Ensemble_Method.ipynb

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   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Requirement already satisfied: pandas in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.5.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.23.2)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (3.7.0)\n",
      "Requirement already satisfied: mlxtend in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (0.23.1)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (0.11.0)\n",
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      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (9.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from mlxtend) (1.10.1)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from mlxtend) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\hp\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.2 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install pandas numpy matplotlib mlxtend scikit-learn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "d={\n",
    "   'sepal length (cm)':data.data[50:,1],\n",
    "    'petal length (cm)':data.data[50:,2],\n",
    "    'Species':np.array([i for i in data.target if i>0])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.2</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  petal length (cm)  Species\n",
       "0                 3.2                4.7        1\n",
       "1                 3.2                4.5        1\n",
       "2                 3.1                4.9        1\n",
       "3                 2.3                4.0        1\n",
       "4                 2.8                4.6        1\n",
       "..                ...                ...      ...\n",
       "95                3.0                5.2        2\n",
       "96                2.5                5.0        2\n",
       "97                3.0                5.2        2\n",
       "98                3.4                5.4        2\n",
       "99                3.0                5.1        2\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will suffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  petal length (cm)  Species\n",
       "8                 2.9                4.6        1\n",
       "95                3.0                5.2        2\n",
       "68                2.6                6.9        2\n",
       "97                3.0                5.2        2\n",
       "70                3.2                5.7        2\n",
       "..                ...                ...      ...\n",
       "2                 3.1                4.9        1\n",
       "99                3.0                5.1        2\n",
       "69                2.2                5.0        2\n",
       "98                3.4                5.4        2\n",
       "55                3.0                6.6        2\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.sample(df.shape[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df.loc[:60,:].sample(10)\n",
    "df_train\n",
    "\n",
    "df_test=df.loc[60:,:].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>petal length (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  petal length (cm)  Species\n",
       "4                 2.8                4.6        1\n",
       "69                2.2                5.0        2\n",
       "50                3.3                6.0        2\n",
       "23                2.8                4.7        1\n",
       "40                2.6                4.4        1"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tt=df_test.sample(5,replace=True)\n",
    "df_tt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = df_tt.iloc[:, :-1].values  # Select all rows and all columns except the last one\n",
    "y_test = df_tt.iloc[:, -1].values   # Select all rows and only the last column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#models -> Random samples\n",
    "from sklearn.tree import DecisionTreeClassifier,plot_tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from mlxtend.plotting import  plot_decision_regions\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def evaluate(model, X, y):\n",
    "    model.fit(X, y)\n",
    "    plot_tree(model)\n",
    "    plt.show()\n",
    "    \n",
    "    filler_feature_values = None\n",
    "    \n",
    "    if X.shape[1] > 2:\n",
    "        # If X has more than 2 features, provide filler values for additional features\n",
    "        filler_feature_values = {i: X[:, i].mean() for i in range(2, X.shape[1])}\n",
    "        \n",
    "    plot_decision_regions(X, y, clf=model, filler_feature_values=filler_feature_values, legend=2)\n",
    "    plt.show()\n",
    "    \n",
    "    y_pred = model.predict(x_test)\n",
    "    print(\"y_test\", y_test)\n",
    "    print(\"y_pred\", y_pred)\n",
    "    \n",
    "    print(\"Accuracy score\", accuracy_score(y_test, y_pred))\n",
    "    \n",
