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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Regresja wielomianowa"
]
},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"\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",
"$$ \\theta - \\text{wektor parametrów modelu} $$ "
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]
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},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"## Funkcja kosztu\n",
"\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."
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]
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},
{
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"cell_type": "markdown",
"metadata": {},
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"source": [
"## Metoda gradientu prostego\n",
""
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]
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},
{
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"cell_type": "markdown",
"metadata": {},
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"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",
"$$ \\alpha - \\text{współczynnik uczenia} $$"
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]
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},
{
"cell_type": "code",
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"execution_count": 1,
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"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",
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"execution_count": 2,
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"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",
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"execution_count": 3,
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"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",
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"execution_count": 4,
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"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",
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"execution_count": 5,
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"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",
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"execution_count": 6,
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"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"outputs": [
{
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sqrMetres</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>318</th>\n",
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" <th>1486</th>\n",
" <td>20</td>\n",
" <td>347633.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224</th>\n",
" <td>42</td>\n",
" <td>289000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <th>1410</th>\n",
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" <th>983</th>\n",
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" <td>496573.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>418</th>\n",
" <td>16</td>\n",
" <td>309978.0</td>\n",
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"</table>\n",
"<p>1674 rows ×
"</div>"
],
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"text/plain": [
" sqrMetres price\n",
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"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",
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"... ... ...\n",
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"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",
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"\n",
"[1674 rows x 2 columns]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"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",
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"execution_count": 7,
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"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",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wielomian 1 stopnia, MSE = 35514312788.779144\n",
"Wielomian 2 stopnia, MSE = 90239672317.31158\n",
"Wielomian 3 stopnia, MSE = 89217346258.50258\n"
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]
},
{
"data": {
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
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2022-06-21 16:42:59 +02:00
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},
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
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"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"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",
