polynomial_regression/05_Regresja_wielomianowa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regresja wielomianowa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# Przydatne funkcje\n",
    "\n",
    "def cost(theta, X, y):\n",
    "    \"\"\"Wersja macierzowa funkcji kosztu\"\"\"\n",
    "    m = len(y)\n",
    "    J = 1.0 / (2.0 * m) * ((X * theta - y).T * (X * theta - y))\n",
    "    return J.item()\n",
    "\n",
    "def gradient(theta, X, y):\n",
    "    \"\"\"Wersja macierzowa gradientu funkcji kosztu\"\"\"\n",
    "    return 1.0 / len(y) * (X.T * (X * theta - y)) \n",
    "\n",
    "def gradient_descent(fJ, fdJ, theta, X, y, alpha=0.1, eps=10**-7):\n",
    "    \"\"\"Algorytm gradientu prostego (wersja macierzowa)\"\"\"\n",
    "    current_cost = fJ(theta, X, y)\n",
    "    logs = [[current_cost, theta]]\n",
    "    while True:\n",
    "        theta = theta - alpha * fdJ(theta, X, y)\n",
    "        current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
    "        if abs(prev_cost - current_cost) > 10**15:\n",
    "            print('Algorithm does not converge!')\n",
    "            break\n",
    "        if abs(prev_cost - current_cost) <= eps:\n",
    "            break\n",
    "        logs.append([current_cost, theta]) \n",
    "    return theta, logs\n",
    "\n",
    "def plot_data(X, y, xlabel, ylabel):\n",
    "    \"\"\"Wykres danych (wersja macierzowa)\"\"\"\n",
    "    fig = plt.figure(figsize=(16*.6, 9*.6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
    "    ax.scatter([X[:, 1]], [y], c='r', s=50, label='Dane')\n",
    "    \n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.margins(.05, .05)\n",
    "    plt.ylim(y.min() - 1, y.max() + 1)\n",
    "    plt.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
    "    return fig\n",
    "\n",
    "def plot_fun(fig, fun, X):\n",
    "    \"\"\"Wykres funkcji `fun`\"\"\"\n",
    "    ax = fig.axes[0]\n",
    "    x0 = np.min(X[:, 1]) - 1.0\n",
    "    x1 = np.max(X[:, 1]) + 1.0\n",
    "    Arg = np.arange(x0, x1, 0.1)\n",
    "    Val = fun(Arg)\n",
    "    return ax.plot(Arg, Val, linewidth='2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(Y_true, Y_pred):\n",
    "    return np.square(np.subtract(Y_true,Y_pred)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Funkcja regresji wielomianowej\n",
    "\n",
    "def h_poly(Theta, x):\n",
    "    \"\"\"Funkcja wielomianowa\"\"\"\n",
    "    return sum(theta * np.power(x, i) for i, theta in enumerate(Theta.tolist()))\n",
    "\n",
    "def get_poly_data(data, deg):\n",
    "    m, n_plus_1 = data.shape\n",
    "    n = n_plus_1 - 1\n",
    "\n",
    "    X1 = data[:, 0:n]\n",
    "    X1 /= np.amax(X1, axis=0)\n",
    "\n",
    "    Xs = [np.ones((m, 1)), X1]\n",
    "\n",
    "    for i in range(2, deg+1):\n",
    "        Xn = np.power(X1, i)\n",
    "        Xn /= np.amax(Xn, axis=0)\n",
    "        Xs.append(Xn)\n",
    "\n",
    "    X = np.matrix(np.concatenate(Xs, axis=1)).reshape(m, deg * n + 1)\n",
    "\n",
    "    y = np.matrix(data[:, -1]).reshape(m, 1)\n",
    "\n",
    "    return X, y\n",
    "\n",
    "\n",
    "def polynomial_regression(X, y, n):\n",
    "    \"\"\"Funkcja regresji wielomianowej\"\"\"\n",
    "    theta_start = np.matrix([0] * (n+1)).reshape(n+1, 1)\n",
    "    theta, logs = gradient_descent(cost, gradient, theta_start, X, y)\n",
    "    return lambda x: h_poly(theta, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_values(model, data, n):\n",
    "    x, y = get_poly_data(np.array(data), n)\n",
    "    preprocessed_x = []\n",
    "    for i in x:\n",
    "        preprocessed_x.append(i.item(1))\n",
    "    return y, model(preprocessed_x), MSE(y, model(preprocessed_x))\n",
    "\n",
    "def plot_and_mse(data, data_test, n):\n",
    "    x, y = get_poly_data(np.array(data), n)\n",
    "    model = polynomial_regression(x, y, n)\n",
    "    \n",
    "    fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "    plot_fun(fig, polynomial_regression(x, y, n), x)\n",
    "\n",
    "    y_true, Y_pred, mse = predict_values(model, data_test, n)\n",
    "    print(f'Wielomian {n} stopnia, MSE = {mse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqrMetres</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78</td>\n",
       "      <td>476118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>459531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>411557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>496416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>406032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>51</td>\n",
