diff --git a/05_Regresja_wielomianowa.ipynb b/05_Regresja_wielomianowa.ipynb
deleted file mode 100644
index a055f58..0000000
--- a/05_Regresja_wielomianowa.ipynb
+++ /dev/null
@@ -1,1044 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "source": [
- "# Regresja wielomianowa"
- ]
- },
- {
- "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 (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",
- " # print(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_data_cost(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], [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",
- " 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",
- " 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",
- " 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, 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": {
- "text/html": [
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " sqrMetres | \n",
- " price | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 160 | \n",
- " 44 | \n",
- " 349668.0 | \n",
- "
\n",
- " \n",
- " 1066 | \n",
- " 54 | \n",
- " 260000.0 | \n",
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- " 679 | \n",
- " 65 | \n",
- " 348000.0 | \n",
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- " 1589 | \n",
- " 97 | \n",
- " 579000.0 | \n",
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- " 132 | \n",
- " 60 | \n",
- " 295120.0 | \n",
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- " \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
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- " 894 | \n",
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- " \n",
- " 368 | \n",
- " 16 | \n",
- " 399000.0 | \n",
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- " \n",
- " 1278 | \n",
- " 51 | \n",
- " 460499.0 | \n",
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- " \n",
- " 1670 | \n",
- " 53 | \n",
- " 339000.0 | \n",
- "
\n",
- " \n",
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\n",
- "
1674 rows × 2 columns
\n",
- "
"
- ],
- "text/plain": [
- " sqrMetres price\n",
- "160 44 349668.0\n",
- "1066 54 260000.0\n",
- "679 65 348000.0\n",
- "1589 97 579000.0\n",
- "132 60 295120.0\n",
- "... ... ...\n",
- "894 68 390000.0\n",
- "937 78 329000.0\n",
- "368 16 399000.0\n",
- "1278 51 460499.0\n",
- "1670 53 339000.0\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 = 24385438317.4621\n",
- "Wielomian 2 stopnia, MSE = 45053827695.02038\n",
- "Wielomian 3 stopnia, MSE = 47184374447.91149\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
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- "needs_background": "light"
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- "output_type": "display_data"
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- {
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\n",
- "text/plain": [
- ""
- ]
- },
- "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": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
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SJA2UduZwTS/5+yhwRWZ+plA9kiRJA2fFwBUR12TmhcDTXGlekiRp/Vbr4XpiRPw74EUR8QEglm7MzBuLViZJkjQgVgtc/xW4DDgVeMeybYnLQkiSJLVlxcCVmVcCV0bEf8nM3+5iTZIkSQNlzWUhDFuSJEkbs551uI5LRGyJiH+KiI+WPpckSVIdtbMsxEa9DrgNOLkjR2s0YGoKZmZgbAwmJ2HU52tLkqT6WrOHKyL+qp22Fb57KvDvgXcff2kt7NsHO3fC618Pb3979b5zZ9UuSZJUU+0MKT596YeI2AL8YJvH/0PgN4CFlXaIiIsjYjoipg8fPrzykRoN2L27ep+fr9rm54+1z821WZIkSVJ3rRi4IuJNEdEAfiAi7m++GsC9wIfXOnBEvBC4NzP3r7ZfZl6emeOZOb5jx46Vd5yagoUVctvCQrVdkiSphlYMXJn51swcBX43M09uvkYz83GZ+aY2jv0cqkVT7wI+ADw3Iv73uiudmTnWs7Xc/DzMzq770JIkSSW1M6T40YgYBoiIn4uId0TE9671pcx8U2aemplnAC8H/i4zf27dlY6NwfBw623Dw7Br17oPLUmSVFI7getdwLci4myq+VhfBP6yaFWtTE7C0ArlDg1V2yVJkmqoncB1NDMTeDHwzsx8J3Bc6zBk5qcz84XrKfDfjI7C3r3V+2JP1/DwsfaRkQ0dXpIkqZR21uFqRMSbgJ8HfqR5l+K2smWtYGICDh2qJsjPzlbDiJOThi1JklRr7QSuSeAVwKsy8ysRcTrwu2XLWsXICOzZ07PTS5IkHa92nqX4FeB9wHc0l3o4kpndn8MlSZLUp9pZaf5lwGeBlwIvA66PiJeULkySJGlQtDOk+J+BZ2XmvQARsQP4FHBlycIkSZIGRTt3KQ4thq2mr7f5PUmSJNFeD9fVEfFx4Irm50ngb8uVJEmSNFjWDFyZ+esR8TPABBDA5Zl5VfHKJEmSBsSKgSsidgFPyMzPZOaHgA8128+PiDMz81+6VaQkSVI/W20u1h8CjRbt32pukyRJUhtWC1xnZObNyxszcxo4o1hFkiRJA2a1wLV9lW0ndboQSZKkQbVa4LohIn5peWNE7AH2lytJkiRpsKx2l+Lrgasi4iKOBaxx4ATgpwvXJUmSNDBWDFyZ+VXgvIj4UeCsZvPHMvPvulKZJEnSgGhnHa5rgWu7UIskSdJA8hE9kiRJhbXzaJ96ajRgagpmZmBsDCYnYXS011VJkiQ9Sn8Grn37YPduWFiA+XkYHoZLL4W9e2FiotfVSZIkPUL/DSk2GlXYajSqsAXV+2L73Fxv65MkSVqm/wLX1FTVs9XKwkK1XZIkqUb6L3DNzBzr2Vpufh5mZ7tbjyRJ0hr6L3CNjVVztloZHoZdu7pbjyRJ0hr6L3BNTsLQCmUPDVXbJUmSaqT/AtfoaHU34ujosZ6u4eFj7SMjva1PkiRpmf5cFmJiAg4dqibIz85Ww4iTk4YtSZJUS/0ZuKAKV3v29LoKSZKkNfXfkKIkSVKfMXBJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCisWuCJie0R8NiI+FxEHIuK/lTqXJElSnZV8tM8DwHMzcy4itgH7IuJvM/MfO3qWRqN6puLMDIyNVc9UHB3t6CkkSZI2oljgyswE5poftzVf2dGT7NsHu3fDwgLMz8PwMFx6KezdWz3gWpIkqQaKzuGKiC0RcRNwL/DJzLy+xT4XR8R0REwfPny4/YM3GlXYajSqsAXV+2L73Nzq35ckSeqSooErMx/OzHOAU4FzI+KsFvtcnpnjmTm+Y8eO9g8+NVX1bLWysFBtlyRJqoGu3KWYmf8KfBr4iY4ddGbmWM/WcvPzMDvbsVNJkiRtRMm7FHdExHc2/z4JeB5we8dOMDZWzdlqZXgYdu3q2KkkSZI2omQP1xOBayPiZuAGqjlcH+3Y0ScnYWiF8oeGqu2SJEk1UPIuxZuBZ5Q6PqOj1d2Iy+9SHBqq2kdGip1akiTpeJRch6u8iQk4dKiaID87Ww0jTk4atiRJUq30d+CCKlzt2dPrKiRJklbksxQlSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpsP6/S3FRo1EtDzEzU61CPzlZrdUlSZLUY4MRuPbte/QCqJdeWi2AOjHR6+okSdIm1/9Dio1GFbYajWMPs56fP9Y+N9fb+iRJ0qbX/4Fraqrq2WplYaHaLkmS1EP9H7hmZo71bC03P1898keSJKmH+j9wjY1Vc7ZaGR6unq8oSZLUQ/0fuCYnYWiFf4yhoWq7JElSD/V/4Bodre5GHB091tM1PHysfWSkt/VJkqRNbzCWhZiYgEOHqgnys7PVMOLkpGFLkiTVwmAELqjC1Z49va5CkiTpUfp/SFGSJKnmDFySJEmFGbgkSZIKG5w5XIt8iLUkSaqZwQpcPsRakiTV0OAMKfoQa0mSVFODE7h8iLUkSaqpwQlcPsRakiTV1OAELh9iLUmSampwApcPsZYkSTU1OIHLh1hLkqSaGqxlIXyItSRJqqHBClzgQ6wlSVLtDF7gWspV5yVJUg0MbuBy1XlJklQTgzNpfilXnZckSTUymIHLVeclSVKNDGbgctV5SZJUI8UCV0ScFhHXRsRtEXEgIl5X6lyP4qrzkiSpRkr2cB0F/mNmfj/wQ8AlEfG0guc7xlXnJUlSjRQLXJl5T2be2Py7AdwG7Cx1vkdw1XlJklQjXVkWIiLOAJ4BXN+N8wGPXHX+wAH4+tfhcY+D22+Hs892PS5JktQ1xQNXRIwAHwRen5n3t9h+MXAxwOmnn97Zk4+MwFOeAm94g+txSZKknonMLHfwiG3AR4GPZ+Y71tp/fHw8p6enO1dAowE7d1bvy42OVj1gDi9KkqQ1RMT+zBxf7/dL3qUYwJ8Bt