{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regresja wielomianowa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![polynomial_regression](regression.png)\n",
    "\n",
    "Celem regresji wielomianowej jest zamodelowanie relacji między zmienną zależną od zmiennych niezależnych jako funkcję wielomianu n-tego stopnia.\n",
    "\n",
    "Postać ogólna regresji wielomianowej:\n",
    "\n",
    "$$ h_{\\theta}(x) = \\sum_{i=0}^{n} \\theta_i x^i $$\n",
    "\n",
    "Gdzie:\n",
    "\n",
    "$$ \\theta - \\text{wektor parametrów modelu} $$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funkcja kosztu\n",
    "![MSE](mse.webp)\n",
    "\n",
    "W celu odpowiedniego dobrania parametrów modelu, trzeba znaleźć minimum funkcji kosztu zdefiniowanej poniższym wzorem:\n",
    "\n",
    "$$ J = \\frac{1}{2m} (X  \\theta - y)^T  (X  \\theta - y) $$\n",
    "\n",
    "Gdzie:\n",
    "\n",
    "$$ m - \\text{ilość przykładów} $$ \n",
    "\n",
    "Za funkcję kosztu przyjmuje się zmodyfikowaną wersję błędu średniokwadratowego. Dodatkowo dodaje się dzielenie przez 2*m zamiast m, aby gradient z funkcji był lepszej postaci."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metoda gradientu prostego\n",
    "![gradient_descent](gradient.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do znalezienia minimum funckji kosztu można użyć metody gradientu prostego. W tym celu iteracyjnie liczy się gradient funkcji kosztu i aktualizuje się za jego pomocą wektor parametrów modelu aż do uzyskania zbieżności (Różnica między obliczoną funkcją kosztu a funkcją kosztu w poprzedniej iteracji będzie mniejsza od ustalonej wcześniej wartości  $\\varepsilon$).\n",
    "\n",
    "Gradient funkcji kosztu:\n",
    "\n",
    "$$ \\dfrac{\\partial J(\\theta)}{\\partial \\theta} = \\frac{1}{m}X^T(X \\theta - y)$$\n",
    "\n",
    "Modyfikacja parametrów modelu:\n",
    "\n",
    "$$ \\theta_{new} = \\theta - \\alpha * \\dfrac{\\partial J(\\theta)}{\\partial \\theta}$$\n",
    "\n",
    "Gdzie:\n",
    "\n",
    "$$ \\alpha - \\text{współczynnik uczenia} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "# Przydatne funkcje\n",
    "cost_functions = dict()\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\"\"\"\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",
    "        # print(current_cost)\n",
    "        if abs(prev_cost - current_cost) > 10**15:\n",
    "            print('Algorytm nie jest zbieżny!')\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\"\"\"\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_data_cost(X, y, xlabel, ylabel):\n",
    "    \"\"\"Wykres funkcji kosztu\"\"\"\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], [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(min(y) - 1, max(y) + 1)\n",
    "    plt.xlim(np.min(X) - 1, np.max(X) + 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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(Y_true, Y_pred):\n",
    "    \"\"\"Błąd średniokwadratowy - Mean Squared Error\"\"\"\n",
    "    return np.square(np.subtract(Y_true,Y_pred)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "    \"\"\"Przygotowanie danych do regresji wielomianowej\"\"\"\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), logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_values(model, data, n):\n",
    "    \"\"\"Funkcja predykcji\"\"\"\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",
    "    \"\"\"Wykres wraz z MSE\"\"\"\n",
    "    x, y = get_poly_data(np.array(data), n)\n",
    "    model, logs = polynomial_regression(x, y, n)\n",
    "    cost_function = [[element[0], i] for i, element in enumerate(logs)]\n",
    "    cost_functions[n] = cost_function\n",
    "    \n",
    "    fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
    "    plot_fun(fig, model, 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": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [
    {
     "data": {
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       "<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>318</th>\n",
       "      <td>62</td>\n",
       "      <td>399000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>50</td>\n",
       "      <td>247705.0</td>\n",
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       "    <tr>\n",