       "      <td>299000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>53</td>\n",
       "      <td>339000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>65</td>\n",
       "      <td>320000.0</td>\n",
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       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>67</td>\n",
       "      <td>364000.0</td>\n",
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       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>50</td>\n",
       "      <td>209000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1674 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqrMetres     price\n",
       "0            78  476118.0\n",
       "1            62  459531.0\n",
       "2            15  411557.0\n",
       "3            14  496416.0\n",
       "4            15  406032.0\n",
       "...         ...       ...\n",
       "1669         51  299000.0\n",
       "1670         53  339000.0\n",
       "1671         65  320000.0\n",
       "1672         67  364000.0\n",
       "1673         50  209000.0\n",
       "\n",
       "[1674 rows x 2 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wczytanie danych (mieszkania) przy pomocy biblioteki pandas\n",
    "\n",
    "alldata = pandas.read_csv('data_flats.tsv', header=0, sep='\\t',\n",
    "                          usecols=['price', 'rooms', 'sqrMetres'])\n",
    "alldata = alldata[['sqrMetres', 'price']]\n",
    "alldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alldata = np.matrix(alldata[['sqrMetres', 'price']])\n",
    "data_train = alldata[0:1600]\n",
    "data_test = alldata[1600:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 31777996749.774563\n",
      "Wielomian 2 stopnia, MSE = 80047128653.54173\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for n in range(1, 3):\n",
    "    plot_and_mse(data_train, data_test, n) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.97959184e-01 4.76118000e+05]\n",
      " [3.16326531e-01 4.59531000e+05]\n",
      " [7.65306122e-02 4.11557000e+05]\n",
      " ...\n",
      " [3.31632653e-01 3.20000000e+05]\n",
      " [3.41836735e-01 3.64000000e+05]\n",
      " [2.55102041e-01 2.09000000e+05]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c76378f10>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 2\n",
    "x, y = get_poly_data(data, n)\n",
    "print(data)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ilość nauki do oceny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>number_courses</th>\n",
       "      <th>time_study</th>\n",
       "      <th>Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4.508</td>\n",
       "      <td>19.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.096</td>\n",
       "      <td>7.734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3.133</td>\n",
       "      <td>13.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7.909</td>\n",
       "      <td>53.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>7.811</td>\n",
       "      <td>55.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>6</td>\n",
       "      <td>3.561</td>\n",
       "      <td>19.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3</td>\n",
       "      <td>0.301</td>\n",
       "      <td>5.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>4</td>\n",
       "      <td>7.163</td>\n",
       "      <td>41.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7</td>\n",
       "      <td>0.309</td>\n",
       "      <td>12.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3</td>\n",
       "      <td>6.335</td>\n",
       "      <td>32.357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_courses  time_study   Marks\n",
       "0                3       4.508  19.202\n",
       "1                4       0.096   7.734\n",
       "2                4       3.133  13.811\n",
       "3                6       7.909  53.018\n",
       "4                8       7.811  55.299\n",
       "..             ...         ...     ...\n",
       "95               6       3.561  19.128\n",
       "96               3       0.301   5.609\n",
       "97               4       7.163  41.444\n",
       "98               7       0.309  12.027\n",
       "99               3       6.335  32.357\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_marks_all = pandas.read_csv('archive(1)/Student_Marks.csv')\n",
    "data_marks_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c754ac580>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_marks_all = data_marks_all[['time_study', 'Marks']]\n",
    "data_marks_all = data_marks_all.sample(frac=1)\n",
    "data_marks = data_marks_all[0:70]\n",
    "data_marks_test = data_marks_all[70:]\n",