7UTtopwPS5JklQDJe9SfA7w88BzI+Km5mt3wfM9mutxSZKkGig2hysz9wFR6vhtWVyPq1Xocj0uSZLUJYO50vyi1dbjOnq0eq6iJElSYYMduJaux7V9+yO3RVR3MO7b15vaJEnSpjHYgQuqpR/uuOPR7UeOVHcv7t4Nc3Pdr0uSJG0agx+4AD72MdiypfU271aUJEmFbY7A5d2KkiSphzZH4Fq8W7GVE06AU0/tbj2SJGlT2RyBa7W7FR98EC67zMnzkiSpmM0RuBbvVlzpMT5zc06elyRJxWyOwAXV3YpvfSuceGLr7U6elyRJhWyewAVw993wwAOtt83Pw4ED3a1HkiRtCpsrcK02eR7gXe9yLpckSeq4zRW4Vps8D9ViqM7lkiRJHba5Atfi5PmV5nEBfPvbcMkl1Sr0kiRJHbC5AhdUk+df85qVtx89CldcATt3OrwoSZI6YvMFLoCnPW31uVwPPVT1cD3veXDPPd2rS5IkDaTNGbjWmsu16IEH4ElPsqdLkiRtyOYMXItzuUZHYevW1fc9csSeLkmStCGbM3BBNZfr0CF4xStg27bV97WnS5IkbcDmDVxQPernj/4Itm9fe197uiRJ0jpt7sAF7S0VseiBB+C00+CNb3TZCEmS1DYDF1TDi3fe2V5P18MPw9vfDjt2wEUXwbvfbfiSJEmriszsdQ3/Znx8PKenp3tXwL591bDhSs9bbOWEE6oHX194IXz/98PTn17dBTk6Wq5OSZLUVRGxPzPH1/19A9cy99xTTZA/cmR931+869EAJknSwDBwlbCenq6VLM4Ne9GL4KSTqon6c3PH3k85xVAmSVLNGbhK2WhP1/HYuhUiqkcO7doFs7PwjW88OpwtDWlnnll9txP7bvR4hkZJ0oAzcJW0bx+84AVV0NDqlg6lnnlmmWDovusL04ZhSdowA1dpc3PwlrfAH/xB9fnBB3tbj3S8SvSg9kvg3Kz7GrSljjNwdcvcHExNwbXXwpVXdmZ+lySV0s+9zv1S52bfd5MFewNXLyzt9cqEhx7qdUWSJHXf8jvzBzGkN0NlnHyygatnFnu9br21el1zTdVuAJMkaTBs3w7btvEdjcYd38x86noPs7WTNW06IyOwZ8+xz0sD2Ne/Xt3h+OEPV9u6cbejJEnqrCNH4MgRzoSxjRzGwNVJywMYPDqEjY5WjwIaHa26Mq+5BoaGnBMmSdIAq9WQYkQcBr4IPB74Wo/L6YotMPQ4OGU7nPggPAhwIpy4BbYuwMNDsGX5+8Nw9AF4oFP7buR4J8L2ETgZ4GsQO3rzH6M24DDgdes/Xrf+5HXrX7cDc5mx3u/XKnAtiojpjUxMU2943fqT160/ed36k9etf2302g11shhJkiQ9moFLkiSpsLoGrst7XYDWxevWn7xu/cnr1p+8bv1rQ9eulnO4JEmSBklde7gkSZIGRq0CV0T8RETcERGzEXFZr+vRyiLiroi4JSJuiojpZtspEfHJiJhpvj+213UKIuI9EXFvRHx+SduK1yoi3tT8Dd4RET/em6q1wnX7rYj4cvN3d1NE7F6yzetWAxFxWkRcGxG3RcSBiHhds93fXI2tct069purzZBiRGwB/hn4MeAgcAPws5l5a08LU0sRcRcwnplfW9L2duC+zHxbMzA/NjPf2KsaVYmI84E54C8z86xmW8trFRFPA64AzgW+B/gU8OTMfLhH5W9aK1y33wLmMvP3lu3rdauJiHgi8MTMvDEiRoH9wE8Bv4C/udpa5bq9jA795urUw3UuMJuZX8jMB4EPAC/ucU06Pi8G3tv8+71U/7KqxzLzOuC+Zc0rXasXAx/IzAcy805gluq3qS5b4bqtxOtWE5l5T2be2Py7AdwG7MTfXK2tct1WctzXrU6Baydw95LPB1n9H1a9lcAnImJ/RFzcbHtCZt4D1b+8wHf1rDqtZaVr5e+w/n41Im5uDjkuDkt53WooIs4AngFcj7+5vrHsukGHfnN1Clytlsuvx3inWnlOZj4TeAFwSXP4Q/3P32G9vQs4EzgHuAf4/Wa7161mImIE+CDw+sy8f7VdW7R57XqkxXXr2G+uToHrIHDaks+nAod6VIvWkJmHmu/3AldRdaV+tTkOvjgefm/vKtQaVrpW/g5rLDO/mpkPZ+YC8KccG8LwutVIRGyj+h/t92Xmh5rN/uZqrtV16+Rvrk6B6wZgLCK+LyJOAF4OfKTHNamFiBhuTiokIoaB5wOfp7per2zu9krgw72pUG1Y6Vp9BHh5RJwYEd8HjAGf7UF9amHxf7Cbfprqdwdet9qIiAD+DLgtM9+xZJO/uRpb6bp18je3tbMlr19mHo2IXwU+DmwB3pOZB3pcllp7AnBV9e8nW4H3Z+bVEXED8NcRsQf4EvDSHtaopoi4ArgAeHxEHATeDLyNFtcqMw9ExF8DtwJHgUu8W6o3VrhuF0TEOVRDF3cBvwxet5p5DvDzwC0RcVOz7TfxN1d3K123n+3Ub642y0JIkiQNqjoNKUqSJA0kA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJqoWImGu+nxERr+jwsX9z2ef/28njS9JaDFyS6uYM4LgCV0RsWWOXRwSuzDzvOGuSpA0xcEmqm7cBPxIRN0XEGyJiS0T8bkTc0HyA7C8DRMQFEXFtRLwfuKXZ9n+aD1Q/sPhQ9Yh4G3BS83jva7Yt9qZF89ifj4hbImJyybE/HRFXRsTtEfG+5krUkrQutVlpXpKaLgP+U2a+EKAZnL6Zmc+KiBOBz0TEJ5r7nguclZl3Nj+/KjPvi4iTgBsi4oOZeVlE/GpmntPiXD9D9VDas4HHN79zXXPbM4CnUz0f7TNUK1Hv6/Q/rKTNwR4uSXX3fOA/NB+3cT3wOKrnlgF8dknYAnhtRHwO+EeqB8uOsboJ4Irmw2m/Cvw98Kwlxz7YfGjtTVRDnZK0LvZwSaq7AH4tMz/+iMaIC4D5ZZ+fB/