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"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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
"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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
"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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
"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,
2022-06-20 20:34:11 +02:00
"metadata": {},
"outputs": [
{
"data": {
2022-06-21 16:42:59 +02:00
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
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},
{
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
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},
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},
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"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"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",
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"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"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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
"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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
"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,
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"metadata": {},
"outputs": [],
"source": [
"# Ilość nauki do oceny"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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"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 ×
"</div>"
],
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"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]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
"data_marks_all = pandas.read_csv('Student_Marks.csv')\n",
"data_marks_all"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"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",
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"execution_count": 13,
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"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wielomian 1 stopnia, MSE = 381.16937283505433\n",
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"Wielomian 2 stopnia, MSE = 394.1863119057109\n",
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"Wielomian 3 stopnia, MSE = 391.5017110730558\n"
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]
},
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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},
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAFkCAYAAACHEodbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/qElEQVR4nO3deXzV5Zn38c+dfSdACIQQFk0AAQUEV6jFDZS665D26bTWsWMXp1OH9qm2T592pqt1ptP6TNvpWGu1i4pVrFRpBRWsUVF22QmyJwEStpzsyTn388fvhIRwzsk5Sc6a7/v1yivJbzm5OAa9vO/rd13GWouIiIiIRE5StAMQERERGWyUgImIiIhEmBIwERERkQhTAiYiIiISYUrARERERCJMCZiIiIhIhKVEO4BgFBQU2PHjx0c7DBER6aO6hlZqTreQn5VKydCsaIcjMmDWr19fZ60dEep9cZGAjR8/nnXr1kU7DBER6aNPP/E+f9tdy0/LZ3DbzOJohyMyYIwxB/pyX1wkYCIiEgdcLliyBCoroawMysshN5eWdjfv7T0OwJzSgigHKRIblICJiEj/VVTAwoXg8UBjI2Rnw+LFsHw560ZOprXDw5SiPEbkpkc7UpGYoCJ8ERHpH5fLSb5cLif5Auez9/hb26sA+MhErX6JdFICJiIi/bNkibPy5YvHw9/W7wPgqrKQ65RFEpYSMBER6Z/Kyq6Vrx6OmAx2tKWSkZrErHFDIxyYSOxSAiYiIv1TVubUfPmwcupVgLP6lZGaHMmoRGKaEjAREemf8nJI8v2fk1fPvxSABVNHRTIikZinBExERPonNxeWL3c+d66EZWdzumAUa8ZeSHKS4doLCqMbo0iMURsKERHpv7lzobraKcjfswdKS3ljyjw6XtrJlecNIz8rLdoRisQUJWAiIjIwcnLg3nvPfPvq79YD2n4U8UVbkCIiMuBa2t28ubsWgOunjIxyNCKxRwmYiIgMuL/trqW53c1FY4YwOj8z2uGIxBxtQYqISN/4mf0IsGL7UUDbjyL+KAETEZHQrVgBt94Kbje0t0NW1pnZjx1XXMnrO5wEbL62H0V80hakiIiEZsUKWLAAWlqc5AugqenM7Mf3d1Rxsqmd81I7KP3P78HjjzvnROQMJWAiIhI8lwtuucX/eY+HFb//CwDz17yMeeQReOABKC6GiorIxCgSB5SAiYhI8JYscbYd/bCNjaysTwVg/va3nIONjWdWx2hoiESUIjFPCZiIiASvshI6Ovye3jp6IlV5hRS6jjOjevfZJz0eJ4ETESVgIiISgrIyp+DejxWllwEwv3INSdizTzY2Ol3yRUQJmIiIhKC8HJKT/Z5+9bKPAbCg8t1zT2ZnQ2lpuCITiStKwEREJHjdB293roSlpEB6Ovte/Cu7k3LIbW3ksoNbz703KclJ4EREfcBERCREPgZvU17OivVHgZ1ce14+admZTs1XY6Oz8pWU5CRuOTnRjl4kJigBExGR0HUO3u7shv/d7/Jq5uVAGgvmXegzQVPyJdJFCZiIiPRNRYXTWsLj4RhpbPinq0jraOOq43vgwo84CZqI+KQETEREgtN99mNJCXz962f6eq2YcRUAV+3bSPYtP3VWwLTiJeKXEjAREeldt9UuGhshPR1aW8+cXlF2OQDzK991RhTdfz/87GdnhnOLyNn0FKSIiATW2cXe5XKSLzgr+apPy+LdcReR5HFz7Z73nfmQTz+t8UMiASgBExGRwJYscVa+/Fh1/iW0J6dyyeHtDG+udw52dGj8kEgASsBERCSwysqulS8fVkx0th8X7PbRfFXjh0R8UgImIiKBlZU5vbx8aElOZfWEWQBcX7nm3As0fkjEJyVgIiISWHm500jVh7fHz6AxPYupR/ZQUn/s3As0fkjEJyVgIiISWPfxQ50rYdnZkJHBislzAFjga/ULNH5IxA+1oRARkd75GD/kvuFGXvvRagDm+6r/ysnR+CERP8KagBlj9gMuwA10WGtnG2