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       "      <td>15</td>\n",
       "      <td>349668.0</td>\n",
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       "      <td>20</td>\n",
       "      <td>347633.0</td>\n",
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       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>42</td>\n",
       "      <td>289000.0</td>\n",
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       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>14</td>\n",
       "      <td>330255.0</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>21</td>\n",
       "      <td>327584.0</td>\n",
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       "      <th>983</th>\n",
       "      <td>78</td>\n",
       "      <td>496573.0</td>\n",
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       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>16</td>\n",
       "      <td>309978.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1674 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqrMetres     price\n",
       "318          62  399000.0\n",
       "1294         50  247705.0\n",
       "593          15  349668.0\n",
       "1486         20  347633.0\n",
       "224          42  289000.0\n",
       "...         ...       ...\n",
       "1410         50  315000.0\n",
       "424          14  330255.0\n",
       "103          21  327584.0\n",
       "983          78  496573.0\n",
       "418          16  309978.0\n",
       "\n",
       "[1674 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "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 = alldata.sample(frac=1)\n",
    "alldata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alldata = np.matrix(alldata[['sqrMetres', 'price']])\n",
    "data_train = alldata[0:1600]\n",
    "data_test = alldata[1600:]\n",
    "data_train = np.matrix(data_train).astype(float)\n",
    "data_test = np.matrix(data_test).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 35514312788.779144\n",
      "Wielomian 2 stopnia, MSE = 90239672317.31158\n",
      "Wielomian 3 stopnia, MSE = 89217346258.50258\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"
    },
    {
     "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"
    },
    {
     "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": [
    "cost_fun_slices = []\n",
    "for n in range(1, 4):\n",
    "    plot_and_mse(data_train, data_test, n)\n",
    "    \n",
    "    cost_data = cost_functions.get(n)\n",
    "    cost_x = [line[1] for line in cost_data[:250]]\n",
    "    cost_y = [line[0] for line in cost_data[:250]]\n",
    "    cost_fun_slices.append((cost_x, cost_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>araba_fiyat</th>\n",
       "      <th>araba_max_hiz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>250</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1000</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>750</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>400</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>500</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>200</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3000</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    araba_fiyat  araba_max_hiz\n",
       "7           250            240\n",
       "12         1000            365\n",
       "13         2000            365\n",
       "3           100            200\n",
       "11          750            360\n",
       "2            80            200\n",
       "8           300            300\n",
       "0            60            180\n",
       "9           400            350\n",
       "4           120            200\n",
       "1            70            180\n",
       "10          500            350\n",
       "6           200            240\n",
       "5           150            220\n",
       "14         3000            365"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wczytanie danych (mieszkania) przy pomocy biblioteki pandas\n",
    "\n",
    "alldata_p2 = pandas.read_csv('polynomial-regression_2.csv')\n",
    "alldata_p2 = alldata_p2.sample(frac=1)\n",
    "alldata_p2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alldata = np.matrix(alldata[['sqrMetres', 'price']])\n",
    "data_train_p2 = alldata_p2[0:12]\n",