xwZn4rIj4NbG/j2Ct5YMnfD+N/X0raAHu4JNVNAxhd8vnjwK9ExDaAiHhyRAy3+N53AN9ohq2nAj+0ZNtDi99f5jpgsjlPbAdwPvDZjvxTSNIS/j82SXVzM3C0OTT4F8A7qYbzbmxOXD8M/FSL710NvDoibgbuoBpWXHQ5cHNE3JiZFy1pvwr4YeBzQAK/kZlfaQY2SeqYyMxe1yBJkjTQHFKUJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFfb/AdfAbDyCLCYvAAAAAElFTkSuQmCC\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "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": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " number_courses | \n",
- " time_study | \n",
- " Marks | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 0 | \n",
- " 3 | \n",
- " 4.508 | \n",
- " 19.202 | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " 4 | \n",
- " 0.096 | \n",
- " 7.734 | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " 4 | \n",
- " 3.133 | \n",
- " 13.811 | \n",
- "
\n",
- " \n",
- " 3 | \n",
- " 6 | \n",
- " 7.909 | \n",
- " 53.018 | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " 8 | \n",
- " 7.811 | \n",
- " 55.299 | \n",
- "
\n",
- " \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " 95 | \n",
- " 6 | \n",
- " 3.561 | \n",
- " 19.128 | \n",
- "
\n",
- " \n",
- " 96 | \n",
- " 3 | \n",
- " 0.301 | \n",
- " 5.609 | \n",
- "
\n",
- " \n",
- " 97 | \n",
- " 4 | \n",
- " 7.163 | \n",
- " 41.444 | \n",
- "
\n",
- " \n",
- " 98 | \n",
- " 7 | \n",
- " 0.309 | \n",
- " 12.027 | \n",
- "
\n",
- " \n",
- " 99 | \n",
- " 3 | \n",
- " 6.335 | \n",
- " 32.357 | \n",
- "
\n",
- " \n",
- "
\n",
- "
100 rows × 3 columns
\n",
- "
"
- ],
- "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 = 381.1693728350544\n",
- "Wielomian 2 stopnia, MSE = 394.1863119057109\n",
- "Wielomian 3 stopnia, MSE = 391.50171107305584\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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- "output_type": "display_data"
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- {
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "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",
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+kuQXk5zWvt4JfGXQhUmSJI2zfkLazwGvAr5Kc3H/PwauHGRRkiRJ466fYcvHaZ4EIEmSpE1ywpCWZDvwNuD83u2r6mcHV5YkSdJ462fiwK3A/wt8FnC2pSRJ0iboJ6T9naq6ZuCVSJIk6W/1M3HgU+39zSRJkrRJ+glp76QJan+T5JtJ5pOs9gxOSZIkrVM/szunNqOQDTc/D3v3woEDMD0Nu3fD1Gj+KZIkafz0M7vzNcu1V9XnN76cDbJvH+zaBQsLcOwYTE7C1VfD7bfDzp3Drk6SJOmE+pk48G973p8JXALsB147kIrWa36+CWjz88fbjh1rlrt2waFDsG3bcGqTJEnq0wmvSauqH+55vQF4OfDY4Etbo717mx605SwsNOslSZI6rp+JA0sdpAlq3XTgwPGes6WOHYO5uc2tR5IkaQ36uSbt/wSq/TgBXAx8aYA1rc/0dHMN2nJBbXISduzY/JokSZJOUj89abM016DtB/4MuKaq/tlAq1qP3bthYoU/a2KiWS9JktRxK/akJbmzql4HXDhSTxyYmmpmcS6d3Tkx0bQ7aUCSJI2A1YY7z07yvwA/kuRmIL0rq+rugVa2Hjt3NrM49+5trkHbsaPpQTOgSZKkEbFaSPvfgWuBc4EblqwrunoLjkXbtsGePcOuQpIkaU1WDGlV9XHg40n+t6r6lU2sSZIkaez1c580A5okSdImW8t90iRJkjRghjRJkqQOOmFIS/L7/bRJkiRp4/TTk3ZR74ckpwD/aDDlSJIkCVYJaUmuSzIP/MMk32xf88DjwK2bVqEkSdIYWjGkVdWvVtUU8BtV9Zz2NVVVz6+q6zaxRkmSpLHTz3Dnp5JMAiT5Z0luSPL3BlyXJEnSWOsnpH0A+FaSVwD/DvgL4PcGWpUkSdKY6yekPV1VBVwGvL+q3g9MDbYsSZKk8bbaszsXzSe5Dvhp4