2022-06-12 17:45:18 +02:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Regresja wielomianowa"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 1,
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2022-06-12 17:45:18 +02:00
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"import ipywidgets as widgets\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 2,
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2022-06-12 17:45:18 +02:00
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"metadata": {
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"slideshow": {
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"slide_type": "notes"
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}
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},
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"outputs": [],
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"source": [
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"# Przydatne funkcje\n",
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"\n",
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"def cost(theta, X, y):\n",
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" \"\"\"Wersja macierzowa funkcji kosztu\"\"\"\n",
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" m = len(y)\n",
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" J = 1.0 / (2.0 * m) * ((X * theta - y).T * (X * theta - y))\n",
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" return J.item()\n",
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"\n",
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"def gradient(theta, X, y):\n",
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" \"\"\"Wersja macierzowa gradientu funkcji kosztu\"\"\"\n",
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" return 1.0 / len(y) * (X.T * (X * theta - y)) \n",
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"\n",
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"def gradient_descent(fJ, fdJ, theta, X, y, alpha=0.1, eps=10**-7):\n",
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" \"\"\"Algorytm gradientu prostego (wersja macierzowa)\"\"\"\n",
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" current_cost = fJ(theta, X, y)\n",
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" logs = [[current_cost, theta]]\n",
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" while True:\n",
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" theta = theta - alpha * fdJ(theta, X, y)\n",
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" current_cost, prev_cost = fJ(theta, X, y), current_cost\n",
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" if abs(prev_cost - current_cost) > 10**15:\n",
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" print('Algorithm does not converge!')\n",
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" break\n",
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" if abs(prev_cost - current_cost) <= eps:\n",
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" break\n",
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" logs.append([current_cost, theta]) \n",
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" return theta, logs\n",
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"\n",
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"def plot_data(X, y, xlabel, ylabel):\n",
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" \"\"\"Wykres danych (wersja macierzowa)\"\"\"\n",
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" fig = plt.figure(figsize=(16*.6, 9*.6))\n",
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" ax = fig.add_subplot(111)\n",
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" fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)\n",
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" ax.scatter([X[:, 1]], [y], c='r', s=50, label='Dane')\n",
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" \n",
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" ax.set_xlabel(xlabel)\n",
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" ax.set_ylabel(ylabel)\n",
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" ax.margins(.05, .05)\n",
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" plt.ylim(y.min() - 1, y.max() + 1)\n",
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" plt.xlim(np.min(X[:, 1]) - 1, np.max(X[:, 1]) + 1)\n",
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" return fig\n",
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"\n",
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"def plot_fun(fig, fun, X):\n",
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" \"\"\"Wykres funkcji `fun`\"\"\"\n",
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" ax = fig.axes[0]\n",
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" x0 = np.min(X[:, 1]) - 1.0\n",
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" x1 = np.max(X[:, 1]) + 1.0\n",
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" Arg = np.arange(x0, x1, 0.1)\n",
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" Val = fun(Arg)\n",
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" return ax.plot(Arg, Val, linewidth='2')"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 3,
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2022-06-18 11:52:01 +02:00
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"metadata": {},
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2022-06-12 17:45:18 +02:00
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"outputs": [],
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"source": [
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2022-06-18 11:52:01 +02:00
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"def MSE(Y_true, Y_pred):\n",
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" return np.square(np.subtract(Y_true,Y_pred)).mean()"
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2022-06-12 17:45:18 +02:00
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 4,
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2022-06-12 17:45:18 +02:00
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"# Funkcja regresji wielomianowej\n",
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"\n",
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"def h_poly(Theta, x):\n",
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" \"\"\"Funkcja wielomianowa\"\"\"\n",
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" return sum(theta * np.power(x, i) for i, theta in enumerate(Theta.tolist()))\n",
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"\n",
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"def get_poly_data(data, deg):\n",
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" m, n_plus_1 = data.shape\n",
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" n = n_plus_1 - 1\n",
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"\n",
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" X1 = data[:, 0:n]\n",
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" X1 /= np.amax(X1, axis=0)\n",
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"\n",
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" Xs = [np.ones((m, 1)), X1]\n",
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"\n",
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" for i in range(2, deg+1):\n",
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" Xn = np.power(X1, i)\n",
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" Xn /= np.amax(Xn, axis=0)\n",
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" Xs.append(Xn)\n",
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"\n",
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" X = np.matrix(np.concatenate(Xs, axis=1)).reshape(m, deg * n + 1)\n",
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"\n",
