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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"# Algorytm najszybszego spadku dla regresji wielomianowej. "
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"Skład grupy:\n",
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"- Nowak Anna,\n",
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"- Łaźna Patrycja,\n",
"- Bregier Damian"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Podstawowe informacje o zbiorze danych"
]
},
{
"attachments": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Ze względu na specyfikę wytycznych projektu zakładającego wykorzystanie jedynie dwóch cech: x i y oraz modelowanie zależności y od x za pomocą funkcji wielomianowej dobrany został przez nas odpowiedni dataset. Obejmuje on jedynie trzy kolumny, czyli: płeć, wzrost w calach oraz wagę w funtach, z czego ze względu na specyfikę projektu wykorzystywane są jedynie wzrost oraz waga. Każdy z parametrów zawiera po 10 tysięcy unikalnych wartości.\n",
"\n",
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"\n",
"\n",
"Dokładne informacje na temat tego zbioru, jego zawartości i samych danych można znaleźć pod adresem: https://www.kaggle.com/mustafaali96/weight-height."
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]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import ipywidgets as widgets\n",
"np.set_printoptions(suppress=True)"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Wczytywanie i preprocessing danych"
]
},
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{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Stopień wielomianu\n",
"degree = 4\n",
"X_plot = np.linspace(0, 100, 1000)\n",
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"initial_theta = np.matrix([0] * (degree + 1)).reshape(degree + 1, 1)"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Wybór dwóch kolumn - dotyczących wzrostu i wagi\n",
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"data = pd.read_csv('weight-height.csv')[[\"Height\", \"Weight\"]]\n",
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"# Czyszczenie tabeli i wartości pustych\n",
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"data = data.dropna()\n",
"data_matrix = np.matrix(data)\n",
"\n",
"m, n_plus_1 = data_matrix.shape\n",
"n = n_plus_1 - 1\n",
"X = (np.ones((m, 1)))\n",
"\n",
"for i in range(1, degree + 1):\n",
" Xn = np.power(data_matrix[:, 0:n], i)\n",
" Xn /= np.amax(Xn, axis=0)\n",
" X = np.concatenate((X, Xn), axis=1)\n",
"\n",
"X = np.matrix(X).reshape(m, degree * n + 1)\n",
"Y = np.matrix(data_matrix[:, -1])"
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]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## 2. Metody regresji wielomianowej"
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]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
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"source": [
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"def print_theta(theta):\n",
" print(\"f(x) = \", end=\"\")\n",
" for i,x in enumerate(theta.tolist()[:-1]):\n",
" x = x[0]\n",
" print(f\"{x}x^{i}\", end=\" + \")\n",
" print(f\"{theta.tolist()[-1][0]}^{len(theta) - 1}\")\n",
"\n",
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"# Implementacja wzrosu na regresję wielomianową\n",
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"def polynomial_regression(theta, x):\n",