    "data_marks = np.matrix(data_marks).astype(float)\n",
    "n = 1 # Wielomian pierwszego stopnia\n",
    "\n",
    "x, y = get_poly_data(np.array(data_marks), n)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c755c13d0>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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unTudbceCAiVfIq3QKROwCYN6kNc7g+LSct5Yt5fPnRbgh46ISCfQUA975bj+pCUH+SchIwNmz45iVCLxr1Mu/RhjuN6/1P7UUvUEE5E44fXC/Pkwd67z6PWGPL2qtp5/frQL4NjPRBGJjk6ZgAF84YwckhM9vFO8jx1lKsYXkRhXVAQ5OTBnDtx/v/OYk+M8H8Qrn+zhUGUtp+V05bScblELVUQ6cQLWPS2ZK07vh7UqxheRGBeoY30YXeqf8e8AFJwZYvWrhatqIhKeTpuAAVx/lvND59nlO6mtV2d8EYlRoTrWB+lSv3V/Be9vPkCXpARmjOsf+NpWrKqJSHg6dQI2cVAPhvfOYJ+3mjfWlbodjohI64TqWB+kS33DTNzLx/Sja2rSyde1clVNRMLTqROwpsX46owvIjErVMf6AF3qa+t9PL9iJwDXN+1831QrVtVEJHydOgED+MJ4pxj/bRXji0isCtWxvr7eWbFq4o11e9lfXk1e7wzOyO3ReKBpvdfzz7d4VU1EwtfpE7Ae6clMP60v1sKzy9WSQkRiUNOO9ampJx8/5ZTj6rae9hffXzcpF2OM8+SJ9V5LlgT/epr9KNJmnT4Bg8b+N4XLdlCnYnwRiUX5+bBhA1h7/PNVVcfVbe06dJS3i/eRnODh6vE5zjmB6r2qq4N/Lc1+FGkzJWDApCE9GZadTqm3mjfWqxhfRGLUokWQGKSbvb9u69llO7AWpp7Wl57pyc6xUPVeAMn+8zT7UaTdKAFDxfgiEieauRuyvngTz/lLLa4/c2B41wFcfDHMmwcPPujMhczPb8egRTonJWB+M88YQHKCh7c27mPnQRXji0gMCnU3ZEoKb28+SMnhKgb1SGXy0F7hXZeeDjNnwr33OvMgtfIl0i6UgPn1SE9m2un+YvxlKsYXkRgU6m7I6mqerstyTlv8FzzvvRvedar3EokIJWBNHCvGX65ifBGJQU3vhjxhRas0vQdvDJ9EYn0ds1YsOr6ZaqDrVO8lElFKwJo4a0hPhmals/dINW9u2Od2OCIiLZef79RpPfggfO5zxwronzv9Euo9CVy8aSm9Kw6d3Ey16XWq9xKJuCC3y7QfY0wCsBzYZa29whgzBHgG6AWsAG601tZEOo5wNBTj/3zxOp5eup1LR/VxOyQRkZN5vU7yVFzs1G9Nn+6sVDV8XlAA114Lzz0HNTX4MDw75lIArvv4Vec1KirgH/+AjRsbr8nMdOq8RCTijD2xZ0x7fwFj7gYmAl39CdizwAvW2meMMX8CVltr/y/Ua0ycONEuX748onE2KKuoYfIv3qDO5+OduReR071LVL6uiEhYioqchMvnc5Ko1FSn11fDY3q6c8wYpwt+dTXv5Y7hhut/Qf8jpbzzp1tIsP4Si5QUp99XerpT67V4sVa8RFrIGLPCWjuxpddFdAvSGDMAuByY7//cABcBz/tPeQK4KpIxtFTP9GSmntYXn3Uas4qIdBiBGqZWVR3/WFEBR49CZeWxZqpPj50KwDUfv96YfEFjs1UN2RaJukjXgP0W+C7Q8B3fCzhkra3zf74TyIlwDC12g78Y/1l1xheRjqS5hqkBlHXpyisjzsFYH9d+/BokJAQ/WUO2RaImYgmYMeYKoNRau6KV199mjFlujFm+b190C+InD+3JkKx09hypYomK8UWko2iuYWoAL4y+kJrEJM7fvJIc7z7IzQ1+soZsi0RNJFfApgBXGmO24hTdXwQ8CHQ3xjQU/w8AdgW62Fr7sLV2orV2YnZ2dgTDPJlTjO90iVZnfBHpMEI1TA3AAs/4tx+vW/2Kc+2554Zuuqoh2yJREbEEzFr7P9baAdbawcB1wH+stV8E3gRm+U+7CVgQqRjaYuYZA0hKMLy5oZSSQ0fdDkdEJHTD1ABW5IxkU1YuWeUHufizpc61992npqsiHYAbfcDmAncbYzbh1IQ96kIMzeqVkcLU0U4x/rPLVYwvIh1AoIapqanHP6anQ5cukJbGMxMuB2DWhrdISk9zru3XT01XRTqAiLehaA/RbEPR1Hub9nPD/A/p1y2VorkXkeAxUY9BROQk5eVOsfymTc6W4eWXw6JFjZ8XFHCkqo5Jvy6iyhqWDNrH4BtnHZ9cnfgaBQVKvkRaobVtKCLeiDWWTR7ai8G90th6oJK3NpZy0alqzCoiLjmx+WpD49QGJzRQXbB2G1XWcPbQXgy+7fKTXy8jQ01XRVykUUQheDzm2HzIpz5UMb6IuMDrhblzoVcvuP12uP9+mDMHcnKcpqxBPOO/geg6/w1FItKxKAFrxswJTjH+f9aXsvuwivFFJIqKiqB/fyfpqq2FGv/UtmYap67ZeZhPSo7QPS2JqaP7RjloEQmHErBmZGWkcFlDMf6ynW6HIyKdRTid6YM0Tn16mbP69YXxA0hNCtF4VURcowQsDA2d8QuXbafe1/FvWhCROBBO1/sAjVMrqutYuKoE0PajSEemBCwMZw/txaBeaZQcruLtjeqMLyJREE7X+wCNUxet2U15dR1n5HZnRJ/MIBeKiNuUgIXB4zFcd6a/GF+d8UUkGsLpeh+gcWpj8X2IkUMi4jolYGGaNWEAiR6nGH/P4Sq3wxGReNdc1/uMjJMap67ddZiV2w+RkZLIFWP6RSFIEWktJWBhys5M4bLRfaj3WZ5TZ3wRibRAXe9TUiA52WlLsXs35Ocfd8mjRVsAKDhzIGnJavMo0pHpO7QFbpg0iMVr9vDMsh1848Lh6owvIpGVnw8lJWF1rN99+Cj/Wl2Cx8CXpwyOfqwi0iJKwFrgnGG9yO2ZxvaySt4u3seFp/R2OyQRiXdhdqx/4r1t1Pksl4/px4AeaVEITETaQluQLeDxmGO3daszvohEjdcL8+c7W4/z5zufN1Fx4BBPvVMMwC1la046LiIdjxKwFrpmwkCSEzy8vm4v2w40c4u4iEhbFRU5Y4fmzAk8hqioiOc/fwtHfB4m7PyU8fNub3ZMkYi4TwlYC2VnpjBjXH+sbSx4FRGJiIZu+F5vY0+wpmOIdu+m/vLLeez0qQDcsuzFZscUiUjHoASsFW49bygAzy7fwcGKGpejEZG4Faobvs8H8+bx2qAz2NajPwMP7eGy4g+OPx5gTJGIdAxKwFphRJ9MLjglm6paH3/7YJvb4YhIvArVDb+iAjZs4NHTpwHwleULSLC+44+fMKZIRDoOJWCtdNu5zirYE+9vo6q23uVoRCQuheqGn5zMqpGTWDZwNJlV5Vyz5vXjjwcYUyQiHYcSsFY6e1gvRvfvyv7yahas2uV2OCISj0J1w6+p4dHDTj+wG1a/QkbN0eOPBxhTJCIdhxKwVjLGcKt/FeyRd7bg81mXIxKRuNPQDT9A49VdmdksHj6ZxPo6bl73RuNKWXp6yOtEpGNQAtYGl4/pR79uqWwqLWfJxlK3wxGReJSfD/fe64whauLxiZ+n3pPA5Zs+oN8PvwsPPgjz5jmPJSUnjSkSkY5FCVgbJCV4+MqUIQA8/PZml6MRkbi1YwdUVx/71JvchWfGOq0nZn/wPOzc6XTLv/de51ErXyIdnhKwNrpu0kAyUxL5YHMZa3YedjscEYlHJxTjPzvmUrwp6UzavoYx3t0qtheJQUrA2igzNYnrz8oF4JF3tAomIhHQpBi/znj4y4QrAX/jVRXbi8QkJWDt4OZzBpPoMSxas5udByvdDkdE4k1DUX1mJq+cfiE7u/dl8KHdXLJ3nYrtRWKUErB20L97F64Y0496n+Uv7251OxwRiUf5+VBSwvyrbwdgdl4anpJdKrYXiVFKwNrJLf6WFM8s3c7ho7UuRyMi8WjFgVo+qkqmW5ckZt5xrVa+RGKYErB2clpON6YM70VFTT1PL93udjgiEoceLXLqTL94Vi5pyYkuRyMibaEErB01NGb9y7tbqKkLMkBXRKQVdpRV8vLaPSQlGG46Z7Db4YhIGykBa0fnj8jmlD6Z7D1Szb8/LnE7HBGJI4+9uwWfhc+P7U+frqluhyMibaQErB0ZY7jl3MbGrNZqPJGItN3ho7U8u2wHALPzh7gcjYi0ByVg7ezKcf3Jzkxh/R4vRZv2ux2OiMSBwmXbqaip55xhvRjdv5vb4YhIO1AC1s5SEhO42V+fofFEItJWtfU+Hve3t2lYYReR2KcELAKcO5QSeKd4P+t2H3E7HBGJYYvX7KbkcBVDs9O5YERvt8MRkXaiBCwCuqclc+3EgYDGE4lI61lrebRoCwC35A/F4zEuRyQi7UUJWITMzh+Cx8C/Vpew53CV2+GISAxatvUgH+88TI+0JL5wRo7b4YhIO1ICFiEDe6Yx7bR+1NZbHn9vq9vhiEgMmu9fQb9x8iBSkxJcjkZE2pMSsAhqKJj9+4fbKK+uczkaEYklW/ZX8Nq6vSQnePivswe5HY6ItDMlYBE0PrcHkwb3xFtVR6G/h4+ISDj+8u4WrIWrxvend6Yar4rEGyVgEXbrec54oseKtlBXr/FEItK8Q5U1PLd8JwCz84eGPtnrhfnzYe5c59HrjUKEItJWmuYaYRef2puhWels3l/B4rV7uHJsf7dDEpEO7qml2zlaW8+5eVmc0jcz+IlFRTB9Ovh8UFEB6elw992weDHk50cvYBFpMa2ARZjHY7jFP6T7EY0nEpFm1NT5eMJ/407Dz46AvF4n+fJ6neQLnMeG58vLIx+siLSaErAo+MIZOfRKT2bNrsN8uKXM7XBEpAP798cl7D1SzYg+GZyXlxX8xMJCZ+UrEJ/POS4iHZYSsChITUrgRv9dTI9oPJGIBGGtZf47jY1XjQnReLW4uHHl60QVFbBpUwQiFJH2ogQsSm6cPIiURA9vrC9lU6mKZEXkZO9vPsCnu4+QlZHMleOaqRfNy3NqvgJJT4fhw9s/QBFpN0rAoqRXRgqzJgwAOPY/XBGRph71/2y4cfLg0I1XvV6oqoLa2sDHPR4oKIhAhCLSXpSARdHs/CEYAy+s3EWpV+OJRKTRptJy3lhfSnKih/+anBv8xKIiyMmBefOgpub4Y+npkJnp3AWZkRHZgEWkTZSARdHQ7AwuHdmHmnofT76/ze1wRKQDeexdZ/Vr5hk59MpICXxSoDsfGyQlwS9/CSUlakEhEgOUgEVZQ2PWJz/YxtGaepejEZGOoKyihn+saGi8OiT4iYWFUB/k50ZyMqSmauVLJEYoAYuyiYN6MG5gdw5V1vL8Co0nEhH46/tbqa7zceEp2QzvHaLx6ptvQmVl4GO681EkpigBizJjDLf5V8HmF22h3qfGrCKd2eHKWh4tcrYfb/OuDz5SyOuFF14I/kJpabrzUSSGKAFzwdTRfRnYswvbDlTy2qd73A5HRFz0yDub8VbVMWXHGs7+3jfg/vthzhyn0L6oqPHEwkLn7sZg6ut156NIDFEC5oIEj+EW/4Ddh9WYVaTTOlBezWNFzs+Au5c8EXqkUHFx8O1HgFmzVP8lEkOUgLnkmokD6NYliZXbD7Fsq8YTiXRGf357M5W1Pi7cupIJJetPPqHpSKHmGq9eeGHkAhWRdhexBMwYk2qMWWqMWW2M+cQY8xP/80OMMR8aYzYZYwqNMcmRiqEjS0tO5Cb/eKJfv7pBQ7pFOpnSI1XHhm7fveSJwCc1LayfPj34HZBqvCoScyK5AlYNXGStHQuMAz5njJkM/BJ4wFo7HDgIzI5gDB3a7HOH0q1LEh9sLuPdTQfcDkdEouihNzdRXedjanoVp5cHqQVtGClUVASnnnry8dRUNV4ViVHNJmDGmDuMMT1a+sLW4S9eIMn/ywIXAc/7n38CuKqlrx0vunVJ4qvnO7Vg/++V9VoFE+kkdh06ytNLd2AM3HXDlODF9R6Ps/LV0Hy16oQJGtbCxo1qvCoSg8JZAesDLDPGPGuM+ZwxxoT74saYBGPMKqAUeA34DDhkra3zn7ITyAly7W3GmOXGmOX79u0L90vGnJvPGUxWRgqrdx7m1U/3uh2OiETBH/5TTE29jyvG9OfUYX2dFazMzMYar6YjhRYtcmrBAklMdI6LSMxpNgGz1v4AyAMeBW4Gio0xvzDGDAvj2npr7ThgADAJCLCGHvTah621E621E7Ozs8O9LOakJSdyx0VO755fv7pBfcFE4ty2AxU8t3wnHgNzLslznszPd0YIPfigM+PxwQcbRwoVF588dqiBmq+KxKzEcE6y1lpjzB5gD1AH9ACeN8a8Zq39bhjXHzLGvAmcDXQ3xiT6V8EGALtaH358uG7SQB5+ezMb95azcPUurh4/wO2QRCRCHnyjmDqfZdaEAQzLblK3lZEBswOUxDbc/RgoCWuoERORmBNODdidxpgVwP3Au8Dp1tqvAxOAmSGuyzbGdPd/3AW4FFgHvAnM8p92E7CgLb+BeJCSmHDsf8IPvFZMbX2Q7QYRiWmbSst58aNdJHoMd16cF95FBQWha8R096NITAqnBqwn8AVr7VRr7XPW2loAa60PuCLEdf2AN40xHwPLgNestf8G5gJ3G2M2Ab1wtjY7vavH5zA0O53tZZU8u1wzIkXi0W9f34jPwrVnDmRgz7TwLmqoBQtWI6a7H0VikomFO+8mTpxoly9f7nYYEbfo493c/tRK+nRN4a3vXEhqUoLbIYlIO1m3+wjTHnyH5EQPb33nAvp169KyFygvd5qybtrkbDsWFCj5EukAjDErrLUTW3pdWDVgEh3TTuvL6P5d+aTkCE++v41b/UO7RST2/ea1jQDcMCm35ckXBK8RE5GYpFFEHYjHY/jvqacA8Mclm/BW1bockYi0h493HuK1T/eSmuThGxc2ewO5iHQCSsA6mAtGZHPm4B4crKzlsaKtbocjIsF4vTB/Psyd6zx6vUFP/fWrzurXTWcPpndmarQiFJEOTAlYB2OM4b8vc1bBHnlnMwcralyOSEROUlQEOTkwZw7cf7/zmJPjPH+C5VvLeGvjPtKTE/jq+Vr9EhGHErAO6KyhvThvRDbl1XX86a3P3A5HRJryehtHAzX05qqoaHy+vPy40xtWv2bnD6FnenK0oxWRDkoJWAf1Hf8q2OPvbWXvkapmzhaRqCksDD4ayOdzjvu9t2k/728+QNfURGaf67+ppgVblyISv5SAdVCnD+jGtNP6Ul3n4/f/KXY7HBFpEOZoIGstv/bf+XjbeUPp1iWpRVuXIhLflIB1YHdfOgKPgWeW7mD7gUq3wxERaBwNFEiT0UBLNu5jxbaD9ExP5uYpQ1q8dSki8U0JWAeW1yeTq8bnUOez/PaNjW6HIyIQ1mggay2/8dd+fe38oWSkJLZo61JE4p8SsA7urktGkJRg+OdHu9i4V7UiIq4LYzTQq5/uZc2uw2RnpnDj5MHOOc1tXT7yiGrCRDoRJWAd3MCeaVx3Zi7Wcux/1CLisvx8KCmBBx+EefOcx5ISyM/H52tc/br9gmF0SfaPFAu1dQnw4YeqCRPpRDQLMgaUHqnivP/3JlW1PhZ+cwpjBnR3OySR+OP1OtuAxcVOslRQ4KxqtdDC1SV86+mP6N8tlTe/cwEpiQmNr5+TE94KV2amk9Bp1qNIh9faWZBaAYsBvbumctM5gwH4lVbBRNpfO92dWFfv47evO9+jd1yc15h8QeCty2BUEyYS95SAxYivnTeMzJRE3t64jw82H3A7HJH40Y53J764qoTN+yrI7ZnGrAkDTj6h6dblWWcFf6Em7SxEJD4pAYsRPdKTucXfyPFXr2wgFraORWJCO92dWFvv40H/3cp3XpxHUkKQH68ZGTB7NtxyS1jtLEQkPikBiyGzz3VGmSzfdpAlG/a5HY5IfAizsWpznlu+kx1lRxmWnc5V43OavyCMdhYiEr+UgMWQjJREvnGBM8z3/72yAZ9Pq2AibRZmY9VQqmrrj02smHPJCBI8pvmvG0Y7CxGJX0rAYsx/TR5E366pfLr7CC+t3eN2OCKxrx1Wop5Zup3dh6s4tW8ml5/eL/yvHaKdhYjENyVgMSY1KYE7Lnb+R/7r1zZQVx+kdkVEwtPGlaijNfX84c3PAP/4sHBWv5pqqAm7917nUStfIp1CotsBSMtdO3Egf35rM5v3VfDCR7u4duJAt0MSiW0NK1GFhU7N1/DhzspXGMnQX9/fyv7yasYM6Malo/pEIVgRiQdKwGJQUoKHuy8dwZzCVTz4ejEzxvU/vt+QiBwvnCarDStRLVBeXcef3mpc/TKmhatfItJpaQsyRn1+bH9O6ZPJrkNHefrD7W6HI9JxtVOT1UD+UrSFg5W1TBjUg/NHZLf59USk81ACFqMSPIZvXzYCgD+8+RmVNXUuRyTSAbVjk9UTHSiv5uF3NgPw7cu0+iUiLaMELIZdOqoPYwd2Z395NY+/t9XtcEQ6nnZqshrIL19ej7eqjnPzsjhnWFarX0dEOiclYDHMGMN3LjsFgD8t+YzDR2tdjkikg2mnJqsnWr61jGeX7yQ5wcNPrhzdhgBFpLNSAhbjpgzvxdlDe3Gkqo6H3/7M7XBEYkd6OgwYAPPnw9y5zqPX2+xldfU+fvDiWgBuO28oQ7PVNkJEWs7EwkzBiRMn2uXLl7sdRoe1YttBZv7fe6Qkenj1rvMY1CtIV2+RzsTrhX79gq+AdekCCQlgrXNOerrTeHXx4pCNUB8r2sJP//0pOd278Prd59MlWXcgi3RmxpgV1tqJLb1OK2BxYMKgHlw9PofqOh8/WvCJBnWLANxzT/DkC6C+3inCb0Fx/t4jVfzmNWfg9k+uHH188uX1tng1TUQ6LyVgceJ700fSNTWRtzbuY9Ga3W6HI+Iurxd++9vQ5wT7j0qI4vyfL1pHeXUdl4zszSVNm65GsNWFiMQnJWBxIjszhbnTTgXgp//6lCNVKsiXTqywEEK1hfB4oDbI90iQ4vz3Nu1n4eoSUhI9/PjzTQrvI9jqQkTilxKwOHL9mbmMz+1Oqbea37y60e1wRNxTXAzV1cGPG9M49/FE6enOKKImaup8/HCBU3h/x0XDGdgzrfFgBFtdiEj8UgIWRzwewy+uPp0Ej+GJ97fy8c5Dbock4o68vOAJFsAddzirYIF4PM6ooibmF23ms30VDM1K59bzhh5/foRaXYhIfFMCFmdG9uvK7PwhWAvf++ca6n0qyJdOqKAgeIKVkuI8fv3rzvzHhkQtPd2ZD7l48XFDuHcerOT3bzhJ1E9mjD557mqoZC/AapqICCgBi0t3XpxH/26prN11hCff3+p2OCLR15BIZWY2Jkepqc6jMU6B/kMPOZ9/85swbx48+CCUlJzUguKn//qUo7X1XD6mH+fmBZj3GCrZC7CaJiICSsDiUnpKIv/r7879q1c3sudwlcsRibggP99JqB58EO6+u/Guxyr/90NFhVMg/8c/wve/D7NnH7fyBfDm+lJe/XQv6ckJ/PDyUYG/TqBkL8hqmohIAyVgceqy0X25dFQfyqvr+Nm/P3U7HBF3ZGQ4idXIkZCYGPicIIXyVbX1/HjhJwDMuWQEfbv5V9AC9ftqmuyFWE0TEWkQ5CeSxIP/vXI0727az6I1u5m1oZQLT+ntdkgi7mhFofwfl3zG9rJKTumTyc1TBjtPFhU5rSV8vsbu+Xff3dg9f/bsyP0eRCSuaAUsjuV078Jdl4wA4EcL1nK0pt7liERc0sJC+a37K/jTW85s1Z9ddRpJCR71+xKRdqUELM7dPGUwp/bNZEfZUf7wZrHb4Yi4I1ShfF2dk0D5WWv50cJPqKnz8YUzcpg0pKdzQP2+RKQdKQGLc0kJHn5+9ekYAw+/vZnivZpPJ51Q00L5hrshGxgDp5xybGzQy2v38PbGfXRNTeR/po1sPE/9vkSkHSkB6wQmDOrB9ZNyqa23fP+fazWsWzqn/HzYsOHk56uqjm0jVpQd5qf+m1a+M/UUsjNTwntt9fsSkRZSAtZJzJ16KlkZySzdWsbzK3a6HY6IOxYtgoSEwMd8Pn73yMvsPlzF6TnduOGsQY3HvF6nXUUwxqjfl4i0iBKwTqJbWhI/8Pcx+sXidZRV1LgckYgLQmwjbuzSi0cPpmGMU3if4GkyzLuwsLGPWCC3365+XyLSIkrAOpEZ4/pzzrBeHKys5b6X1rkdjkj0Bbkb0gI//Nzt1OHh+km5jBvY/fgTQtV/AXz8sbNKJiISJiVgnYgxhp9ddRrJCR6eXb6TpVvK3A5JJLqC3A354qgL+DBnND3Tkvju1FNOvq654d7/+Q/k5Bwr5BcRaY4SsE5mWHYGX79gGADf/+caauqC3FYvEo8CjA063CObn198CwDzpo+ke1ryydeFamMBUF2tfmAi0iJKwDqhr18wjMG90iguLWd+0Wa3wxGJrhPGBj3wnT+wP607Ewb1YNYZAwJf0zRxSw6QoDVQPzARCZMSsE4oNSmBe646HYDfvVHMjrJKlyMSiTL/jMi135zHX4+k4zHwsxmn4WlaeH+ihsTtoouCn6N+YCISJiVgnVR+XhYzxvWnqtbHDxeoN5h0Pj6f5QcvrsVn4eZzhjCqf9fmL8rIgJkzWzTWSEQkECVgndj3Lx9JZmoiSzbs4+W1e9wORySqnl2+g1U7DtE7M4W7Ls0LfqLXC/Pnw9y5zuP06cHrwTwe9QMTkbAoAevEememMvdzpwLwv//6BG9VrcsRiURHWUUN9728Hmj4j0hS4BOLipy7G+fMgfvvdx5PPRXuu++4Qn7S0xvrxNQPTETCkOh2AOKuGybl8vyKnazacYjfvLaRH39+tNshiUTc/S+v51BlLecM68WVY/sHPqnhrsam/b0aeoHNmwcbNzqd9TdtcrYdCwqUfIlI2CK2AmaMGWiMedMY86kx5hNjzJ3+53saY14zxhT7H3tEKgZpnsdj+PnVTtfvJ97bytpdh90OSSSiVmw7yDPLdpCUYPjpjNMwJkjhfWGhc1djID6fk3zNng333us8KvkSkRaI5BZkHfBta+0oYDJwuzFmFDAPeMNamwe84f9cXDS6fze+fM5gfBa+98811PtUkC8x5MQarRAd6b1VtXz72VUA3HLuUIb3DpE0hep+r7sdRaSNIrYFaa3dDez2f+w1xqwDcoAZwAX+054AlgBzIxWHhOeuS0ewaM1uPt55mL9/uI0vnT3Y7ZAkXnm9zupScbHTYb6gwKmfao2iImebsL4eKishMRG++U1YuBAuu+y4U621fP+fa9l6oJJT+2Zy58UhCu+hsft9oCRMdzuKSBuZaLQfMMYMBt4GTgO2W2u7+583wMGGz0+45jbgNoDc3NwJ27Zti3icnd0rn+zhq0+uIDMlkdfuPp++3VLdDkniTUPC5PM5iU16unPn4OLFTp+tlvB6nQL5YCter7xyXBJWuGw7c/+xhrTkBP51Rz7DsjMaXydQQhjq9TMznZ5g2nYU6fSMMSustRNbel3E74I0xmQA/wDmWGuPND1mnewvYAZorX3YWjvRWjsxOzs70mEKcNmoPlwysjfe6jrufOYj6uo1pkjaUdOi9oZVpYqK1o/wKSx0Vr6CmTHj2Gtu3Ovlxws/AeCeq05rTL4C3eXYdKbj178OSUmN3e91t6OItJOIJmDGmCSc5Ovv1toX/E/vNcb08x/vB5RGMgYJnzGGX3zhdLIzU/hwSxkPvL7R7ZAknjRX1N7SET7Fxc62YzD19VBYyNGyQ9z+0BtU1fqYmVnJF/K6OcdDJYRTp0L//vDQQ1BbC8Y4Sdg3v+msfLV0tU5E5ASRvAvSAI8C66y1v2lyaCFwk//jm4AFkYpBWq53Ziq/u248HgMPvfkZb25QfiztJNpF7bW18Oab/OTmn1Fck8SwAzv46a++1rjCFSohrKx0Vs8a4q2uhpoa+OMf2zdGEem0IrkCNgW4EbjIGLPK/2s6cB9wqTGmGLjE/7l0IGcP68W3LzsFgLsKV1Fy6KjLEUlcaChqD6SlRe1eb/PJUFoaC9bs4ZlRF5FcV8MfFvyS9ENljStfn3wSPCEMRsO2RaSdRCwBs9YWWWuNtXaMtXac/9dia+0Ba+3F1to8a+0l1tqySMUgrff184dx/ohsDlXWcvtTK6mpUz2YtFFBQfuN8CkshGZuINqS2ZvvXfw1AH78+sOM3Le18aDPB2VlwRPCYNR+QkTaiUYRSUAej+GBgnH065bKR9sPcb9/bItIqzUUr7fHCJ9Q25lAdWoX7rjhp1Qkd+Hy9e9ww+qXjz+hogKysoInhMGo/YSItBMlYBJUz/Rk/nDDeBI9hvlFWzSwW9ouP98pYn/wQWecz4MPwoYNsH59WI1Ujwm1nZmSwr3ff5S1yT0ZeHgv9770e07qdZ+eDqNGBU4IMzKgS5fAr61h2yLSTqLSB6ytJk6caJcvX+52GJ3W/Hc2c8+idWSmJrLojnPJ7ZXmdkgSL1rbFyxEj65XxlzIV6d9mySP4flnv8fYz1affH3TPl7l5c6WZtOZjqtWtV+/MhGJa63tA6YETJplreW2J1fw2qd7OS2nK89/7RxSkxLcDktiXVsbnQZI3nZ27c302b/nSC384PKR3GJKWp9IBUrM1PtLRE7Q2gQsYqOIJH4YY/jVrLFc8Yd3WLvrCD9ftI6fXXWa22FJrAunL9i11wYfW9SwnelPkmqHDedbVSM4svMIF5/am9n5Q8AMPe6cFiVSGRnOkG0RkQjQCpiE7eOdh5j1f+9TU+/j99eP5/Nj+7sdksSyuXOd7vPBfPGLzkzHMFevfvnyev5vyWf07ZrK4jvPpWd6cnhxtOdsShHpdDrsKCKJH2MGdOeHV4wEYN4/PmbzvhaOjhFpKlQhfVoaPP982GOL3tq4j/9b8hkeA7+7fnz4yVdzo4hERCJECZi0yH9NHsQVY/pRUVPPN/6+kqraELP4REIJ1RfM54OEIHWGJzRDLT1Sxd2FqwC465IRTBrSM7yv396zKUVEWkAJmLSIMYb7Zo5haFY66/d4+fGCT9wOSWJVqL5gX/hC8DmPTZqh1vsscwpXcaCihinDe/GNC8Po0eX1Ou0urrnGGS8UiDrei0iEKQGTFstISeShL55BSqKHwuU7eH7FTrdDklgVqC9YSQlceGFYY4seenMT7312gKyMZB4oGEeC56SOX8druuX4yivOjMdA1PFeRCJMRfjSas8u28F3//ExqUkeFtyezyl9Vbgs7SSMFhUfllZz/SMfYIG/fmUS5+Zlt/41T5Se7iSDugtSRJqhInyJumsmDmDmGQOoqvXxjb+voKK6zu2QJF40M7aozCTzrWc+wmeduaXHJV9eL/zhD/C5zzm//vCHxjsdg7W9OJE63otIhKkPmLSaMYafXTWaNbsOsXFvOd/75xp+WzAOY5rZBpLYFc2WDSf0+Wro4WXT0/nvJ5az90g1Ewf14O5LRzTG9eabzt2TTWu7XnkFvvtduPrqkPMjgeNbXajpqohEkLYgpc02lXq58g/vUllTzy+uPp0bzsp1OySJhNaODWpnDaOxunVJYvGd55KzdoUTV3198MJ9gJQU587KQOckJ8PFF8PMmep4LyItolFE4qoFq3Zx5zOrSE708MLXz+G0nG5uhyTtqa1jg9rydZusuK3Kn8asv66mzmd55EsTuXRgWvh1XcnJYEzgwvtI/h5EJK6pBkxcNWNcDjeclUtNnY/bn1rJkapat0OS9hTO2KD2dkKT1MPf/R53PPgKdT7Ll6cM5tJRfVpW11VTA7NmBa0rU/IlItGkBEzazY+uGMWofl3ZdqCSuc9/TCysrkqYiouD109FomXDCU1SfRj+57zZ7