3_Linear_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b225fbf1",
   "metadata": {},
   "source": [
    "# Linear Classification in Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b67d08c",
   "metadata": {},
   "source": [
    "Below are two linear classification models used in scikit learn trained and tested on simplified antarctic penguin data taken from the following source: https://www.kaggle.com/datasets/parulpandey/palmer-archipelago-antarctica-penguin-data\n",
    "\n",
    "\n",
    "The two linear classification models used are logistic regression and SVC's (support vector classifiers)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5306e7",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f57b6a22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T00:01:05.303944Z",
     "start_time": "2024-03-01T00:01:05.299304Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7e7b50",
   "metadata": {},
   "source": [
    "# a) Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e35f06de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T16:36:17.241770Z",
     "start_time": "2024-02-29T16:36:17.209063Z"
    }
   },
   "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>species</th>\n",
       "      <th>culmen_length_mm</th>\n",
       "      <th>culmen_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>47.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>214.0</td>\n",
       "      <td>4925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>334 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species  culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g\n",
       "0    Adelie              39.1             18.7              181.0       3750.0\n",
       "1    Adelie              39.5             17.4              186.0       3800.0\n",
       "2    Adelie              40.3             18.0              195.0       3250.0\n",
       "4    Adelie              36.7             19.3              193.0       3450.0\n",
       "5    Adelie              39.3             20.6              190.0       3650.0\n",
       "..      ...               ...              ...                ...          ...\n",
       "338  Gentoo              47.2             13.7              214.0       4925.0\n",
       "340  Gentoo              46.8             14.3              215.0       4850.0\n",
       "341  Gentoo              50.4             15.7              222.0       5750.0\n",
       "342  Gentoo              45.2             14.8              212.0       5200.0\n",
       "343  Gentoo              49.9             16.1              213.0       5400.0\n",
       "\n",
       "[334 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"penguin-sizes.csv\")\n",
    "df = df.dropna()\n",
    "df = df.drop(columns = df.columns[[1, 6]], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8c991c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T16:36:17.249433Z",
     "start_time": "2024-02-29T16:36:17.243570Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (334, 4) \n",
      "\n",
      "Feature names:  culmen_length_mm   culmen_depth_mm   flipper_length_mm   body_mass_g   \n",
      "\n",
      "Feature values of first sample:  [  39.1   18.7  181.  3750. ] \n",
      "\n",
      "y shape:  (334,) \n",
      "\n",
      "Target names:  species \n",
      "\n",
      "Target values (penguin species):  ['Adelie' 'Chinstrap' 'Gentoo']\n"
     ]
    }
   ],
   "source": [
    "X = df[df.columns[[1,2,3,4]]].to_numpy()\n",
    "y = df[df.columns[0]].to_numpy()\n",
    "\n",
    "print('X shape: ', X.shape, '\\n') # 334 samples, 4 features\n",
    "print('Feature names: ', df.columns[1], ' ', df.columns[2], ' ',\n",
    "      df.columns[3], ' ', df.columns[4], ' ', '\\n')\n",
    "print('Feature values of first sample: ', X[0,:], '\\n')\n",
    "print('y shape: ', y.shape,'\\n')\n",
    "print('Target names: ', df.columns[0], '\\n')\n",
    "print('Target values (penguin species): ', np.unique(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e9eb5f3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T16:36:17.256399Z",
     "start_time": "2024-02-29T16:36:17.251689Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Adelie penguin samples:  146\n",
      "Number of Chainstrap penguin samples:  68\n",
      "Number of Gentoo penguin samples:  120\n",
      "Total samples:  334\n"
     ]
    }
   ],
   "source": [
    "# Converting target strings to numerical values\n",
    "y = np.reshape(y,(-1,1))\n",
    "y_enc = OrdinalEncoder().fit_transform(y).ravel()\n",
    "#print(\"Encoded target values: \", y_enc)\n",
    "\n",
    "def count_freq(arr, n):\n",
    "    count = 0\n",
    "    for i in arr:\n",
    "        if i == n:\n",
    "            count += 1\n",
    "    return count\n",
    "\n",
    "num_0 = count_freq(y_enc, 0)\n",
    "num_1 = count_freq(y_enc, 1)\n",
    "num_2 = count_freq(y_enc, 2)\n",
    "\n",
    "print(\"Number of Adelie penguin samples: \", num_0)\n",
    "print(\"Number of Chainstrap penguin samples: \", num_1)\n",
    "print(\"Number of Gentoo penguin samples: \", num_2)\n",
    "print(\"Total samples: \", num_0 + num_1 + num_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b94c654",