    "    return model\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3XHgwAFYWVnJpE4XFxd8/PhR6LZPnz7h06dPiI+Px969e9GmTRvs27cPDg4OMqn7W/Xr15fLcQlRFkoGCCEVsnPnTvj7+7PrdnZ26NevH0xMTJCQkICjR4/i69eviIyMhKenJ6KiomBsbCyz+m1sbNCmTRvY2dnB1NQUWlpayMjIwP379xEVFQUul4sHDx6ga9euiI6ORvPmzYUeZ9GiRfj06ZNEde7Zs4d9KJWbmxsaNWpU7j5Dhw5F+/btyy2nq6srUQyEyBVDyDdiY2MZAExsbKyyQyEq5tWrV4yuri4DgAHA/PTTT0xxcbFAmcTERKZZs2YCZWTh8OHDzJs3b8SWSUhIYBwcHNi63d3dpa73y5cvjLGxMXvMffv2iSwbEBDAlgsKCpK6bnmg9k2EoTEDhBCJLVmyBAUFBQBKuu3/+usvaGpqCpSxtbXFkSNHoK2tDaDkksKLFy+krnvw4MGoW7eu2DJNmzZFaGgoux4ZGYkPHz5IVe/hw4fZx1PXqlULgwYNkup4hKgiSgaISnn79i1++eUXODo6wsjICDVr1kTLli0xf/58JCcnA5Bs1L4kdxPwDyArdfv2bfj4+KBRo0bQ19eHqakpnJ2d8eeff7L/BEWp7ncT5ObmIjw8nF1fvHgxNDSEf4U4Ojpi4MCBAIDi4mLs379fITECgIODAywsLAAADMMgKSlJquPt3LmTXR41ahT09PSkOh4hqoiSAaIyjh8/Dnt7e6xduxbx8fH48uULPn/+jLi4OKxZswZOTk44fvy4XOpmGAbz58+Hs7Mz9u7di1evXiE/Px9ZWVm4desWZs+ejY4dOyItLU0u9VcFFy5cwNevXwEAJiYm6Nmzp9jygwcPZpePHTsm19j4FRUVCYzQNzAwqPSxnj59iujoaHZ9/PjxUsVGiKqiAYREJVy8eBFDhgxBUVERAMDU1BT9+/eHnZ0dPn/+jMuXLyM2NhbDhg3DpEmTZF7/0qVLsWbNGujp6cHb2xuOjo7Q0tLC/fv3cfLkSfB4PDx+/Bg+Pj44d+6czOuvCmJjY9llZ2dnkb0Cpbp27coux8fHo7CwEDo6OnKLr9TmzZuRl5cHALC0tESLFi0qfSz+XoH27dujVatWEu/7zz//YMOGDUhKSkJBQQFq166Npk2bolu3bhg3bhzdkUBUCiUDROm+fPmCsWPHsomAp6cnDhw4ABMTE4FywcHBGD9+PDZv3izzGJYvX46OHTviyJEjZb6ko6Ki0Lt3b+Tn5yMiIgJ37txBp06dZB7Dt2xtbdlLI7IQFBQEX1/fSu8fHx/PLjdp0qTc8ubm5qhZsyY+ffqE4uJiJCQkoGXLlpWuXxQej4esrCw8evQIwcHB2LdvH7tt9erVZcY0SKqwsBB79+5l1ydMmFCh/fl7FADg3bt3ePfuHa5du4bffvsNs2bNwqpVq6ClRV/DRPnor5AoXXBwMN68eQOg5Da1o0ePQl9fv0w5X19fpKam4tdff5V5DHXr1sW5c+dgampaZpurqyumTZuGdevWAQDCw8MVkgyomvfv37PL9erVk2ifevXqsbfvvX//XmbJwMyZM7Fp0yaR2w0MDLBjxw6MHDmy0nUcP34cGRkZAIAaNWpg+PDhEu9rZmYGFxcXNG/eHDVr1kRubi7+/fdfXLp0CZ8/f0ZRURHWrl2LuLg4nDx5stIJCyGyQskAUbqQkBB2ef78+UITgVJz5szBunXrJL4/XFJz5swRmgiUGjx4MJsMPHjwQKZ1i1KR++Al0aFDB6n2z8nJYZdr1Kgh0T781+v595eniRMnYs2aNahVq5ZUx+G/RDBkyBAYGRmVu0+nTp1w6dIldO/eXehllM+fP2Px4sVs79bZs2cREBCAlStXShUrIdKiZIAoVX5+Pu7fv8+u9+vXT2x5PT09eHp6Ctw+Jgve3t5itzdr1oxdlvZWNUlVtFta3koHDwKQ+No//8j70uv4stCvXz+2d6KoqAjp6em4e/cuoqOjsWPHDly/fh0bN26Eh4dHpY6fnJyMy5cvs+uSfhZeXl5itxsbG2PTpk0wNDTEb7/9BgDYsGEDpk2bBktLy0rFSogsUDJAlCo5OZkdK2BhYcHeEiaOk5OTzJOBBg0aiN3Of1aornPJ8/fYFBYWSrRPfn4+uyzNqP5vde/eHd27dy/z+7i4OIwePRoPHz6El5cX1q9fj5kzZ1b4+Lt27WKfb+Dg4IAuXbpIG7KAgIAA7N+/H8nJyfj69StOnTpFdyoQpaJbC4lSZWVlscu1a9eWaB9Jy1VEefeO889FIMlDcKoj/oTo22cSiMLfGyBJN7u0HB0dceXKFdSpUwc8Hg9z5szB3bt3K3QMHo+HoKAgdn3cuHGyDhM6Ojro378/u/7tYENCFI16BghRUf/8849Mxwx4eXlJ9eAe/gcOlQ74LA9/OVk9sKg8JiYmmDJlChYtWgQej4fAwEB07NhR4v0jIiLYuHV1dTFmzBi5xMl/R4aiLj0RIgolA0Sp+Ad5iXoi3bckLVfVrVq1Sqa3FpqZmUmVDDg4OLCTPj1//rzc8hkZGWwyo6WlJTDuQt5at27NLvOPSZEE/8DB/v37y6UnChDsbSJE2egyAVEqW1tbdg77tLQ0iWb4e/z4sbzDIkK0bduWXb516xYYhhFb/vr16+xyixYtFDLhUKni4mJ2mcvlSrzfhw8fcPr0aXZdntfx+Z/XQIMHibJRMkCUSk9PT+CfzIkTJ8SWL534Rx0kJSWBYRiZ/Ugz4RAAeHh4sGMrMjMzBUbbC3PkyBF2ecCAAVLVXVF37txhlyWdEwEoeVRx6YBWOzs79OjRQ+axASV3QPBP0ayO81YQ1ULJAFE6/olhfv/9d4Fb2L61fv16mc8xQCRjaGjIPnwIAFasWCGyd+Dff/9FWFgYAEBTU1OqyX8qKjU1FX///Te73rt3b4n33bVrF7s8duxYuXXlr1y5kn2Akq6uLn744Qe51EOIpCgZIErn6+vLPpr21atXGDhwILKzs8uU27t3LwICAuhaqxItX76c7e6PiorCjBkzynTDJycnY9CgQewZ9oQJE8ROX1z6pEcOh4Pg4GChZX7++WesXr0ab9++FRvf1atX4ebmxt6lYmlpCT8/P4leW1RUFJ49ewagJIGRdL9S69atw6xZs/Dff/+JLJOTk4M5c+Zg+fLl7O9mzpwJa2vrCtVFiKzRAEKidEZGRti1axf69u2L4uJinDt3Do0aNcKAAQPQsGFD5OTk4PLly4iJiYGuri78/f3ZGdwoMVAsOzs7bN68mX1Y1F9//YWzZ8+iX79+MDExQUJCAo4ePcreUujo6IjVq1dLXW9aWhrWrVuHRYsWoVWrVmjdujXq1asHQ0ND5OfnIyUlBTdv3kRCQgK7j6GhIQ4dOlTmGRei8A8c9Pb2Rp06dSoUY25uLjZu3IiNGzeiadOmaN++PWxtbWFkZIS8vDw8ffoUFy9eFOjZ8vT0pNkHiUqgZICohN69e+PQoUPw8/NDTk4OMjMzBbpsgZKkYc+ePcjMzGR/J27qYiIf/v7+4PF4mDt3LvLy8vDy5Uts2LChTDlXV1ccPHhQ6mmBAbBT+zIMg4cPH+Lhw4diy3fp0gXbtm2Dk5OTRMf/9OmTwBgHaQcOPnv2jO1lEEZHRwdz587F0qVL6UFFRCXQXyFRGQMHDkTnzp2xadMmnDlzBsnJydDQ0ED9+vXh7e2NyZMnw87OTuAfT82aNZUYsfqaPHkyvLy8sHPnTpw5cwYpKSnIzc2FpaUl2rVrh+HDh2PQoEEy67nZtm0bRo0ahaioKMTExOD58+d49+4d8vLyoKuri5o1a7Jn4wMHDqzwjIH79+9nx6pYW1ujT58+FY5xxowZaN++Pe7cuYN79+7h9evXyMjIQFZWFnR1dWFqaoqWLVvCzc0NY8aMoTsIiErhMOXdH0TUzv3799GuXTvExsYKjPRXFePHj2d7DSIiIio0QIwQdafq7ZsoBw0gJFVKcXGxwK2F7dq1U2I0hBBSPVAyQKqUwMBApKamAgBcXFxgZmam5IgIIaTqo2SAqISsrCxMnToViYmJQrdzuVwEBgZi7ty57O8q8zQ6QgghZdEAQqISuFwutm7disDAQLRr1w4dO3ZknzyXkpKCixcvCszTP3ToUIEJcAghhFQeJQNEpTAMg5iYGMTExAjdzuFwMGHCBGzZskXBkRFCSPVFyQBRCbVr10ZUVBTOnDmDu3fv4u3bt0hPT8fnz59Rs2ZN2NjYwM3NDX5+fhLfO04IIUQylAwQlcDhcNC1a1d07dpV2aEQQojaoQGEhBBCiJqjZIAQQghRc5QMEEIIIWqOkgFCCCFEzVEyQEg11q1bN3A4HHA4HCQlJSk7HEKIiqJkgBBC5OTRo0eYNWsWOnbsiNq1a0NbWxtGRkZo1KgRBgwYgKCgIPZpiYQoE91aSAghMlZYWIgpU6Zg165d+PbBsF++fMGXL1/w6tUrHD9+HEuXLsW+ffvg6uqqpGgJoWSAEEJkbsSIEQgPD2fXmzdvDldXV9SrVw/Z2dmIj4/HhQsXwDAMUlJS0Lt3b9y8eZMeKUyUhpIBQgiRofPnz7OJgIaGBnbs2AE/Pz9oaAhelY2Li0P//v3x8uVL5OfnY968ebh8+bIyQiaExgwQQogsHT16lF0ePXo0xo0bVyYRAABHR0ds376dXb969Sry8/MVEiMh36JkgCjdo0ePMGPGDLRr1w4mJibQ0tKCvr4+6tSpg65du2LhwoW4cuUKCgsLhe7P5XJx5coVLFiwAN27d0fdunWhp6cHfX191KtXD3369MGWLVuQl5dXbiy+vr7s6PurV68CAP79919MnToVzZo1g6GhISwsLODm5obQ0FDweDyB/b9+/YqdO3fC1dUV1tbW0NPTQ6NGjTBjxgx8+PBBbN3BwcFs3UuXLgUApKWlYcWKFWjbti1q164NAwMDNG3aFNOmTcOzZ8/Kf3MrKCEhAQsWLEDHjh1haWkJHR0dmJubo3PnzggICCj3NZSS9jOtyvifrtmlSxexZV1cXNhlHo+HrKwsucVFiFgMId+IjY1lADCxsbFyrYfH4zG//PILo6GhwQAo9+fUqVNCj1OvXj2J9re2tmZu3rwpNiYfHx+2fGRkJPPPP/8wurq6Io85YsQIhsvlMgzDMP/99x/TpEkTkWXNzc2ZuLg4kXUHBQWxZQMCApjr168zVlZWIo+nq6vLbNu2TezrcXNzY8snJiaKLFdYWMhMmzaN0dTUFPseGhgYMLt37xZ5HFl9plVZ//792de3atUqsWVfv34t8N4WFhbKPT5FtW9StdCYAaI0W7ZswZo1a9j1Ll26oHPnzjA3NweXy0VGRgbi4+MRHR0t9qw+PT0dAGBnZ4f27dujUaNGMDY2RkFBAV68eIGIiAhkZGTg3bt38PDwQExMDJo3b15ufGfOnMH69euhoaEBb29vtG/fHlpaWrh9+zbOnj0LADhw4ABatGiB8ePHo2fPnnjz5g2aN28OLy8vmJubIyUlBaGhocjKykJ6ejqGDBmChw8fQltbW2zdr1+/Rr9+/ZCZmYnGjRujT58+sLCwwJs3b3Dy5EmkpqaioKAAkyZNgo6ODvz8/CR5y4UqKiqCl5cXe71aU1MT7u7uaNu2LWrVqoXMzExERUXhzp07yMvLw9ixY1FYWAh/f/8yx5LVZ1qVubq64vjx4wCAXbt2YcqUKahZs6bQsuvWrWOXhw8fXu7fBSFyo+xshKgeRZ052NjYMAAYbW1t5vz58yLL5efnM2FhYczjx4+Fbp89e7bYM+78/Hxmzpw57BlYz549RZbl7xkAwNStW5e5f/9+mXIHDhxgy5iYmDDe3t4Mh8Nh1q9fz/B4PIGyb968YV8rAObgwYNC6+bvGeBwOAwAZtGiRUxxcbFAuby8PMbPz48ta2hoyCQlJQk9piQ9AzNmzGDLuLq6iix35swZxtjYmO2VePbsWZkysvpMJRUQECBRD4SkP25ublLFwzAM8+nTJ4HPu1GjRszOnTuZFy9eMF+/fmXevXvHXLp0ienbty9bpmPHjkxWVpbUdUuCegaIMJQMkDIU8WWRkZHBfhH27dtXbvXw+/7779k6X7x4IbQMfzKgqakp9j3o2bOnwD+SuXPniiy7d+9ettzgwYOFluFPBgAwI0eOFHk8LpfLdO3alS07depUoeXKSwaeP3/Oduk7OTkxeXl5IutkGIYJDw9njzdhwgSBbcr4TFUxGWAYhnn58iXj6OhYbn02NjbM0qVLmYKCApnUKwlKBogwdJmAKAWXy2WXFdVdPGLECJw6dQoAcP36dTRq1Ehs+X79+om977tv3764dOkSAEBHRwfz588XWfb7779nlx88eFBurBwOB6tWrRK5XUNDA6tWrWInqtm3bx82bdokdNS6OIGBgewgyBUrVkBfX19s+R9//BGNGzfGixcvcPLkSezYsYPdpozP1MPDA4aGhjI7Xv369WVyHDs7O9y7dw9hYWFYsmSJ0KmgNTQ00K5dO3Tu3Bk6OjoyqZeQyqJkgCiFhYUFrK2t8e7dO1y5cgUbNmzA1KlTpf5STElJwePHj/H+/Xt8+fIFxcXF7LaXL1+yywkJCeUeq3fv3mK329nZscvt2rVD7dq1RZatVasWTE1NkZmZiffv35dbd/v27dGgQQOxZVxcXGBhYYG0tDR8+vQJcXFxcHJyKvfY/EqTGQ0NDfTq1UuifVq3bo0XL17gw4cPSElJgY2NDQD5fabiODs7w9nZWW7Hl0ZQUBC2bt2KpKQkNG7cGL1790bdunXx9etXPHz4EBERETh27BiOHTuGwYMHY+/evdDT01N22ERNUTJAlGb+/PmYMWMGAGDOnDlYtWoVevfuDRcXF3Ts2BFt2rSBpqZmucfh8XjYuXMnNm/ejPj4eInqluQWrtJ/cqLwn5GWV7a0fGZmJnJzc8st27p163LLcDgcODk5sf/QExISKpQMcLlc9v3i8XgwMDCQeN9SaWlpAq9dVp9pVfb161eMHj0a4eHh4HA42LhxI6ZNm1am1yYxMREDBw7EgwcPEBYWBi0tLRw4cEBJURN1R/MMEKWZPn06Vq9ezXZNZ2Zm4uDBg5gyZQo6dOgAExMTDBkyBBcvXhR5jIKCAvzwww/w9/eXOBEAINHkLuWdpXE4HInL8pdnvpmrXhhxvQz8zMzM2OXMzEyJ9imVlZVVZp6Eivo2sZHFZ1rVzZs3j52BcN68eZgxY4bQyzcNGzbE2bNnYWRkBAA4ePAgbty4odBYCSlFPQNEqebPn49x48bh4MGDuHjxIm7evMmetefk5CAsLAxhYWHo27cvDh06hBo1agjsv3LlSpw5cwYAYGBggIkTJ8LT0xNNmzaFhYUF9PT02DPRyMhIuLu7K/YFqjD+Syg6OjpixyiIwn+ppJS0n2lFREdHIzo6utL7f6t+/foYOnRopffPyMhgx1FwOBzMmjVLbHkrKyuMGDGCnYnwyJEjAhMREaIolAwQpTM3N8f06dMxffp0MAyD+Ph4XL16FeHh4ewsgKdPn8akSZOwb98+dj8ul4vAwEAAgJaWFiIjI9GxY0eR9WRnZ8vzZcjUx48fJSqXkZHBLpuamlaoDv7eh8LCQsycORNaWrL5SqjsZ1pRFy5cwLJly2QSMwC4ublJlQzcvn0bRUVFAEp6baysrMrdh//SjjxmlSREEnSZgKgUDocDR0dHTJ06FZGRkTh48CDbvX7gwAGBrvCEhAR2vWvXrmITAaDkwTBVxaNHjyQq9/jxY3a5WbNmFapDW1tb4I4K/mPJUkU+06ru8+fP7DL/ZSRJVfRuEEJkhf7yiEobNmwY+0+Ox+Ph+fPn7Db+QYCSnIGFhYXJPkA5iYmJwevXr8WWuXHjBtLS0gAANWvWhKOjY4Xr8fDwYJcPHjxY4f0rQ9xnWlFLly4FUzJfikx+SnstKot/DEd6erpEd47wJ2HW1tZS1U9IZVEyQKoU/hHv/N3c5Z1J79ixA0+ePJFbXLLG4/Hw66+/itzOMIzA9lGjRlXqrHLKlCnsflu3bpVoDgT+GGShMncxqKr27duzY1QYhsHmzZvFln/37p3AHQRubm5yjY8QUSgZIEoRFRUFLy8vnDp1CgUFBULLMAyDjRs34r///gNQctZlb2/Pbm/WrBksLS0BlDxZcOnSpQIT35QeY/v27Zg6dWqlum2VhcPhYO/evQgICCjzmvLz8zFhwgRcu3YNAFCjRg3MnTu3UvU4ODjgp59+AlByS1zPnj0RFhYm9h99TEwMJk+ejF9++UXg97L4TKs6U1NT9O/fn11fs2YNtm7dKvT9fPXqFby8vJCTkwOgpFfgxx9/VFSohAigAYREKXg8HiIiIhAREQEjIyN06tQJjo6OqF27NrhcLt69e4eIiAiBx8GuWrVKYIAbh8PB/Pnz2RHby5YtQ2hoKHr27Alra2ukpaUhIiICCQkJ0NTUxMKFCys1Yl4ZfH19ceLECSxfvhwHDx5E3759YW5ujtTUVJw4cQJv3rxhy27cuBG2traVruvPP//Eq1evcPbsWWRmZmLIkCFo2LAh3N3dYWNjA21tbWRnZ+PZs2e4d+8eUlNTAZT0KvCTxWdaHaxfvx7R0dF49+4deDwepk6dik2bNqF3796oU6cO8vPz8eDBA0RERLCDDbW0tLBz585q1UtCqhhFzHlMqhZFzF1+48YNieeL19fXZzZt2iT0ODwej/H39xe7v6GhIbN//34mMjKS/Z2Pj4/Q4337CGNxJDkevwYNGrDlhfn2EcZRUVGMhYWFyNelq6vLbN26VWydkj7CmMvlMkuWLGH09fUl+kx0dXXLfCay+kyrg4SEBKZDhw4SvRdWVlbM2bNnFRYbPZuACFO9UnJSZXz33XdISUlBREQEbt68icePHyMlJQWfP38Gh8OBqakp7O3t0aNHD/j6+qJu3bpCj8PhcLBt2zb0798f27Ztw507d/Dx40cYGxujfv366NOnD8aPHw9bW1upB4cpWteuXfHo0SNs27YNJ06cQFJSEgoKClC3bl14eHhg+vTpFb6DQBQNDQ0sW7YMU6ZMQXBwMK5cuYL4+Hh8/PgRxcXFMDY2RsOGDeHk5AR3d3d4e3vDxMRE4Biy+kyrg6ZNm+L27du4cOECwsLCcO/ePbx+/RpfvnyBrq4uzM3N0aZNG3h7e2PEiBHUI0CUjsMwMhoFRKqN+/fvo127doiNjRX7oB4iW8HBwfDz8wMABAQEYOnSpcoNiFRL1L6JMDSAkBBCCFFzlAwQQgghao6SAUIIIUTNUTJACCGEqDlKBgghhBA1R8kAIYQQouZongFCVISvry98fX2VHQYhRA1RzwAhhBCi5igZIIQQQtQcJQOEEEKImqNkgBBCCFFzlAwQtXL16lVwOBxwOBwarKcktra27GfA/9O6dWtlh6ZSgoODhb5PHA6nyj10i6g+upuAEKLyGIbBvXv3cPnyZdy6dQvx8fH48OEDCgsLUatWLTRp0gQuLi7w9fWFvb29ssMFAOTm5iIsLAwnTpzAkydP8P79ewCAlZUVbGxs0LVrV7i7u8PNzU3JkRJCyQAhRInWrVvHLltYWAgtc/LkSfz0009ITU0Vuj09PR3p6emIjo7GunXrMGHCBGzYsAE1atSQS8ySOHLkCGbNmoU3b96U2fby5Uu8fPkSkZGRWL9+Pb58+SL0GB06dBB4f0JDQxETEyO3mIl6o2SAEKI0c+fOLbfMv//+K5AINGzYEN999x0aNGgAAwMDvH79GufOnUNycjIYhsGOHTuQkJCA8+fPQ1dXV57hC7Vu3Tr8/PPP7HrTpk3h5uaGunXrAgDevXuHlJQU3LhxAzweT+RxHBwc4ODgwK7HxcVRMkDkhpIBQojKMzQ0xPjx4+Hn5wcnJ6cy27lcLlatWoWAgAAAwLVr17BmzRosWbJEoXGGhISwiUCdOnWwc+dOeHl5CS1bVFSEu3fvKjI8QkSiAYSEEJXm7e2NpKQk/Pnnn0ITAQDQ1NTEkiVLMGnSJPZ3W7duFXvmLWtv377FtGnTAADm5ua4ffu2yEQAALS1tfHdd98pKjxCxKJkgMhU8+bN2RHPkp71JCUlQUNDAxwOB+bm5igqKipT5vHjx1izZg2+//57NGrUCDVq1ICOjg4sLCzg4uKCxYsXi7ymXFG+vr4VGrXdrVs3tnxSUlK55WNiYjBr1iy0bt0aZmZm0NHRgaWlJbp164a1a9fi8+fP0r+IasTJyQm1a9eWqOyMGTPY5bS0NLx48UJeYZWxfv16ZGdnAwD++OMP1K9fX2F1EyItSgaITI0ePZpd3rdvn0T7hISEgGEYAMCwYcOgra1d5pitWrXC/Pnzcfr0abx69Qp5eXkoKipCeno6bt68iZUrV8LOzg7btm2T3YuRsZycHAwbNgwdOnTAxo0b8ejRI3z8+BFFRUVIS0vDtWvX8Msvv6Bhw4Y4d+6cssOtkho2bCiw/vHjR4XUm5+fj6CgIABArVq1MGzYMIXUS4is0JgBIlOjR4/G4sWLwTAMDh06hA0bNpT55/4t/qRhzJgxZbanp6cDAExNTdGlSxc0b94cpqam4PF4SE1NRWRkJBISElBYWIjJkyfD0NAQo0aNku0Lk1J2djZcXV3x5MkTAICuri48PDzQsmVLGBkZIS0tDRcvXkRcXBwyMzPx/fff4+TJk/D29lZy5FXLt71D5ubmCqn39u3byMrKAgB07twZOjo6SElJQWBgIE6dOoXk5GRoaGjA2toarq6uGDduHDp37qyQ2AiRBCUDRKZsbGzg5uaGq1evIiMjA+fOncMPP/wgsvydO3fw7NkzAIC9vT06dOhQpkyvXr0wb948dOvWDZqamkKPExISgvHjx6OgoADTpk3DgAEDlHpr2bd8fX3ZRGDgwIH4+++/hf6jCg4OxsSJE1FUVITRo0fj5cuXqFWrVqXrXbp0KZYtW1bp/b9V+tmqqsOHD7PLderUQaNGjRRS761bt9jlJk2aIDQ0FBMmTEBOTo5AuZycHDx79gw7d+6Ej48Ptm/frpQ7Hgj5Fl0mIDLHf3a/d+9esWX5t/NfYuA3Z84c9OjRQ2QiAACjRo3Cb7/9BqDkLDw0NLQiIcvVlStXcOLECQCAp6cnDh8+LPKM1dfXF7///jsAIDMzE4GBgQqLs6p7//491q5dy65PnDgRHA5HIXU/f/6cXY6JicGoUaOQk5MDKysr+Pv7Y9WqVZg9e7bAhEh79uzBwIED2UtkhCgT9QwQmRs0aBCmTp2KvLw8nD59GtnZ2ULPbouKith/2hoaGlJ37Y8YMQJz5swBAERFRWHs