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" x = x/data[\"Height\"].max()\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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"# Implementacja wzoru na RMSE, czyli pierwiastek z błędu średniokwadratowego\n",
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"def mean_squared_error(theta, X, Y):\n",
" J = 1.0 / (2.0 * m) * ((X * theta - Y).T * (X * theta - Y))\n",
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" return J.item()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Wybrane rodzaje gradientów"
]
},
{
"attachments": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Metoda gradientu prostego to algorytm numeryczny mający na celu znalezienie minimum lokalnego dla zadanej funkcji celu. Jak sama nazwa wskazuje jest to jedna z prostszych metod, która opiera się na iteracyjnym poszukiwaniu minimum. Proces ten (dla pierwszych czterech kroków), został pokazany na poniższym rysunku.\n",
""
]
},
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{
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Metoda gradientu prostego\n",
""
]
},
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{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
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"# Wzór na gradient prosty\n",
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"def gradient(theta, X, Y):\n",
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" return 1.0 / len(Y) * (X.T * (X * theta - Y)) "
]
},
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{
"attachments": {
"image.png": {
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}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Metoda najszybszego spadku jest iteracyjnym algorytmem wyszukiwania minimym zadanej funkcji celu i stanowi modyfikację gradientu prostego. Na samym początku algorytmu wybierany jest punkt startowy, w którym jest obliczany antygradient funkcji celu stanowiący kierunek poszukiwań w procedurze. Kolejny krok jest obliczany w oparciu o poprzedni i jeżeli nie spełnia on warunku stopu to obliczenia są powtarzane do skutku. \n",
"\n",
"Podstawową różnicę w stosunku do gradientu prostego stanowi sposób wyszukiwania wartości, która jest dokonywana przez minimalizację kierunkową funkcji. \n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Steepest descent\n",
"def steepest_descent(X, Y, theta, cost_function = mean_squared_error, \n",
" single_step=0.01, epochs=30):\n",
" cost = cost_function(theta, X, Y)\n",
" logs = [[cost, theta]]\n",
" for i in range(epochs):\n",
" direction = gradient(theta, X, Y)\n",
" j = 0\n",
" while(True):\n",
" j+=1\n",
" next_theta = theta - single_step * direction\n",
" next_cost = cost_function(next_theta, X, Y)\n",
" if(next_cost >= cost or type(next_cost) != float):\n",
" break\n",
" cost = next_cost\n",
" theta = next_theta\n",
" logs.append([next_cost, theta])\n",
" return theta, logs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ponadto w toku prac projektowych przetestowane zostaly także inne algorytmy takie jak batch gradient descend (BGD), BGD z momentum, mini-batch gradient descend oraz stochastic gradient descend. \n",
"\n",
"BGD to metoda w której błąd obliczany jest dla każdego parametru ze zbioru treningowego, ale sam model jest aktualizowany dopiero po ocenie wszystkich danych. \n",
"\n",
"MBGD opiera się na podziale danch treningowych na niewielkie pakiety, które są potem wykorzystywane do obliczania wartości błędu i aktualizacji modelu.\n",