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" y = np.matrix(data[:, -1]).reshape(m, 1)\n",
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"\n",
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" return X, y\n",
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"\n",
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"\n",
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2022-06-15 20:06:32 +02:00
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"def polynomial_regression(X, y, n):\n",
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2022-06-12 17:45:18 +02:00
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" \"\"\"Funkcja regresji wielomianowej\"\"\"\n",
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" theta_start = np.matrix([0] * (n+1)).reshape(n+1, 1)\n",
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" theta, logs = gradient_descent(cost, gradient, theta_start, X, y)\n",
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" return lambda x: h_poly(theta, x)"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 5,
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2022-06-18 11:52:01 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_values(model, data, n):\n",
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" x, y = get_poly_data(np.array(data), n)\n",
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" preprocessed_x = []\n",
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" for i in x:\n",
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" preprocessed_x.append(i.item(1))\n",
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" return y, model(preprocessed_x), MSE(y, model(preprocessed_x))\n",
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"\n",
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"def plot_and_mse(data, data_test, n):\n",
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" x, y = get_poly_data(np.array(data), n)\n",
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" model = polynomial_regression(x, y, n)\n",
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" \n",
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" fig = plot_data(x, y, xlabel='x', ylabel='y')\n",
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" plot_fun(fig, polynomial_regression(x, y, n), x)\n",
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"\n",
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" y_true, Y_pred, mse = predict_values(model, data_test, n)\n",
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" print(f'Wielomian {n} stopnia, MSE = {mse}')"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 6,
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2022-06-12 17:45:18 +02:00
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"metadata": {
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"slideshow": {
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2022-06-18 11:52:01 +02:00
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"slide_type": "notes"
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2022-06-12 17:45:18 +02:00
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}
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},
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"outputs": [
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{
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"data": {
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2022-06-18 11:52:01 +02:00
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sqrMetres</th>\n",
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" <th>price</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>470</th>\n",
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" <td>40</td>\n",
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" <td>1140000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 12:59:24 +02:00
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" <th>1171</th>\n",
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" <td>90</td>\n",
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2022-06-18 17:22:26 +02:00
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" <td>855000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>1128</th>\n",
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" <td>37</td>\n",
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" <td>288405.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>254</th>\n",
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" <td>49</td>\n",
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" <td>290000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>508</th>\n",
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" <td>91</td>\n",
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" <td>375606.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>389</th>\n",
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" <td>56</td>\n",
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" <td>325000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>1403</th>\n",
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" <td>69</td>\n",
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" <td>399000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>957</th>\n",
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" <td>94</td>\n",
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" <td>595000.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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" <th>356</th>\n",
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" <td>53</td>\n",
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" <td>339200.0</td>\n",
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2022-06-18 11:52:01 +02:00
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" </tr>\n",
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" <tr>\n",
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2022-06-18 17:22:26 +02:00
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|
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" <th>160</th>\n",
|
|
|
|
|
|
" <td>44</td>\n",
|
|
|
|
|
|
" <td>349668.0</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
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" </tbody>\n",
|
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"</table>\n",
|
|
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|
|
"<p>1674 rows × 2 columns</p>\n",
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"</div>"
|
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|
],
|
2022-06-12 17:45:18 +02:00
|
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"text/plain": [
|
2022-06-18 12:59:24 +02:00
|
|
|
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" sqrMetres price\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"470 40 1140000.0\n",
|
|
|
|
|
|
"1171 90 855000.0\n",
|
|
|
|
|
|
"1128 37 288405.0\n",
|
|
|
|
|
|
"254 49 290000.0\n",
|
|
|
|
|
|
"508 91 375606.0\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"... ... ...\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"389 56 325000.0\n",
|
|
|
|
|
|
"1403 69 399000.0\n",
|
|
|
|
|
|
"957 94 595000.0\n",
|
|
|
|
|
|
"356 53 339200.0\n",
|
|
|
|
|
|
"160 44 349668.0\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"[1674 rows x 2 columns]"
|
2022-06-12 17:45:18 +02:00
|
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|
]
|
|
|
|
|
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},
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 6,
|
2022-06-12 17:45:18 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 11:52:01 +02:00
|
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|
"output_type": "execute_result"
|
|
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|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# Wczytanie danych (mieszkania) przy pomocy biblioteki pandas\n",