   "metadata": {},
   "source": [
    "# b) Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ccc634a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T03:11:25.943131Z",
     "start_time": "2024-03-01T03:11:25.767771Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Culmen Depth (mm)')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "# Discretize the colormap to represent 3 classes\n",
    "cmap = cm.cool\n",
    "cmaplist = [cmap(i) for i in range(cmap.N)]\n",
    "cmap = mpl.colors.LinearSegmentedColormap.from_list(\n",
    "    'Penguin classes', cmaplist, cmap.N)\n",
    "bounds = np.linspace(0, 2, 4)\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "# Plot\n",
    "s = ax.scatter(X[:,0],X[:,1], s=40, cmap=cmap, norm=norm, c=y_enc)\n",
    "fig.colorbar(s, ticks=[0, 1, 2])\n",
    "\n",
    "ax.set_xlabel('Culmen Length (mm)')\n",
    "ax.set_ylabel('Culmen Depth (mm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b95fd7c",
   "metadata": {},
   "source": [
    "# c) Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56f466e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T16:36:17.543145Z",
     "start_time": "2024-02-29T16:36:17.509601Z"
    }
   },
   "outputs": [],
   "source": [
    "def label_array(X, y, n): # Function to create array of sample values of a specified feature for each class\n",
    "    X_num = []\n",
    "    for i in range(len(X)):\n",
    "        if int(y[i]) == n:\n",
    "            X_num.append(X[i])\n",
    "    X_num = np.array(X_num)\n",
    "    return X_num\n",
    "        \n",
    "# Create array of culmen length values for each class\n",
    "X0_0 = label_array(X[:,0], y_enc, 0)\n",
    "X0_1 = label_array(X[:,0], y_enc, 1)\n",
    "X0_2 = label_array(X[:,0], y_enc, 2)\n",
    "\n",
    "# Create array of culmen depth values for each class\n",
    "X1_0 = label_array(X[:,1], y_enc, 0)\n",
    "X1_1 = label_array(X[:,1], y_enc, 1)\n",
    "X1_2 = label_array(X[:,1], y_enc, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "057ac65a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T03:12:27.659467Z",
     "start_time": "2024-03-01T03:12:27.518566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Culmen Length\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "alpha = 0.4\n",
    "cmap = mpl.colormaps['cool']\n",
    "\n",
    "ax.hist(X0_0, label=\"Adelie\", alpha=alpha, color=cmap(0.0))\n",
    "ax.hist(X0_1, label=\"Chainstrap\", alpha=alpha, color=cmap(0.5))\n",
    "ax.hist(X0_2, label=\"Gentoo\", alpha=alpha, color=cmap(1.0))\n",
    "\n",
    "plt.legend(loc='upper right') \n",
    "plt.title(\"Culmen Length of Different Penguin Species\")\n",
    "ax.set_xlabel(\"Culmen Length (mm)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f4cdcbd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T03:12:37.536317Z",
     "start_time": "2024-03-01T03:12:37.395125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAIhCAYAAABg21M1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABUiElEQVR4nO3de3zP9f//8fvbDu+dpzlsc9omzPkUySGHchaFSikRlRxDSkhGfUYqlUT1ybETOpAQybmPMznEEjWHajM5bAwz2/P3R7+9v942bDN778Xterm8Lxev1+v5ej4f7/de4+75fr5fb5sxxggAAACwgEKuLgAAAADILsIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrbjm7du3Sk08+qYiICHl5ecnPz0+1a9fWhAkTdOLEiRz3t3r1atlsNq1evTrvi73BMmrPeHh6eqpYsWJq2LChRo4cqUOHDuVLHUuWLFFUVFSWx2w2m/r373/Dazh48KDatWunoKAg2Ww2DRo06Iptw8PDHa9ZoUKFFBgYqEqVKumJJ57QDz/8kOU5Npst03NcsWKF6tSpI19fX9lsNi1YsECSNHfuXFWpUkXe3t6y2WzasWNH3jzJPHb27FlFRUVl+9o/ePCg0/VWqFAhFSlSRG3bttWGDRtubLE3QHh4uHr06JGnfW7atEkdO3ZUmTJlZLfbFRwcrPr16+v555/P03Fyq2nTpmratKmry8Atzt3VBQD56b///a/69u2ryMhIvfDCC6pcubJSU1O1detWffDBB9qwYYPmz5/v6jLzXXR0tJo1a6a0tDQdP35cmzZt0vTp0/X222/rv//9rx577LEbOv6SJUv0/vvvXzHA5ofBgwc7nndISIhCQ0Ov2r5hw4Z68803JUlnzpzRvn37NGfOHLVq1UqdO3fWF198IQ8PD0f7DRs2qFSpUo5tY4wefvhhVahQQQsXLpSvr68iIyN17NgxdevWTa1bt9aUKVNkt9tVoUKFG/Okr9PZs2c1ZswYScpRoBkwYIC6du2qtLQ07dmzR2PGjFGzZs20YcMG1apV6wZVm/fmz5+vgICAPOtv8eLF6tChg5o2baoJEyYoNDRUcXFx2rp1q+bMmaO33norz8bKrSlTpri6BEAywC1i/fr1xs3NzbRu3dqcP38+0/GUlBTz7bff5rjfVatWGUlm1apVeVBl/sqo/csvv8x07Pjx46ZWrVrG3d3d7Nq164bW0a9fP3Olv44kmX79+t3Q8Y0xply5cqZNmzbZahsWFmbatWuX5bHRo0cbSebFF1+8ah9//vmnkWRef/11p/0//fSTkWTmzp2bvcKz4ezZsyY9PT3P+stw7NgxI8mMHj06W+1jY2ONJPPGG2847V+xYoWRZJ566qk8r9FKGjdubG6//XaTmpqa6VhaWpoLKgIKJpYN4JYRHR0tm82mjz76SHa7PdNxT09PdejQwbGd1du8UvbeKuzRo4f8/Pz066+/qlWrVvL19VVoaKjGjx8vSdq4caMaNWokX19fVahQQbNmzXI6/9ixY+rbt68qV64sPz8/FS9eXPfcc4/WrVvn1C7jbdg333xTEydOVEREhPz8/FS/fn1t3Lgxm69M1oKCgvThhx/q4sWLevvtt52O7d+/X127dlXx4sVlt9tVqVIlvf/++05tMpYkfPrppxoyZIhCQkLk7e2tJk2a6Oeff3Z6rTLOvfQt5YMHDzr198knn6hSpUry8fFRjRo1tGjRomw9j8OHD+vxxx93qvWtt95Senq6U50HDhzQ999/f8XxsysqKkpVqlTR5MmTdf78ecf+S6+nqKgoxyzssGHDZLPZHNdVo0aNJEldunSRzWZzmtHcunWrOnTooKCgIHl5ealWrVqaN2+e0/gzZ86UzWbTDz/8oJ49e6pYsWLy8fFRSkqKpH+XJNSvX1++vr7y8/NTq1atnH4e0v9dvwcOHFDbtm3l5+en0qVL6/nnn3f0c/DgQRUrVkySNGbMGMfrlpu30e+66y5Jclqm8uOPP+ree+9VQECAfHx81LBhQ61YsSLTa22z2bRnzx49+uijCgwMVHBwsHr27KnExESntqdOnVKvXr0UFBQkPz8/tWvXTn/88Uem3/MePXooPDw8U40ZY13q8r8LMq6lL774QiNHjlSJEiUUEBCg5s2ba9++fdd8HY4fP66iRYvK3T3zm6KFCjn/cx0eHq777rtP8+fPV/Xq1eXl5aWyZctq0qRJmc5NSkrS0KFDFRERIU9PT5UsWVKDBg1ScnKyU7v09HS99957qlmzpry9vVW4cGHdddddWrhwoaNNVssGLly4oNdee00VK1aU3W5XsWLF9OSTT+rYsWNO7VauXKmmTZuqSJEi8vb2VpkyZdS5c2edPXv2mq8NcCnCK24JaWlpWrlype644w6VLl06X8ZMTU1Vp06d1K5dO3377bdq06aNhg8frhEjRqh79+7q2bOn5s+fr8jISPXo0UPbtm1znJux9nb06NFavHixZsyYobJly6pp06ZZri98//33tXz5cr3zzjv67LPPlJycrLZt22b6Bzyn6tatq9DQUK1du9axb+/evapbt65++eUXvfXWW1q0aJHatWungQMHOt5CvtSIESP0xx9/6OOPP9bHH3+sv//+W02bNtUff/whSRo1apQefPBBSf++tZ7xuPRt+8WLF2vy5MkaO3asvv76awUFBaljx46OPq7k2LFjatCggX744Qe9+uqrWrhwoZo3b66hQ4c61tHWrl1bGzZsUEhIiBo2bJjl+DnVvn17nT17Vlu3bs3y+FNPPaVvvvlG0r9voWcsVxk1apQjyEdHR2vDhg2Ot2lXrVqlhg0b6tSpU/rggw/07bffqmbNmurSpYtmzpyZaYyePXvKw8NDn3zyib766it5eHgoOjpajz76qCpXrqx58+bpk08+0enTp3X33Xdr7969TuenpqaqQ4cOuvfee/Xtt9+qZ8+eevvtt/X6669LkkJDQ7V06VJJUq9evRyv26hRo3L8eh04cECSHGH4008/VcuWLRUQEKBZs2Zp3rx5CgoKUqtWrTIFWEnq3LmzKlSooK+//lovvfSSPv/8cw0ePNhxPD09Xe3bt9fnn3+uYcOGaf78+apXr55at26d41qzY8SIETp06JA+/vhjffTRR9q/f7/at2+vtLS0q55Xv359bdq0SQMHDtSmTZuUmpp61fY7duzQoEGDNHjwYM2fP18NGjTQc88951jOIv27tKNJkyaaNWuWBg4cqO+//17Dhg3TzJkz1aFDBxljHG179Oih5557TnXr1tXcuXM1Z84cdejQ4ar/kUtPT9f999+v8ePHq2vXrlq8eLHGjx+v5cuXq2nTpjp37pyk/1tT7unpqenTp2vp0qUaP368fH19deHChWy8qsAlXD31C+SH+Ph4I8k88sgj2T5HV3g7NCwszHTv3t2xndWyge7duxtJ5uuvv3bsS01NNcWKFTOSzPbt2x37jx8/btzc3MyQIUOuWMvFixdNamqquffee03Hjh0d+zPehq1WrZq5ePGiY//mzZuNJPPFF19c9TlebdlAhnr16hlvb2/HdqtWrUypUqVMYmKiU7v+/fsbLy8vc+LECae+a9eu7fSW9cGDB42Hh4fTW8TXWjYQHBxskpKSHPvi4+NNoUKFzLhx4676/F566SUjyWzatMlpf58+fYzNZjP79u1z7LvaUoDLXavt1KlTM731f/n1dKW30K/0M6lYsaKpVatWpreU77vvPhMaGup4W3nGjBlGknniiSec2h0+fNi4u7ubAQMGOO0/ffq0CQkJMQ8//LBjX8b1O2/ePKe2bdu2NZGRkY7t3C4beP31101qaqo5f/682bZtm6lbt66RZBYvXmySk5NNUFCQad++vdO5aWlppkaNGubOO+907MtYojFhwgSntn379jVeXl6O627x4sVGkpk6dapTu3HjxmWqv3v37iYsLCxT7RljXepKfxe0bdvWqd28efOMJLNhw4arvj7//POPadSokZFkJBkPDw/ToEEDM27cOHP69OlMY9tsNrNjxw6n/S1atDABAQEmOTnZ8RwLFSpktmzZ4tTuq6++MpLMkiVLjDHGrF271kgyI0eOvGqNTZo0MU2aNHFsf/HFF5n+rjPGmC1bthhJZsqUKU7jXV4vkBvMvAI3iM1mU9u2bR3b7u7uKleunEJDQ50+lBIUFKTixYtn+mT/Bx98oNq1a8vLy0vu7u7y8PDQihUrFBMTk2msdu3ayc3NzbFdvXp1ScqTuwWYS2Zmzp8/rxUrVqhjx47y8fHRxYsXHY+2bdvq/PnzmZYrdO3a1ent1rCwMDVo0ECrVq3Kdg3NmjWTv7+/Yzs4ODjL1+xyK1euVOXKlXXnnXc67e/Ro4eMMVq5cmW2a8iJS1+zvHDgwAH9+uuvjg/OXf66x8XFZXpbunPnzk7by5Yt08WLF/XEE084ne/l5aUmTZpkmtG32Wxq3769077q1avnyTU1bNgweXh4yMvLS3fccYcOHz6sDz/8UG3bttX69et14sQJde/e3anO9PR0tW7dWlu2bMn0dvely30y6jx//rwSEhIkSWvWrJEkPfzww07tHn300et+LlnJqh7p2r+PRYoU0bp167RlyxaNHz9e999/v3777TcNHz5c1apV0z///OPUvkqVKqpRo4bTvq5duyopKUnbt2+XJC1atEhVq1ZVzZo1nV7PVq1aOd0l5fvvv5ck9evXL0fPddGiRSpcuLDat2/v1H/NmjUVEhLi6L9mzZry9PTUM888o1mzZl3zXRPgarjbAG4JRYsWlY+Pj2JjY/NtTB8fH3l5eTnt8/T0VFBQUKa2np6eTusjJ06cqOeff17PPvusXn31VRUtWlRubm4aNWpUluG1SJEiTtsZa3oz3rK7HocPH1aJEiUk/bsm7+LFi3rvvff03nvvZdn+8n9gQ0JCMrUJCQnRzp07s13D5c9P+vc5Xuv5HT9+PMv1i5c+nxshI6RkjHO9jh49KkkaOnSohg4dmmWby1/3y5c9ZPRRt27dLM+/fE1lVtev3W53uk5z67nnntPjjz+uQoUKqXDhwoqIiHD8ByejzoylJFk5ceKEfH19HdvXuv6PHz8ud3f3TL97wcHB1/1csnK9v4916tRRnTp1JP27fGPYsGF6++23NWHCBE2YMMHR7kq/W9L/XdtHjx7VgQMHnO58camM6+bYsWNyc3PLss+rOXr0qE6dOiVPT8+r9n/77bfrxx9/1IQJE9SvXz8lJyerbNmyGjhwoJ577rkcjQkQXnFLcHNz07333qvvv/9ef/75p9Mti67Ebrc7PpxyqRsVeC716aefqmnTppo6darT/tOnT9/wsS+1efNmxcfHq1evXpKk2267TW5uburWrdsVZ2giIiKctuPj4zO1iY+PzzKQ5rUiRYooLi4u0/6///5b0r//qclrxhh999138vX1dQSQ65VR5/Dhw9WpU6cs20RGRjptX/7hoow+vvrqK4WFheVJXblVqlSpK742GXW+9957jg9yXS6nobNIkSK6ePGiTpw44RRgs7o2vby8svy9v/w/B/nFw8NDo0eP1ttvv61ffvnF6diVfrek/wvQRYsWlbe3t6ZPn55l/xmvd7FixZSWlqb4+PgcrfcuWrSoihQp4lj/fLlL3zG5++67dffddystLU1bt27Ve++9p0GDBik4OFiPPPJItscECK+4ZQwfPlxLlizR008/rW+//TbTTEFqaqqWLl3qeKs0PDxcu3btcmqzcuVKnTlz5obXarPZMt0RYdeuXdqwYUO+feDsxIkTevbZZ+Xh4eH48IuPj4+aNWumn3/+WdWrV7/ibMulvvjiCw0ZMsQRpg4dOqT169friSeecLS5dGbK29s7z57Dvffeq3Hjxmn79u2qXbu2Y//s2bNls9nUrFmzPBsrw5gxY7R3716NGDEi08xlbkVGRqp8+fLauXOnoqOjc9VHq1at5O7urt9//z3TkoLcyssZ/gwNGzZU4cKFtXfv3jz7coomTZpowoQJmjt3rvr06ePYP2fOnExtw8PDlZCQoKNHjzpC8oULF7Rs2bI8qeVq4uLisgyOGe+2XD6Tv2fPHu3cudNp6cDnn38uf39/x/V+3333KTo6WkWKFMn0H8tLtWnTRuPGjdPUqVM1duzYbNd83333ac6cOUpLS1O9evWydY6bm5vq1aunihUr6rPPPtP27dsJr8gRwituGfXr19fUqVPVt29f3XHHHerTp4+qVKmi1NRU/fzzz/roo49UtWpVR3jt1q2bRo0apVdeeUVNmjTR3r17NXnyZAUGBt7wWu+77z69+uqrGj16tJo0aaJ9+/Zp7NixioiI0MWLF/N8vP3792vjxo1KT093fEnBtGnTlJSUpNmzZ6tKlSqOtu+++64aNWqku+++W3369FF4eLhOnz6tAwcO6Lvvvsu0jjQhIUEdO3bU008/rcTERI0ePVpeXl4aPny4o021atUkSa+//rratGkjNze3bIfjqxk8eLBmz56tdu3aaezYsQoLC9PixYs1ZcoU9enT57pu/n/q1CnH+t7k5GTHlxSsW7dODz/8cJZ3XrgeH374odq0aaNWrVqpR48eKlmypE6cOKGYmBht375dX3755VXPDw8P19ixYzVy5Ej98ccfat26tW677TYdPXpUmzdvlq+vb45r9vf3V1hYmL799lvde++9CgoKUtGiRbNcqpFdfn5+eu+999S9e3edOHFCDz74oIoXL65jx45p586dOnbsWKZ3JK6ldevWatiwoZ5//nklJSXpjjvu0IYNGzR79mxJzksmunTpoldeeUWPPPKIXnjhBZ0/f16TJk265p0C8kKrVq1UqlQptW/fXhUrVlR6erp27Niht956S35+fpneXi9RooQ6dOigqKgohYaG6tNPP9Xy5cv1+uuvy8fHR5I0aNAgff3112rcuLEGDx6s6tWrKz09XYcPH9YPP/yg559/XvXq1dPdd9+tbt266bXXXtPRo0d13333yW636+eff5aPj48GDBiQZc2PPPKIPvvsM7Vt21bPPfec7rzzTnl4eOjPP//UqlWrdP/996tjx4764IMPtHLlSrVr105lypTR+fPnHbPBzZs3v7EvLG4+rv28GJD/duzYYbp3727KlCljPD09ja+vr6lVq5Z55ZVXTEJCgqNdSkqKefHFF03p0qWNt7e3adKkidmxY0e27zbg6+ubaewmTZqYKlWqZNp/+afXU1JSzNChQ03JkiWNl5eXqV27tlmwYEGmT0Jf6RPrxlz5bgmXyqg94+Hu7m6KFCli6tevb0aMGGEOHjyY5XmxsbGmZ8+epmTJksbDw8MUK1bMNGjQwLz22muZ+v7kk0/MwIEDTbFixYzdbjd333232bp1q1N/KSkp5qmnnjLFihUzNpvNSDKxsbGO55HVlxRc/nO4kkOHDpmuXbuaIkWKGA8PDxMZGWneeOONTDd9z+ndBjJeM5vNZvz8/ExkZKTp1q2bWbZsWZbnXP7zyOndBowxZufOnebhhx82xYsXNx4eHiYkJMTcc8895oMPPnC0ybjbwOWfLs+wYMEC06xZMxMQEGDsdrsJCwszDz74oPnxxx8dba50/Wb1ifsff/zR1KpVy9jtdiPpqj+Tq12vl1uzZo1p166dCQoKMh4eHqZkyZKmXbt2Tq9LRj3Hjh1zOjfjNci4howx5sSJE+bJJ580hQsXNj4+PqZFixZm48aNRpJ59913nc5fsmSJqVmzpvH29jZly5Y1kydPztHdBi7/2WU87xkzZlz1Oc+dO9d07drVlC9f3vj5+RkPDw9TpkwZ061bN7N3795MY7dr18589dVXpkqVKsbT09OEh4ebiRMnZur3zJkz5uWXXzaRkZHG09PTBAYGmmrVqpnBgweb+Ph4R7u0tDTz9ttvm6pVqzra1a9f33z33XeONpffbcCYf++k8uabb5oaNWoYLy8v4+fnZypWrGh69+5t9u/fb4wxZsOGDaZjx44mLCzM2O12U6RIEdOkSROzcOHCq74mQFZsxuTxx2IBQP/esL1Zs2b68ssvr/rhG8BVPv/8cz322GP63//+pwYNGri6nBwJDw9X1apVs/1lHcDNhGUDAICb3hdffKG//vpL1apVU6FChbRx40a98cYbaty4seWCK3CrI7wCAG56/v7+mjNnjl577TUlJycrNDRUPXr00Guvvebq0gDkEMsGAAAAYBl8wxYAAAAsg/AKAAAAyyC8AgAAwDJu+g9spaen6++//5a/v3+mr0sEAACA6xljdPr0aZUoUcLpi0OyctOH17///jvfvk4TAAAAuXfkyBGVKlXqqm1u+vDq7+8v6d8XIyAgwMXVAAAA4HJJSUkqXbq0I7ddzU0fXjOWCgQEBBBeAQAACrDsLPHkA1sAAACwDMIrAAAALIPwCgAAAMu46de8AgCAgskYo4sXLyotLc3VpeAGc3Nzk7u7e57ctpTwCgAA8t2FCxcUFxens2fPuroU5BMfHx+FhobK09PzuvohvAIAgHyVnp6u2NhYubm5qUSJEvL09OSLhG5ixhhduHBBx44dU2xsrMqXL3/NLyK4GsIrAADIVxcuXFB6erpKly4tHx8fV5eDfODt7S0PDw8dOnRIFy5ckJeXV6774gNbAADAJa5n9g3Wk1c/b64aAAAAWAbhFQAAAJbBmlcAAFBgfJTP4z2TD2NERUVpwYIF2rFjR7baHzx4UBEREfr5559Vs2ZNrV69Ws2aNdPJkydVuHDhG1qrFTDzCgAAkEPr16+Xm5ubWrdufcPHatCggeLi4hQYGHjDx7ICwisAAEAOTZ8+XQMGDNBPP/2kw4cP39CxPD09FRISwu3E/j/CKwAAQA4kJydr3rx56tOnj+677z7NnDnT6fj48eMVHBwsf39/9erVS+fPn8/Ux4wZM1SpUiV5eXmpYsWKmjJlyhXHW716tWw2m06dOuXYt379ejVu3Fje3t4qXbq0Bg4cqOTk5Lx6igUa4RUAACAH5s6dq8jISEVGRurxxx/XjBkzZIyRJM2bN0+jR4/Wf/7zH23dulWhoaGZgul///tfjRw5Uv/5z38UExOj6OhojRo1SrNmzcrW+Lt371arVq3UqVMn7dq1S3PnztVPP/2k/v375/lzLYgIrwAAADkwbdo0Pf7445Kk1q1b68yZM1qxYoUk6Z133lHPnj311FNPKTIyUq+99poqV67sdP6rr76qt956S506dVJERIQ6deqkwYMH68MPP8zW+G+88Ya6du2qQYMGqXz58mrQoIEmTZqk2bNnZznLe7MhvAIAAGTTvn37tHnzZj3yyCOSJHd3d3Xp0kXTp0+XJMXExKh+/fpO51y6fezYMR05ckS9evWSn5+f4/Haa6/p999/z1YN27Zt08yZM53Ob9WqleNrd2923CoLAAAgm6ZNm6aLFy+qZMmSjn3GGHl4eOjkyZPXPD89PV3Sv0sH6tWr53TMzc0tWzWkp6erd+/eGjhwYKZjZcqUyVYfVkZ4BQAAyIaLFy9q9uzZeuutt9SyZUunY507d9Znn32mSpUqaePGjXriiSccxzZu3Oj4c3BwsEqWLKk//vhDjz32WK7qqF27tvbs2aNy5crl7olYHOEVAHBLyu+b4edUftw8HzmzaNEinTx5Ur169cp0z9UHH3xQ06ZN00svvaTu3burTp06atSokT777DPt2bNHZcuWdbSNiorSwIEDFRAQoDZt2iglJUVbt27VyZMnNWTIkGvWMWzYMN11113q16+fnn76afn6+iomJkbLly/Xe++9l+fPu6AhvAIAgAKjIIf2adOmqXnz5ll+WUDnzp0VHR2t8uXL65VXXtGwYcN0/vx5de7cWX369NGyZcscbZ966in5+PjojTfe0IsvvihfX19Vq1ZNgwYNylYd1atX15o1azRy5EjdfffdMsbo9ttvV5cuXfLqqRZoNpNxb4ebVFJSkgIDA5WYmKiAgABXlwMAKCCYeXWd8+fPKzY2VhEREfLy8nJ1OcgnV/u55ySvcbcBAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWIZLw+vUqVNVvXp1BQQEKCAgQPXr19f333/vON6jRw/ZbDanx1133eXCigEAAOBKLr3Pa6lSpTR+/HjHN0TMmjVL999/v37++WdVqVJFktS6dWvNmDHDcY6np6dLagUAAIDruTS8tm/f3mn7P//5j6ZOnaqNGzc6wqvdbldISIgrygMAAEABU2DWvKalpWnOnDlKTk5W/fr1HftXr16t4sWLq0KFCnr66aeVkJBw1X5SUlKUlJTk9AAAAMDNweVfD7t7927Vr19f58+fl5+fn+bPn6/KlStLktq0aaOHHnpIYWFhio2N1ahRo3TPPfdo27ZtstvtWfY3btw4jRkzJj+fAgAAyCPbtubveHfUuTH92mw2zZ8/Xw888ECu++jRo4dOnTqlBQsW5FldNwOXz7xGRkZqx44d2rhxo/r06aPu3btr7969kqQuXbqoXbt2qlq1qtq3b6/vv/9ev/32mxYvXnzF/oYPH67ExETH48iRI/n1VAAAwC0iPj5eAwYMUNmyZWW321W6dGm1b99eK1asyLMx3n33Xc2cOTPP+gsPD9c777yTZ/25istnXj09PR0f2KpTp462bNmid999Vx9++GGmtqGhoQoLC9P+/fuv2J/dbr/irCwAAMD1OnjwoBo2bKjChQtrwoQJql69ulJTU7Vs2TL169dPv/76a56MExgYmCf95ERaWppsNpsKFXL5/OYVFbjKjDFKSUnJ8tjx48d15MgRhYaG5nNVAAAA/+rbt69sNps2b96sBx98UBUqVFCVKlU0ZMgQbdy40dHun3/+UceOHeXj46Py5ctr4cKFjmNpaWnq1auXIiIi5O3trcjISL377rtO4/To0cNp2UHTpk01cOBAvfjiiwoKClJISIiioqKczomKilKZMmVkt9tVokQJDRw40HHuoUOHNHjwYMftRyVp5syZKly4sBYtWqTKlSvLbrfr0KFD2rJli1q0aKGiRYsqMDBQTZo00fbt253Gstlsmjp1qtq0aSNvb29FREToyy+/zIuX+KpcGl5HjBihdevW6eDBg9q9e7dGjhyp1atX67HHHtOZM2c0dOhQbdiwQQcPHtTq1avVvn17FS1aVB07dnRl2QAA4BZ14sQJLV26VP369ZOvr2+m44ULF3b8ecyYMXr44Ye1a9cutW3bVo899phOnDghSUpPT1epUqU0b9487d27V6+88opGjBihefPmXXX8WbNmydfXV5s2bdKECRM0duxYLV++XJL01Vdf6e2339aHH36o/fv3a8GCBapWrZok6ZtvvlGpUqU0duxYxcXFKS4uztHn2bNnNW7cOH388cfas2ePihcvrtOnT6t79+5at26dNm7cqPLly6tt27Y6ffq0Uz2jRo1S586dtXPnTj3++ON69NFHFRMTk6vXNrtcumzg6NGj6tatm+Li4hQYGKjq1atr6dKlatGihc6dO6fdu3dr9uzZOnXqlEJDQ9WsWTPNnTtX/v7+riwbAADcog4cOCBjjCpWrHjNtj169NCjjz4qSYqOjtZ7772nzZs3q3Xr1vLw8HD6gHlERITWr1+vefPm6eGHH75in9WrV9fo0aMlSeXLl9fkyZO1YsUKtWjRQocPH1ZISIiaN28uDw8PlSlTRnfeeackKSgoSG5ubvL39890C9LU1FRNmTJFNWrUcOy75557nNp8+OGHuu2227RmzRrdd999jv0PPfSQnnrqKUnSq6++quXLl+u9997TlClTrvn65JZLw+u0adOueMzb21vLli3Lx2oAAACuzhgjSY633a+mevXqjj/7+vrK39/f6ZafH3zwgT7++GMdOnRI586d04ULF1SzZs1s9yn9+3mgjD4feughvfPOOypbtqxat26ttm3bqn379nJ3v3rc8/T0zNRvQkKCXnnlFa1cuVJHjx5VWlqazp49q8OHDzu1u/T2phnbO3bsuOp416vArXkFAAAoqMqXLy+bzZatt8Y9PDyctm02m9LT0yVJ8+bN0+DBg9WzZ0/98MMP2rFjh5588klduHAh132WLl1a+/bt0/vvvy9vb2/17dtXjRs3Vmpq6lX79Pb2zhTGe/TooW3btumdd97R+vXrtWPHDhUpUuSa9WXUdCMRXgEAALIpKChIrVq10vvvv6/k5ORMx0+dOpWtftatW6cGDRqob9++qlWrlsqVK6fff//9uuvz9vZWhw4dNGnSJK1evVobNmzQ7t27Jf07w5qWlpbt+gYOHKi2bduqSpUqstvt+ueffzK1u/QDahnb2VlScT0IrwAAADkwZcoUpaWl6c4779TXX3+t/fv3KyYmRpMmTcr0NvqVlCtXTlu3btWyZcv022+/adSoUdqyZct11TVz5kxNmzZNv/zyi/744w998skn8vb2VlhYmKR/7/O6du1a/fXXX1kG0cvr++STTxQTE6NNmzbpsccek7e3d6Z2X375paZPn67ffvtNo0eP1ubNm9W/f//reh7X4vL7vAIAAGS4Ud94lZciIiK0fft2/ec//9Hzzz+vuLg4FStWTHfccYemTp2arT6effZZ7dixQ126dJHNZtOjjz6qvn376vvvv891XYULF9b48eM1ZMgQpaWlqVq1avruu+9UpEgRSdLYsWPVu3dv3X777UpJSXGs383K9OnT9cwzz6hWrVoqU6aMoqOjNXTo0EztxowZozlz5qhv374KCQnRZ5995vim1BvFZq5W+U0gKSlJgYGBSkxMVEBAgKvLAQAUEB+5uoBreMbVBdxA58+fV2xsrCIiIuTl5eXqcpBLOf0K3Kv93HOS11g2AAAAAMsgvAIAAMAyWPMKAACAHHPVylNmXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBrfKAgAABUd+f/XZzfxVZjcpZl4BAAByKD4+Xs8995zKlSsnLy8vBQcHq1GjRvrggw909uzZPBunadOmGjRoUJ71dzNg5hUAACAH/vjjDzVs2FCFCxdWdHS0qlWrposXL+q3337T9OnTVaJECXXo0MHVZd60mHkFAADIgb59+8rd3V1bt27Vww8/rEqVKqlatWrq3LmzFi9erPbt20uSEhMT9cwzz6h48eIKCAjQPffco507dzr6iYqKUs2aNfXJJ58oPDxcgYGBeuSRR3T69GlJUo8ePbRmzRq9++67stlsstlsOnjwoCRpzZo1uvPOO2W32xUaGqqXXnpJFy9edPSdkpKigQMHqnjx4vLy8lKjRo20ZcuW/HuRbiDCKwAAQDYdP35cP/zwg/r16ydfX98s29hsNhlj1K5dO8XHx2vJkiXatm2bateurXvvvVcnTpxwtP3999+1YMECLVq0SIsWLdKaNWs0fvx4SdK7776r+vXr6+mnn1ZcXJzi4uJUunRp/fXXX2rbtq3q1q2rnTt3aurUqZo2bZpee+01R78vvviivv76a82aNUvbt29XuXLl1KpVK6exrYrwCgAAkE0HDhyQMUaRkZFO+4sWLSo/Pz/5+flp2LBhWrVqlXbv3q0vv/xSderUUfny5fXmm2+qcOHC+uqrrxznpaena+bMmapataruvvtudevWTStWrJAkBQYGytPTUz4+PgoJCVFISIjc3Nw0ZcoUlS5dWpMnT1bFihX1wAMPaMyYMXrrrbeUnp6u5ORkTZ06VW+88YbatGmjypUr67///a+8vb01bdq0fH29bgTWvAIAAOSQzWZz2t68ebPS09P12GOPKSUlRdu2bdOZM2dUpEgRp3bnzp3T77//7tgODw+Xv7+/Yzs0NFQJCQlXHTsmJkb169d3qqFhw4Y6c+aM/vzzT506dUqpqalq2LCh47iHh4fuvPNOxcTE5Or5FiSEVwAAgGwqV66cbDabfv31V6f9ZcuWlSR5e3tL+ndGNTQ0VKtXr87UR+HChR1/9vDwcDpms9mUnp5+1RqMMZnCszHGcf6lf77WeVbEsgEAAIBsKlKkiFq0aKHJkycrOTn5iu1q166t+Ph4ubu7q1y5ck6PokWLZns8T09PpaWlOe2rXLmy1q9f7wipkrR+/Xr5+/urZMmSKleunDw9PfXTTz85jqempmrr1q2qVKlSDp5twUR4BQAAyIEpU6bo4sWLqlOnjubOnauYmBjt27dPn376qX799Ve5ubmpefPmql+/vh544AEtW7ZMBw8e1Pr16/Xyyy9r69at2R4rPDxcmzZt0sGDB/XPP/8oPT1dffv21ZEjRzRgwAD9+uuv+vbbbzV69GgNGTJEhQoVkq+vr/r06aMXXnhBS5cu1d69e/X000/r7Nmz6tWr1w18ZfIHywYAAEDBYYFvvLr99tv1888/Kzo6WsOHD9eff/4pu92uypUra+jQoerbt69sNpuWLFmikSNHqmfPnjp27JhCQkLUuHFjBQcHZ3usoUOHqnv37qpcubLOnTun2NhYhYeHa8mSJXrhhRdUo0YNBQUFqVevXnr55Zcd540fP17p6enq1q2bTp8+rTp16mjZsmW67bbbbsRLkq9s5tI555tQUlKSAgMDlZiYqICAAFeXAwAoIPL7W0hzygIZLtfOnz+v2NhYRUREyMvLy9XlIJ9c7eeek7zGsgEAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAOASN/lnxnGZvPp5E14BAEC+yvhWqbNnz7q4EuSnjJ/35d8qllPc5xUAAOQrNzc3FS5cWAkJCZIkHx+fm+JrS5E1Y4zOnj2rhIQEFS5cWG5ubtfVH+EVAADku5CQEElyBFjc/AoXLuz4uV8PwisAAMh3NptNoaGhKl68uFJTU11dDm4wDw+P655xzUB4BQAALuPm5pZnoQa3Bj6wBQAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyXBpep06dqurVqysgIEABAQGqX7++vv/+e8dxY4yioqJUokQJeXt7q2nTptqzZ48LKwYAAIAruTS8lipVSuPHj9fWrVu1detW3XPPPbr//vsdAXXChAmaOHGiJk+erC1btigkJEQtWrTQ6dOnXVk2AAAAXMRmjDGuLuJSQUFBeuONN9SzZ0+VKFFCgwYN0rBhwyRJKSkpCg4O1uuvv67evXtnq7+kpCQFBgYqMTFRAQEBN7J0AICFfOTqAq7hGVcXAOSjnOS1ArPmNS0tTXPmzFFycrLq16+v2NhYxcfHq2XLlo42drtdTZo00fr166/YT0pKipKSkpweAAAAuDm4PLzu3r1bfn5+stvtevbZZzV//nxVrlxZ8fHxkqTg4GCn9sHBwY5jWRk3bpwCAwMdj9KlS9/Q+gEAAJB/XB5eIyMjtWPHDm3cuFF9+vRR9+7dtXfvXsdxm83m1N4Yk2nfpYYPH67ExETH48iRIzesdgAAAOQvd1cX4OnpqXLlykmS6tSpoy1btujdd991rHONj49XaGioo31CQkKm2dhL2e122e32G1s0AAAAXMLlM6+XM8YoJSVFERERCgkJ0fLlyx3HLly4oDVr1qhBgwYurBAAAACu4tKZ1xEjRqhNmzYqXbq0Tp8+rTlz5mj16tVaunSpbDabBg0apOjoaJUvX17ly5dXdHS0fHx81LVrV1eWDQAAABdxaXg9evSounXrpri4OAUGBqp69epaunSpWrRoIUl68cUXde7cOfXt21cnT55UvXr19MMPP8jf39+VZQMAAMBFCtx9XvMa93kFAGSF+7wCBYcl7/MKAAAAXAvhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJbh0vA6btw41a1bV/7+/ipevLgeeOAB7du3z6lNjx49ZLPZnB533XWXiyoGAACAK7k0vK5Zs0b9+vXTxo0btXz5cl28eFEtW7ZUcnKyU7vWrVsrLi7O8ViyZImLKgYAAIArubty8KVLlzptz5gxQ8WLF9e2bdvUuHFjx3673a6QkJD8Lg8AAAAFTIFa85qYmChJCgoKctq/evVqFS9eXBUqVNDTTz+thISEK/aRkpKipKQkpwcAAABuDjZjjHF1EZJkjNH999+vkydPat26dY79c+fOlZ+fn8LCwhQbG6tRo0bp4sWL2rZtm+x2e6Z+oqKiNGbMmEz7ExMTFRAQcEOfAwDgXx+5uoCbwDOuLgDIR0lJSQoMDMxWXisw4bVfv35avHixfvrpJ5UqVeqK7eLi4hQWFqY5c+aoU6dOmY6npKQoJSXFsZ2UlKTSpUsTXgEgHxFerx/hFbeSnIRXl655zTBgwAAtXLhQa9euvWpwlaTQ0FCFhYVp//79WR632+1ZzsgCAADA+lwaXo0xGjBggObPn6/Vq1crIiLimuccP35cR44cUWhoaD5UCAAAgILEpR/Y6tevnz799FN9/vnn8vf3V3x8vOLj43Xu3DlJ0pkzZzR06FBt2LBBBw8e1OrVq9W+fXsVLVpUHTt2dGXpAAAAcAGXzrxOnTpVktS0aVOn/TNmzFCPHj3k5uam3bt3a/bs2Tp16pRCQ0PVrFkzzZ07V/7+/i6oGAAAAK7k8mUDV+Pt7a1ly5blUzUAAAAo6ArUfV4BAACAqyG8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDLcXV0AABREH7m6gGt4xtUFAICLMPMKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALCMXIXXsmXL6vjx45n2nzp1SmXLls12P+PGjVPdunXl7++v4sWL64EHHtC+ffuc2hhjFBUVpRIlSsjb21tNmzbVnj17clM2AAAALC5X4fXgwYNKS0vLtD8lJUV//fVXtvtZs2aN+vXrp40bN2r58uW6ePGiWrZsqeTkZEebCRMmaOLEiZo8ebK2bNmikJAQtWjRQqdPn85N6QAAALAw95w0XrhwoePPy5YtU2BgoGM7LS1NK1asUHh4eLb7W7p0qdP2jBkzVLx4cW3btk2NGzeWMUbvvPOORo4cqU6dOkmSZs2apeDgYH3++efq3bt3TsoHAACAxeUovD7wwAOSJJvNpu7duzsd8/DwUHh4uN56661cF5OYmChJCgoKkiTFxsYqPj5eLVu2dLSx2+1q0qSJ1q9fn2V4TUlJUUpKimM7KSkp1/UAAACgYMlReE1PT5ckRUREaMuWLSpatGieFWKM0ZAhQ9SoUSNVrVpVkhQfHy9JCg4OdmobHBysQ4cOZdnPuHHjNGbMmDyrCwAAAAVHrta8xsbG5mlwlaT+/ftr165d+uKLLzIds9lsTtvGmEz7MgwfPlyJiYmOx5EjR/K0TgAAALhOjmZeL7VixQqtWLFCCQkJjhnZDNOnT89RXwMGDNDChQu1du1alSpVyrE/JCRE0r8zsKGhoY79CQkJmWZjM9jtdtnt9hyNDwAAAGvI1czrmDFj1LJlS61YsUL//POPTp486fTILmOM+vfvr2+++UYrV65URESE0/GIiAiFhIRo+fLljn0XLlzQmjVr1KBBg9yUDgAAAAvL1czrBx98oJkzZ6pbt27XNXi/fv30+eef69tvv5W/v79jjWtgYKC8vb1ls9k0aNAgRUdHq3z58ipfvryio6Pl4+Ojrl27XtfYAAAAsJ5chdcLFy7kyczn1KlTJUlNmzZ12j9jxgz16NFDkvTiiy/q3Llz6tu3r06ePKl69erphx9+kL+//3WPDwAAAGuxGWNMTk8aNmyY/Pz8NGrUqBtRU55KSkpSYGCgEhMTFRAQ4OpyAFjER64u4BqecXUB11DQXz8rKOg/YyAv5SSv5Wrm9fz58/roo4/0448/qnr16vLw8HA6PnHixNx0