    "data_test_p2 = alldata_p2[12:]\n",
    "data_train_p2 = np.matrix(data_train_p2).astype(float)\n",
    "data_test_p2 = np.matrix(data_test_p2).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 13592.268823271697\n",
      "Wielomian 2 stopnia, MSE = 9030.04916509696\n",
      "Wielomian 3 stopnia, MSE = 9795.339194991013\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"
    },
    {
     "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"
    },
    {
     "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": [
    "cost_fun_slices = []\n",
    "for n in range(1, 4):\n",
    "    plot_and_mse(data_train_p2, data_test_p2, n)\n",
    "    \n",
    "    cost_data = cost_functions.get(n)\n",
    "    cost_x = [line[1] for line in cost_data[:250]]\n",
    "    cost_y = [line[0] for line in cost_data[:250]]\n",
    "    cost_fun_slices.append((cost_x, cost_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "image/png": 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kdYgZQ125zcj5wJeBPwH+utw4WJIkSR2ikZ66W4F3jN8zTpIkSZ2nkWvqNreiEEmSJM1dI6NfJUmS1OEMdZIkSV2g0dGvy4EfrV8+M7/arKIkSZI0O42Mfv0w1a1MHgXGB0skYKiTJEnqEI301F0O/HhmHmhyLZIkSZqjRq6p+zZwdLMLkSRJ0tw10lP3AvBwRNwL/ENvXWb+WtOqkiRJ0qw0Euo2lpckSZI6VCM3H749IpYCrylNj2fmD5tbliRJkmajkdGvFwG3A98BAjgtItZ6SxNJkqTO0cjp148Al2Tm4wAR8Rrgs8Drm1mYJEmSGtfI6NejxwMdQGb+HxwNK0mS1FEa6akbiYhbgP9Rpt8JjDSvpDaq1WDDBtixAwYGYHgY+vvbXZUkSdKMGgl1vwpcBYzfwuRvgE82raJ22boVVq+GsTHYvx96e+Gaa2DTJhgaand1kiRJ04rMbHcNLTU4OJgjIxM6Gms1WL68ep+ovx9274a+vtYUKEmSFqWI2JaZg3Ndf8pr6iLirvL+SER8Y+JrrjvsSBs2VD10kxkbq+ZLkiR1sOlOv76vvL+5FYW01Y4d1SnXyezfD6Ojra1HkiRplqbsqcvMPeXjezPzu/Uv4L2tKa9FBgaqa+gm09sLK1e2th5JkqRZauSWJr8wSdtl811IWw0PQ88Uf4qenmq+JElSB5vumrpfjYhHgNdOuJ7uCeCR1pXYAv391SjX/v7DPXa9vYfbHSQhSZI63HTX1H0G+BLwB8B1de21zNzb1KraYWioGuW6YUN1Dd3KlVUPnYFOkiQtAFOGusx8Hng+Ij4G7M3MGkBEHB8RF2TmA60qsmX6+mDdunZXIUmSNGuNXFN3E7CvbnpfaZMkSVKHaCTURdbdoTgzx2jgSRQRcVpE3BcRj0bE9oh4X2k/KSK2RMSO8n5iaY+I+HhEjJZr986r29basvyOiFhb1/76ch+90bJuzObLS5IkdYtGQt23I+LXIuLo8nof8O0G1jsIvD8zzwYuBK6KiLOprs+7NzMHgHs5fL3eZcBAea2n9AZGxEnADcAFwPnADeNBsCzznrr1VjVQlyRJUtdpJNT9CvAGYBewkypcrZ9ppczck5lfK59rwGPAcmANcHtZ7Hbg8vJ5DXBHVu4HToiIU4FLgS2ZuTcznwO2AKvKvOMz8/7Sk3hH3bYkSZIWlRlPo2bm08AVR7KTiDgD+CngAeCUuhsbfw84pXxeDjxZt9rO0jZd+85J2ifb/3pKED399NOP4JtIkiR1pkaujVtGdYrzjPrlM/OXGtlBRPQBnwN+PTN/UH/ZW2ZmROSUK8+TzLwZuBlgcHCw6fuTJElqtRlDHXAP8DfAXwKHZrPxiDiaKtB9OjM/X5qfiohTM3NPOYX6dGnfBZxWt/qK0rYLuGhC+1+V9hWTLC9JkrToNHJN3T/KzGsz867M/Nz4a6aVykjUW4HHMvOjdbM2AuMjWNdShcbx9ivLKNgLgefLadrNwCURcWIZIHEJsLnM+0FEXFj2dWXdtiRJkhaVRnrqvhgRqzNz0yy3/UbgXcAjEfFwaftt4EPAXRGxDvgu8PYybxOwGhgFXgDeDZCZeyPiA8BDZbnfq3uixXuBTwHHUT394kuzrFGSJKkrRN0t6CZfIKIG9AIHgB8CQXU53PHNL2/+DQ4O5sjISLvLkCRJepmI2JaZg3Ndv5HRr/1z3bgkSZJao5HRrz87WXtmfnX+y5EkSdJcNHJN3W/VfT6W6qkO24CLm1KRJEmSZq2R06//sn46Ik4D/rhZBUmSJGn2GrmlyUQ7gZ+Y70IkSZI0d41cU/dfgfEhsj3AucDXmliTJEmSZqmRa+rq7/9xEPhsZv5tk+qRJEnSHEwZ6iLi3sx8E3B2Zl7bwpokSZI0S9P11J0aEW8A3hIRd1LddPgfZKanYCVJkjrEdKHuPwP/CVgBfHTCvMRbmkiSJHWMKUNdZt4N3B0R/ykzP9DCmiRJkjRLjdynbvEFuloNNmyAHTtgYACGh6Hfp6VJkqTO1cjo18Vl61ZYvRrGxmD/fujthWuugU2bYGio3dVJkiRNai43H+5etVoV6Gq1KtBB9T7evm9fe+uTJEmawoyhLiL+rJG2rrBhQ9VDN5mxsWq+JElSB2qkp+6c+omIWAK8vjnltNmOHYd76Cbavx9GR1tbjyRJUoOmDHURcX1E1IB/GhE/KK8a8DRwT8sqbKWBgeoausn09sLKla2tR5IkqUFThrrM/IPM7Af+KDOPL6/+zDw5M69vYY2tMzwMPVP8SXp6qvmSJEkdqJHTr1+MiF6AiPi3EfHRiPjRJtfVHv391SjX/v7DPXa9vYfb+/raW58kSdIUGrmlyU3A6yLidcD7gVuAO4B/3szC2mZoCHbvrgZFjI5Wp1yHhw10kiSpozUS6g5mZkbEGuBPMvPWiFjX7MLaqq8P1nX3V5QkSd2lkVBXi4jrgXcB/ywieoCjm1uWJEmSZqORa+qGgQPAL2Xm94AVwB81tSpJkiTNyoyhrgS5TwOviog3Ay9m5h1Nr0ySJEkNa+SJEm8HHgT+DfB24IGIeFuzC5MkSVLjGrmm7j8CP52ZTwNExDLgL4G7m1mYJEmSGtfINXU944GueLbB9SRJktQijfTUfTkiNgOfLdPDwJeaV5IkSZJma8ZQl5m/FRH/ChgqTTdn5heaW5YkSZJmY8pQFxErgVMy828z8/PA50v7UESclZn/t1VFSpIkaXrTXRv3x8APJml/vsyTJElSh5gu1J2SmY9MbCxtZ8y04Yi4LSKejohv1rX9TkTsioiHy2t13bzrI2I0Ih6PiEvr2leVttGIuK6u/cyIeKC0b4iIpQ18X0mSpK40Xag7YZp5xzWw7U8BqyZpvzEzzy2vTQARcTZwBXBOWeeTEbEkIpYAnwAuA84G3lGWBfhw2dZK4DnAh7VKkqRFa7pQNxIR75nYGBH/Dtg204Yz86vA3gbrWAPcmZkHMvMJYBQ4v7xGM/PbmfkScCewJiICuJjD98q7Hbi8wX1JkiR1nelGv/468IWIeCeHQ9wgsBR46xHs8+qIuBIYAd6fmc8By4H765bZWdoAnpzQfgFwMvD9zDw4yfKvEBHrgfUAp59++hGULkmS1Jmm7KnLzKcy8w3A7wLfKa/fzcyfKc+DnYubgLOAc4E9wEfmuJ1ZycybM3MwMweXLVvWil1KkiS1VCP3qbsPuG8+dpaZT41/jog/Bb5YJncBp9UtuqK0MUX7s8AJEXFU6a2rX16SJGnRaenjviLi1LrJtwLjI2M3AldExDERcSYwADwIPAQMlJGuS6kGU2zMzKQKmm8r668F7pn3gms1uOUWuPba6r1Wm/ddSJIkzYdGHhM2JxHxWeAi4NURsRO4AbgoIs4Fkup07i8DZOb2iLgLeBQ4CFyVmYfKdq4GNgNLgNsyc3vZxbXAnRHxQeDvgFvn9Qts3QqrV8PYGOzfD729cM01sGkTDA3NvL4kSVILRdXptXgMDg7myMjI9AvVarB8+eQ9c/39sHs39PU1p0BJkrQoRcS2zByc6/otPf26YGzYUPXQTWZsrJovSZLUQQx1k9mxozrlOpn9+2F0tLX1SJIkzcBQN5mBgeoausn09sLKla2tR5IkaQaGuskMD0PPFH+anp5qviRJUgcx1E2mv78a5drff7jHrrf3cLuDJCRJUodp2i1NFryhoWqU64YN1TV0K1dWPXQGOkmS1IEMddPp64N169pdhSRJ0ow8/SpJktQFDHWSJEldwFAnSZLUBQx1kiRJXcBQJ0mS1AUMdZIkSV3AUCdJktQFDHWSJEldwFAnSZLUBQx1kiRJXcDHhDWiVqueAbtjBwwMVM+A7e9vd1WSJEn/wFA3k61bYfVqGBuD/fuhtxeuuQY2bYKhoXZXJ0mSBHj6dXq1WhXoarUq0EH1Pt6+b19765MkSSoMddPZsKHqoZvM2Fg1X5IkqQMY6qazY8fhHrqJ9u+H0dHW1iNJkjQFQ910Bgaqa+gm09sLK1e2th5JkqQpGOqmMzwMPVP8iXp6qvmSJEkdwFA3nf7+apRrf//hHrve3sPtfX3trU+SJKnwliYzGRqC3burQRGjo9Up1+FhA50kSeoohrpG9PXBunXtrkKSJGlKnn6VJEnqAoY6SZKkLmCokyRJ6gKGOkmSpC7QtFAXEbdFxNMR8c26tpMiYktE7CjvJ5b2iIiPR8RoRHwjIs6rW2dtWX5HRKyta399RDxS1vl4RESzvoskSVKna2ZP3aeAVRPargPuzcwB4N4yDXAZMFBe64GboAqBwA3ABcD5wA3jQbAs85669SbuS5IkadFoWqjLzK8Ceyc0rwFuL59vBy6va78jK/cDJ0TEqcClwJbM3JuZzwFbgFVl3vGZeX9mJnBH3baap1aDW26Ba6+t3mu1pu9SkiSpEa2+T90pmbmnfP4ecEr5vBx4sm65naVtuvadk7RPKiLWU/UAcvrpp8+t8q1bYfVqGBuD/furJ0tcc031ZImhobltU5IkaZ60baBE6WHLFu3r5swczMzBZcuWzX4DtVoV6Gq1KtBB9T7evm/f/BYsSZI0S60OdU+VU6eU96dL+y7gtLrlVpS26dpXTNLeHBs2VD10kxkbq+ZLkiS1UatD3UZgfATrWuCeuvYryyjYC4Hny2nazcAlEXFiGSBxCbC5zPtBRFxYRr1eWbet+bdjx+Eeuon276+eCStJktRGTbumLiI+C1wEvDoidlKNYv0QcFdErAO+C7y9LL4JWA2MAi8A7wbIzL0R8QHgobLc72Xm+OCL91KNsD0O+FJ5NcfAQHUN3WTBrrcXVq5s2q4lSZIaEdWlbYvH4OBgjoyMzG6lWg2WL598tGt/P+zeDX1981OgJElalCJiW2YOznV9nyjRiP7+apRrf3/VMwfV+3i7gU6SJLVZq29psnANDVU9chs2VNfQrVwJw8MGOkmS1BEMdbPR1wfr1rW7CkmSpFfw9KskSVIXMNRJkiR1AUOdJElSF/Caurmq1apBEzt2VPexGx6uRsNKkiS1gaFuLrZurZ75OjZW3ZC4txeuuaa6vcnQULurkyRJi5CnX2erVqsCXa12+AkT+/cfbt+3r731SZKkRclQN1sbNlQ9dJMZG6vmS5IktZihbrZ27Jj8GbBQtY+OtrYeSZIkDHWzNzBw+FFhE/X2Vk+akCRJajFD3WwND0PPFH+2np5qviRJUosZ6marv78a5drff7jHrrf3cLvPgpUkSW3gLU3mYmgIdu+uBkWMjlanXIeHDXSSJKltDHVz1dcH69a1uwpJkiTAUHfkfLKEJEnqAIa6I+GTJSRJUodwoMRc+WQJSZLUQQx1c+WTJSRJUgcx1M2VT5aQJEkdxFA3Vz5ZQpIkdRBD3Vz5ZAlJktRBDHVz5ZMlJElSB/GWJkei/skS27fDs8/CySfDt74Fr3ud96uTJEktY6g7Un198OM/Dr/xG96vTpIktY2nX4+U96uTJEkdwFB3pLxfnSRJ6gCGuiPl/eokSVIHMNQdKe9XJ0mSOkBbQl1EfCciHomIhyNipLSdFBFbImJHeT+xtEdEfDwiRiPiGxFxXt121pbld0TE2nZ8l2nvV3fwYHVdnSRJUpO1s6fu5zLz3MwcLNPXAfdm5gBwb5kGuAwYKK/1wE1QhUDgBuAC4HzghvEg2FL196s79tiXz4uoRsZu3drysiRJ0uLSSadf1wC3l8+3A5fXtd+RlfuBEyLiVOBSYEtm7s3M54AtwKoW11wZGoLHH39l+4svOgpWkiS1RLtCXQJ/ERHbImJ9aTslM/eUz98DTimflwNP1q27s7RN1d4ef/7nsGTJ5PMcBStJkpqsXTcfHsrMXRHxI8CWiPhW/czMzIjI+dpZCY7rAU4//fT52uzLOQpWkiS1UVt66jJzV3l/GvgC1TVxT5XTqpT3p8viu4DT6lZfUdqmap9sfzdn5mBmDi5btmw+v8ph042CXboUVqxozn4lSZJoQ6iLiN6I6B//DFwCfBPYCIyPYF0L3FM+bwSuLKNgLwSeL6dpNwOXRMSJZYDEJaWtPaYbBfvSS3DddQ6YkCRJTdOO06+nAF+IiPH9fyYzvxwRDwF3RcQ64LvA28vym4DVwCjwAvBugMzcGxEfAB4qy/1eZu5t3deYYHwU7GWXTT4oYt++asDE7t3V82IlSZLmUWTO26VrC8Lg4GCOjIw0bwd/8ifwm78JBw68cl5vL3zsY7BuXfP2L0mSFqSI2FZ3q7dZ66RbmnSHJ5+cPNBBNWDic5+rbnMiSZI0jwx18226ARMAX/kKLF/u9XWSJGleGerm23QDJqDqxfOGxJIkaZ4Z6uZb/WPDli6dejlvSCxJkuaRoa4ZhoaqUa4XXzz1Mvv3w/btratJkiR1NUNds/T1wb/+19NfX3fTTV5bJ0mS5oWhrplmur7uxRfh538e9uyZehlJkqQGGOqaafz6umOOmXqZAwfgx37MHjtJknREDHXNNjQE733v9MvYYydJko6Qoa4Vzj57+mvrwB47SZJ0RAx1rTDTtXXj7LGTJElzZKhrhUaurRt34ACcfjq8851wyy0+UkySJDXEUNcqQ0PwxBNw7LEzL3vwIHzmM3DVVXDyyXDttYY7SZI0LUNdK516KmzZ0liPHcBLL8EPfwh/+IeGO0mSNK3IzHbX0FKDg4M5MjLS3iL27KkGRbz44uzXPeooeNOb4Cd+As45p7per79//muUJEktFRHbMnNwzusb6tpk69ZqUMSBA3PfxniP31veAscdVz3FYt8+OOkkA58kSQuMoW6WOibUwZH12M3kqKOq9/FevbPOqqZHR+G55w4HwInvJ510eNknn4SBAcOhJEktYKibpY4KdVD12K1eDYcOwQsvtLuaV1q6FMbGqnB41llTB8DZhMVuWbbT6jSES9KCZqibpY4LdVD9x3nDBrjvPrj77iM7JavFzRDePcsulDpdtnOWne32vEyn4xjqZqkjQ129ffvggx+Ej360GvkqSVIzTHVddicF1cWybAnYcfzxhrrZ6PhQN85wJ0nS4nDssXD00byqVnv8+czXznUz3qeuU/X1wYc+BHv3VvenW7oUjj663VVJkqT59uKLUKtxFgwcyWbsqVsoxq+7e/RRePbZ6n8A99xTzWvG6FlJktRS58HY1zKXzHX9RRfqIuIZ4LvAq4G/b3M5R2QJ9JwMJx0Hxy6Bo8bg0FI4pg+OBwiIdtc4354BlrW7CM2ax21h8rgtXB67helbwL7MOf+3e9GFunERMXIkFyOqPTxuC5PHbWHyuC1cHruF6UiPm9fUSZIkdQFDnSRJUhdYzKHu5nYXoDnxuC1MHreFyeO2cHnsFqYjOm6L9po6SZKkbrKYe+okSZK6xqILdRGxKiIej4jRiLiu3fVoehHxnYh4JCIejoiR0nZSRGyJiB3l/cR217nYRcRtEfF0RHyzrm3S4xSVj5ff4Dci4rz2Vb64TXHcficidpXf3MMRsbpu3vXluD0eEZe2p2pFxGkRcV9EPBoR2yPifaXd31wHm+a4zdtvblGFuohYAnwCuAw4G3hHRJzd3qrUgJ/LzHPrhnlfB9ybmQPAvWVa7fUpYNWEtqmO02VUd00fANYDN7WoRr3Sp3jlcQO4sfzmzs3MTQDl38orgHPKOp8s/6aq9Q4C78/Ms4ELgavK8fE319mmOm4wT7+5RRXqgPOB0cz8dma+BNwJrGlzTZq9NcDt5fPtwOXtK0UAmflVYO+E5qmO0xrgjqzcD5wQEae2pFC9zBTHbSprgDsz80BmPgGMUv2bqhbLzD2Z+bXyuQY8BizH31xHm+a4TWXWv7nFFuqWA0/WTe9k+j+o2i+Bv4iIbRGxvrSdkpl7yufvAae0pzTNYKrj5O+w811dTtPdVnd5g8etA0XEGcBPAQ/gb27BmHDcYJ5+c4st1GnhGcrM86hOH1wVET9bPzOr4dsO4e5wHqcF5SbgLOBcYA/wkbZWoylFRB/wOeDXM/MH9fP8zXWuSY7bvP3mFluo2wWcVje9orSpQ2XmrvL+NPAFqq7np8ZPHZT3p9tXoaYx1XHyd9jBMvOpzDyUmWPAn3L4dI/HrYNExNFUweDTmfn50uxvrsNNdtzm8ze32ELdQ8BARJwZEUupLkDc2OaaNIWI6I2I/vHPwCXAN6mO2dqy2FrgnvZUqBlMdZw2AleWEXkXAs/XnTJSm0241uqtVL85qI7bFRFxTEScSXXR/YOtrk/VaFbgVuCxzPxo3Sx/cx1squM2n7+5o+a35M6WmQcj4mpgM7AEuC0zt7e5LE3tFOAL1e+Ao4DPZOaXI+Ih4K6IWAd8F3h7G2sUEBGfBS4CXh0RO4EbgA8x+XHaBKymuuj3BeDdLS9YwJTH7aKIOJfq1N13gF8GyMztEXEX8CjVKL6rMvNQG8oWvBF4F/BIRDxc2n4bf3Odbqrj9o75+s35RAlJkqQusNhOv0qSJHUlQ50kSVIXMNRJkiR1AUOdJElSFzDUSZIkdQFDnaRFIyL2lfczIuIX53nbvz1h+n/N5/YlaSaGOkmL0RnArEJdRMx0X8+XhbrMfMMsa5KkI2Kok7QYfQj4ZxHxcET8RkQsiYg/ioiHykO1fxkgIi6KiL+JiI1UNwAlIv5nRGyLiO0Rsb60fQg4rmzv06VtvFcwyra/GRGPRMRw3bb/KiLujohvRcSnyx3nJWlOFtUTJSSpuA74zcx8M0AJZ89n5k9HxDHA30bEX5RlzwN+MjOfKNO/lJl7I+I44KGI+FxmXhcRV2fmuZPs619RPaj7dcCryzpfLfN+CjgH2A38LdUd57fO95eVtDjYUydJ1XOFryyP7nkAOJnqOYsAD9YFOoBfi4ivA/dTPWx7gOkNAZ8tD+x+Cvhr4Kfrtr2zPMj7YarTwpI0J/bUSRIE8O8zc/PLGiMuAvZPmP554Gcy84WI+Cvg2CPY74G6z4fw32RJR8CeOkmLUQ3or5veDPxqRBwNEBGviYjeSdZ7FfBcCXSvBS6sm/fD8fUn+BtguFy3twz4WeDBefkWklTH/1coaTH6BnConEb9FPAxqlOfXyuDFZ4BLp9kvS8DvxIRjwGPU52CHXcz8I2I+FpmvrOu/QvAzwBfBxL4D5n5vRIKJWneRGa2uwZJkiQdIU+/SpIkdQFDnSRJUhcw1EmSJHUBQ50kSVIXMNRJkiR1AUOdJElSFzDUSZIkdQFDnSRJUhf4/1fqhvs04mnSAAAAAElFTkSuQmCC\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": [
    "#WYKRESY FUNKCJI KOSZTU\n",
    "for fig in cost_fun_slices:\n",
    "    cost_x, cost_y = fig\n",
    "    fig = plot_data_cost(cost_x, cost_y, \"Iteration\", \"Cost function value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "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"
    },
    {
     "data": {
      "image/png": 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+0socrr3z/j4MXJGZXy5UjyRJUt9ZMnBFxLWZeT5wlivNS5Ikrd5yPVw/GBHnAq+IiI8CMX9jZjqsKEmS1ILlAte/B34fOA1414JtictCSJIktWTJwJWZVwJXRsTvZ+YfdLAmSZKkvrLishCGLUmSpLVp5S7FNYmIDVR3Ot6XmS9f8wEbDZiYgMlJGBmB8XEY9nGPkiSpvooHLuBS4Dbg5DUfac8e2L4dZmdhZgYGB2HnTti9G8bG1nx4SZKkElYcUoyI/9lK2xLfPQ34V8D7jr+0BRqNKmw1GlXYgup9rn16es2nkCRJKqGVR/s8c/6H5hDhj7V4/D8BfheYXWqHiLg4IvZGxN4DBw4sfaSJiapnazGzs9V2SZKkGloycEXEWyOiAfzLiHik+WoADwKfXOnAEfFy4MHMvGG5/TJzV2aOZuboli1blt5xcvJoz9ZCMzMwNbVSSZIkSV2xZODKzHdk5jDwx5l5cvM1nJmnZuZbWzj2T1Atmno38FHgxRHxV6uudGSkmrO1mMFB2LZt1YeWJEkqqZUhxU9HxCBARPxSRLwrIn5opS9l5lsz87TMPAN4FfB3mflLq650fBwGlih3YKDaLkmSVEOtBK7Lge9GxNnAbwP/CPxl0aoWMzxc3Y04PHy0p2tw8Gj70FDHS5IkSWpFK8tCHM7MjIgLgfdk5vsjYsfxnCQzPw98fhX1Pd7YGOzfX02Qn5qqhhHHxw1bkiSp1loJXI2IeCvwy8BPRsQAsKlsWcsYGoIdx5X3JEmSuqqVIcVx4BDwusx8gOph1n9ctCpJkqQ+0sqzFB8APgw8sbnUw8HM7PwcLkmSpB7Vykrzvwh8BfgF4BeB6yPilaULkyRJ6hetzOH6d8BzM/NBgIjYAvwtcGXJwiRJkvpFK3O4BubCVtO3W/yeJEmSaK2H6+qIuAa4ovl5HPibciVJkiT1lxUDV2b+TkT8PDDWbNqVmVeVLUuSJKl/LBm4ImIb8JTM/HJmfgL4RLN9LCLOzMx/7FSRkiRJvWy5uVh/AjyySPvDzW2SJElqwXKB6ymZecvCxmbbGcUqkiRJ6jPLBa4nLbPtpDbXIUmS1LeWC1x7I+LXFjZGxL8BbihXkiRJUn9Z7i7FNwFXRcRFHA1Yo8AJwM8VrkuSJKlvLBm4MvObwLkR8SLgWc3mz2Tm33WkMkmSpD7Ryjpc1wHXdaAWSZKkvtTKSvP11GjAxARMTsLICIyPw/Bwt6uSJEk6Rm8Grj17YPt2mJ2FmRkYHISdO2H3bhgbW/n7kiRJHdR7D6FuNKqw1WhUYQuq97n26enu1idJkrRA7wWuiYmqZ2sxs7PVdkmSpBrpvcA1OXm0Z2uhmRmYmupsPZIkSSvovcA1MlLN2VrM4CBs29bZeiRJklbQe4FrfBwGlih7YKDaLkmSVCO9F7iGh6u7EYeHj/Z0DQ4ebR8a6m59kiRJC/TmshBjY7B/fzVBfmqqGkYcHzdsSZKkWurNwAVVuNqxo9tVSJIkraj3hhQlSZJ6jIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVFixwBURmyPiKxHxtYjYFxH/sdS5JEmS6qzko30OAS/OzOmI2ATsiYi/ycx/aOtZGo3qmYqTkzAyUj1TcXi4raeQJElai2KBKzMTmG5+3NR8ZVtPsmcPbN8Os7MwMwODg7BzJ+zeXT3gWpIkqQaKzuGKiA0RcRPwIPC5zLx+kX0ujoi9EbH3wIEDrR+80ajCVqNRhS2o3ufap6eX/74kSVKHFA1cmXkkM88BTgOeFxHPWmSfXZk5mpmjW7Zsaf3gExNVz9ZiZmer7ZIkSTXQkbsUM/OfgeuAl7XtoJOTR3u2FpqZgamptp1KkiRpLUrepbglIp7U/Psk4KeA29t2gpGRas7WYgYHYdu2tp1KkiRpLUr2cP0gcF1E3Ax8lWoO16fbdvTxcRhYovyBgWq7JElSDZS8S/Fm4Nmljs/wcHU34sK7FAcGqvahoWKnliRJOh4l1+Eqb2wM9u+vJshPTVXDiOPjhi1JklQrvR24oApXO3Z0uwpJkqQl+SxFSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVFjvLwsxp9Go1uOanKwe+zM+Xi2OKkmS1GX9Ebj27Dl2xfmdO6sV58fGul2dJEla53p/SLHRqMJWo1GFLaje59qnp7tbnyRJWvd6P3BNTFQ9W4uZna22S5IkdVHvB67JyaM9WwvNzFTPWJQkSeqi3g9cIyPVnK3FDA5WD7SWJEnqot4PXOPjMLDEf8bAQLVdkiSpi3o/cA0PV3cjDg8f7ekaHDzaPjTU3fokSdK61x/LQoyNwf791QT5qalqGHF83LAlSZJqoT8CF1ThaseOblchSZJ0jN4fUpQkSao5A5ckSVJh/TOkOMdnKkqSpJrpr8DlMxUlSVIN9c+Qos9UlCRJNdU/gctnKkqSpJrqn8DlMxUlSVJN9U/g8pmKkiSppvoncPlMRUmSVFP9E7h8pqIkSaqp/loWwmcqSpKkGuqvwAU+U1GSJNVO/wWu+Vx1XpIk1UD/Bi5XnZckSTXRP5Pm53PVeUmSVCP9GbhcdV6SJNVIfwYuV52XJEk1UixwRcTpEXFdRNwaEfsi4tJS5zqGq85LkqQaKdnDdRj47cw8C3gBcElEnFXwfEe56rwkSaqRYoErM+/PzBubfzeA24Ctpc73OK46L0mSaqQjy0JExBnAs4HrO3E+4PGrzu/bB9/+Npx6Ktx+O5x9tutxSZKkjikeuCJiCPg48KbMfGSR7RcDFwM89alPbe/Jh4bg6U+HN7/Z9bgkSVLXRGaWO3jEJuDTwDWZ+a6V9h8dHc29e/e2r4BGA7Zurd4XGh6uesAcXpQkSSuIiBsyc3S13y95l2IA7wduayVsFeF6XJIkqQZK3qX4E8AvAy+OiJuar+0Fz3cs1+OSJEk1UGwOV2buAaLU8Vsytx7XYqHL9bgkSVKH9OdK83OWW4/r8OHquYqSJEmF9Xfgmr8e1+bNj98WUd3BuGdPd2qTJEnrRn8HLqiWfrjjjmPbDx6s7l7cvh2mpztflyRJWjf6P3ABfOYzsGHD4tu8W1GSJBW2PgKXdytKkqQuWh+Ba+5uxcWccAKcdlpn65EkSevK+ghcy92t+OijcNllTp6XJEnFrI/ANXe34lKP8ZmedvK8JEkqZn0ELqjuVnzHO+DEExff7uR5SZJUyPoJXAD33AOHDi2+bWYG9u3rbD2SJGldWF+Ba7nJ8wCXX+5cLkmS1HbrK3AtN3keqsVQncslSZLabH0FrrnJ80vN4wL43vfgkkuqVeglSZLaYH0FLqgmz7/hDUtvP3wYrrgCtm51eFGSJLXF+gtcAGedtfxcrsceq3q4XvISuP/+ztUlSZL60voMXCvN5Zpz6BD88A/b0yVJktZkfQauublcw8OwcePy+x48aE+XJElak/UZuKCay7V/P7zmNbBp0/L72tMlSZLWYP0GLqge9fOe98DmzSvva0+XJElapfUduKC1pSLmHDoEp58Ob3mLy0ZIkqSWGbigGl68667WerqOHIE/+iPYsgUuugje9z7DlyRJWlZkZrdr+P9GR0dz79693Stgz55q2HCp5y0u5oQTqgdfn38+/OiPwjOfWd0FOTxcrk5JktRREXFDZo6u+vsGrgXuv7+aIH/w4Oq+P3fXowFMkqS+YeAqYTU9XUuZmxv2ilfASSdVE/Wnp4++n3KKoUySpJozcJWy1p6u47FxI0RUjxzatg2mpuA73zk2nM0PaWeeWX23Hfsut4+BUJIkA1dRe/bABRdUwWM9WzhM2s6w10/7Gk4lqW8ZuEqbnoa3vx3e/e7q86OPdrce1dv8cHrmmfUOiKV6RUvta6CV1EUGrk6ZnoaJCbjuOrjyyvbM75J0fOxtrf++99wDIyMGY/UdA1c3zO/1yoTHHut2RZJUH/OXy5nr6a17D6r7OtViBQaubprr9br11up17bVVuwFMkqRKJ6ZalAyRzcAYJ59s4KqN+QHs29+u7nD85CerbZ2421GSJLXX5s2waRNPbDTueDjzGas9zMZ21rTuDQ3Bjh2Pb1sYwoaHq0cBDQ9Xyfraa2FgwDlhkiTV0cGDcPAgZ8LIWg5j4CptsRA231wgm5qC006r2u6889hwNv/91FOrNcLate9S+8wFQnCYVJKkNajVkGJEHAC+ATwZ+FaXyxGwAQZOhVNOgs0bYOMROHwIDgGcCCdugI2zcGQANvwznPAkeHTu83L7Lnzv9X1PhM1DcDJAQHT6Oq3FAWBLt4vQcfO69SavW++6HZjOXPW/77UKXHMiYu9aJqapO7xuvcnr1pu8br3J69a71nrtBtpZjCRJko5l4JIkSSqsroFrV7cL0Kp43XqT1603ed16k9etd63p2tVyDpckSVI/qWsPlyRJUt+oVeCKiJdFxB0RMRURl3W7Hi0tIu6OiFsi4qaI2NtsOyUiPhcRk8337+t2nYKI+EBEPBgRX5/Xtui1isp/bf4Gb46I53Sv8vVtiev2HyLivubv7qaI2D5v21ub1+2OiPjp7lStiDg9Iq6LiFsjYl9EXNps9zdXY8tct7b95moTuCJiA/DfgQuAs4BXR8RZ3a1KK3hRZp4z7zbZy4BrM3MEuLb5Wd33QeBlC9qWulYXUK2mPAJcDFzeoRp1rA9y7HUDeHfzd3dOZu4GaP5b+Srgmc3vvLf5b6o67zDw25l5FvAC4JLm9fE3V29LXTdo02+uNoELeB4wlZl3ZuajwEeBC7tck47PhcCHmn9/CPjZ7pWiOZn5ReChBc1LXasLgb/Myj8AT4qIH+xIoXqcJa7bUi4EPpqZhzLzLmCK6t9UdVhm3p+ZNzb/bgC3AVvxN1dry1y3pRz3b65OgWsrcM+8z/ey/H+suiuBz0bEDRFxcbPtKZl5f/PvB4CndKc0tWCpa+XvsP5+szn09IF5w/ZetxqKiDOAZwPX42+uZyy4btCm31ydApd6y1hmPoeqO/ySiHjh/I1Z3f7qLbA9wGvVUy4HzgTOAe4H3tnVarSkiBgCPg68KTMfmb/N31x9LXLd2vabq1Pgug84fd7n05ptqqHMvK/5/iBwFVVX6jfnusKb7w92r0KtYKlr5e+wxjLzm5l5JDNngT/n6BCG161GImIT1f9ofzgzP9Fs9jdXc4tdt3b+5uoUuL4KjETE0yLiBKrJaJ/qck1aREQMRsTw3N/AS4GvU12v1zZ3ey3wye5UqBYsda0+Bfzr5p1TLwAenjcMoi5bMLfn56h+d1Bdt1dFxIkR8TSqCdhf6XR9qu46BN4P3JaZ75q3yd9cjS113dr5m9vY3pJXLzMPR8RvAtcAG4APZOa+LpelxT0FuKr6v082Ah/JzKsj4qvAxyJiB/AN4Be7WKOaIuIK4DzgyRFxL/A24A9Z/FrtBrZTTQD9LvCrHS9YwJLX7byIOIdqOOpu4NcBMnNfRHwMuJXqbqtLMvNIF8oW/ATwy8AtEXFTs+338DdXd0tdt1e36zfnSvOSJEmF1WlIUZIkqS8ZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUtSLUTEdPP9jIh4TZuP/XsLPv/vdh5fklZi4JJUN2cAxxW4ImKlNQUfF7gy89zjrEmS1sTAJalu/hD4yYi4KSLeHBEbIuKPI+KrzQfI/jpARJwXEV+KiE9RLT5IRPyv5gPV9809VD0i/hA4qXm8Dzfb5nrTonnsr0fELRExPu/Yn4+IKyPi9oj4cHMlaklaldqsNC9JTZcB/zYzXw7QDE4PZ+ZzI+JE4MsR8dnmvs8BnpWZdzU/vy4zH4qIk4CvRsTHM/OyiPjNzDxnkXP9PNVDac8Gntz8zheb254NPBPYD3yZaiXqPe3+j5W0PtjDJanuXkr1rLmbgOuBU6meWwbwlXlhC+CNEfE14B+oHiw7wvLGgCuaD6f9JvAF4Lnzjn1v86G1N1ENdUrSqtjDJanuAvitzLzmcY0R5wEzCz6/BPjxzPxuRHwe2LyG8x6a9/cR/PdS0hrYwyWpbhrA8LzP1wC/ERGbACLiRyJicJHvPRH4TjNsPQN4wbxtj819f4EvAePNeWJbgBcCX2nLf4UkzeP/xyapbm4GjjSHBj8I/CnVcN6NzYnrB4CfXeR7VwOvj4jbgDuohhXn7AJujogbM/Oiee1XAT8OfA1I4Hcz84FmYJOktonM7HYNkiRJfc0hRUmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJh/w+7QotArs4kuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 691.2x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WYKRESY FUNKCJI KOSZTU\n",
    "for fig in cost_fun_slices:\n",
    "    cost_x, cost_y = fig\n",
