg6GDBmC0NBQZGdno127doiNjYVdiRKAn5+8Z0BliTgZ2fPe3vxxxe2vDlMsZUoIIflYbNUgcyErY5qaqnzNGNkcAV3LmD579gzh4eF48+YNunbtinXr1lFZUEKI2evQoQNu3LgBjuPw008/ITw8HCLZ+E6O4+vve3ryk21lE27zf8lnjP/CXrMm/9B1OFB+BUqZAkDruk/h7pyD1HR7hepFMrI5AnopZUoIIQVQImBkhipj6uLigoiICNy7dw+jRo0CJ8TdLkIIMbIbN26gcuXK2LhxIzp27Kh8J0dHvvRm/fr8OMvcXH6YjZ0d/+VaVnJUaPlKmcqIbIClA+LRe5G/4UuZEkJIATQ0yAToo4ypWCzGlClTsHv3bvm2bt26YfTo0ZQEEEIsRps2bRAfH686CcjP1pavuuPlxScFXl787/pIAmSv966UaX49m6dih6FLmRJCiBIWWT7UXEvpCVXG9P79+wgJCcGZM2dQoUIF3L59G+XLlxe+wYQQozL3mKcL2Xt//vy5ace3rCx5KdOCDFrKlBBi1vQV7+n2ggmRlTHVhWxtgIyMDDg5OWHVqlWmfZEkhBAdiAxZ71kbjo78BOTExEJPyUqZFkuIUqaEEKIEDQ2yENnZ2RgyZIh8bYAWLVogPj4effr0MXbTCCHEujVoYPxSpoQQogT1CFiArKwsNGvWDImJifK1AcLDw6ksKCGEmAKO40uS2tvzPQMFK0Mo258xviegQQNhSpkSQogSlAhYAEdHR7Rr1w6ZmZnYsGEDOnToYOwmEUIIyc/YpUwJIUQJmixspp49ewaxWAxXV1cAQG5uLrKzs1GxYkUjt4wQYijWFPMKMvv3LhYbtpQpIcSs0WRhInf8+HGEhoaibt26OHz4MEQiEezt7WFPi80QQoh5kJUyJYQQI6LJwmYkLy8PP/74Izp27Ii0tDQ8fPgQT548MXazCCGEEEKIGaJEwEzcu3cPbdq0wZw5c8AYw+DBg/Hvv//CjVaaJIQQQgghWjCZRGDVqlVo2LAhypYti7Jly8Lf3x9//fWXsZtlErZt2wZfX1+cO3cOTk5O2Lp1K9asWQMHBwdjN40QQrRCMZ8QQozPZBIBd3d3zJs3DxcvXsS///6LDh06oHv37rh27Zqxm2ZUb9++RXh4ODIzM+Hv709rAxBCLALFfEIIMT6TrhpUoUIFLFiwAIMGDVJrf7OvIqHC5cuXsWPHDoSHh8OWqkkQQt6xtJinScy3tPdOCCFFsaqqQRKJBNu3b0d2djb8/f1V7vfmzRu8efNG/ntGRgYA/sMyV4wxrF69GlKpFMOHDwcA1KhRA+PHj0dOTo6RW0cIMSWyWGfC93PUok7Mt8R4Twgh6tJbvGcm5MqVK8zBwYGJRCLm5OTEDhw4UOT+4eHhDAA96EEPelj1486dOwaK0sLSJOZTvKcHPehBD+HjvUkNDXr79i2Sk5ORkZGBHTt2YO3atfj7779Rr149pfsXvEP08uVLeHh4IDk5GU5OToZqtkXIzMxEtWrVkJKSQt3sGqLPTjf0+WkvIyMD1atXx4sXL1CuXDljN0djmsR8ivfCov932qPPTnv02WlPX/HepIYGlShRArVr1wYANGnSBBcuXMDSpUuxevVqpfuXLFkSJUuWLLTdycmJ/sC0JKvgQTRHn51u6PPTno2NydR90IgmMZ/ivX7Q/zvt0WenPfrstCd0vDfpq4dUKlW4A0QIIcRyUcwnhBDDMpkegUmTJuGTTz5B9erVkZWVhc2bNyMmJgaHDh0ydtMIIYQIjGI+IYQYn8kkAk+ePEG/fv2QlpYGJycnNGzYEIcOHUKnTp3UPkfJkiURHh6utPuYFI0+O+3RZ6cb+vy0Z86fna4x35zfuymgz0979Nlpjz477enrszOpycKEEEIIIYQQwzDpOQKEEEIIIYQQ/aBEgBBCCCGEECtEiQAhhBBCCCFWiBIBQgghhBBCrJDZJQIrVqyAp6cnSpUqhebNm+P8+fNF7r99+3Z4e3ujVKlS+PDDD/Hnn38aqKWmR5PPLiIiAhzHKTxKlSplwNaajpMnT6Jbt26oUqUKOI7D7t27iz0mJiYGjRs3RsmSJVG7dm1ERETovZ2mSNPPLiYmptDfHcdxePTokWEabELmzp0LPz8/ODo6olKlSujRowdu3rxZ7HGWFPMo3uuGYr7mKN7rhmK+dowZ780