CwAAAFxVrsLrrl27VLNmTUnSL7/84nTsSrewAgAAAK5XrsLrqlWr8roOAAAA4JpydassAAAAwBVyNfParFmzqy4PWLlyZa4LAgAAAK4kV+E1Y71rhtTUVO3YsUO//PKLunfvnhd1Ac7y+6PLfMwXAIACKVfh9e23385yf1RUlM6cOXNdBQEAAABXkqdrXh9//HFNnz49L7sEAAAAHPI0vG7YsEFeXl552SUAAADgkKtlA506dXLaNsYoLi5OW7dutcS3bgEAAMCachVeAwMDnbYLFSqkyMhIjR07Vi1btsyTwgAAAIDL5Sq8zpgxI6/rAAAAAK4pV+E1w7Zt2xQTEyObzabKlSurVq1aeVUXAAAAkEmuwmtCQoIeeeQRrV69WoULF5YxRomJiWrWrJnmzJmjYsWK5XWdAAAAQO7uNjBgwAAlJSVpz549OnHihE6ePKlffvlFSUlJGjhwYF7XCAAAAEjK5czr0qVL9eOPP6pSpUqOfZUrV9b777/PB7YAAABww+Rq5jU9PV0eHh6Z9nt4eCg9Pf26iwIAAACykqvwes899+i5557T33//7dj3119/afDgwbr33nvzrDgAAADgUrkKr5MnT9bp06cVHh6u22+/XeXKlVNERIROnz6t9957L69rBAAAACTlcs1r6dKltX37di1fvly//vqrjDGqXLmymjdvntf1AQAAAA45mnlduXKlKleurKSkJElSixYtNGDAAA0cOFB169ZVlSpVtG7duhtSKAAAAJCj8PrOO+/o6aefVkBAQKZjgYGB6t27tyZOnJhnxQEAAACXylF43blzp1q3bn3F4y1bttS2bduuuygAAAAgKzkKr0ePHs3yFlkZ3N3ddezYsesuCgAAAMhKjsJryZIltXv37ise37Vrl0JDQ6+7KAAAACArOQqvbdu21SuvvKLz589nOnbu3DmNHj1a9913X54VBwAAAFwqR7fKevnll/XNN9+oQoUK6t+/vyIjI2Wz2RQTE6P3339faWlpGjly5I2qFQAAALe4HIXX4OBgrV+/Xn369NHw4cNljJEk2Ww2tWrVSlOmTFFwcPANKRQAAADI8ZcUhIWFacmSJTp58qQOHDggY4zKly+v22677UbUBwAAADjk6hu2JOm2225T3bp187IWAAAA4KpyHV6Bm9pH+TzeM/k83s3+/G4B+f0jBICCIkd3GwAAAABcifAKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDLcXV0AAADI7CNXF3ANz7i6ANyymHkFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFiGS8Pr2rVr1b59e5UoUUI2m00LFixwOt6jRw/ZbDanx1133eWaYgEAAOByLg2vycnJqlGjhiZPnnzFNq1bt1ZcXJzjsWTJknysEAAAAAWJS79hq02bNmrTps1V29jtdoWEhORTRQAAACjICvya19WrV6t48eKqUKGCnn76aSUkJFy1fUpKipKSkpweAAAAuDkU6PDapk0bffbZZ1q5cqXeeustbdmyRffcc49SUlKueM64ceMUGBjoeJQuXTofKwYAAMCN5NJlA9fSpUsXx5+rVq2qOnXqKCwsTIsXL1anTp2yPGf48OEaMmSIYzspKYkACwAAcJMo0OH1cqGhoQoLC9P+/fuv2MZut8tut+djVQAAAMgvBXrZwOWOHz+uI0eOKDQ01NWlAAAAwAVcOvN65swZHThwwLEdGxurHTt2KCgoSEFBQYqKilLnzp0VGhqqgwcPasSIESpatKg6duzowqoBAADgKi4Nr1u3blWzZs0c2xlrVbt3766pU6dq9+7dmj17tk6dOqXQ0FA1a9ZMc+fOlb+/v6tKBgAAgAu5NLw2bdpUxpgrHl+2bFk+VgMAAICCzlJrXgEAAHBrI7wCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMiz19bC4go9cXQCuGz9DAACyhZlXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBluLu6AAAACpKzW10zrk8d14wLWA0zrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAy3B3dQEAbgEfuWDMZ1wwJmAxZ7fm/txt1zHuHXWu42Tc8ph5BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAluHS8Lp27Vq1b99eJUqUkM1m04IFC5yOG2MUFRWlEiVKyNvbW02bNtWePXtcUywAAABczqXhNTk5WTVq1NDkyZOzPD5hwgRNnDhRkydP1pYtWxQSEqIWLVro9OnT+VwpAAAACgKXfj1smzZt1KZNmyyPGWP0zjvvaOTIkerUqZMkadasWQoODtbnn3+u3r1752epAAAAKAAK7JrX2NhYxcfHq2XLlo59drtdTZo00fr16694XkpKipKSkpweAAAAuDm4dOb1auLj4yVJwcHBTvuDg4N16NChK543btw4jRkz5obWBgC3mrNbXTOuTx3XjAug4CqwM68ZbDab07YxJtO+Sw0fPlyJiYmOx5EjR250iQAAAMgnBXbmNSQkRNK/M7ChoaGO/QkJCZlmYy9lt9tlt9tveH0AAADIfwV25jUiIkIhISFavny5Y9+FCxe0Zs0aNWjQwIWVAQAAwFVcOvN65swZHThwwLEdGxurHTt2KCgoSGXKlNGgQYMUHR2t8uXLq3z58oqOjpaPj4+6du3qwqoBAADgKi4Nr1u3blWzZs0c20OGDJEkde/eXTNnztSLL76oc+fOqW/fvjp58qTq1aunH374Qf7+/q4qGQAAAC7k0vDatGlTGWOueNxmsykqKkpRUVH5VxQAAAAKrAK75hUAAAC4HOEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYhrurCwCAvHDooPP2P1uvr7+z2WjjU+f6xgAA5BwzrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAy3B3dQEAbk1r87i/C5dt/5LH/RckZ7e6uoL8cys9V6tZdx3nbsuzKq7smXwYA67BzCsAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDLcXV3ATekjVxcAIL9VzMff+wsHpV865d94gBXxT/H1e8bVBVwBM68AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCnR4jYqKks1mc3qEhIS4uiwAAAC4SIH/hq0qVaroxx9/dGy7ubm5sBoAAAC4UoEPr+7u7sy2AgAAQFIBXzYgSfv371eJEiUUERGhRx55RH/88cdV26ekpCgpKcnpAQAAgJtDgQ6v9erV0+zZs7Vs2TL997//VXx8vBo0aKDjx49f8Zxx48YpMDDQ8ShdunQ+VgwAAIAbqUCH1zZt2qhz586qVq2amjdvrsWLF0uSZs2adcVzhg8frsTERMfjyJEj+VUuAAAAbrACv+b1Ur6+vqpWrZr2799/xTZ2u112uz0fqwIAAEB+KdAzr5dLSUlRTEyMQkNDXV0KAAAAXKBAh9ehQ4dqzZo1io2N1aZNm/Tggw8qKSlJ3bt3d3VpAAAAcIECvWzgzz//1KOPPqp//vlHxYoV01133aWNGzcqLCzM1aUBAADABQp0eJ0zZ46rSwAAAEABUqCXDQAAAACXIrwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLKNB3GwAA4FZxdqurKwCsgZlXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBluLu6AAB5b62rC7hFXPpd9BcOuqwMwHIu/d3JLz518n9M3BjMvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALMPd1QUAwI1Q9RtXVwCgIDm71TXj+tRxzbg3M2ZeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZVgivE6ZMkURERHy8vLSHXfcoXXr1rm6JAAAALhAgQ+vc+fO1aBBgzRy5Ej9/PPPuvvuu9WmTRsdPnzY1aUBAAAgnxX48Dpx4kT16tVLTz31lCpVqqR33nlHpUuX1tSpU11dGgAAAPKZu6sLuJoLFy5o27Zteumll5z2t2zZUuvXr8/ynJSUFKWkpDi2ExMTJUlJSUk3rtDLncu/oYCsJLu6ABe4kHLtNjeT82dcXQGA7LDlY/zIa/lZekZOM8Zcs22BDq///POP0tLSFBwc7LQ/ODhY8fHxWZ4zbtw4jRkzJtP+0qVL35AaAcAlJrq6AAA3u0EuGPP06dMKDAy8apsCHV4z2Gw2p21jTKZ9GYYPH64hQ4Y4ttPT03XixAkVKVLkiufg+iQlJal06dI6cuSIAgICXF0OCgiuC2SF6wJZ4bqAMUanT59WiRIlrtm2QIfXokWLys3NLdMsa0JCQqbZ2Ax2u112u91pX+HChW9UibhEQEAAf+kgE64LZIXrAlnhuri1XWvGNUOB/sCWp6en7rjjDi1fvtxp//Lly9WgQQMXVQUAAABXKdAzr5I0ZMgQdevWTXXq1FH9+vX10Ucf6fDhw3r22WddXRoAAADyWYEPr126dNHx48c1duxYxcXFqWrVqlqyZInCwsJcXRr+P7vdrtGjR2daroFbG9cFssJ1gaxwXSAnbCY79yQAAAAACoACveYVAAAAuBThFQAAAJZBeAUAAIBlEF4BAABgGYRXZMvatWvVvn17lShRQjabTQsWLLhi2969e8tms+mdd97Jt/rgGtm5LmJiYtShQwcFBgbK399fd911lw4fPpz/xSJfXevaOHPmjPr3769SpUrJ29tblSpV0tSpU11TLPLFuHHjVLduXfn7+6t48eJ64IEHtG/fPqc2xhhFRUWpRIkS8vb2VtOmTbVnzx4XVYyCivCKbElOTlaNGjU0efLkq7ZbsGCBNm3alK2vd4P1Xeu6+P3339WoUSNVrFhRq1ev1s6dOzVq1Ch5eXnlc6XIb9e6NgYPHqylS5fq008/VUxMjAYPHqwBAwbo22+/zedKkV/WrFmjfv36aePGjVq+fLkuXryoli1bKjk52dFmwoQJmjhxoiZPnqwtW7YoJCRELVq00OnTp11YOQocA+SQJDN//vxM+//8809TsmRJ88svv5iwsDDz9ttv53ttcJ2srosuXbqYxx9/3DUFocDI6tqoUqWKGTt2rNO+2rVrm5dffjkfK4MrJSQkGElmzZo1xhhj0tPTTUhIiBk/fryjzfnz501gYKD54IMPXFUmCiBmXpEn0tPT1a1bN73wwguqUqWKq8tBAZCenq7FixerQoUKatWqlYoXL6569epddckJbh2NGjXSwoUL9ddff8kYo1WrVum3335Tq1atXF0a8kliYqIkKSgoSJIUGxur+Ph4tWzZ0tHGbrerSZMmWr9+vUtqRMFEeEWeeP311+Xu7q6BAwe6uhQUEAkJCTpz5ozGjx+v1q1b64cfflDHjh3VqVMnrVmzxtXlwcUmTZqkypUrq1SpUvL09FTr1q01ZcoUNWrUyNWlIR8YYzRkyBA1atRIVatWlSTFx8dLkoKDg53aBgcHO44BkgW+HhYF37Zt2/Tuu+9q+/btstlsri4HBUR6erok6f7779fgwYMlSTVr1tT69ev1wQcfqEmTJq4sDy42adIkbdy4UQsXLlRYWJjWrl2rvn37KjQ0VM2bN3d1ebjB+vfvr127dumnn37KdOzyf0eMMfzbAifMvOK6rVu3TgkJCSpTpozc3d3l7u6uQ4cO6fnnn1d4eLiry4OLFC1aVO7u7qpcubLT/kqVKnG3gVvcuXPnNGLECE2cOFHt27dX9erV1b9/f3Xp0kVvvvmmq8vDDTZgwAAtXLhQq1atUqlSpRz7Q0JCJCnTLGtCQkKm2Vjc2givuG7dunXTrl27tGPHDsejRIkSeuGFF7Rs2TJXlwcX8fT0VN26dTPdCue3335TWFiYi6pCQZCamqrU1FQVKuT8T5Cbm5tjxh43H2OM+vfvr2+++UYrV65URESE0/GIiAiFhIRo+fLljn0XLlzQmjVr1KBBg/wuFwUYywaQLWfOnNGBAwcc27GxsdqxY4eCgoJUpkwZFSlSxKm9h4eHQkJCFBkZmd+lIh9d67p44YUX1KVLFzVu3FjNmjXT0qVL9d1332n16tWuKxr54lrXRpMmTfTCCy/I29tbYWFhWrNmjWbPnq2JEye6sGrcSP369dPnn3+ub7/9Vv7+/o4Z1sDAQHl7e8tms2nQoEGKjo5W+fLlVb58eUVHR8vHx0ddu3Z1cfUoUFx7swNYxapVq4ykTI/u3btn2Z5bZd0asnNdTJs2zZQrV854eXmZGjVqmAULFriuYOSba10bcXFxpkePHqZEiRLGy8vLREZGmrfeesukp6e7tnDcMFldD5LMjBkzHG3S09PN6NGjTUhIiLHb7aZx48Zm9+7drisaBZLNGGPyNS0DAAAAucSaVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwC3hNWrV8tms+nUqVOuLqXA6tGjhx544IFcndu4cWN9/vnneVtQNqSkpKhMmTLatm1bvo8NwDUIrwAKvPj4eA0YMEBly5aV3W5X6dKl1b59e61YscLVpeWJgwcPymazOR7+/v6qUqWK+vXrp/3799+w8Xbs2JEn/S1atEjx8fF65JFH8qS/nLDb7Ro6dKiGDRuW72MDcA3CK4AC7eDBg7rjjju0cuVKTZgwQbt379bSpUvVrFkz9evXz9Xl5akff/xRcXFx2rlzp6KjoxUTE6MaNWoU+JA+adIkPfnkkypUyDX/pDz22GNat26dYmJiXDI+gPxFeAVQoPXt21c2m02bN2/Wgw8+qAoVKqhKlSoaMmSINm7cKCnrmcRTp07JZrNp9erVWfY7c+ZMFS5cWIsWLVJkZKR8fHz04IMPKjk5WbNmzVJ4eLhuu+02DRgwQGlpaY7zLly4oBdffFElS5aUr6+v6tWr5zRGRr/Lli1TpUqV5Ofnp9atWysuLu6az7VIkSIKCQlR2bJldf/99+vHH39UvXr11KtXL6cavvvuO91xxx3y8vJS2bJlNWbMGF28eNFx3GazaerUqWrTpo28vb0VERGhL7/80nE8IiJCklSrVi3ZbDY1bdrUqY4333xToaGhKlKkiPr166fU1NQr1vzPP//oxx9/VIcOHZz222w2ffjhh7rvvvvk4+OjSpUqacOGDTpw4ICaNm0qX19f1a9fX7///rvjnKioKNWsWVPTp09XmTJl5Ofnpz59+igtLU0TJkxQSEiIihcvrv/85z+ZXrcGDRroiy++uOZrDMD6CK8ACqwTJ05o6dKl6tevn3x9fTMdL1y48HX1f/bsWU2aNElz5szR0qVLtXr1anXq1ElLlizRkiVL9Mknn+ijjz7SV1995TjnySef1P/+9z/NmTNHu3bt0kMPPaTWrVs7vb1/9uxZvfnmm/rkk0+0du1aHT58WEOHDs1xfYUKFdJzzz2nQ4cOOdZ0Llu2TI8//rgGDhyovXv36sMPP9TMmTMzBbpRo0apc+fO2rlzpx5//HE9+uijjpnJzZs3S/q/md5vvvnGcd6qVav0+++/a9WqVZo1a5ZmzpypmTNnXrHGn376yRFOL/fqq6/qiSee0I4dO1SxYkV17dpVvXv31vDhw7V161ZJUv/+/Z3O+f333/X9999r6dKl+uKLLzR9+nS1a9dOf/75p9asWaPXX39dL7/8suM/LhnuvPNOrVu3LpuvLABLMwBQQG3atMlIMt98881V28XGxhpJ5ueff3bsO3nypJFkVq1aZYwxZtWqVUaSOXnypDHGmBkzZhhJ5sCBA45zevfubXx8fMzp06cd+1q1amV69+5tjDHmwIEDxmazmb/++stp/HvvvdcMHz78iv2+//77Jjg4OEf1Z4iJiTGSzNy5c40xxtx9990mOjraqc0nn3xiQkNDHduSzLPPPuvUpl69eqZPnz5XHa979+4mLCzMXLx40bHvoYceMl26dLli7W+//bYpW7Zspv2SzMsvv+zY3rBhg5Fkpk2b5tj3xRdfGC8vL8f26NGjjY+Pj0lKSnLsa9WqlQkPDzdpaWmOfZGRkWbcuHFO47377rsmPDz8inUCuHm4uyYyA8C1GWMk/fsW9I3g4+Oj22+/3bEdHBys8PBw+fn5Oe1LSEiQJG3fvl3GGFWoUMGpn5SUFBUpUuSK/YaGhjr6yKnLX4Nt27Zpy5YtTjOtaWlpOn/+vM6ePSsfHx9JUv369Z36qV+/frY+oFWlShW5ubk51b579+4rtj937py8vLyyPFa9enXHn4ODgyVJ1apVc9p3/vx5JSUlKSAgQJIUHh4uf39/pzZubm5O62kv/Zlk8Pb21tmzZ6/5/ABYH+EVQIFVvnx52Ww2xcTEXPUWThnBJiPoSbrqOs0MHh4eTts2my3Lfenp6ZKk9PR0ubm5adu2bU4BT5JT4M2qj0try4mMt/oz1qmmp6drzJgx6tSpU6a2VwqRl9ZxLVd7/lkpWrSoTp48ec2+MsbOat+l/ef0Z5LhxIkTKlas2BXrBHDzYM0rgAIrKChIrVq10vvvv6/k5ORMxzPu2ZoRWi79UFRe3QbqUrVq1VJaWpoSEhJUrlw5p0dISEiej5eenq5JkyYpIiJCtWrVkiTVrl1b+/btyzR+uXLlnGYnL18TunHjRlWsWFGS5OnpKUlOHwLLrVq1aik+Pv6KATa//PLLL47XCMDNjfAKoECbMmWK0tLSdOedd+rrr7/W/v37FRMTo0mTJjneGvf29tZdd92l8ePHa+/evVq7dq1efvnlPK+lQoUKeuyxx/TEE0/om2++UWxsrLZs2aLXX39dS5Ysue7+jx8/rvj4eP3xxx9auHChmjdvrs2bN2vatGmOmd5XXnlFs2fPVlRUlPbs2aOYmBjNnTs30/P98ssvNX36dP32228aPXq0Nm/e7PhwVPHixeXt7a2lS5fq6NGjSkxMzHXNtWrVUrFixfS///0v9088D6xbt04tW7Z0aQ0A8gfhFUCBFhERoe3bt6tZs2Z6/vnnVbVqVbVo0UIrVqzQ1KlTHe2mT5+u1NRU1alTR88995xee+21G1LPjBkz9MQTT+j5559XZGSkOnTooE2bNql06dLX3Xfz5s0VGhqqatWq6aWXXlKlSpW0a9cuNWvWzNGmVatWWrRokZYvX666devqrrvu0sSJExUWFubU15gxYzRnzhxVr15ds2bN0meffabKlStLktzd3TVp0iR9+OGHKlGihO6///5c1+zm5qaePXvqs88+y3Uf12vDhg1KTEzUgw8+6LIaAOQfm8ntQiwAQIFks9k0f/78XH/Va04dPXpUVapU0bZt2zKF6Pzw0EMPqVatWhoxYkS+jw0g/zHzCgC4LsHBwZo2bZoOHz6c72OnpKSoRo0aGjx4cL6PDcA1mHkFgJtMfs+8AkB+4lZZAHCTYU4CwM2MZQMAAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAy/h8CNbc2s3uJlQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Culmen Depth\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "ax.hist(X1_0, label=\"Adelie\", alpha=alpha, color=cmap(0.0))\n",
    "ax.hist(X1_1, label=\"Chainstrap\", alpha=alpha, color=cmap(0.5))\n",
    "ax.hist(X1_2, label=\"Gentoo\", alpha=alpha, color=cmap(1.0))\n",
    "\n",
    "plt.legend(loc='upper right') \n",
    "plt.title(\"Culman Depth of Different Penguin Species\")\n",
    "ax.set_xlabel(\"Culmen Depth (mm)\")\n",
    "ax.set_ylabel(\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30bed85",
   "metadata": {},
   "source": [
    "# d) Train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bb0942db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T03:14:42.749115Z",