2LFSHU9WtmzZwi6vWbMGGhric/ApU6ZgyZIlyM3NxYkTJ7Bw4cJK1+3h4QFDQ8NK7/8tVR0Ux+VyMWbMGLar3tbWFvPmzVNY/aX1Av/rJRgyZAh2794t0EO1bt06rF69Gr/++isA4MyZM9i2bRsmT56ssFgJEYaSASJzRkZG6N+/Pw4cOICCggKEhobC39+/TLkzZ86wA7y6desm0T+azMxMPHr0CMnJycjJyUFBQYHQcgkJCdK9CBnh8XiIjIwEUDINrahb4/jp6urC3t4eMTExuH//Png8XrkJhCjOzs5wdnau1L5VyfTp03Hx4kUAgI6ODsLCwmBgYKCw+r+9HODo6IiQkJAy42U0NDSwaNEiPHv2jO0V+/333+Hv71/pz5gQWaBkgMjFmDFjcODAAQAlAwSFJQPlDRzkFxkZiRUrVuDatWsS3TvOf6amTMnJyeztZu/fv69wt3VxcTEyMzNhZmYmh+iqh19//ZW9nKKhoYGgoCC0b99eoTHo6+sLrM+bN0/swNmFCxeyyUBKSgqePHmCVq1ayTVGQsShVJTIRc+ePWFtbQ0AuHnzJl69eiWwPSsrC6dPnwYA1KhRAwMHDhR5rJUrV8Ld3R2RkZESTyKTn59fychlSxa3tuXm5sogkuopICAAq1atAlCSCOzevRsjRoxQeBxGRkYC6+7u7mLLN2vWDHXq1GHXY2Nj5RIXIZKingEiF5qamhg5ciT++OMPACW9AKVTxQIlD10pLCwEAAwYMEDkde3Lly9j8eLF7PrgwYMxePBgODk5wcrKCgYGBuwZGMMwKtfVWlxczC5bWlpKNBf/t0xMTCpdf3R0NKKjoyu9/7fq16+PoUOHyux40li8eDFWrlwJ4H89AuX1MMlLaeILABwOR+AfvSj16tXD27dvAQAZGRlyi40QSVAyQORmzJgxbDIQEhIikAxIeolg8+bN7PL69esxe/ZskWVLu+Olxd+VL8lI77y8PJHb+GfO09bWrlQyII0LFy7I/NZCVUgGFixYwN51oampieDgYKXOLeHo6Mgul85GWR7+Moq664EQUVTrNIpUKy1btkTr1q0BAC9evGDPUF++fMku161bFz169BB5jNKR2Xp6epgyZYrY+uLi4mQQNQR6KUQ9XpZfSkqKyG2lT9YDSibEUdSMeNXZzz//LJAI7Nu3T+mTTPGPUeDxeOwZvzj8jzcW9fhmQhSFkgEiV/xn/aW9Afy9AiNHjhTbtV86ENDExKTcyVn4J5yRhqWlJbtcOiGSKA8ePMCHDx9EbtfR0YGbmxsAsLMyKtLSpUvBMIzMfpQ94dCcOXOwbt06AICWlhYOHjyI4cOHKzUmoCTxbdKkCbt++fJlseWfPXsmMFtix44d5RYbIZKgZIDI1YgRI6ClVXI1KjQ0FAUFBRW6i6C0m/3Dhw94//69yHKPHz/GP//8I4OIgbZt27LLR48eFVmOYRgsWrSo3OOVPskOAJYvX47Xr19LHAtNSPM/M2fOxIYNGwCUXHI5fPgwBg8erOSo/sfPz49d/uOPP4Q+cKtU6aBHoGTmTf7JiAhRBkoGiFxZWlrCw8MDQMlZ/oIFC9g7C9q2bQsHBwex+5eeVfN4PIwfPx6fPn0qU+bGjRvo3bs3OyBRWu7u7uygvejoaGzatKlMmby8PEyYMEGiBwp5eXmhb9++AEqepOfi4iL2zJHH4yEqKgojR44UGDOhzqZPn85+Drq6uggPD8eAAQOkOib/0yZ9fX2ljnHmzJnsXBlPnjzB6NGjy9wJwuPx8NtvvwnMvCnLMR2EVBYNICRyN2bMGJw9exYAsHHjRoHfl2fevHkIDw8Hl8vFmTNn0KhRI/Tr1w8NGzZEbm4uoqOjERUVBaDkNjNZfLHq6elhwYIF+PnnnwGUfMkfPnwY7u7u0NPTQ2JiIk6dOoW0tDS4u7ujoKAAN2/eFHvM/fv3w93dHbGxsUhJSUHPnj3RokULuLm5oU6dOuBwOMjKysLTp09x9+5ddnR5mzZtpH49Vd3q1avx119/sevdu3dHQkKCRBNLDR06VGGzJurr6+PQoUPo1asX8vLyEBoaimvXrmHAgAGoX78+Pn78iLNnz+Lp06fsPhMnTlSp3g2ixhhCvhEbG8sAYGJjY2VyvK9fvzI1a9ZkALA/WlpaTFpamkT7BwcHMzo6OgL78/9wOBxm9uzZDI/HY3/XoEEDoceKjIxky/j4+Iiss7i4mBkzZozIOgEwHh4eTGZmJuPm5sb+LjExUez7MHnyZEZLS0vscUt/DA0NmbCwMIneo6qkQYMG7GuUhI+Pj0Tvl7CfyMhIkcd1dXVly40dO1ZGr45hrl27xtja2oqNS0tLi/n1118ZHo8n8XH53wdxr6s8sm7fpHqgywRE7vT09Mqc/Xh6ekr8eFkfHx88ePAA48ePh52dHXR1dWFkZISmTZtiwoQJuHnzJtavXy/T27M0NTWxZ88eHDt2DH369IGFhQW0tbVhbW0NDw8P7N+/HxERERWaA0BPTw+BgYF4/vw5li5dim7duqFOnTrQ1dWFrq4uLCws0KVLF0yePBnh4eH48OEDBg0aJLPXRP6HYRj8+++/7LosByG6uroiLi4OgYGB6NmzJ+rWrQsdHR3UqlULrVu3xpw5c/D06VOsWLGCbikkKoPDMDRCiQi6f/8+2rVrh9jYWIHBdITIgq2tLZKTkwEob4DkkydP2OdEuLi44Pr160qJoyJ8fX2xZ88eACXTc3fr1q1Sx6H2TYShngFCiNrhv0WSBvARQgMICSFKVDpDJVAy8Y6iphMuTQZcXV3LfY6AssTHxwvcrRIfH6/EaEh1R8kAIURp5s2bxy63atVKIckAwzDsHSiq3Ctw7949gfeHEHmiZIAQolY4HA7S09OVHQYhKoWSAUKIQiUlJSk7hCrB19dXJpMhESIJGkBICCGEqDlKBgghhBA1R8kAIYQQouYoGSCEEELUHCUDhBBCiJqjZIAQQghRc5QMEEIIIWqOkgFCCCFEzVEyQAghhKg5SgYIIYQQNUfJACGEEKLmKBkghBBC1Bw9qIiI9PTpU2WHQAiRMWrXRBhKBkgZZmZmMDAwwKhRo5QdCiFEDgwMDGBmZqbsMIgK4TAMwyg7CKJ6UlJSkJGRoewwlC4zMxODBw9G27ZtsXbtWnA4HGWHRCqAYRjMmzcPDx48QFhYGExNTZUdkkowMzODjY2NssMgKoSSAUJEYBgGgwcPxrVr1xAfHw8LCwtlh0Qq4cOHD3BwcEC3bt0QFhZGCR0hQtAAQkJECA0NRXh4OAIDAykRqMIsLS0RGBiI8PBwHD58WNnhEKKSqGeAECHev38PBwcH9OzZE6GhocoOh8jAkCFDcPnyZcTHx8PKykrZ4RCiUigZIOQbDMNgwIABuHXrFuLj42mgVTWRnp4OBwcHODs749ixY3S5gBA+dJmAkG8cOHAAJ06cwLZt2ygRqEbMzc3x999/48SJEzhw4ICywyFEpVDPACF83r59C0dHR3h5eWH//v3KDofIwfDhw3H+/HnEx8fD2tpa2eEQohIoGSDk/zEMgx9++AExMTGIi4tD7dq1lR0SkYOPHz/CwcEBHTt2xIkTJ+hyASGgywSEsPbt24fTp09j+/btlAhUY7Vr18b27dtx6tQp7Nu3T9nhEKISqGeAEACpqalwcHDADz/8gL179yo7HKIAo0ePxqlTpxAfH4+6desqOxxClIqSAaL2GIZBnz598PDhQ8THx8PExETZIREFyMzMhKOjI9q0aYPTp0/T5QKi1ugyAVF7QUFBOHfuHP755x9KBNSIqakpduzYgbNnzyI4OFjZ4RCiVNQzQNTa69ev4ejoiB9//BFBQUHKDocoga+vL44dO4a4uDjUr19f2eEQohSUDBC1xTAMPD09ER8fj7i4ONSqVUvZIRElyM7OhoODA1q2bIlz587R5QKilugyAVFbO3fuxIULF7Bz505KBNRYrVq1sHPnTpw/fx67du1SdjiEKAX1DBC1lJycDEdHRwwbNgz//POPssMhKmD8+PE4fPgwnjx5ggYNGig7HEIUipIBonYYhkGvXr3w7NkzxMXFwdjYWNkhERXw6dMnODo6olmzZrh48SJdLiBqhS4TELWzfft2XL58Gbt27aJEgLBq1qyJXbt24fLly9i+fbuywyFEoSgZINUKj8fDkiVL8PbtW6HbExMTMXfuXPj7+6NXr14Kjo6oOg8PD0ycOBFz585FYmKi0DKpqalYsmQJqFOVVCd0mYBUK69fv4aNjQ3OnDkDb29vgW08Hg89evRAYmIinjx5AiMjIyVFSVRZTk4OWrZsCTs7O1y6dAkaGoLnTGfOnEHfvn3x+vVr1KtXT0lREiJb1DNAqpXSszlbW9sy2wIDA3H16lXs3r2bEgEikpGREXbt2oXIyEj8/fffZbaX/m2J6jkgpCqiZIBUK0lJSQDKJgMvX77EL7/8gp9++gnu7u6KD4xUKT169MDkyZPx888/4+XLlwLbSv+2Sv/WCKkOKBkg1UpiYiIsLS1hYGDA/o7H48HPzw+WlpZYs2aNEqMjVcnatWthYWGBsWPHgsfjsb+vUaMGLCwsqGeAVCuUDJBqJTExsUyvwF9//YXr168jKCgIhoaGygmMVDmGhoYICgpCVFQUtmzZIrDN1taWkgFSrVAyQKqVxMRENGzYkF1/9uwZFixYgOnTp8PNzU2JkZGqqFu3bpg2bRrmz5+P58+fs79v2LAhJQOkWqFkgFQr/MkAl8uFn58f6tati99++61M2efPn+Pw4cOKDpGoqMOHDwv8wy+1evVq1KlTB35+fuByuQAoGSDVDyUDpNooLCxEamoqmwxs2rQJt27dQlBQEGrUqMGWy8rKwuzZs+Hg4IDly5cLXA8m6onH42H58uVwcHDAnDlzkJ2dzW6rUaMGgoKCEB0djU2bNgEoSQbevHmDoqIiJUVMiGxRMkCqjdevX4PH46Fhw4b477//sGjRIsycORMuLi4AgKKiImzZsgVNmjTBjh07EBAQgHv37pW5j5yoHw0NDdy9exdLlizB9u3b0bhxY2zduhXFxcUAgK5du2LGjBlYtGgREhIS0LBhQ/B4PLx+/VrJkRMiIwwh1cTFixcZAExCQgLTqVMnpmnTpkxubi7DMAxz9uxZxt7enuFwOIyfnx/z9u1bJUdLVFVqairj5+fHcDgcxt7enjl37hzDMAyTm5vLNGnShOncuTPz33//MQCYS5cuKTlaQmSDTolItZGYmAgOh4MjR47g3r17CA4ORmJiIjw9PeHt7Q0LCwvExMRg9+7dsLa2Vna4REXVqVMHu3fvRkxMDMzNzeHl5QUvLy8kJSUhODgYd+7cwdGjR8HhcGjcAKk2KBkg1UbpHAPLli3DpEmTEBISglatWuH58+cIDw9HZGQk2rZtq+wwSRXRtm1bXL16FeHh4Xj27BmcnJxw