    "    fig = plot_data_cost(cost_x, cost_y, \"Iteration\", \"Cost function value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ilość nauki do oceny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_marks_all = pandas.read_csv('Student_Marks.csv')\n",
    "data_marks_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_marks_all = data_marks_all[['time_study', 'Marks']]\n",
    "# data_marks_all = data_marks_all.sample(frac=1)\n",
    "data_marks_train = data_marks_all[0:70]\n",
    "data_marks_test = data_marks_all[70:]\n",
    "data_marks_train = np.matrix(data_marks_train).astype(float)\n",
    "data_marks_test = np.matrix(data_marks_test).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 381.16937283505433\n",
      "Wielomian 2 stopnia, MSE = 394.1863119057109\n",
      "Wielomian 3 stopnia, MSE = 391.5017110730558\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"
    },
    {
     "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"
    },
    {
     "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": [
    "cost_fun_slices = []\n",
    "\n",
    "for n in range(1, 4):\n",
    "    plot_and_mse(data_marks_train, data_marks_test, n)\n",
    "    \n",
    "    cost_data = cost_functions.get(n)\n",
    "    cost_x = [line[1] for line in cost_data[:250]]\n",
    "    cost_y = [line[0] for line in cost_data[:250]]\n",
    "    cost_fun_slices.append((cost_x, cost_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "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"
    },
    {
     "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": [
    "#WYKRESY FUNKCJI KOSZTU\n",
    "for fig in cost_fun_slices:\n",
    "    cost_x, cost_y = fig\n",
    "    fig = plot_data_cost(cost_x, cost_y, \"Iteration\", \"Cost function value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>27</th>\n",
       "      <td>55</td>\n",
       "      <td>female</td>\n",
       "      <td>32.775</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>12268.63225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>17.195</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>14455.64405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>54</td>\n",
       "      <td>female</td>\n",
       "      <td>46.700</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>11538.42100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>30.590</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>16796.41194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>23.980</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>22192.43711</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>827</th>\n",
       "      <td>36</td>\n",
       "      <td>male</td>\n",
       "      <td>28.025</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>20773.62775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>28.500</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3537.70300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>23.210</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1121.87390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>28.500</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>35147.52848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>18.715</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21595.38229</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",
       "27     55  female  32.775         2     no  northwest  12268.63225\n",
       "412    26  female  17.195         2    yes  northeast  14455.64405\n",
       "547    54  female  46.700         2     no  southwest  11538.42100\n",
       "983    27  female  30.590         1     no  northeast  16796.41194\n",
       "661    57  female  23.980         1     no  southeast  22192.43711\n",
       "...   ...     ...     ...       ...    ...        ...          ...\n",
       "827    36    male  28.025         1    yes  northeast  20773.62775\n",
       "212    24    male  28.500         2     no  northwest   3537.70300\n",
       "940    18    male  23.210         0     no  southeast   1121.87390\n",
       "476    24    male  28.500         0    yes  northeast  35147.52848\n",
       "1027   23    male  18.715         0     no  northwest  21595.38229\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ins = data_ins[['age', 'charges']]\n",
    "data_ins_train = data_ins[0:1200]\n",
    "data_ins_test = data_ins[1200:]\n",