"\n"
]
},
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{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
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"# Batch gradient descend (BGD)\n",
"def BGD(X, Y, theta, cost_function = mean_squared_error, learning_rate=0.1, \n",
" eps=10**-5, max_steps = 10000000000):\n",
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" cost = cost_function(theta, X, Y)\n",
" logs = [[cost, theta]]\n",
" \n",
" for i in range(max_steps):\n",
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" theta = theta - learning_rate * gradient(theta, X, Y)\n",
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" next_cost = cost_function(theta, X, Y)\n",
" logs.append([next_cost, theta])\n",
" if abs(cost - next_cost) <= eps:\n",
" break\n",
" cost = next_cost\n",
" return theta, logs\n",
"\n",
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"# BGD z momentum\n",
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"def momentum(X, Y, theta, cost_function = mean_squared_error, \n",
" learning_rate=0.1, momentum = 0.9, epochs=30):\n",
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" cost = cost_function(theta, X, Y)\n",
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" logs = [[cost, theta]]\n",
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" delta_history = [np.matrix([0]*(degree+1)).T]\n",
" for i in range(epochs):\n",
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" delta_history.append(momentum * delta_history[-1] + \n",
" learning_rate * gradient(theta, X, Y))\n",
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" theta = theta - delta_history[-1]\n",
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" next_cost = cost_function(theta, X, Y)\n",
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" logs.append([next_cost, theta])\n",
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" cost = next_cost\n",
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" return theta, logs\n",
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"\n",
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"# Mini-batch gradient descend (MBGD)\n",
"def MBGD(X, Y, theta, cost_function = mean_squared_error, \n",
" learning_rate=0.1, epochs=5, batch_size=16):\n",
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" cost = cost_function(theta, X, Y)\n",
" logs = [[cost, theta]]\n",
" start, end = 0, batch_size\n",
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" \n",
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" steps = m / batch_size\n",
" for i in range(epochs):\n",
" zipped_XY = list(zip(X, Y))\n",
" random.shuffle(zipped_XY)\n",
" X_shuffled, Y_shuffled = zip(*zipped_XY)\n",
" X_shuffled = np.concatenate(X_shuffled, axis=0) \n",
" Y_shuffled = np.concatenate(Y_shuffled, axis=0) \n",
" for j in range(int(steps)):\n",
" batch = X_shuffled[start:end,:], Y_shuffled[start:end,:]\n",
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" theta = theta - learning_rate * gradient(theta, batch[0], batch[1])\n",
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" cost = cost_function(theta, X, Y)\n",
" logs.append([cost, theta])\n",
"\n",
" if start + batch_size < batch_size:\n",
" start += batch_size\n",
" else:\n",
" start = 0\n",
" end = min(start + batch_size, m)\n",
" return theta, logs\n",
"\n",