OGAUuA8cB+YJG19mQ44xARkQHQOX7Ia93e4xzPGsK400eY1FznHExLA2th8WL45jeVfIn4EYkVsKuttXXdvn8IeN1a+7Ax5iHv9w9GIA4RERlAK7YfBWDBgtmY2Y9q7qNICKKxBXkrMM/79VPAapSAiYjEFWstr247AsD8GSUwfnqUIxKJL+EuwrfACmPMemPMfd5jI621Nd6vjwAjfd1ojLnPGLPOGLOutrY2zGGKiEgottfUc/hkMwU56Vw8dmi0wxGJO+FeAZtrra0yxhQCK40xO7uftNZaY4z1daO19jHgMYDZs2f7vEZERKLj1W3O9uP1U0aSlGSiHI1I/AnrCpi1tsr7+RjwInApcNQYUwTg/eyjcYyIiMSyFZ3bj1N9bmKISC/CloAZY7KNMbmdXwPzga3AMuBu72V3Ay+FKwYRERl4B483sfOIi5z0FK48f3i0wxGJS+HcghwJvGiM6fw5T1tr/2qMWQs8Z4y5FzgALApjDCIiMsA6i++vnlxIekpylKMRiU9hS8CstXuBcx6LsdYeB64N188VEZHwWrHdScAWaPtRpM80ikhERIJW62pl3YGTpCUn8dGJI6IdjkjcUgImIiJBe23HUayFOaXDyc1IjXY4InFLCZiIiASt8+nHBVNHRTkSkfimBExERILiamnn7T3HMQauvUD1XyL9oQRMRESCsnpXLW1uD7PHDWVEbnq0wxGJa0rAREQkKGeGb2v7UaTflICJiEivWjvcrNrpDC6ZP0UJmEh/KQETEZFevfPhcRpaO5g8Kpexw7OiHY5I3FMCJiIivdLTjyIDSwmYiIgE5PZYVnrrvzR8W2RgKAETEZGANh48SV1DG2OGZjKlKC/a4YgkBCVgIiIS0Kvdth+NMVGORiQxhG0Yt4iIxDmXC/vsElbsyQeTyfwJudGOSCRhaAVMRETOVVEBxcXs+t6POWAyGd50mtlXTnOOi0i/KQETEZGzuVywcCG4XLw6ZgYA11WuIbm+3jne0BDd+EQSgBIwERE525Il4PEAsKLscgDmV65xznk8znkR6RclYCIicrbKSmhs5FBeIdtGlZLd2sSc/Zucc42NsGdPVMMTSQRKwERE5GxlZZCdzYqJzurXvH3ryXC3O+eys6G0NIrBiSQGJWAiInK28nJISmJF2RUAzN+9putcUpJzXkT6RW0oRETkbLm5HH/xZda+eppUdwdXf7jWWflKSoLlyyEnJ9oRisQ9JWAiInKO1/Mm4En6gLlZ7eQt/mdn27G8XMmXyABRAiYiIudYtrkagBsWzIbL7ohyNCKJRzVgIiJylprTzbz9YR1pyUl87MKiaIcjkpCUgImIyFn+tLEaa+G6KYUMyUqNdjgiCUkJmIiInGGtZemGwwDcMXNMlKMRSVxKwERE5IytVfVUHmtgWHYaH500ItrhiCQsJWAiInLGC97Vr1umjyY1Wf+JEAkXPQUpIiIAtLs9Z55+vPPiANuPLpczD7Ky0umaX14OubkRilIkMSgBExERAN7cVcuJxjbKCnOYVpzn+6KKCli40BnK3djoNGhdvNhp0Dp3bmQDFoljWl8WEREAlm70Ft9fPAZjzLkXuFxO8uVyOckXOJ87jzc0RDBakfimBExERDjd1M5r249hDNw2c7Tvi5YscVa+fPF4nPMiEhQlYCIiwitbamhze5hzfgFFQzJ9X1RZ2bXy1VNjI+zZE74ARRKMEjAREenq/XVxsf+Lysqcmi9fsrOdeZEiEhQlYCIig9yB442sO3CSrLRkFkwd5f/C8nJI8vOfjaQk57yIBEUJmIjIILd0QxUAN0wbRXZ6gIfjc3Odpx1zc7tWwrKzu47n5EQgWpHEoDYUIiKDmLW26+nHYEYPzZ0L1dVOwf2ePc62Y3m5ki+RECkBExEZxNYdOMmhE82MysvgivOHB3dTTg7ce294AxNJcNqCFBEZxDqL72+bWUxyko/eXyISFkrAREQGqZZ2Ny9/UAP08vSjiAw4bUGKiAxSr+04iqulgwuLhzBxZIBZjpr9KDLglICJiAxSnU8/Blz90uxHkbDQFqSIyCBU62r
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"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",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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},
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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},
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"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",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"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 ×
"</div>"
],
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"text/plain": [
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" 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",
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"\n",
"[1338 rows x 7 columns]"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
],
"source": [
"data_ins = pandas.read_csv('insurance.csv')\n",
"data_ins = data_ins.sample(frac=1)\n",
"data_ins"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"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",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wielomian 1 stopnia, MSE = 178360681.2241408\n",
"Wielomian 2 stopnia, MSE = 179170879.99336478\n"
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]
},
{
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"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: "
]
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},
{
"data": {
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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},
{
"data": {
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"text/plain": [
"<Figure size 691.2x388.8 with 1 Axes>"
]
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},
"metadata": {
"needs_background": "light"
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},
"output_type": "display_data"
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}
],
"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",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"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": {
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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},
"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",
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"version": "3.8.12"
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},
"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
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}