J+2sztPG2xZkiRJ462fnrTdwJPAz1bV14BzgN8YaFWSJEljrp9nd34N+Ajwd5P8EPBEVZ3wmrQkH07yeJL7etreneSrSe5pX7t61l2XZC7JQ0neuMa/R5IkaUvo54kDPwncBfwE8JPAF5L8eB/H/l3gTcu0v6+qLm5ft7ffcSFwOc2Nc98E/HY7rCpJkjSW+rkm7X8Fvq+qHgdIsh34LPDx1Xaqqs8nOb/POi4Dbq6qJ4GHk8wBlwB/1uf+kiRJW0o/16RNLAa01tf73G8l70jy5XY49Llt2znAoz3bHGzbniXJlUlmk8weOXJkHWVIkiR1Vz9h6zNJ/ijJzyT5GeDTwB+u8fs+AFwAXAwcBt7btmeZbWu5A1TVjVU1U1Uz27dvX2MZkiRJ3XbC4c6q+rdJfgzYSROmbqyqT67ly6rqscX3ST4IfKr9eBA4r2fTc4FDa/kOSZKkrWC1B6zvSPJqgKr6g6q6uqr+FfD1JBes5cuSnN3z8UeBxZmftwGXJzkjyUuAaZrJCpIkSWNptZ60/wN41zLt32rX/fBqB07yMeBS4AVJDgK/DFya5GKaocxHgLcDVNX9SW4BHgCeBq6qqmf6/zNWMT8Pe/fCgQMwPQ27d8OUD0yQJEndluaJT8usSO6rqpevsO7eqvqfBlpZH2ZmZmp2dnblDfbtg127YGEBjh2DyUmYmIDbb4edOzevUEmSNFaS7K+qmfUcY7WJA2eusu6s9XzpppifbwLa/HwT0KBZLrYfPTrc+iRJklaxWkj7YpK3LW1MsgfYP7iSNsjevU0P2nIWFpr1kiRJHbXaNWm/BHwyyVs5HspmgNNpLvrvtgMHjvegLXXsGMzNbW49kiRJJ2HFkNbeLuNVSb4fWLw27dNV9f9sSmXrNT3dXIO2XFCbnIQdOza/JkmSpD6tOHFgFKw6cWB+Hs45p1kuNTUFhw7Btm2DLVCSJI2lQU8cGG1TU80szqmppucMmuViuwFNkiR1WD8PWB9dO3c2PWZ79zbXoO3Y0dwnzYAmSZI6bmuHNGgC2Z49w65CkiTppGzd4U5JkqQRZkiTJEnqIEOaJElSBxnSJEmSOsiQJkmS1EGGNEmSpA4ypEmSJHWQIU2SJKmDDGmSJEkdZEiTJEnqIEOaJElSBxnSJEmSOmjrP2C91/w87N0LBw7A9DTs3g1TU8OuSpIk6VnGJ6Tt2we7dsHCAhw7BpOTcPXVcPvtsHPnsKuTJEn6DuMx3Dk/3wS0+fkmoEGzXGw/enS49UmSJC0xHiFt796mB205CwvNekmSpA4Zj5B24MDxHrSljh2DubnNrUeSJOkExiOkTU8316AtZ3ISduzY3HokSZJOYDxC2u7dMLHCnzox0ayXJEnqkPEIaVNTzSzOqanjPWqTk8fbt20bbn2SJElLjM8tOHbuhEOHmkkCc3PNEOfu3QY0SZLUSeMT0qAJZHv2DLsKSZKkExqP4U5JkqQRM7CQluTDSR5Pcl9P2/OS3JHkQLt8bs+665LMJXkoyRsHVZckSdIoGGRP2u8Cb1rSdi1wZ1VNA3e2n0lyIXA5cFG7z28nOWWAtUmSJHXawEJaVX0e+MaS5suAm9r3NwFv7mm/uaqerKqHgTngkkHVJkmS1HWbfU3ai6rqMEC7fGHbfg7waM92B9u2Z0lyZZLZJLNHjhwZaLGSJEnD0pWJA1mmrZbbsKpurKqZqprZvn37gMuSJEkajs2+BcdjSc6uqsNJzgYeb9sPAuf1bHcucGhgVczPN/dLO3CgeWTU7t3NjW0lSZI6YrND2m3AFcD17fLWnvaPJrkBeDEwDdw1kAr27YNdu2BhoXm4+uQkXH118+SBnTsH8pWSJEkna2AhLcnHgEuBFyQ5CPwyTTi7Jcke4C+BnwCoqvuT3AI8ADwNXFVVz2x4UfPzTUCbnz/eduxYs9y1q3kigU8gkCRJHTCwkFZVb1lh1etW2P49wHsGVQ/QDHEuLCy/bmGhWe8TCSRJUgd0ZeLA5jhw4HjP2VLHjjXP9JQkSeqA8Qpp09PNNWjLmZxsHrouSZLUAeMV0nbvhokV/uSJiWa9JElSB4xXSJuaamZxTk0d71GbnDze7qQBSZLUEZt9C47h27mzmcW5d29zDdqOHU0PmgFNkiR1yPiFNGgCmbM4JUlSh43XcKckSdKIMKRJkiR1kCFNkiSpg8bzmrRFPmhdkiR11PiGNB+0LkmSOmw8hzt7H7S++JioY8eOtx89Otz6JEnS2BvPkNbPg9YlSZKGaDxDmg9alyRJHTeeIc0HrUuSpI4bz5Dmg9YlSVLHjWdI80HrkiSp48b3Fhw+aF2SJHXYePakLVp80Pq73gVV8Cu/Ah/6UHMrDkmSpCEa3560Rd7UVpIkddB496R5U1tJktRR4x3SvKmtJEnqqPEOad7UVpIkddR4hzRvaitJkjpqvEPaaje1ffrp5ro0SZKkIRjvkNZ7U9szz/zOdQl8z/c0sz8lSZI22XiHNGhus/HQQ89uf+IJZ3lKkqShMaQBfPrTcMopy69zlqckSRoCQxo4y1OSJHWOIQ2c5SlJkjrHkAarz/J86in4m7/xeZ6SJGlTDSWkJXkkyb1J7kky27Y9L8kdSQ60y+duWkG9szyX9qh9+9tw7bVwzjnO9JQkSZtmmD1p319VF1fVTPv5WuDOqpoG7mw/b56dO+HQIfi1X4PTT//OdT7PU5IkbbIuDXdeBtzUvr8JePOmV7BtG5xxBpx22vLrn3wSbrpp+XWSJEkbaFghrYA/TrI/yZVt24uq6jBAu3zhcjsmuTLJbJLZI0eObHxlq830fOopuPpqhz0lSdLADSukvbqqXgn8IHBVktf0u2NV3VhVM1U1s3379o2vbLWZntAENYc9JUnSgA0lpFXVoXb5OPBJ4BLgsSRnA7TLx4dR26ozPRc57ClJkgZs00NakskkU4vvgR8A7gNuA65oN7sCuHWzawOOz/Rc6bo0cNhTkiQN3DB60l4E7EvyJeAu4NNV