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"# Stochastic gradient descend (SGD)\n",
"def SGD(X, Y, theta, cost_function = mean_squared_error, learning_rate=0.1,\n",
" epochs=5):\n",
" return MBGD(X, Y, theta, cost_function, learning_rate, epochs, 1)"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Zestawienie wyników"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"SGD:\n",
"f(x) = 34.06033951445318x^0 + 43.85258948907885x^1 + 49.46046316253574x^2 + 52.05621808156889x^3 + 52.51952473344481^4\n",
"600001 updates\n",
"\n",
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"BGD:\n",
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"f(x) = -65.64506277392528x^0 + 68.77648241256031x^1 + 114.75349304333466x^2 + 102.20675310280708x^3 + 52.80014652046691^4\n",
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"82705 updates\n",
"\n",
"MBGD:\n",
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"f(x) = -32.29122418991072x^0 + 53.876193091384174x^1 + 86.86638770732357x^2 + 84.88913589397598x^3 + 60.97817901045005^4\n",
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"37501 updates\n",
"\n",
"momentum:\n",
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"f(x) = 8.015549563834169x^0 + 38.129978604712115x^1 + 58.33870778356634x^2 + 71.22019895802788x^3 + 78.72511869002662^4\n",
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"61 updates\n",
"\n",
"steepest descent:\n",
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"f(x) = 13.63864883159118x^0 + 39.09249361981945x^1 + 55.99541645170295x^2 + 66.58927282539138x^3 + 72.568097240031^4\n",
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"61 updates\n",
"\n"
]
}
],
"source": [
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"final_theta_SGD, logs_3 = SGD(X, Y, initial_theta, epochs = 60)\n",
"print(\"SGD:\")\n",
"print_theta(final_theta_SGD)\n",
"print(f\"{len(logs_3)} updates\\n\")\n",
"\n",
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"final_theta_BGD, logs_1 = BGD(X, Y, initial_theta)\n",
"print(\"BGD:\")\n",
"print_theta(final_theta_BGD)\n",
"print(f\"{len(logs_1)} updates\\n\")\n",
"\n",
"final_theta_MBGD, logs_2 = MBGD(X, Y, initial_theta, epochs = 60)\n",
"print(\"MBGD:\")\n",
"print_theta(final_theta_MBGD)\n",
"print(f\"{len(logs_2)} updates\\n\")\n",
"\n",
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"\n",
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"final_theta_momentum, logs_4 = momentum(X, Y, initial_theta, epochs = 60)\n",
"print(\"momentum:\")\n",
"print_theta(final_theta_momentum)\n",
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"print(f\"{len(logs_4)} updates\\n\")\n",
"\n",
"final_steepest_descent, logs_5 = steepest_descent(X, Y, initial_theta, epochs = 60)\n",
"print(\"steepest descent:\")\n",
"print_theta(final_steepest_descent)\n",
"print(f\"{len(logs_5)} updates\\n\")"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"Wniosek:\n",
"Metoda najszybszego spadku oraz momentum cechują się najszybszą zbieżnością do minimum lokalnego danej funkcji. Przeciętnie, dla zadanych parametrów, minimum jest osiągane w ciągu 61 aktualizacji. Podczas gdy SGD wymaga ich ponad 600 tysięcy. "
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## 4. Reprezentacja graficzna regresji wielomianowej i funkcji kosztu"
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]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"BGD:\n"
]
},
{
"data": {
2021-06-27 17:34:20 +02:00