OjWh9NLNzPv3IHNx3Wi5GSnvUWgthdR3EIVEQEV4Us7Sk1K4I9fPIPP/76Il9bu4fH3tvLlKUPcDkvaQ8PYoEDJTpO+XGFrrpi/ycqWBX528S0sPjWfjOpKfv/Kb0n5R5rTIiJUXCdKTm6s71J7CRFxmVbApF0Nzkrn/lljAPjF4nV8tP2gyxFJuwg1NqilLRvCmb/YZGXrN+f+F3+ZOIPkulr++OK9DC7Z3LjiFiquprp0gZde0jajiHQYSsCk3U07vR9fnjKY2nrLV59cwZb9YW4RScfVTF+usBObcOcv+le2/jRpJr8/5zoSfPX8buH9nLf1o+NX3ALFlZbm3PE4eTJMm+b0ASst1TajiHQo2oKUiPifaSNZv9vL+5sPcMMjH1B429nk9kpzOyxpiyB9uVq0qhROMf/s2VBQwJOPvsR9538ZY338atEDfK74fee8E1fc2iMuEZEoUwImEZGc6OHRmydy82PLWLq1jOsf+YDCr05mQA8lYRETjSapba2fCrOY/4Xiw/zw/K8AcM+SR7n60yWhm6SqrktEYowSMImYtOREHvvymXzp0Q9Zuf0Q1z/yAc9+9Wz6devidmjxJ1CT1LvvjnqT1GaFUcz/8trd/PdzqwH43sVD+WLe5+GikVrZEpG4okasEnFHqmq5cf6HrN55mCFZ6Txz22T6dE11O6z44VaT1NZoJtYl76zl1mfXUltv+dbFec6YIRGRDkyNWKXD6pqaxF+/chaj+3dly/4KbnjkA/Z5A3Qjl9Zxo0lqa4Uo5v/wyYV89blPqK23zM4fwl2X5Lkbq4hIBCkBk6jolpbE32afxal9M/lsXwVfnP8BB8qVhLWLaDdJbauGovkmzVBXL1/P7JXVVNf5uO7Mgfzg8pEa6i4icU0JmERNj/Rk/nbLWeT1zmDj3nK+OP9DDlbUuB1W7GuoqwqkNU1So6GhaP7ee1l/+TV86em1lFfXceXY/vz86tOVfIlI3FMCJlGVlZHC3289i6HZ6azf4+XGxz7kcKXmRrZJezZJjbIt+yv4r/lLOXy0lktG9uHX144lwaPkS0TinxIwibremak8fetkBvdKY+2uI3zpsQ81vLst2qtJapTtOnSULz7yAfvLq5kyvBd/uGE8SQlNfiR5vTB/Psyd6zwGKtwXEYlRugtSXFNy6CgFD7/PjrKjTBjUgye+MomMFHVGabXy8phpRlrqreLaP73P1gOVTBjUgydnTyItucmffaC2Gg09wDpSWw0R6fRaexekEjBx1Y6ySq57+AN2HTrKpME9efwrZx7/D7F0XK1s/HqwoobrHv6ADXu9jO7fladunUy3LknHv26stNUQkU5PbSgkJg3smcZTt55F366pLN1axuzHl3O0pt7tsKQ54QzUhpO2Eb37D3LzX5ayYa+X4b0z+OtXJh2ffEFstdUQEWklLTWI6wb1SuepW8+i4OEPeH/zAW57cjmPfGkiqUkJbocmgTQdqN2goQ3G9OmNK1QnbCMe7daD2W8dYnXOSAb27MLfZp9Fr4yUk18/1tpqiIi0glbApEMYmp3B07eeRa/0ZN4p3s/X/7aC6jqthHVI4axQNU3SKiqoTkjka5fdydKckfQtP8BT159O324nTENoWC1bvRqSkwO/fkdtqyEi0kJKwKTDGN47k6dunUyPtCTe3LCP2//+ETV1Qf6hF/eEs0LVJEmrMx7u/Px3eGvoRHpVHOJvL97DwFcXHn9d0y3NV16BmiD94Tp4Ww0RkXApAZMO5ZS+mfztlrPo1iWJ19ft5c5nPqKuvpMnYR2tHUM4jV/9SZoPw3enz+HlU6bQtaqcvz77Q4bvKj5+G/GE1bKgr9vB22qIiLSEEjDpcEb378bfZp9FZmoiL63dw13Pro7vJCxUghVusXs0hdP4NS8Pb/cs7pjxXV447SLSao7yl+f+l9GlW07eRgy1pZmSAtOmOWOLSkrUgkJE4oaK8KVDOn1AN/76lUnc+OhS/rW6hESP4VfXxGGX9ED9ru6+21npGTs2vGL3aGtYiQrWpysjg08vuJzbC2BL936kV1fyyAv3MKFkvXP9iduIobY0q6ud92H27Mj/vkREokgrYNJhjc/tweNfPpO05AT++dEurn/kA3YfPup2WO0n0NZbRUXj80880THbMXi9sH69kxTNnOkkjP4VKjtlCoXLtnP146vY0r0fpx7Yzr+e+x7nbP84+DZiLM6yFBFpI62ASYc2cXBPnvjKJL7x95Us3VLGtAff4ZczxzB1dF+3Q2u7UFtv1dXwq191vHYMITrUVyan8oPnVvPCyl0AFEwcyE8uzid1cnLo7vwFBU4SF4iK7kUkTikBkw7vzME9eenOc/nv51azZMM+vvrkCv5rci4/uHxUbPcKC7X1VlMD27YFv9aNlaEQ/b82ffEWvvGt/2Pjvkq6JCVwz1WnMXPCAOec5rYPw9jSFBGJN0rAJCZkZaTw2E1n8pf3tnLfS+v42wfbWbblIL+7fjyn9G1+/E2H1LD1FiwJC8WNlaEgK3YvjrqA7039JpX7KhneO4M/fvEMRvRp4Z9Jfr5T0xYjsyxFRNpKsyAl5qzddZhvPf0Rm/dXkJLo4YdXjOKLZ+ViTIwV6IeaeRhKSgq8/nr07wicO9e5E9OvKiGJn1xyG0+PmwbAVZTy85/cSLoGqotIJ6JZkNJpnJbTjX/dkc81EwZQXefjBy+u5Wt/W8GhyiDNOzuqhq23zMzgReiB3H67O+0YmhTLb+3ejy/c+CueHjeN5Loa7v3Pn3lguK91yVdH63MmIhIFWgGTmLZg1S5+8M+1eKvr6Nctld8WjOOsob3cDqtlysudrbd//AP+8x+nAD+Y9HTnjkM32jL4V+wW9x/Dd6fdSXlKGoPLSnhowb2MPrq/dW0xQhT1q+eXiMSC1q6AKQGTmLejrJJvPfMRH20/hMfAHRflccdFw0lMiLEF3nC2JDMzXev/VVPn4xfz3+Dxrc5K4/T1Rdz39ny61lW3LmEK9ft18fcpItIS2oKUTmtgzzSe/erZ3H7hMCzw4BvFXP/IB+w6FGM9w5puSaaeMKg6NdXVUTw7D1ZyzZ/f5/GtNSR5DP+bfYSHToWu/+++1neoD2eot4hInFK1rMSFpAQP35l6KlOGZTGncBXLth5k2m/f5pczxzDt9H5uhxe+pncDfvopHDgAvXrBqFGu3RX4xrq93P3sag4frSWnexce+uIZjBvYHbg++EVer/N7KC52ascKCpwEsqlwhnqLiMQpJWASV84ZnsXLc87jO8+t5o31pXz97yu5flIuP7piFF2SY6RnWEZGhxi9U1vv41evbuDPb20G4OJTe/Pra8fSPS059IWhxis1XSkL1YZDHfBFJM6pBkzikrWWJ97byi8Wr6em3kde7wx+d/14Rvbr6nZo4QlnBSmC9hyu4o6nV7Js60ESPIbvTj2FW88diqe5WZwtqetSDZiIxAHVgIk0YYzh5ilDePH2KQzLTqe4tJwZD73LX9/fSof/T0dRkZOYzJnj9N2aM8f5vKgo4l+61FvF/S+v59IH3mLZ1oP06ZrCM7dN5qvnD2s++YKW1XUFasMRbF6kiEic0RakxLVR/bvyrzvy+em/PuWZZTv40YJPWPTxbm49dygXntqbhHCSimgKMe6H6dMjtir02b5y5r+zmX+s2EVNvZNAXXhKNr+6Ziy9MlLCf6GW1nWpA76IdFIRS8CMMY8BVwCl1trT/M/1BAqBwcBW4Fpr7cFIxSACkJacyH0zx3BuXjb/88LHfLiljA+3lJHbM40vnT2IayYOpFuXJLfDdISzgtSO9WErtx/kz299xquf7sVaMAY+N7ovt50/lDNye7T8BVtT19VBat5ERKIpYjVgxpjzgHLgr00SsPuBMmvtfcaYeUAPa+3c5l5LNWDSXg5X1vLs8h088f5Wdh502lSkJScw84wB3HTOYIb3dnnl5YRxPyeZNw/uvbdNX8Lns/xnfSkPv72ZpVvLAEhO9DDzjAHceu4QhmYHeQ/CqUtTXZeIdDKtrQGL2AqYtfZtY8zgE56eAVzg//gJYAnQbAIm0l66pSVx63lD+Ur+EN5Yt5fH39vKe58d4MkPtvHkB9s4Ny+LL08ZzAUjeodX89TeInhnYE2djwWrdvHw25spLi0HoGtqIjeePYibzhlM78zU4BeHe2djQ/1WsO72Sr5ERIAI3wXpT8D+3WQF7JC1trv/YwMcbPg8FK2ASSRt2OPlife38sLKnVTVOtt/g3ul8aWzBzNr4gC6pkZxezICK0hHqmp5+sPtPPbuFvYeccYc9euWyuz8IVw3KZeM5uY3tiamhvFKqusSkTjXIUcRhUrA/J8ftNYGLDQxxtwG3AaQm5s7Ydu2bRGLUwTgUGWNsz353rZjXfTTkxOYNWEAXzpnMMOCbc211Ylbe7m5MGtWm+cj7j1SxWPvbuGpD7bjra4D4NS+mdx23lA+P7Y/SeGOapo/37kTM9iqnFuzKUVEOoAOtwUZxF5jTD9r7W5jTD+gNNiJ1tqHgYfBWQGLVoDSeXVPS+a284YxO38or6/by+PvbuX9zQd44v1tPPH+Ns4fkc3NUwZzfl52+21PBhtG/fzzsGNHq1aQNpV6efjtzfzzo13U1jvfOpOH9uRr5w/j/BHZOIvPLaCO9SIi7S7aCdhC4CbgPv/jgih/fZFmJXgMU0f3Zerovqzfc4Qn3tvKCyt38dbGfby1cR9DstK56exBzJwwgMy2bE+Gajkxa1bY241lFTUU7/VSXFrOkg37eH3dXgA8Bi4/vR+3nTeUsQO7n/y1w230GqouLSnJidPrjWqjWBGRWBfJuyCfxim4zwL2Aj8GXgSeBXKBbThtKMqaey3VgInbDlbUULh8B0++37g9mZLoYUhWOgN7ppHb5NfAnmkM6NGF1KRmRh+1YGvPWsu+8mo27S2nuLSc4lIvxXvL2VRazoGKmuMuTUn0cM3EAdySP5TBWenHv67XC/fcAw884PScqKlpfoszVA0YQFoaJCS0eItURCQedMgasPaiBEw6irp6H6+v28tj725l6ZbQ/3fo2zX1WEKW2zON3F5djn2enZGCmTfvpJYTFtiT2YviXrkUF3yZTZPOp9ifdB0+Whvw62SkJDK8dwZ5vTM4tV9XZozrT1ag5qlFRTBtmlMgH0ioIv+GrdL6eqisbPn1IiJxSgmYSJQdrqxle1nlcb92+B93HTpKvS/491ZqkodcU03uhtUMOFBCRXIXirMGsqlXLuUpaQGv6ZqayIg+meT1yWB470zyemeQ1yeDvl1Tm6/ram4VC5ovqC8vh9tvh6eegrq6ll8vIhKHYqUIXyRudEtL4vS0bpw+oNtJx+rqfew+XHUsITsxQTtYWctGktg4ZCIMOf7anpWHyTu4i7wrLyEvpwd5vTMY3ifDWTVraQF9g1Ad9hs0V1CfkQF9+wZOvsK5XkREjlECJhIBiQkeBvq3G88JcPxIVS07yirZ8e5Ktv/+YbrUVJNXUkxe5X561VT466nGt19Aoe5kbBBOo9cINooVEelMtAUp4rZoNC0NVfDfIJwaLo0aEhE5jrYgRWJVW4ZRh9tOoqDAGR0UKoZwRgVp1JCISLvQCpiIm1rSj+tEwZq4BmsHceL5KSlgLdx1F/zgBy1LnjRqSEQE0F2QIrGnpQlUU63dClTiJCLSrrQFKRJLQnXBnzYNdu8OnRiFuqvR53OOB9rWbMt2p4iItJswp/GKSLsKlUCVlzvd6kPRfEYRkZimBEzEDc21hXjggeAd66GxHUQgbWkH4fU6d0zOnes8hmrcKiIiraYETMQNeXmQnBz8uDHOKlkwBQVOvVggHo9zvKWKipy6sjlznBFJc+Y4nxcVtfy1REQkJCVgIm4oKHDuQAymujr0NmJDO4jMzMaVsPT0xuebK6w/caWrpKSxJq1hZa6iorFWLdRqnIiItJiK8EXckJnptH84YRj3MeFsI+bnO4lTS+9qDHT35Te/6ay6BRKqqF9ERFpFK2AibgnVeyvcbURrnV8+X+PHoTS9+7LpSld1NVRVBb5GRf0iIu1OK2AibsnMhJdeCq+rfKCGratXOy0ramudBColxVlVe+ml4H3EwhnKfSLNeBQRaXdKwETcFM42YqAtw7vuaky8GlRXO7+mTnVea9GikzvshzOU+0StLeoXEZGglICJuC1Uc9RQDVuDqayEIUMgMbExYbv7bmdVraF9RaDXSE11HhMSNONRRCTClICJdGSt2TKExtUwaEy2pk+HDRuCD+VOSoKNG52VM40qEhGJKCVgIh1N03qv1atbvmUYjM/nrGYtXhy87qxvX93tKCISBUrARDqSE+u9QjVrbamGuxlnz25d+woREWk3SsBEOopA9V41Ne33+k3vZtRQbhERV6kPmEhH0Vy9V0qK89jQ8f6hh5xEqmGVLDnZOZaWFvh63c0oItJhaAVMpKNorkXERRfB2LHHbxl+6UsnbyWuWhVebzEREXGNEjCRjiJUi4j0dJg58+Rtw0Bbia0dUSQiIlFjbHOjSzqAiRMn2uXLl7sdhkhkeb2Qk3N8DViDzEwnqVISJSLSoRhjVlhrJ7b0OtWAiXQUmZnONmFmprPiBY31Xto+FBGJK9qCFOlItH0oItIpKAET6WjUIkJEJO7FRA2YMWYfsM3tODqBLGC/20F0MnrPo0vvd/TpPY8uvd/Rd4q1NrOlF8XECpi1NtvtGDoDY8zy1hQSSuvpPY8uvd/Rp/c8uvR+R58xplV3CaoIX0RERCTKlICJiIiIRJkSMGnqYbcD6IT0nkeX3u/o03seXXq/o69V73lMFOGLiIiIxBOtgImIiIhEmRKwTswY09MY85oxptj/2CPIefXGmFX+XwujHWesM8Z8zhizwRizyRgzL8DxFGNMof/4h8aYwS6EGVfCeM9vNsbsa/L3+hY34owXxpjHjDGlxpi1QY4bY8zv/H8eHxtjzoh2jPEkjPf7AmPM4SZ/v38U7RjjjTFmoDHmTWPMp8aYT4wxdwY4p0V/z5WAdW7zgDestXnAG/7PAzlqrR3n/3Vl9MKLfcaYBOAhYBowCrjeGDPqhNNmAwettcOBB4BfRjfK+BLmew5Q2OTv9fyoBhl/Hgc+F+L4NCDP/+s24P+iEFM8e5zQ7zfAO03+fv80CjHFuzrg29baUcBk4PYAP1da9PdcCVjnNgN4wv/xE8BV7oUStyYBm6y1m621NcAzOO97U03/HJ4HLjbGmCjGGG/Cec+lHVlr3wbKQpwyA/irdXwAdDfG9ItOdPEnjPdb2pm1dre1dqX/Yy+wDsg54bQW/T1XAta59bHW7vZ/vAfoE+S8VGPMcmPMB8aYq6ITWtzIAXY0+XwnJ3/THjvHWlsHHAZ6RSW6+BTOew4w079N8LwxZmB0Quu0wv0zkfZztjFmtTHmJWPMaLeDiSf+MpHxwIcnHGrR3/OY6IQvrWeMeR3oG+DQ95t+Yq21xphgt8QOstbuMsYMBf5jjFljrf2svWMViaJ/AU9ba6uNMV/FWYG8yOWYRNrLSpyf2+XGmOnAizjbYtJGxpgM4B/AHGvtkba8lhKwOGetvSTYMWPMXmNMP2vtbv8yaWmQ19jlf9xsjFmCk/krAQvPLqDp6soA/3OBztlpjEkEugEHohNeXGr2PbfWNn1/5wP3RyGuziyc7wNpJ00TA2vtYmPMH40xWdZazYhsA2NMEk7y9Xdr7QsBTmnR33NtQXZuC4Gb/B/fBCw48QRjTA9jTIr/4yxgCvBp1CKMfcuAPGPMEGNMMnAdzvveVNM/h1nAf6wa9LVFs+/5CXUZV+LUc0jkLAS+5L9LbDJwuEn5g7QzY0zfhjpSY8wknH/r9Z+6NvC/n48C66y1vwlyWov+nmsFrHO7D3jWGDMb2AZcC2CMmQh8zVp7CzAS+LMxxofzTXyftVYJWJistXXGmG8CrwAJwGPW2k+MMT8FlltrF+J8Uz9pjNmEU1h7nXsRx74w3/NvGWOuxLmzqQy42bWA44Ax5mngAiDLGLMT+DGQBGCt/ROwGJgObAIqgS+7E2l8COP9ngV83RhTBxwFrtN/6tpsCnAjsMYYs8r/3PeAXGjd33N1whcRERGJMm1BioiIiESZEjARERGRKFMCJiIiIhJlSsBEREREokwJmIiIiEiUKQETERERiTIlYCIiIiJRpgRMRDoFY8yZ/uHbqcaYdGPMJ8aY09yOS0Q6JzViFZFOwxhzD5AKdAF2WmvvdTkkEemklICJSKfhnw25DKgCzrHW1rsckoh0UtqCFJHOpBeQAWTirISJiLhCK2Ai0mkYYxYCzwBDgH7W2m+6HJKIdFKJbgcgIhINxpgvAbXW2qeMMQnAe8aYi6y1/3E7NhHpfLQCJiIiIhJlqgETERERiTIlYCIiIiJRpgRMREREJMqUgImIiIhEmRIwERERkShTAiYiIiISZUrARERERKJMCZiIiIhIlP1/MhIA5d/lb1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 2 # Wielomian drugiego stopnia\n",
    "\n",
    "x, y = get_poly_data(np.array(data_marks), n)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.024\n",
      "[[1.         0.80130703 0.64209295]]\n",
      "[0.8013070252607767]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([37.16059534])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "n = 2 # Wielomaian pierwszego stopnia\n",
    "x, y = get_poly_data(np.array(data_marks), n)\n",
    "model = polynomial_regression(x, y, n)\n",
    "\n",
    "index = 2\n",
    "print(data_marks[index].item(1))\n",
    "print(x[index])\n",
    "print([x[index].item(1)])\n",
    "model([x[index].item(1)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 465.8122515203192\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# data_marks_test\n",