|
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|
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|
"\n",
|
|
|
|
|
|
"alldata = pandas.read_csv('data_flats.tsv', header=0, sep='\\t',\n",
|
|
|
|
|
|
" usecols=['price', 'rooms', 'sqrMetres'])\n",
|
|
|
|
|
|
"alldata = alldata[['sqrMetres', 'price']]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"alldata = alldata.sample(frac=1)\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"alldata"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 7,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# alldata = np.matrix(alldata[['sqrMetres', 'price']])\n",
|
|
|
|
|
|
"data_train = alldata[0:1600]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_test = alldata[1600:]\n",
|
|
|
|
|
|
"data_train = np.matrix(data_train).astype(float)\n",
|
|
|
|
|
|
"data_test = np.matrix(data_test).astype(float)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 8,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"Wielomian 1 stopnia, MSE = 34232245972.377033\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 84918416526.0791\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 83924720748.3686\n"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
2022-06-12 17:45:18 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 17:22:26 +02:00
|
|
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2022-06-18 11:52:01 +02:00
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2022-06-12 17:45:18 +02:00
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2022-06-18 11:52:01 +02:00
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2022-06-18 17:22:26 +02:00
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2022-06-18 11:52:01 +02:00
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"<Figure size 691.2x388.8 with 1 Axes>"
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},
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"output_type": "display_data"
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2022-06-12 17:45:18 +02:00
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}
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],
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"source": [
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2022-06-18 12:59:24 +02:00
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"for n in range(1, 4):\n",
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" plot_and_mse(data_train, data_test, n) "
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]
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"execution_count": 145,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Ilość nauki do oceny"
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]
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},
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{
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"cell_type": "code",
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2022-06-18 17:22:26 +02:00
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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" <th></th>\n",
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" <th>number_courses</th>\n",
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" <th>time_study</th>\n",
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" <th>Marks</th>\n",
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" <td>4.508</td>\n",
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" <td>19.202</td>\n",
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|
|
" <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]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 9,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_marks_all = pandas.read_csv('Student_Marks.csv')\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"data_marks_all"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 10,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"outputs": [],
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"source": [
|
|
|
|
|
|
"data_marks_all = data_marks_all[['time_study', 'Marks']]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"# data_marks_all = data_marks_all.sample(frac=1)\n",
|
|
|
|
|
|
"data_marks_train = data_marks_all[0:70]\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"data_marks_test = data_marks_all[70:]\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"data_marks_train = np.matrix(data_marks_train).astype(float)\n",
|
|
|
|
|
|
"data_marks_test = np.matrix(data_marks_test).astype(float)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 17,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"Wielomian 1 stopnia, MSE = 381.1693728350544\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 394.1863119057109\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 391.50171107305584\n"
|
2022-06-18 12:59:24 +02:00
|
|
|
|
]
|
2022-06-18 11:52:01 +02:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"image/png": "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
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 691.2x388.8 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
|
},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 17:22:26 +02:00
|
|
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"for n in range(1, 4):\n",
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" plot_and_mse(data_marks_train, data_marks_test, n) "
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]
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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" <th></th>\n",
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" <th>age</th>\n",
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" <th>sex</th>\n",
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" <th>bmi</th>\n",
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" <th>955</th>\n",
|
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" <td>31</td>\n",
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" <td>male</td>\n",
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" <td>39.490</td>\n",
|
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" <td>1</td>\n",
|
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" <td>no</td>\n",
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" <td>southeast</td>\n",
|
|
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|
" <td>3875.73410</td>\n",
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|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>644</th>\n",
|
|
|
|
|
|
" <td>43</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>35.310</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
|
|
|
|
|
" <td>18806.14547</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>1210</th>\n",
|
|
|
|
|
|
" <td>36</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>30.875</td>\n",
|
|
|
|
|
|
" <td>1</td>\n",
|
|
|
|
|
|
" <td>no</td>\n",
|
|
|
|
|
|
" <td>northwest</td>\n",
|
|
|
|
|
|
" <td>5373.36425</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>260</th>\n",
|
|
|
|
|
|
" <td>58</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>female</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>25.200</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southwest</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>11837.16000</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>740</th>\n",
|
|
|
|
|
|
" <td>45</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>24.035</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>2</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>northeast</td>\n",
|
|
|
|
|
|
" <td>8604.48365</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </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",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>808</th>\n",
|
|
|
|
|
|