9RngeuANSQ4Ab2g/D8fOnXDDDc0kgpU47ClJkgZo00NaVX2lql7Rvi6qqve07V+vqtdV1XS7/MZm1/Ydrrji2bfiWMphT0mSNCBdugVHtzjsKUmShsiQtpp+hz1f/3o4fHjz6pIkSVueIe1E+h32/O7vhre+FT70IZ/zKUmS1s2QdiL9DHsCPP00fPSjcNVV8PznwzXXGNYkSdKaGdL60c+w56Knnmoeyv7rv25YkyRJa2ZI61c/w55LGdYkSdIaGdL6tTjs2U9v2lKGNUmSdJJSVcOuYc1mZmZqdnZ2c7/08GF46UvhiSfWfowzzoAf+RE46yx43vPgoouax1FNTW1cnZIkaWiS7K+qmXUdw5C2Bvv2NU8beOYZ+Na31n+8U09tlq97HfyDf2BokyRpxBnShhXSoHkc1N698LnPwcc/3tyGY6Oceiok8Au/ADt2wKOPwvS0wU2SpBFhSBtmSOt19Cj8h//QzAD99rcH8x2nnw4LC/a2SZI0AgxpXQlpizYjrC1aOkR6wQXNZ3vdJEkaOkNa10Laos0Ma8vp7XW74IKmHicoSJK0aQxpXQ1pixbD2vve13x+6qnh1rP0Wre5OfjrvzbASZK0wQxpXQ9pi3onGXziE03bem7hMQi9w6e9vW8Oo0qSdNIMaaMS0notBrYHHmhed97ZtA9jWPRkLTeMum2bw6mSJC1hSBvFkLbUcqFtYmJjb+mxWRafxrB4o97FAGeQkySNGUPaVghpSy2Gtrk5OPdc+G//DX7rt6BqNHrbTmSl6+IMdJKkLcSQthVD2nJGeYh0PQx0kqQRZUgbl5C2VG9o+/rXmwe3v/SlW6/X7WScaKi1N9AtToZwdqskaUAMaeMa0lazNMBNTTVhZJSvddsMJ5rdulIvnr15kqRlGNIMaf1beq0bwFe+Ml7Dp4O2nt48w58kbSmGNEPaxliu921+3mHUrhhU+FtpW4OhJK2bIc2QtnlWCnKLyyeegFtvbbbt2o16dfJWejbsesKfoVLSGDGkGdK6ZbUg53VxGqSTDZXDCJNbeVuDsvQshjRD2mhZ6bo4A500+kap93XQ245KnW470P/Tcurb3vZfnq565dp+UA1Dmrqp30DnUKskqWvOPJNXPvHEwt1Vp6znMKduVD3Shtq2Dfbs6X/7E10zt3QyBDi7VZI0GE88wQRMrPcwhjRtDScb6nqdaHYrrNyLZ2+eJGlADGnSegJer7X25hn+JEnLGOlr0pIcAf6i/fgC4K+GWI7WxvN2AqfAxPPheWfBmafAqQvwzAScsnT5DDz9JDwJcAacsZZtT4cztsFzAAJZqaYjwPZN+eu1kTxvo8nzNpr+HDhateK/R/sx0iGtV5LZ9U511ebzvI0mz9to8ryNJs/baNqI87bui9okSZK08QxpkiRJHbSVQtqNwy5Aa+J5G02et9HkeRtNnrfRtO7ztmWuSZMkSdpKtlJPmiRJ0pYx8iEtyZuSPJRkLsm1w65HK0vySJJ7k9yTZLZte16SO5IcaJfPHXad4y7Jh5M8nuS+nrYVz1OS69rf30NJ3jicqgUrnrt3J/lq+7u7J8munnWeuyFLcl6SzyV5MMn9Sd7Ztvub67BVztuG/t5GergzySnAfwXeABwEvgi8paoeGGphWlaSR4CZqvqrnrZfB75RVde3Ifu5VXXNsGoUJHkNcBT4vap6edu27HlKciHwMeAS4MXAZ4GXVdUzQyp/rK1w7t4NHK2q31yyreeuA5KcDZxdVXcnmQL2A28GfgZ/c521ynn7STbw9zbqPWmXAHNV9ZWqegq4GbhsyDXp5FwG3NS+v4nmH3INUVV9HvjGkuaVztNlwM1V9WRVPQzM0fwuNQQrnLuVeO46oKoOV9Xd7ft54EHgHPzNddoq520lazpvox7SzgEe7fl8kNX/R9JwFfDHSfYnubJte1FVHYbmH3rghUOrTqtZ6Tz5GxwN70jy5XY4dHHYzHPXMUnOB74X+AL+5kbGkvMGG/h7G/WQttzjFkZ3/Hbre3VVvRL4QeCqdmhGo83fYPd9ALgAuBg4DLy3bffcdUiSbcAngF+qqm+utukybZ63IVnmvG3o723UQ9pB4Lyez+cCh4ZUi06gqg61y8eBT9J09T7Wju0vjvE/PrwKtYqVzpO/wY6rqseq6pmqWgA+yPEhFs9dRyQ5jeY/9B+pqj9om/3Nddxy522jf2+jHtK+CEwneUmS04HLgduGXJOWkWSyvbiSJJPADwD30ZyvK9rNrgBuHU6FOoGVztNtwOVJzkjyEmAauGsI9WkFi/+hb/0oze8OPHedkCTA7wAPVtUNPav8zXXYSudto39vp25cyZuvqp5O8g7gj4BTgA9X1f1DLkvLexHwyeafa04FPlpVn0nyReCWJHuAvwR+Yog1CkjyMeBS4AVJDgK/DFzPMuepqu5PcgvwAPA0cJWzzIZnhXN3aZKLaYZWHgHeDp67Dnk18NPAvUnuadvehb+5rlvpvL1lI39vI30LDkmSpK1q1Ic7JUmStiRDmiRJUgcZ0iRJkjrIkCZJktRBhjRJkqQOMqRJGllJjrbL85P81AYf+11LPv9/G3l8SToRQ5qkreB84KRCWpJTTrDJd4S0qnrVSdYkSetiSJO0FVwP/NMk9yT5V0lOSfIbSb7YPuj47QBJLk3yuSQfBe5t2/7vJPuT3J/kyrbteuCs9ngfadsWe+3SHvu+JPcm2d1z7D9J8vEkf57kI+1dySVpTUb6iQOS1LoW+DdV9UMAbdj6H1X1fUnOAP5Tkj9ut70EeHlVPdx+/tmq+kaSs4AvJvlEVV2b5B1VdfEy3/VjNA9PfgXwgnafz7frvhe4iOaZfP+J5q7k+zb6j5U0HuxJk7QV/QDwz9vHtXwBeD7Ns/IA7uoJaAC/mORLwH+meQDyNKvbCXysfYjyY8CfAt/Xc+yD7cOV76EZhpWkNbEnTdJWFOBfVtUffUdjcilwbMnn1wP/pKq+leRPgDP7OPZKnux5/wz+