4MABTJo0CcuWLYOlpSUlA6Ta0FJ2AITISmJiInJyclCrVi3s378fDMPg999/x7Rp06Crq6vs8EgVxOFw8OOPP6JPnz7YvHkzVq5cCQ6HA2NjY+Tk5ODVq1fKDpEQmaAHFZFqw8bGBq9fv4aGhgb8/f2xbNkymJubKzssUo2kpaUhICAAO3bsAI/Hg42NDZKTk5UdFiFSo2SAVBv16tWDgYEBjh49CkdHR2WHQ6qxuLg4/Pjjj8jLy8ObN2+UHQ4hUqNkgFQbDMOAw+EoOwyiRuhvjlQXlAwQQgghak7iAYQpKSnIyMiQZyyEECUpKCigQZaEVFNmZmawsbERW0aiZCAlJQX29vbIy8uTSWCEENWiqanJzrtPCKleDAwM8PTpU7EJgUTJQEZGBvLy8hASEgJ7e3uZBUgIUb6zZ89i8eLF1L4JqYaePn2KUaNGISMjQ/pkoJS9vT1N2kJINfP06VMA1L4JUWc0AyEhhBCi5igZIIQQQtQcJQOEEEKImqNkgBBCCFFzlAwQQgghao6SAUIIIUTNUTJACCGEqDlKBgghhBA1R8kAIYQQouYoGSCEEELUHCUDhBBCiJqjZKCSfH19weFwwOFwcPXqVYXUGRwczNa5dOlShdRJiDqi9k3UDSUDRGUlJydjyZIlaNu2LczMzKCvr4+GDRti4MCBOHLkCBiGkVvd8fHxmDNnDhwdHWFiYoIaNWqgcePGGDNmDC5evCi3eglRF9S+VUuFnlpIiKLs2LEDs2fPRm5ursDvk5KSkJSUhKNHj6J79+44cOAArKysZFr3smXLsHLlShQXFwv8/uXLl3j58iX27duHQYMGISgoCIaGhjKtmxB1QO1b9VAyQFTOzp074e/vz67b2dmhX79+MDExQUJCAo4ePYqvX78iMjISnp6eiIqKgrGxsUzqXrx4MVauXMmuOzk5wdPTEwYGBnj06BFOnTqF4uJiHDlyBJ8+fcKZM2egra0tk7oJUQfUvlUUI4HY2FgGABMbGytJcUIq7dWrV4yuri4DgAHA/PTTT0xxcbFAmcTERKZZs2YCZWTh5s2b7DEBMKtXry5T5v79+4ylpSVbZu3atTKpW5lCQkKofROFoPateJL+/6ZkgKiUUaNGsQ3RxcWF4XK5Qss9efKE0dbWZgAwWlpazPPnz6Wu28XFha17xIgRIsudP3+eLVezZk0mOztb6rqViZIBoijUvhVP0v/fajeA8O3bt/jll1/g6OgIIyMj1KxZEy1btsT8+fORnJwMQLJRvZKMNra1tWXLlLp9+zZ8fHzQqFEj6Ovrw9TUFM7Ozvjzzz9RUFAgNvbqPto4NzcX4eHh7PrixYuhoSH8T9TR0REDBw4EABQXF2P//v1S1f3q1SvcuHEDAMDhcLBkyRKRZT08PNC5c2cAwKdPn3D8+HGp6iayQ+1bdVH7Vm1qlQwcP34c9vb2WLt2LeLj4/Hlyxd8/vwZcXFxWLNmDZycnOT2wTMMg/nz58PZ2Rl79+7Fq1evkJ+fj6ysLNy6dQuzZ89Gx44dkZaWJpf6q4ILFy7g69evAAATExP07NlTbPnBgwezy8eOHZOq7hMnTrDLTk5OaNasmcLqJrJB7Vu1UftWbWozgPDixYsYMmQIioqKAACmpqbo378/7Ozs8PnzZ1y+fBmxsbEYNmwYJk2aJPP6ly5dijVr1kBPTw/e3t5wdHSElpYW7t+/j5MnT4LH4+Hx48fw8fHBuXPnZF5/VRAbG8suOzs7izxrKNW1a1d2OT4+HoWFhdDR0ZG6bhcXl3LL89d9//79StVJZIfat+qj9q3a1CIZ+PLlC8aOHct+UXh6euLAgQMwMTERKBccHIzx48dj8+bNMo9h+fLl6NixI44cOYL69esLbIuKikLv3r2Rn5+PiIgI3LlzB506dZJ5DN+ytbVlu05lISgoCL6+vpXePz4+nl1u0qRJueXNzc1Rs2ZNfPr0CcXFxUhISEDLli0VUnfjxo3Z5devX+Pz588yG/FMKobat3DUvitftzq2b7W4TBAcHIw3b94AKLmN5ejRo2W+KICS64TLli2Ty2QXdevWxblz58p8UQCAq6srpk2bxq7zX1dTJ+/fv2eX69WrJ9E+/OX495d33aUTlZT68OFDpesm0qH2XTVQ+1ZtatEzEBISwi7Pnz8f+vr6IsvOmTMH69atw6dPn2Qaw5w5c2Bqaipy++DBg7Fu3ToAwIMHD2RatyiLFi2S6evs0KGDVPvn5OSwy/wNURwDAwOh+yuq7tJJU6Spm0iH2rdw1L6lr1ud2ne1Twby8/MFrvn069dPbHk9PT14enoiNDRUpnF4e3uL3c4/oEVRWeiECRMUUo+kSgcXAZD42qCenh67nJeXVyXrJpVH7Vs0at+qUXdVUe0vEyQnJ7PXEi0sLGBhYVHuPk5OTjKPo0GDBmK3GxkZsctfvnyRef1VAf8ZXWFhoUT75Ofns8v8ZxFVqW5SedS+qw5q36qt2icDWVlZ7HLt2rUl2kfSchXBn2UKw3+vMo/Hk3n9VQH/F+a3c5aLwp+x8+9fleomlUftu+qg9q3aqv1lAiLaP//8I9Nril5eXnBwcKj0/vwPJCkdEFYe/nLSPNDEysqKHWQkSd3Z2dkCXyqWlpaVrpsQeaD2LVg3tW/xqn0yUKtWLXb548ePEu0jabmqbtWqVTK99cjMzEyqLwsHBwd2Upjnz5+XWz4jI4P9stPS0ip3IpHy6n748KHEdb948YJdrl+/frW/7UhVUfsWjdq3YN3UvsWr9pcJbG1t2adOpaWlSTQD2OPHj+UdFhGibdu27PKtW7fKvQXs+vXr7HKLFi0qPSHJt3XfvHmz3PL8dbdp06bS9RLpUPuuOqh9q7Zqnwzo6ekJ/CHwT0spTOnEIOogKSkJTMnDqmTyI82EJEDJnOCl114zMzNx+fJlseWPHDnCLg8YMECquvlHoT969AjPnj1TWN2k8qh9i0bt+3+ofZev2icDADBy5Eh2+ffffxe4zeRb69evl/k9yEQyhoaG7MNJAGDFihUizx7+/fdfhIWFAQA0NTUFPuPKaNSoEb777jsAJfPMr1ixQmTZS5cuITo6GgBgbGyM/v37S1U3kQ6176qB2rdqU4tkwNfXF3Xr1gVQ8vSqgQMHIjs7u0y5vXv3IiAgQGDkL1Gs5cuXs92BUVFRmDFjBrhcrkCZ5ORkDBo0iL2lbMKECWKnGC19EhyHw0FwcLDIcr///ju7HBISwk4Sw+/Ro0cYM2YMu75w4UKB69ZE8ah9Vx3UvlVXtR9ACJTcFrJr1y707dsXxcXFOHfuHBo1aoQBAwagYcOGyMnJweXLlxETEwNdXV34+/uz85fTF4di2dnZYfPmzezDZP766y+cPXsW/fr1g4mJCRISEnD06FH2th9HR0esXr1aJnW7uLhg/vz57JfGzz//jP3798PT0xMGBgZ49OgRTp06xX5J9ejRA7NmzZJJ3aTyqH1XHdS+VZdaJAMA0Lt3bxw6dAh+fn7IyclBZmYmdu3aJVDGyMgIe/bsQWZmJvs7cVObEvnw9/cHj8fD3LlzkZeXh5cvX2LDhg1lyrm6uuLgwYMyzdxXr14NbW1trF69GsXFxXj06BEePXpUptyPP/6I4OBgqQY1Edmh9l11UPtWTWpxmaDUwIED8fTpU8ybNw8tWrRAjRo1YGRkhBYtWmDu3Ll4+PAhBgwYIHBNsWbNmkqMWH1NnjwZ8fHxWLRoEVq3bg1TU1Po6urCxsYGAwYMwOHDh3H16lXUqVNH5nUvX74cDx48wMyZM9GiRQvUrFkT+vr6sLOzw8iRIxEREYHw8HC1mIikKqH2XXVQ+1ZBjARiY2MZAExsbKwkxau8cePGMQAYAExERISywyFErkJCQqh9E1JNSfr/W616BiRRXFwscOtRu3btlBgNIUSWqH0TIhwlA98IDAxEamoqgJIBJ2ZmZkqOiBAiK9S+CRFObZKBrKwsTJ06FYmJiUK3c7lcBAYGYu7cuezvZs6cqaDoCCHSoPZNiHTU5m4CLpeLrVu3IjAwEO3atUPHjh1Rp04d8Hg8pKSk4OLFiwLzeA8dOlRgggxCiOqi9k2IdNQmGSjFMAxiYmIQExMjdDuHw8GECROwZcsWBUdGCJEWtW9CKkdtkoHatWsjKioKZ86cwd27d/H27Vukp6fj8+fPqFmzJmxsbODm5gY/Pz84OTkpO1xCSAVQ+yZEOmqTDHA4HHTt2hVdu3ZVdiiEEBmj9k2IdNRmACEhhBBChKNkgBBCCFFzlAwQQgghao6SAUIIIUTNUTKgZrp168Y++zspKUnZ4RBCZIjaN6kstbmbgBBlSk1NxYULF3Dt2jU8efIEiYmJyMnJQY0aNWBtbY327dtj0KBB6Nu3LzQ1NZUdLiFECtevX8eRI0dw9epVvH37Fp8/f4aZmRmsrKzQvn17dOvWDd7e3ir11ExKBgiRo8+fP8PLywu3bt0CwzBltn/69AmfPn3Cf//9h5CQELRs2RJ79uxBmzZtlBAtIUQaiYmJmDJlCs6dO1dm29u3b/H27Vvcv38fO3bsQFhYGAYNGqSEKIWjZIAQOcrLy0N0dDS7bmRkBGdnZzg6OqJ27dr4/Pkzbt26hWvXrgEAnjx5Ajc3N1y+fBkdOnRQVtiEkAqKi4tDz5498eHDBwBAzZo14e7ujmbNmsHIyAjp6el4//497t27h5cvXyo52rIoGSBEATw8PODv74++fftCR0enzPbo6GgMHDgQ79+/R05ODkaPHo0nT55AW1tbCdESQioiIyODTQQ4HA4WLlyI+fPnw9DQUGj558+fw8DAQMFRikfJACFyZGRkhDt37qBjx45iyzk7O+PYsWNwdnYGwzBISEjA+fPn0bdvXwVFSgiprJ9++ontEQgMDMSkSZPElm/SpIkiwqoQlbyb4NGjR5gxYwbatWsHExMTaGlpQV9fH3Xq1EHXrl2xcOFCXLlyBYWFhUL353K5uHLlChYsWIDu3bujbt260NPTg76+PurVq4c+ffpgy5YtyMvLKzcWX19fdnTu1atXAQD//vsvpk6dimbNmsHQ0BAWFhZwc3NDaGgoeDyewP5fv37Fzp074erqCmtra+jp6aFRo0aYMWMG+8cjSnBwMFv30qVLAQBpaWlYsWIF2rZti9q1a8PAwABNmzbFtGnT8OzZs/Lf3ApKSEjAggUL0LFjR1haWkJHRwfm5ubo3LkzAgICyn0NpaT9TKuqGjVqlJsIlOrcubNAWf7LC9UJte8S1L6rhydPniAsLAwA0L1793ITAZXFSCA2NpYBwMTGxkpSvNJ4PB7zyy+/MBoaGgyAcn9OnTol9Dj16tWTaH9ra2vm5s2bYmPy8fFhy0dGRjL//PMPo6urK/KYI0aMYLhcLsMwDPPff/8xTZo0EVnW3NyciYuLE1l3UFAQWzYgIIC5fv06Y2VlJfJ4urq6zLZt28S+Hjc3N7Z8YmKiyHKFhYXMtGnTGE1NTbHvoYGBAbN7926Rx5HVZ6ouhg0bxr4XEydOVEidISEh1L6pfVP7rqRJkyaxr+/w4cPKDqcMSf9/q9Rlgi1btmDNmjXsepcuXdC5c2eYm5uDy+UiIyMD8fHxiI6OFpv1p6enAwDs7OzQvn17NGrUCMbGxigoKMCLFy8QERGBjIwMvHv3Dh4eHoiJiUHz5s3Lje/MmTNYv349NDQ04O3tjfbt20NLSwu3b9/G2bNnAQAHDhxAixYtMH78ePTs2RNv3rxB8+bN4eXlBXNzc6SkpCA0NBRZWVlIT0/HkCFD8PDhw3KvDb9+/Rr9+vVDZmYmGjdujD59+sDCwgJv3rzByZMnkZqaioKCAkyaNAk6Ojrw8/OT5C0XqqioCF5eXrh8+TIAQFNTE+7u7mjbti1q1aqFzMxMREVF4c6dO8jLy8PYsWNRWFgIf3//MseS1WeqLlJTU9llc3NzJUYie9S+RaP2XXXx3zng6uqK4uJi7NmzByEhIfj333+RnZ0NMzMztG7dGgMGDICPj49qjgWSZWYhLRsbGwYAo62tzZw/f15kufz8fCYsLIx5/Pix0O2zZ88Wm5Hn5+czc+bMYbO5nj17iizLf+YAgKlbty5z//79MuUOHDjAljExMWG8vb0ZDofDrF+/nuHxeAJl37x5w75WAMzBgweF1s1/5sDhcBgAzKJFi5ji4mKBcnl5eYyfnx9b1tDQkElKShJ6TEnOHGbMmMGWcXV1FVnuzJkzjLGxMXvW8uzZszJlZPWZSiogIECiMxRJf9zc3KSKpyLevHkjcKYWERGhkHoV1TNA7VsQte+KU7X2/fbtW/ZYRkZGTEpKCtOuXTuxdTZu3Jh58uSJVPVWhKT/v1UmGcjIyGDfrL59+8qtHn7ff/89W+eLFy+EluH/stDU1BT7HvTs2VPgQ587d67Isnv37mXLDR48WGgZ/i8LAMzIkSNFHo/L5TJdu3Zly06dOlVoufK+LJ4/f852+Tk5OTF5eXki62QYhgkPD2ePN2HCBIFtyvhMVe3LoiL4LxE0adKEKSoqUki9ikgGqH2XRe274lStfV+7do09lqmpKePg4MAAYLS0tJgffviBWbp0KRMQEMD06dNH4FJKzZo1hSZX8lDlLhNwuVx2WVHdSSNGjMCpU6cAlMwY1ahRI7Hl+/Xrh7Zt24rc3rdvX1y6dAkAoKOjg/nz54ss+/3337PLDx48KDdWDoeDVatWidyuoaGBVatWwdXVFQCwb98+bNq0CRoaFRsjGhgYyA6SWrFiBfT19cWW//HHH9G4cWO8ePECJ0+exI4dO9htyvhMPTw8RN7OUxn169eX2bHE2bt3Lw4dOsSub9y4EVpaKtM8pUbtWzxq35JRtfadlZXFLmdmZiIzMxN16tTB2bNn0apVK4GyMTEx6Nu3Lz58+IBPnz5h1KhRuHPnjlT1y5LKfNtYWFjA2toa7969w5UrV7BhwwZMnTpV6D3ZFZGSkoLHjx/j/fv3+PLlC4qLi9lt/BM/JCQklHus3r17i91uZ2fHLrdr1w61a9cWWbZWrVowNTVFZmYm3r9/X27d7du3R4MGDcSWcXFxgYWFBdLS0vDp0yfExcXBycmp3GPzK/2y09DQQK9evSTap3Xr1njx4gU+fPiAlJQU2NjYAJDfZyqOs7MznJ2d5XZ8ebhx4wYmTpzIrs+aNQve3t5KjEj2qH2LR+1bMqrWvnNycgTWORwOwsLCyiQCQMlnfPDgQbi7uwMA7t69i8uXL6NHjx4KibVcsuxmkNamTZsEunBMTU2Z4cOHM1u3bmXu3btX5lqaKFwul9m+fTvbZSPJj7+/v9Bj8Xcjnjt3Tmy9V65cYcsOHTq03DhLr7dxOByh2/m7Eb/tohOFvytT2MhWcd2IxcXFEo8KFvVz7949gWPK6jOtrmJiYpiaNWsKdLcq6vJAKUWNGaD2LYjad9V35MgRgdffvXv3cvfp3LkzW37mzJlyj1HS/98qNc/A9OnTsXr1arbrKjMzEwcPHsSUKVPQoUMHmJiYYMiQIbh48aLIYxQUFOCHH36Av78/4uPjJa47Pz+/3DJ6enpit3M4HInL8pdnhMxZ/y1xZyH8zMzM2OXMzEyJ9imVlZVV5j7qisrNzRVYl8VnWl3FxsaiV69e+PTpEwDA29sbR44cqVaXB/hR+xaN2nfVZGRkJLBeetYvDn+Z2NhYmcdUWSr3rTN//nyMGzcOBw8exMWLF3Hz5k32ukxOTg7CwsIQFhaGvn374tChQ6hRo4bA/itXrsSZM2cAAAYGBpg4cSI8PT3RtGlTWFhYQE9Pj30qXGRkpEQfnrrg72LV0dERew1TFP6u1FLSfqYVER0dLdPJeurXr4+hQ4fK7HilYmJi0KtXL2RnZwMA+vTpg/DwcOjq6sq8LlVC7Vt5qH2XJW37tra2FlivV69eufvwl8nIyKh03bKmcskAUHJ/9fTp0zF9+nQwDIP4+HhcvXoV4eHh7Cxhp0+fxqRJk7Bv3z52Py6Xi8DAQACAlpYWIiMjxc7+VvpFXBV8/PhRonL8f1ympqYVqoP/7KSwsBAzZ86U2VlqZT/Tirpw4QKWLVsmk5gBwM3NTebJwJ07d9C7d2+2R6Bv374IDw+X67VWVULtuyxq35JRtfbdrFkzaGlpsYkWf++RKPxlJCmvKCp1mUAYDocDR0dHTJ06FZGRkTh48CD7Bh44cECgqywhIYFd79q1a7nTwMbFxckvcBl79OiRROUeP37MLjdr1qxCdWhrawuMuOY/lixV5DOtbm7dugUPDw82Eejfv79aJQLfovZdgtp31aSjoyMwiPPNmzfl7sNfxsLCQi5xVYbKJwPfGjZsGNsIeDwenj9/zm7jv83Dysqq3GOVziddFcTExOD169diy9y4cQNpaWkASh6f6ejoWOF6PDw82OWDBw9WeP/KEPeZVtTSpUvBlMyfIZOf0rMaWbh58yZ69+6Nz58/AwAGDRqEw4cPq20iIAy1b9Gofatm+x44cCC7XDqrozhXrlxhlyV9bokiVLlk4Fv8j4Hk7wYrL9PesWMHnjx5Ire4ZI3H4+HXX38VuZ1hGIHto0aNqvA9yAAwZcoUdr+tW7dKdI80fwyyoGqP9pSF69evw9PTk70VaejQoTh48KBqTkuqQqh9l6D2rbpGjhzJJvRXr17FrVu3RJaNjIwU2P7jjz/KPT5JqUwyEBUVBS8vL5w6dQoFBQVCyzAMg40bN+K///4DUDKy1t7ent3erFkzWFpaAih58tjSpUsFJsYoPcb27dsxdepUlbpeUx4Oh4O9e/ciICCgzGvKz8/HhAkTcO3aNQAlT8qbO3dupepxcHDATz/9BKDkiWw9e/ZEWFiY2C+CmJgYTJ48Gb/88ovA72XxmVYHpe/Dly9fAJR8ke/fv7/a3jUgDLVv8ah9V10NGjTA9OnTAZS83sGDBwu9BBMbG4vhw4ez697e3ujUqZPC4iyPynwb8Xg8REREICIiAkZGRujUqRMcHR1Ru3ZtcLlcvHv3DhEREUhOTmb3WbVqlcAXKofDwfz58zFr1iwAwLJlyxAaGoqePXvC2toaaWlpiIiIQEJCAjQ1NbFw4cJKjahVBl9fX5w4cQLLly/HwYMH0bdvX5ibmyM1NRUnTpwQuA61ceNG2NraVrquP//8E69evcLZs2eRmZmJIUOGoGHDhnB3d4eNjQ20tbWRnZ2NZ8+e4d69e+zDdaZMmSJwHFl8plXd8+fP4e3tzd6SZWFhgZYtW+LPP/8sd19HR0d4enrKO0SFoPYtHrXvqm3ZsmXsnQ6pqalo164d+vTpg7Zt24JhGMTExODcuXNsole/fn0EBQUpOepvyHLSAmncuHFD4okv9PX1mU2bNgk9Do/HY/z9/cXub2hoyOzfv5+JjIxkf+fj4yP0eN8+4lQcSY7Hr0GDBmx5Yb59xGlUVBRjYWEh8nXp6uoyW7duFVunpI845XK5zJIlSxh9fX2JPhNdXd0yn4msPtOqjP9voqI/kvwNyYIiJh2i9l0Wte/qJSsrixkwYEC570WXLl2Y1NRUhcVV5Z5N8N133yElJQURERG4efMmHj9+jJSUFHz+/BkcDgempqawt7dHjx494Ovri7p16wo9DofDwbZt29C/f39s27YNd+7cwcePH2FsbIz69eujT58+GD9+PGxtbWU6OEwRunbtikePHmHbtm04ceIEkpKSUFBQgLp168LDwwPTp0+v8AhjUTQ0NLBs2TJMmTIFwcHBuHLlCuLj4/Hx40cUFxfD2NgYDRs2hJOTE9zd3eHt7Q0TExOBY8jqMyVVH7Xv8lH7rtpq1aqFo0eP4vLlywgJCcHNmzfx7t07cLlcWFpaonPnzhg2bBj69eun7FCFk2VmQWTr2zMHQuRBUdMRE0HUvokiVMnpiAkhhBCieJQMEEIIIWqOkgFCCCFEzVEyQAghhKg5SgYIIYQQNUfJACGEEKLmVGaeAVKWr68vfH19lR0GIUQOqH0TVUI9A4QQQoiao2SAEEIIUXOUDBBCCCFqTu2TgatXr4LD4YDD4dD1OyWxtbVlPwP+n9atWys7NJUSHBws9H3icDhVbh5+RaH2rXzUviWj7Pat9skAqZoYhsHdu3exevVq/PDDD2jUqBEMDQ2ho6MDCwsLfPfdd/jll1/w9OlTZYcKAEhOTsaSJUvQtm1bmJmZQV9fHw0bNsTAgQNx5MgRsc+TJ0SdVZW2k5ubi+DgYAwYMACNGzeGoaEhDA0N0bhxY7i7uyMgIADXrl1Tdpgi0d0ERKWsW7eOXbawsBBa5uTJk/jpp5/Y56x/Kz09Henp6YiOjsa6deswYcIEbNiwATVq1JBLzOXZsWMHZs+ejdzcXIHfJyUlISkpCUePHkX37t1x4MABWFlZiTxOhw4dBN6f0NBQxMTEyC1uQmRNkvbNT1ZtR96OHDmCWbNm4c2bN2W2vXz5Ei9fvkRkZCTWr1+PL1++CD2Gsts3JQNEpcydO7fcMv/++69AItCwYUN89913aNCgAQwMDPD69WucO3cOycnJYBgGO3bsQEJCAs6fPw9dXV15hl/Gzp074e/vz67b2dmhX79+MDExQUJCAo4ePYqvX78iMjISnp6eiIqKgrGxsdBjOTg4wMHBgV2Pi4ujZIBUKZK071KybDvytG7dOvz888/setOmTeHm5sY+svndu3dISUnBjRs3wOPxRB5H2e2bkgFSJRkaGmL8+PHw8/ODk5NTme1cLherVq1CQEAAAODatWtYs2YNlixZorAYExMTMXXqVHb9p59+wubNm6Gpqcn+LikpCZ6enkhISMCjR4+wYMECbN26VWExEqKKqkrbCQkJYROBOnXqYOfOnfDy8hJatqioCHfv3lVkeBVCYwZIlePt7Y2kpCT8+eefQhMBANDU1MSSJUswadIk9