    "plot_and_mse(data_marks, data_marks_test, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  archive.zip\n",
      "replace insurance.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: ^C\n"
     ]
    }
   ],
   "source": [
    "!unzip archive.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>33.060</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7749.15640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>32.300</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>29186.48236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>26.840</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>17085.26760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>24.225</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>8965.79575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>29.040</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>7243.81360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>20.700</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1242.81600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>37.290</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8978.18510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>34.600</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2020.17700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>25.840</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>5266.36560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>36</td>\n",
       "      <td>male</td>\n",
       "      <td>35.200</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>38709.17600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges\n",
       "309    41  female  33.060         2     no  northwest   7749.15640\n",
       "696    53  female  32.300         2     no  northeast  29186.48236\n",
       "261    20  female  26.840         1    yes  southeast  17085.26760\n",
       "937    39  female  24.225         5     no  northwest   8965.79575\n",
       "891    36  female  29.040         4     no  southeast   7243.81360\n",
       "...   ...     ...     ...       ...    ...        ...          ...\n",
       "584    19    male  20.700         0     no  southwest   1242.81600\n",
       "1066   48    male  37.290         2     no  southeast   8978.18510\n",
       "1025   21  female  34.600         0     no  southwest   2020.17700\n",
       "831    36  female  25.840         0     no  northwest   5266.36560\n",
       "49     36    male  35.200         1    yes  southeast  38709.17600\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ins = pandas.read_csv('insurance.csv')\n",
    "data_ins = data_ins.sample(frac=1)\n",
    "data_ins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33.06 41.  ]\n",
      " [32.3  53.  ]\n",
      " [26.84 20.  ]\n",
      " ...\n",
      " [34.6  21.  ]\n",
      " [25.84 36.  ]\n",
      " [35.2  36.  ]]\n"
     ]
    }
   ],
   "source": [
    "data_ins = data_ins[['bmi', 'age']]\n",
    "data_ins = np.matrix(data_ins).astype(float)\n",
    "print(data_ins)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8c75c617c0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1 # Wielomaian pierwszego stopnia\n",
    "\n",
    "x, y = get_poly_data(np.array(data_ins), n)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.735 1.0\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'a' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_6535/4031094360.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_ins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
     ]
    }
   ],
   "source": [
    "n = 1 # Wielomaian pierwszego stopnia\n",
    "\n",
    "x, y = get_poly_data(np.array(data_ins), n)\n",
    "model = polynomial_regression(x, y, n)\n",
    "\n",
    "index = 10\n",
    "print(data_ins.item(index), x.item(index))\n",
    "a([x.item(index)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2 # Wielomian 2 stopnia\n",
    "x, y = get_poly_data(np.array(data_ins), n)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 3 # Wielomian 3 stopnia\n",
    "x, y = get_poly_data(np.array(data_ins), n)\n",
    "fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "plot_fun(fig, polynomial_regression(x, y, n), x)"
   ]
  }
 ],
 "metadata": {
  "author": "Paweł Skórzewski",
  "celltoolbar": "Slideshow",
  "email": "pawel.skorzewski@amu.edu.pl",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "lang": "pl",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "livereveal": {
   "start_slideshow_at": "selected",
   "theme": "white"
  },
  "subtitle": "5.Regresja wielomianowa. Problem nadmiernego dopasowania[wykład]",
  "title": "Uczenie maszynowe",
  "year": "2021"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}