     "start_time": "2024-03-01T03:14:42.736371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training data:  (250, 4) \n",
      " X test data:  (84, 4) \n",
      "\n",
      "y training data:  (250,) \n",
      " y test data:  (84,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# train_test_split(*arrays, test_size=None, train_size=None,random_state=None, shuffle=True, stratify=None)\n",
    "# Note: test_size and train_size is between 0.0 to 1.0.\n",
    "# If None is specified, test_size = 0.25 and train_size = 1- test_size = 0.75\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split (X, y_enc, random_state=1, shuffle=True)\n",
    "\n",
    "print(\"X training data: \", X_train.shape, \"\\n\", \"X test data: \", X_test.shape,\"\\n\")\n",
    "print(\"y training data: \", y_train.shape, \"\\n\", \"y test data: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039857d9",
   "metadata": {},
   "source": [
    "# e) Training models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "290c6bb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T02:55:10.544606Z",
     "start_time": "2024-03-01T02:55:07.429441Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression scores:  [0.6904761904761905, 0.7857142857142857, 0.7857142857142857, 0.7857142857142857, 0.7857142857142857]\n",
      "Linear SVM scores  [0.8690476190476191, 0.9523809523809523, 0.7738095238095238, 0.7738095238095238, 0.7738095238095238]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, \n",
    "            # intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, \n",
    "            # multi_class='auto', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None)\n",
    "# sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, \n",
    "            #tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', \n",
    "            #break_ties=False, random_state=None)[source]\n",
    "\n",
    "C = np.logspace(-2, 2, 5) # Regularization parameter from 10e-2 to 10e2\n",
    "\n",
    "# Logistic regression\n",
    "logreg_scores = []\n",
    "for i in C:\n",
    "    logreg_scores.append(LogisticRegression(penalty='l1', solver='saga', C=i, max_iter=10000, random_state=1)\n",
    "                         .fit(X_train, y_train).score(X_test, y_test))\n",
    "\n",
    "# Support vector classifier\n",
    "SVC_scores = []\n",
    "for i in C:\n",
    "    SVC_scores.append(LinearSVC(C=i, max_iter=10000, random_state=1, dual=True)\n",
    "                      .fit(X_train, y_train).score(X_test, y_test))\n",
    "\n",
    "# Note: the SVM model never converges for any maximum iteration value\n",
    "print(\"Logistic regression scores: \", logreg_scores)\n",
    "print(\"Linear SVM scores \", SVC_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f8acfafa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T02:55:14.617192Z",
     "start_time": "2024-03-01T02:55:14.610234Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C for logistic regression:  0.1\n",
      "Best C for SVC:  0.1\n"
     ]
    }
   ],
   "source": [
    "best_logreg = np.argmax(logreg_scores)\n",
    "best_logreg_C = C[best_logreg]\n",
    "\n",
    "best_SVC = np.argmax(SVC_scores)\n",
    "best_SVC_C = C[best_SVC]\n",
    "\n",
    "print(\"Best C for logistic regression: \", best_logreg_C)\n",
    "print(\"Best C for SVC: \", best_SVC_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "03d9afe9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T03:02:57.871388Z",
     "start_time": "2024-03-01T03:02:57.232259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression confusion matrix:  \n",
      " [[30  0  5]\n",
      " [ 6 12  3]\n",
      " [ 4  0 24]] \n",
      "\n",
      "SVC confusion matrix:  \n",
      " [[34  0  1]\n",
      " [ 1 19  1]\n",
      " [ 1  0 27]] \n",
      "\n",
      "Logistic regression f1 score:  \n",
      " [0.8        0.72727273 0.8       ] \n",
      "\n",
      "SVC f1 score:  \n",
      " [0.95774648 0.95       0.94736842] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jp/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(penalty='l1', solver='saga', C=best_logreg_C, max_iter=10000, random_state=1).fit(X_train, y_train)\n",
    "SVC = LinearSVC(C=best_SVC_C, max_iter=10000, random_state=1, dual=True).fit(X_train, y_train)\n",
    "\n",
    "y_logreg_pred = logreg.predict(X_test)\n",
    "y_SVC_pred = SVC.predict(X_test)\n",
    "\n",
    "logreg_conf = confusion_matrix(y_test, y_logreg_pred)\n",
    "SVC_conf = confusion_matrix(y_test, y_SVC_pred)\n",
    "\n",
    "average = None\n",
    "logreg_f1 = f1_score(y_test, y_logreg_pred, average=average)\n",
    "SVC_f1 = f1_score(y_test, y_SVC_pred, average=average)\n",
    "\n",
    "print(\"Logistic regression confusion matrix: \", \"\\n\", logreg_conf, \"\\n\")\n",
    "print(\"SVC confusion matrix: \", \"\\n\", SVC_conf, \"\\n\")\n",
    "print(\"Logistic regression f1 score: \", \"\\n\", logreg_f1, \"\\n\")\n",
    "print(\"SVC f1 score: \", \"\\n\", SVC_f1, \"\\n\")"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.12.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}