" <td>18</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>30.140</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>0</td>\n",
|
|
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>1131.50660</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>301</th>\n",
|
|
|
|
|
|
" <td>53</td>\n",
|
|
|
|
|
|
" <td>female</td>\n",
|
|
|
|
|
|
" <td>22.610</td>\n",
|
|
|
|
|
|
" <td>3</td>\n",
|
|
|
|
|
|
" <td>yes</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>northeast</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>24873.38490</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>664</th>\n",
|
|
|
|
|
|
" <td>64</td>\n",
|
|
|
|
|
|
" <td>female</td>\n",
|
|
|
|
|
|
" <td>22.990</td>\n",
|
|
|
|
|
|
" <td>0</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>yes</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
|
|
|
|
|
" <td>27037.91410</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>989</th>\n",
|
|
|
|
|
|
" <td>24</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>female</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>20.520</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>0</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>yes</td>\n",
|
|
|
|
|
|
" <td>northeast</td>\n",
|
|
|
|
|
|
" <td>14571.89080</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" <tr>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <th>1121</th>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>46</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" <td>male</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>38.170</td>\n",
|
|
|
|
|
|
" <td>2</td>\n",
|
2022-06-18 12:59:24 +02:00
|
|
|
|
" <td>no</td>\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
" <td>southeast</td>\n",
|
|
|
|
|
|
" <td>8347.16430</td>\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
" </tr>\n",
|
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
|
"</table>\n",
|
|
|
|
|
|
"<p>1338 rows × 7 columns</p>\n",
|
|
|
|
|
|
"</div>"
|
|
|
|
|
|
],
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
" age sex bmi children smoker region charges\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"955 31 male 39.490 1 no southeast 3875.73410\n",
|
|
|
|
|
|
"644 43 male 35.310 2 no southeast 18806.14547\n",
|
|
|
|
|
|
"1210 36 male 30.875 1 no northwest 5373.36425\n",
|
|
|
|
|
|
"260 58 female 25.200 0 no southwest 11837.16000\n",
|
|
|
|
|
|
"740 45 male 24.035 2 no northeast 8604.48365\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"... ... ... ... ... ... ... ...\n",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"808 18 male 30.140 0 no southeast 1131.50660\n",
|
|
|
|
|
|
"301 53 female 22.610 3 yes northeast 24873.38490\n",
|
|
|
|
|
|
"664 64 female 22.990 0 yes southeast 27037.91410\n",
|
|
|
|
|
|
"989 24 female 20.520 0 yes northeast 14571.89080\n",
|
|
|
|
|
|
"1121 46 male 38.170 2 no southeast 8347.16430\n",
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"[1338 rows x 7 columns]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 13,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"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",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 14,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"outputs": [],
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"source": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
"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)"
|
2022-06-18 11:52:01 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"execution_count": 15,
|
2022-06-18 11:52:01 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
2022-06-18 12:59:24 +02:00
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
2022-06-18 17:22:26 +02:00
|
|
|
|
"Wielomian 1 stopnia, MSE = 195836517.14979035\n",
|
|
|
|
|
|
"Wielomian 2 stopnia, MSE = 196081038.617658\n",
|
|
|
|
|
|
"Wielomian 3 stopnia, MSE = 196407183.82186848\n"
|
2022-06-18 12:59:24 +02:00
|
|
|
|
]
|
|
|
|
|
|
},
|
2022-06-18 11:52:01 +02:00
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2022-06-18 17:22:26 +02:00
|
|
|
|
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2022-06-18 11:52:01 +02:00
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2022-06-18 11:52:01 +02:00
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2022-06-18 12:59:24 +02:00
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2022-06-18 11:52:01 +02:00
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2022-06-18 17:22:26 +02:00
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2022-06-18 11:52:01 +02:00
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2022-06-18 12:59:24 +02:00
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2022-06-18 17:22:26 +02:00
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"image/png": "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2022-06-18 12:59:24 +02:00
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"text/plain": [
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"<Figure size 691.2x388.8 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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2022-06-18 11:52:01 +02:00
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}
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],
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"source": [
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2022-06-18 12:59:24 +02:00
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"for n in range(1, 4):\n",
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" plot_and_mse(data_ins_train, data_ins_test, n) "
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2022-06-12 17:45:18 +02:00
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]
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2022-06-18 17:22:26 +02:00
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},
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{
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"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": []
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2022-06-12 17:45:18 +02:00
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}
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],
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"metadata": {
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"author": "Paweł Skórzewski",
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"celltoolbar": "Slideshow",
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"email": "pawel.skorzewski@amu.edu.pl",
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"kernelspec": {
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2022-06-18 11:52:01 +02:00
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"display_name": "Python 3 (ipykernel)",
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2022-06-12 17:45:18 +02:00
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"language": "python",
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"name": "python3"
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},
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"lang": "pl",
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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2022-06-18 17:22:26 +02:00
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"version": "3.9.12"
|
2022-06-12 17:45:18 +02:00
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"theme": "white"
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},
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"subtitle": "5.Regresja wielomianowa. Problem nadmiernego dopasowania[wykład]",
|
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"title": "Uczenie maszynowe",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
|
2022-06-18 11:52:01 +02:00
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}
|