O1bSOtiTJmkrmAemej7/EfDzSU4DSPKyJJPL7Pd3gb9uA9rfB/7nnnXfXtx/ic8Du9vr3rYDrwHu2pC/QpJ6+P/yJG0FXwaeboctfxd4P81Q493txftHgDcvs99ngJ9L8mXgIZohz0U3Al9OcndVvbWn/ZPAPwG+BBTw76rqa23Ik6QNk6oadg2SJElawuFOSZKkDjKkSZIkdZAhTZIkqYMMaZIkSR1kSJMkSeogQ5okSVIHGdIkSZI6yJAmSZLUQf8/Sgq7Eo8TCI4AAAAASUVORK5CYII=\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "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": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " age | \n",
- " sex | \n",
- " bmi | \n",
- " children | \n",
- " smoker | \n",
- " region | \n",
- " charges | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 1235 | \n",
- " 26 | \n",
- " male | \n",
- " 31.065 | \n",
- " 0 | \n",
- " no | \n",
- " northwest | \n",
- " 2699.56835 | \n",
- "
\n",
- " \n",
- " 234 | \n",
- " 39 | \n",
- " male | \n",
- " 24.510 | \n",
- " 2 | \n",
- " no | \n",
- " northwest | \n",
- " 6710.19190 | \n",
- "
\n",
- " \n",
- " 540 | \n",
- " 34 | \n",
- " female | \n",
- " 38.000 | \n",
- " 3 | \n",
- " no | \n",
- " southwest | \n",
- " 6196.44800 | \n",
- "
\n",
- " \n",
- " 1256 | \n",
- " 51 | \n",
- " female | \n",
- " 36.385 | \n",
- " 3 | \n",
- " no | \n",
- " northwest | \n",
- " 11436.73815 | \n",
- "
\n",
- " \n",
- " 1257 | \n",
- " 54 | \n",
- " female | \n",
- " 27.645 | \n",
- " 1 | \n",
- " no | \n",
- " northwest | \n",
- " 11305.93455 | \n",
- "
\n",
- " \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " 752 | \n",
- " 64 | \n",
- " male | \n",
- " 37.905 | \n",
- " 0 | \n",
- " no | \n",
- " northwest | \n",
- " 14210.53595 | \n",
- "
\n",
- " \n",
- " 954 | \n",
- " 34 | \n",
- " male | \n",
- " 27.835 | \n",
- " 1 | \n",
- " yes | \n",
- " northwest | \n",
- " 20009.63365 | \n",
- "
\n",
- " \n",
- " 1137 | \n",
- " 26 | \n",
- " female | \n",
- " 22.230 | \n",
- " 0 | \n",
- " no | \n",
- " northwest | \n",
- " 3176.28770 | \n",
- "
\n",
- " \n",
- " 106 | \n",
- " 19 | \n",
- " female | \n",
- " 28.400 | \n",
- " 1 | \n",
- " no | \n",
- " southwest | \n",
- " 2331.51900 | \n",
- "
\n",
- " \n",
- " 274 | \n",
- " 25 | \n",
- " male | \n",
- " 27.550 | \n",
- " 0 | \n",
- " no | \n",
- " northwest | \n",
- " 2523.16950 | \n",
- "
\n",
- " \n",
- "
\n",
- "
1338 rows × 7 columns
\n",
- "
"
- ],
- "text/plain": [
- " age sex bmi children smoker region charges\n",
- "1235 26 male 31.065 0 no northwest 2699.56835\n",
- "234 39 male 24.510 2 no northwest 6710.19190\n",
- "540 34 female 38.000 3 no southwest 6196.44800\n",
- "1256 51 female 36.385 3 no northwest 11436.73815\n",
- "1257 54 female 27.645 1 no northwest 11305.93455\n",
- "... ... ... ... ... ... ... ...\n",
- "752 64 male 37.905 0 no northwest 14210.53595\n",
- "954 34 male 27.835 1 yes northwest 20009.63365\n",
- "1137 26 female 22.230 0 no northwest 3176.28770\n",
- "106 19 female 28.400 1 no southwest 2331.51900\n",
- "274 25 male 27.550 0 no northwest 2523.16950\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 = 130138987.51553275\n",
- "Wielomian 2 stopnia, MSE = 130327783.40257515\n",