ndbt24Vm5nL2pIlS1BQUAAAcHFxwV9//SXwZQaUDK46cuQItLW1AZR0i7548UJhMRKiiqpC23n79i2mTZsGADA3N8ft27dFJgIAoK2tje+++05R4VWY3JOB5s2bsyMiJc2KkpKSoKGhAQ6HA3NzcxQVFZUp8/jxY6xZswbff/89GjVqhBo1arCDx1xcXLB48WKR15QrytfXt0KjOrt168aWT0pKKrd8TEwMZs2ahdatW8PMzAw6OjqwtLREt27dsHbtWnz+/Fn6F1GNODk5oXbt2hKVnTFjBruclpamsC+L3NxchIeHs+uLFy+Ghobw5ubo6IiBAwcCAIqLi7F//36FxCgL1L6Tyi1P7btiqkrbWb9+PbKzswEAf/zxB+rXr6+wuuWCkUBsbCwDgImNjZWkuICVK1cyABgAzNSpUyXaZ8WKFWL3GTVqFLtd3I+Ojg7z999/i60rMjKSLe/j4yO0jI+PD1smMjKy3Pjd3NzY8omJiSLLff78mRk6dGi5r8PU1JQ5e/ZsufVWVQ0aNGBfq6zl5+cLvJfR0dEyr0OYo0ePsnWamJgwXC5XbPnw8HC2fKtWrSSqo6J/l6KEhIRQ+6b2LTcVbd+KaDvS+vr1K2NiYsIAYGrVqsUUFBTIvA5ZtW9J/3/LfczA6NGjsXjxYjAMg0OHDmHDhg1st44o+/btY5fHjBlTZnt6ejoAwNTUFF26dEHz5s1hamoKHo+H1NRUREZGIiEhAYWFhZg8eTIMDQ0xatQo2b4wKWVnZ8PV1RVPnjwBAOjq6sLDwwMtW7aEkZER0tLScPHiRcTFxSEzMxPff/89Tp48CW9vbyVHXrV8e/Zobm6ukHpjY2PZZWdnZ5FnNqW6du3KLsfHx6OwsBA6Ojpyi09WqH0LR+278qpC27l9+zaysrIAAJ07d4aOjg5SUlIQGBiIU6dOITk5GRoaGrC2toarqyvGjRuHzp07yzUmqckysxClW7dubIZz4sQJsWVv377NlrW3txda5o8//mAuXbrEFBcXizzOvn37GF1dXTZz+/Lli9Byyjpz6NevH1tm4MCBTFpamtByQUFBjLa2NnsGkZWVVW794gQEBEh01iXpj5ubm1TxMIx8ewZWr17NHrtOnToMj8eTeR3C9O/fn6135syZEu1Ts2ZNdp/Hjx+XW14VegYYhtq3MNS+/6ei7VsRbUdav/32G1vftGnTmEOHDjFGRkZi30sfHx8mPz9f4joU3TOgkAGE/Nn/3r17xZbl3z569GihZebMmYMePXqUGVDCb9SoUfjtt98AlGTpoaGhFQlZrq5cuYITJ04AADw9PXH48GGRZ6y+vr74/fffAQCZmZkIDAxUWJxV3fv377F27Vp2feLEieBwOAqru1S9evUk2oe/HP/+qo7atyBq39KpCm3n+fPn7HJMTAxGjRqFnJwcWFlZwd/fH6tWrcLs2bNhb2/PltuzZw8GDhyoMpMkfUshtxYOGjQIU6dORV5eHk6fPo3s7GzUqlWrTLmioiK2UWtoaEjd9TdixAjMmTMHABAVFYWxY8dKdTxZ2bJlC7u8Zs2acrvBpkyZgiVLliA3NxcnTpzAwoULK123h4cHDA0NK73/t1R10AyXy8WYMWPYrjxbW1vMmzdPYfXn5OSwy5JOdmRgYCB0f1VH7VsQtW/pVIW2U/q9AgC3bt0CAAwZMgS7d+8WiHndunVYvXo1fv31VwDAmTNnsG3bNkyePFnuMVaUQpIBIyMj9O/fHwcOHEBBQQFCQ0MFJpModebMGXz8+BFAyYhdSf4QMzMz8ejRIyQnJyMnJ4e9HeVbCQkJ0r0IGeHxeIiMjAQAWFlZibw1jp+uri7s7e0RExOD+/fvg8fjlfsFI4qzszOcnZ0rtW9VMn36dFy8eBEAoKOjg7CwMIEvDHn7+vUruyzp9Us9PT12OS8vT+YxyQu17/+h9i29qtB2vk04HB0dERISUma8jIaGBhYtWoRnz56xvWK///47/P39K/0Zy4vCJh0aM2YMDhw4AKBkAJGwL4vyBhbxi4yMxIoVK3Dt2jWJ7h3nz+SUKTk5mb0d5f379xXuti4uLkZmZibMzMzkEF318Ouvv7LdrRoaGggKCkL79u0VGoO+vj67XFhYKNE++fn57LIiExdZoPZdgtq39KpC2+GPEQDmzZsnduDswoUL2WQgJSUFT548QatWreQaY0UpLDXp2bMnrK2tAQA3b97Eq1evBLZnZWXh9OnTAEq6hkrvHRVm5cqVcHd3R2RkpMSTyPD/sShT6ZmRNL6dp5v8T0BAAFatWgWgJBHYvXs3RowYofA4jIyM2GVJPy/+Mxr+/asCat8lqH1Lryq0nW/rcHd3F1u+WbNmqFOnDrvOf8eEqlBYz4CmpiZGjhyJP/74A0DJWULpVLFAyUMZSrPAAQMGiLzudfnyZSxevJhdHzx4MAYPHgwnJydYWVnBwMCAzdAYhlG5rpji4mJ22dLSskJzdZcyMTGpdP3R0dGIjo6u9P7fql+/PoYOHSqz40lj8eLFWLlyJYD/9QiUdwYqL/wPTRH28BJh+Msp86ErlUHtuwS1b+lVhbZTmvgCAIfDEfhHL0q9evXw9u1bAEBGRobcYqsshT6bYMyYMeyXRUhIiMCXhaRdiJs3b2aX169fj9mzZ4ssW9pdJy3+rj5JRoKKu2bFP3OetrZ2pb4spHHhwgUsW7ZMZsdzc3NTiWRgwYIF7KhsTU1NBAcHK/XecwcHBxw/fhyA4MhjUTIyMvDp0ycAgJaWFpo1aybP8OSC2je1b1moCm3H0dGRXS6djbI8/GUUdVdTRSg0rW7ZsiVat24NAHjx4gWbwb58+ZJdrlu3Lnr06CHyGKUjN/X09DBlyhSx9cXFxckgagicxYh6/CS/lJQUkdtKn6wHlEyII4tuRXX3888/CyQC+/btU/okNG3btmWXb926Ve4/mevXr7PLLVq0qBITDn2L2je1b1moCm2HfwwSj8djz/jF4e+9kOTxzYqm8D42/rOC0rMF/rOGkSNHiu36Kx0oZGJiUu7jaA8fPixNqCxLS0t2+dmzZ2LLPnjwAB8+fBC5XUdHB25ubgDAztqmSEuXLgXDMDL7kWQud3maM2cO+wxwLS0tHDx4EMOHD1dqTEDJLV6lI5wzMzNx+fJlseWPHDnCLg8YMECusckTtW9q39KqCm2nZcuWaNKkCbteXozPnj0TmA21Y8eOcout0mQ5g5Ek3r9/z2hpabHzTufn5zN2dnbsTEtxcXFi97e0tGQAMBoaGsy7d+9Elnv06BE7QxkApkGDBkLLSTJD2ZkzZ9gyzs7OIuvk8XiMl5eXwKxTwmYoO3v2LLvdwsKCSUlJEfeSy9RR3VR2BsIZM2aw+2lrazNHjx6VU4SVM3LkSDY+V1dXkZ9dfHw8OwudpqYm8+zZM4mOryozEPKj9k3t+1uVad/ybjuywD8LYcuWLZnCwkKRZceMGVPuzJvfUvQMhApPBhiGYby9vdkXOWvWLHa5bdu25e47ZMgQtnyfPn2Y7OzsMmWuX7/OWFlZMRwORyZfFvwPpQDAbNy4sUyZ3NxcZty4cWWmoBQ1XWnfvn3ZMjY2NsylS5dEvmYul8tcu3aNGTFihNC6q7rKfFlMmzaN3UdXV5c5efKk1HHwTzMr6m+hIl6+fMno6OgITFv67RS7SUlJjL29PVtm0qRJEh9fFZMBhqH2zTDUvvlVpn3Lo+3Iun3n5eUx9evXZ485dOjQMtNic7lcZtWqVQJ/M4cPH5bo+NXuQUXCjBkzBmfPngUAbNy4UeD35Zk3bx7Cw8PB5XJx5swZNGrUCP369UPDhg2Rm5uL6OhoREVFASi5zUwWg2n09PSwYMEC/PzzzwCAmTNn4vDhw3B3d4eenh4SExNx6tQppKWlwd3dHQUFBbh586bYY+7fvx/u7u6IjY1FSkoKevbsiRYtWsDNzQ116tQBh8NBVlYWnj59irt377KjT9u0aSP166nqVq9ejb/++otd7969OxISEiSaeGbo0KEKm1XNzs4OmzdvxqRJkwAAf/31F86ePYt+/frBxMQECQkJOHr0KDsgzdHREatXr1ZIbPJE7Zvat7SqQtvR19fHoUOH0KtXL+Tl5SE0NBTXrl3DgAEDUL9+fXz8+BFnz57F06dP2X0mTpyIwYMHKzROickys5DU169fBR4sAYDR0tIS+TCPbwUHBwtkjd/+cDgcZvbs2QyPx5PJmQPDMExxcbFAV4+wHw8PDyYzM1PiR5x+/fqVmTx5MtutWt6PoaEhExYWJtF7VJVU9MyBP2Ou6I+4DNvV1ZUtN3bsWBm9OoYJDAxkDAwMxMbl6urKpKamVui4qtozQO37f+8DtW/pHkQmy7Yjr/Z97do1xtbWVmyMWlpazK+//lqhy0DV8kFF39LT0yuTHXl6ekr8eFkfHx88ePAA48ePh52dHXR1dWFkZISmTZtiwoQJuHnzJtavXy/T2zc0NTWxZ88eHDt2DH369IGFhQW0tbVhbW0NDw8P7N+/HxERERW6R1hPTw+BgYF4/vw5li5dim7duqFOnTrQ1dWFrq4uLCws0KVLF0yePBnh4eH48OEDBg0aJLPXRP6HYRj8+++/7LosByFOnjwZ8fHxWLRoEVq3bg1TU1Po6urCxsYGAwYMwOHDh3H16lWJ7lWuCqh9l6D2LT1ZtR15tm9XV1fExcUhMDAQPXv2RN26daGjo4NatWqhdevWmDNnDp4+fYoVK1ao5C2FLFlmFoRUhjwfYSypx48fszG4uLgoLY6KUNWeAUL4UfuuHLXoGSBE1fDfQiXLSVsIIcpH7bt8ShlASIgopTPYASUTcyhqOuHSLwtXV9dy5xlXlvj4eJw7d05gnZCqhNq3aMpu35QMEJUyb948drlVq1YK+bJgGIYdoa7KZw337t0TeH8IqWqofYum7PZNyQBRexwOB+np6coOgxAiB9S+JUPJAFG6pKQkZYdQJfj6+sLX11fZYRBSIdS+JaPs9k0DCAkhhBA1R8kAIYQQouYoGSCEEELUHCUDhBBCiJqjZIAQQghRc5QMEEIIIWqOkgFCCCFEzVEyQAghhKg5SgYIIYQQNUfJACGEEKLmKBkghBBC1BwlA4QQQoiao2SAEEIIUXMVemrh06dP5RUHIURJEhMTAVD7JqQ6krRdcxiGYcorlJKSAnt7e+Tl5UkdGCFE9WhqaoLL5So7DEKIHBgYGODp06ewsbERWUaiZAAoSQgyMjJkFhwhRHUUFBRAV1dX2WEQQuTAzMxMbCIAVCAZIIQQQkj1RAMICSGEEDVHyQAhhBCi5igZIIQQQtQcJQOEEEKImqNkgBBCCFFzlAwQQgghao6SAUIIIUTNUTJACCGEqDlKBgghhBA1R8kAIYQQouYoGSCEEELUHCUDhBBCiJqjZIAQQghRc5QMEEIIIWqOkgFCCCFEzVEyQAghhKg5SgYIIYQQNUfJACGEEKLmKBkghBBC1BwlA4QQQoiao2SAEEIIUXOUDBBCCCFqjpIBQgghRM1RMkAIIYSoOUoGCCGEEDVHyQAhhBCi5igZIIQQQtQcJQOEEEKImqNkgBBCCFFzlAwQQgghao6SAUIIIUTNUTJACCGEqDlKBgghhBA1R8kAIYQQouYoGSCEEELU3P8BPkdpAIVKYuYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test [1 2 2 1 1]\n",