qEdi6dSvGjh2L8PBwxMXFwcfHB507d8aTJ0+U7n/mzBkEBQVh0KBBuHTpEnr06IEePXogISHBwC03Pk0/O4Bf+S8tLU3+SEpKMmCLTUd2djZ8fHywYsUKtfa/d+8eunbtivbt2yM+Ph5hYWEYPHiwVdZH1/Szk7l586bC316lSpX01ELT9ffff2PEiBE4d+4cjhw5gry8PAQEBCA7O1vlMZYU8yje64ZivnYo3uuGYr52jBrvmRlp1qwZGzFihPx3iUTCqlSpwubOnat0/z59+rCuXbsqbGvevDn75ptv9NpOU6TpZ7du3Trm5ORkoNaZDwBs165dRe4zYcIEVr9+fYVtX331FevcubMeW2b61PnsTpw4wQCwFy9eGKRN5uTJkycMAPv7779V7mNJMY/ivW4o5uuO4r1uKOZrz5Dx3mx6BN6+fYuLFy+iY8eO8m02Njbo2LEjzp49q/SYs2fPKuwPAJ07d1a5v6XS5rMDgFevXsHDwwPVqlVD9+7dce3aNUM01+zR353ufH194ebmhk6dOuH06dPGbo5JyMjIAABUqFBB5T6W8rdH8V43FPMNh/7uhEExX5Eh473ZJALPnj2DRCJB5cqVFbZXrlxZ5ViyR48eabS/pdLms6tTpw7++OMP7NmzBxs3boRUKsVHH32EBw8eGKLJZk3V311mZiZyc3ON1Crz4Obmhl9//RU7d+7Ezp07Ua1aNbRr1w5xcXHGbppRSaVShIWFoWXLlmjQoIHK/Swl5lG81w3FfMOheK8bivmFGTre22rVSmLx/P394e/vL//9o48+Qt26dbF69WrMnDnTiC0jlqxOnTqoU6eO/PePPvoId+7cweLFi7FhwwYjtsy4RowYgYSEBJw6dcrYTSEWimI+MQaK+YUZOt6bTY9AxYoVIRKJ8PjxY4Xtjx8/hqurq9JjXF1dNdrfUmnz2RVkZ2eHRo0a4fbt2/pookVR9XdXtmxZ2NvbG6lV5qtZs2ZW/Xc3cuRI7N+/HydOnIC7u3uR+1pKzKN4rxuK+YZD8V541hzzjRHvzSYRKFGiBJo0aYJjx47Jt0mlUhw7dkzhLkZ+/v7+CvsDwJEjR1Tub6m0+ewKkkgkuHr1Ktzc3PTVTItBf3fCio+Pt8q/O8YYRo4ciV27duH48eOoUaNGscdYyt8exXvdUMw3HPq7E541xnyjxnstJjMbzZYtW1jJkiVZREQEu379Ohs6dCgrV64ce/ToEWOMsb59+7IffvhBvv/p06eZra0tW7hwIbtx4wYLDw9ndnZ27OrVq8Z6C0aj6Wc3ffp0dujQIXbnzh128eJFFhgYyEqVKsWuXbtmrLdgNFlZWezSpUvs0qVLDAD7+eef2aVLl1hSUhJjjLEffviB9e3bV77/3bt3WenSpdn48ePZjRs32IoVK5hIJGIHDx401lswGk0/u8WLF7Pdu3ezW7dusatXr7IxY8YwGxsbdvToUWO9BaMZNmwYc3JyYjExMSwtLU3+yMnJke9jyTGP4r1uKOZrh+K9bijma8eY8d6sEgHGGFu2bBmrXr06K1GiBGvWrBk7d+6c/Lm2bduy/v37K+y/bds25uXlxUqUKMHq16/PDhw4YOAWmw5NPruwsDD5vpUrV2affvopi4uLM0KrjU9W3qzgQ/Z59e/fn7Vt27bQMb6+vqxEiRKsZs2abN26dQZvtynQ9LObP38+q1WrFitVqhSrUKECa9euHTt+/LhxGm9kyj43AAp/S5Ye8yje64ZivuYo3uuGYr52jBnvuXcNIIQQQgghhFgRs5kjQAghhBBCCBEOJQKEEEIIIYRYIUoECCGEEEIIsUKUCBBCCCGEEGKFKBEghBBCCCHEClEiQAghhBBCiBWiRIAQQgghhBArRIkAIYQQQgghVogSAUIIIYQQQqwQJQKEEEIIIYRYIUoECCGEEEIIsUKUCBBShNOnT4PjOHAch23btind559//kGZMmXAcRzGjx9v4BYSQggRAsV7Yo04xhgzdiMIMWXdu3fH3r174e3tjYSEBIhEIvlzN2/eRMuWLZGeno7+/ftj3bp14DjOiK0lhBCiLYr3xNpQjwAhxZg7dy5EIhESExOxceNG+faHDx+ic+fOSE9Px2effYa1a9fSRYEQQswYxXtibahHgBA1DB48GL///jtq1KiBmzdvIjs7G23atMHVq1fRqlUrHD58GPb29sZuJiGEEB1RvCfWhBIBQtSQmpqKDz74ALm5uVi8eDF27dqFkydP4sMPP8TJkydRrlw5YzeREEKIACjeE2tCQ4MIUUPVqlUxevRoAMB3332HkydPwtPTE4cOHVJ6UXj16hWmTZuGzz77DK6uruA4DgMGDDBsowkhhGiM4j2xJpQIEKKm0aNHw8aG/y9ToUIFHD58GG5ubkr3ffbsGaZPn464uDg0bdrUkM0khBCiI4r3xFrYGrsBhJgDsViMb775BlKpFACQk5NT5BhRNzc3PHjwAFWrVsXr169pPCkhhJgJivfEmlCPACHFYIxh8ODB2L9/P1xcXFCjRg28fv0a4eHhKo8pWbIkqlatasBWEkII0RXFe2JtKBEgpBgTJkzA+vXrUaZMGRw4cACzZ88GAKxfvx7Xr183cusIIYQIheI9sTaUCBBShIULF2LhwoWws7NDdHQ0/Pz8EBgYiIYNG0IikWDSpEnGbiIhhBABULwn1ogSAUJUiIyMxIQJE8BxHCIiItCpUycAAMdxmDlzJgBg7969OH36tDGbSQghREcU74m1okSAECX+/PNPDBo0CIwx/PzzzwgODlZ4/vPPP0fz5s0BABMnTjRGEwkhhAiA4j2xZpQIEFLA2bNn8eWXX0IsFmPixIkICwtTup9s7Ojp06exZ88eA7aQEEKIECjeE2tH5UMJKcDf3x/Z2dnF7vfxxx+DFuYmhBDzRfGeWDvqESCEEEIIIcQKUY8AIXqyfPlyvHz5EmKxGABw5coVzJo1CwDQpk0btGnTxpjNI4QQIhCK98RccYz6ugjRC09PTyQlJSl9Ljw8HNOmTTNsgwghhOgFxXtirigRIIQQQgghxArRHAFCCCGEEEKsECUChBBCCCGEWCFKBAghhBBCCLFClAgQQgghhBBihSgRIIQQQgghxApRIkAIIYQQQogVokSAEEIIIYQQK0SJACGEEEIIIVaIEgFCCCGEEEKsECUChBBCCCGEWCFKBAghhBBCCLFC/wcv3lzA0KQhSgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c22d85ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a8e47731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=100, degree=2, 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-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=100, degree=2, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=100, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b96b5540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(C=0.01, degree=2, gamma=&#x27;auto&#x27;, 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-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=0.01, degree=2, gamma=&#x27;auto&#x27;, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(C=0.01, degree=2, gamma='auto', kernel='poly')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1, gamma=\"auto\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"auto\")\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5bf9b31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$degree=2, C=0.01, \\\\epsilon = 0.1$')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd2a9647",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}