- "Wielomian 3 stopnia, MSE = 130351122.7970451\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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\n",
- "text/plain": [
- ""
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- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "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": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
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- "needs_background": "light"
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- "output_type": "display_data"
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\n",
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\n",
- "text/plain": [
- ""
- ]
- },
- "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\")"
- ]
- }
- ],
- "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": "3.9.12"
- },
- "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
-}
diff --git a/Regresja_wielomianowa.ipynb b/Regresja_wielomianowa.ipynb
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+++ b/Regresja_wielomianowa.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "# Regresja wielomianowa"
+ ]
+ },
+ {
+ "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": "markdown",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "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": 47,
+ "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": 48,
+ "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": 49,
+ "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": 50,
+ "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": 51,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "notes"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " sqrMetres price\n",
+ "1102 73 550000.0\n",
+ "1375 78 496573.0\n",
+ "1644 40 310000.0\n",
+ "1434 22 377652.0\n",
+ "1444 30 345180.0\n",
+ "... ... ...\n",
+ "384 76 269984.0\n",
+ "214 42 249000.0\n",
+ "24 47 299000.0\n",
+ "1004 57 293848.0\n",
+ "1632 112 329000.0\n",
+ "\n",
+ "[1674 rows x 2 columns]"
+ ],
+ "text/html": "\n\n
\n \n \n | \n sqrMetres | \n price | \n
\n \n \n \n 1102 | \n 73 | \n 550000.0 | \n
\n \n 1375 | \n 78 | \n 496573.0 | \n
\n \n 1644 | \n 40 | \n 310000.0 | \n
\n \n 1434 | \n 22 | \n 377652.0 | \n
\n \n 1444 | \n 30 | \n 345180.0 | \n
\n \n ... | \n ... | \n ... | \n
\n \n 384 | \n 76 | \n 269984.0 | \n
\n \n 214 | \n 42 | \n 249000.0 | \n
\n \n 24 | \n 47 | \n 299000.0 | \n
\n \n 1004 | \n 57 | \n 293848.0 | \n
\n \n 1632 | \n 112 | \n 329000.0 | \n
\n \n
\n
1674 rows × 2 columns
\n
"
+ },
+ "metadata": {},
+ "execution_count": 51
+ }
+ ],
+ "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": 52,
+ "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": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Wielomian 1 stopnia, MSE = 49522013685.367744\n",
+ "Wielomian 2 stopnia, MSE = 148367826800.28735\n",
+ "Wielomian 3 stopnia, MSE = 145689760074.80402\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": "