      "y_pred [2 2 2 2 1]\n",
      "Accuracy score 0.6\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#first tree\n",
    "\n",
    "df_t=df_train.sample(8,replace=True)\n",
    "\n",
    "x_train=df_t.iloc[:,:-1].values\n",
    "y_train=df_t.iloc[:,-1].values\n",
    "\n",
    "\n",
    "dt1=DecisionTreeClassifier()\n",
    "bag1=evaluate(dt1,x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test [1 2 2 1 1]\n",
      "y_pred [2 1 2 2 1]\n",
      "Accuracy score 0.4\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#second tree\n",
    "\n",
    "df_t=df_train.sample(8,replace=True)\n",
    "\n",
    "x_train=df_t.iloc[:,:-1].values\n",
    "y_train=df_t.iloc[:,-1].values\n",
    "\n",
    "\n",
    "dt2=DecisionTreeClassifier()\n",
    "bag2=evaluate(dt2,x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test [1 2 2 1 1]\n",
      "y_pred [1 2 2 2 1]\n",
      "Accuracy score 0.8\n"
     ]
    }
   ],
   "source": [
    "#Third tree\n",
    "\n",
    "df_t=df_train.sample(8,replace=True)\n",
    "\n",
    "x_train=df_t.iloc[:,:-1].values\n",
    "y_train=df_t.iloc[:,-1].values\n",
    "\n",
    "\n",
    "dt3=DecisionTreeClassifier()\n",
    "bag3=evaluate(dt3,x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggrigation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction 1:  [2]\n",
      "Prediction 2:  [1]\n",
      "Prediction 3:  [2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction 1: \",bag1.predict(np.array([2.5,4.9]).reshape(1,2)))\n",
    "print(\"Prediction 2: \",bag2.predict(np.array([2.5,4.9]).reshape(1,2)))\n",
    "print(\"Prediction 3: \",bag3.predict(np.array([2.5,4.9]).reshape(1,2)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB,GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1=KNeighborsClassifier()\n",
    "m2=LogisticRegression()\n",
    "m3=MultinomialNB()\n",
    "m4=GaussianNB()\n",
    "m5=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=VotingClassifier([('knn',m1),('lr',m2),('mn',m3),('nb',m4),('dt',m5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>petal length (cm)</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length (cm)  petal length (cm)  Species\n",
       "8                 2.9                4.6        1\n",
       "95                3.0                5.2        2\n",
       "68                2.6                6.9        2\n",
       "97                3.0                5.2        2\n",
       "70                3.2                5.7        2\n",
       "..                ...                ...      ...\n",
       "2                 3.1                4.9        1\n",
       "99                3.0                5.1        2\n",
       "69                2.2                5.0        2\n",
       "98                3.4                5.4        2\n",
       "55                3.0                6.6        2\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(df.iloc[:,:-1].values,df.iloc[:,-1].values,test_size=0.2,random_state=42)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;knn&#x27;, KNeighborsClassifier()),\n",
       "                             (&#x27;lr&#x27;, LogisticRegression()),\n",
       "                             (&#x27;mn&#x27;, MultinomialNB()), (&#x27;nb&#x27;, GaussianNB()),\n",
       "                             (&#x27;dt&#x27;, DecisionTreeClassifier())])</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\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;knn&#x27;, KNeighborsClassifier()),\n",
       "                             (&#x27;lr&#x27;, LogisticRegression()),\n",
       "                             (&#x27;mn&#x27;, MultinomialNB()), (&#x27;nb&#x27;, GaussianNB()),\n",
       "                             (&#x27;dt&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>knn</label></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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mn</label></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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>nb</label></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\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>dt</label></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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('knn', KNeighborsClassifier()),\n",
       "                             ('lr', LogisticRegression()),\n",
       "                             ('mn', MultinomialNB()), ('nb', GaussianNB()),\n",
       "                             ('dt', DecisionTreeClassifier())])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adaboost\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>AdaBoostClassifier(n_estimators=4, random_state=0)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(n_estimators=4, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(n_estimators=4, random_state=0)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc=AdaBoostClassifier(n_estimators=4,random_state=0)\n",
    "abc.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=abc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred,y_test)"
   ]
  }
 ],
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