    "data_ins_train = np.matrix(data_ins_train).astype(float)\n",
    "data_ins_test = np.matrix(data_ins_test).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wielomian 1 stopnia, MSE = 178360681.2241408\n",
      "Wielomian 2 stopnia, MSE = 179170879.99336478\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_5896/1100858333.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\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----> 4\u001b[0;31m     \u001b[0mplot_and_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_ins_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_ins_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mcost_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcost_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_5896/3773219878.py\u001b[0m in \u001b[0;36mplot_and_mse\u001b[0;34m(data, data_test, n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;34m\"\"\"Wykres wraz z MSE\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_poly_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpolynomial_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mcost_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0melement\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\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[1;32m     14\u001b[0m     \u001b[0mcost_functions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcost_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_5896/1168100012.py\u001b[0m in \u001b[0;36mpolynomial_regression\u001b[0;34m(X, y, n)\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;34m\"\"\"Funkcja regresji wielomianowej\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0mtheta_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mh_poly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_5896/1641407440.py\u001b[0m in \u001b[0;36mgradient_descent\u001b[0;34m(fJ, fdJ, theta, X, y, alpha, eps)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\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[1;32m     18\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheta\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfdJ\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mcurrent_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprev_cost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfJ\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;31m# print(current_cost)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/tmp/ipykernel_5896/1641407440.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(theta, X, y)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\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[1;32m     11\u001b[0m     \u001b[0;34m\"\"\"Wersja macierzowa gradientu funkcji kosztu\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtheta\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my\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[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfJ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdJ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m7\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~/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__array_finalize__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__array_finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\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[1;32m    168\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "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"
    },
    {
     "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": [
    "cost_fun_slices = []\n",
    "\n",
    "for n in range(1, 4):\n",
    "    plot_and_mse(data_ins_train, data_ins_test, n)\n",
    "    \n",
    "    cost_data = cost_functions.get(n)\n",
    "    cost_x = [line[1] for line in cost_data[:250]]\n",
    "    cost_y = [line[0] for line in cost_data[:250]]\n",
    "    cost_fun_slices.append((cost_x, cost_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#WYKRESY FUNKCJI KOSZTU\n",
    "for fig in cost_fun_slices:\n",
    "    cost_x, cost_y = fig\n",
    "    fig = plot_data_cost(cost_x, cost_y, \"Iteration\", \"Cost function value\")"
   ]
  }
 ],
 "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",
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