"image/png": "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
2021-06-27 14:09:23 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
2021-06-27 18:45:49 +02:00
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAE9CAYAAABk/zSyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAd1UlEQVR4nO3df7BfdX3n8eeLRAKiIMiFYgIm1lQL1GJzl0VrW7e0JbauoR2ocbSkLbNZGLqibdeS9Q/rzrDVta1KLXQYUYJlQUrpkjqi0tBqO0XwotQQEElFIRIhVopZrbGB9/7x/aT9crm5Cfr9cc+9z8fMd77n+z7nc87neLyZF+fH56SqkCRJUrccNO4OSJIk6ekzxEmSJHWQIU6SJKmDDHGSJEkdZIiTJEnqIEOcJElSBy0e1oqTfAB4NfBIVZ08bd5vA+8CJqrq6622ATgXeBx4Y1V9vNVXAVcChwIfBS6sqkqyBLgKWAX8E/Daqvry/vp19NFH1/Llywexi5IkSUN1xx13fL2qJmaaN7QQRy94vY9e0Po3SY4HfhZ4oK92IrAWOAl4HvBXSX6oqh4HLgPWA5+mF+JWAzfRC3yPVtULk6wF3gm8dn+dWr58OVNTU9/3zkmSJA1bkq/sa97QLqdW1aeAb8ww693AW4D+UYbXANdW1e6quh/YBpya5Djg8Kq6tXqjEl8FnNnXZmObvh44PUkGvyeSJElzz0jviUvyGuCrVfUP02YtBR7s+7291Za26en1J7Wpqj3AY8Bzh9BtSZKkOWeYl1OfJMkzgbcCPzfT7BlqNUt9tjYzbXs9vUuynHDCCfvtqyRJ0lw3yjNxPwisAP4hyZeBZcBnk/wAvTNsx/ctuwx4qNWXzVCnv02SxcARzHz5lqq6vKomq2pyYmLGewMlSZI6ZWQhrqq2VNUxVbW8qpbTC2E/VlVfAzYBa5MsSbICWAncXlU7gF1JTmv3u50D3NhWuQlY16bPAm5p981JkiTNe0MLcUmuAW4FXpRke5Jz97VsVW0FrgPuBj4GXNCeTAU4H3g/vYcd/pHek6kAVwDPTbIN+E3goqHsiCRJ0hyUhXbyanJyshxiRJIkdUGSO6pqcqZ5vrFBkiSpgwxxkiRJHWSIG7AHv/Ftrrn9AR791nfH3RVJkjSPGeIG7O4d32TDDVt46LF/GXdXJEnSPGaIkyRJ6iBDnCRJUgcZ4iRJkjrIECdJktRBhjhJkqQOMsRJkiR1kCFuSBbY28wkSdKIGeIGLOPugCRJWhAMcZIkSR1kiJMkSeogQ5wkSVIHGeIkSZI6yBAnSZLUQYY4SZKkDjLEDVjiICOSJGn4DHGSJEkdZIiTJEnqIEOcJElSBxniJEmSOsgQJ0mS1EGGOEmSpA4yxA1J1bh7IEmS5jND3IA5SpwkSRoFQ5wkSVIHDS3EJflAkkeS3NVXe1eSLyT5fJK/SPKcvnkbkmxLcm+SM/rqq5JsafMuSXslQpIlST7c6rclWT6sfZEkSZprhnkm7kpg9bTazcDJVfUS4IvABoAkJwJrgZNam0uTLGptLgPWAyvbZ+86zwUeraoXAu8G3jm0PZEkSZpjhhbiqupTwDem1T5RVXvaz08Dy9r0GuDaqtpdVfcD24BTkxwHHF5Vt1ZVAVcBZ/a12dimrwdOjy8ulSRJC8Q474n7deCmNr0UeLBv3vZWW9qmp9ef1KYFw8eA5w6xv5IkSXPGWEJckrcCe4Cr95ZmWKxmqc/WZqbtrU8ylWRq586dT7e7kiRJc87IQ1ySdcCrgde3S6TQO8N2fN9iy4CHWn3ZDPUntUmyGDiCaZdv96qqy6tqsqomJyYmBrUrs6qZ86QkSdJAjDTEJVkN/A7wmqr6dt+sTcDa9sTpCnoPMNxeVTuAXUlOa/e7nQPc2NdmXZs+C7ilLxSOjXflSZKkUVg8rBUnuQZ4JXB0ku3A2+g9jboEuLk9g/DpqjqvqrYmuQ64m95l1guq6vG2qvPpPel6KL176PbeR3cF8KEk2+idgVs7rH2RJEmaa4YW4qrqdTOUr5hl+YuBi2eoTwEnz1D/DnD299NHSZKkrvKNDZIkSR1kiJMkSeogQ5wkSVIHGeKGZPzPyUqSpPnMEDdgDjEiSZJGwRAnSZLUQYY4SZKkDjLESZIkdZAhTpIkqYMMcZIkSR1kiJMkSeogQ9yQOEycJEkaJkPcgAUHipMkScNniJMkSeogQ5wkSVIHGeIkSZI6yBAnSZLUQYY4SZKkDjLESZIkdZAhbkiqHClOkiQNjyFu0BwmTpIkjYAhTpIkqYMMcZIkSR1kiJMkSeogQ5wkSVIHGeIkSZI6yBA3JA4wIkmShskQN2COMCJJkkZhaCEuyQeSPJLkrr7aUUluTnJf+z6yb96GJNuS3JvkjL76qiRb2rxLkqTVlyT5cKvflmT5sPZFkiRprhnmmbgrgdXTahcBm6tqJbC5/SbJicBa4KTW5tIki1qby4D1wMr22bvOc4FHq+qFwLuBdw5tTyRJkuaYoYW4qvoU8I1p5TXAxja9ETizr35tVe2uqvuBbcCpSY4DDq+qW6v3HqurprXZu67rgdP3nqWTJEma70Z9T9yxVbUDoH0f0+pLgQf7ltveakvb9PT6k9pU1R7gMeC5M200yfokU0mmdu7cOaBdkSRJGp+58mDDTGfQapb6bG2eWqy6vKomq2pyYmLie+yiJEnS3DHqEPdwu0RK+36k1bcDx/cttwx4qNWXzVB/Upski4EjeOrlW0mSpHlp1CFuE7CuTa8Dbuyrr21PnK6g9wDD7e2S664kp7X73c6Z1mbvus4Cbmn3zc0Jc6cnkiRpPlo8rBUnuQZ4JXB0ku3A24B3ANclORd4ADgboKq2JrkOuBvYA1xQVY+3VZ1P70nXQ4Gb2gfgCuBDSbbROwO3dlj78nT4bIUkSRqFoYW4qnrdPmadvo/lLwYunqE+BZw8Q/07tBAoSZK00MyVBxskSZL0NBjiJEmSOsgQJ0mS1EGGOEmSpA4yxEmSJHWQIW5oHChOkiQNjyFuwBwlTpIkjYIhTpIkqYMMcZIkSR1kiJMkSeogQ5wkSVIHGeIkSZI6yBA3JOUII5IkaYgMcQMWxxiRJEkjYIiTJEnqIEOcJElSBxniJEmSOsgQJ0mS1EGGOEmSpA4yxEmSJHWQIW5IHCZOkiQNkyFuwIIDxUmSpOEzxEmSJHWQIU6SJKmDDHGSJEkdZIiTJEnqIEOcJElSB40lxCV5c5KtSe5Kck2SQ5IcleTmJPe17yP7lt+QZFuSe5Oc0VdflWRLm3dJEh8NlSRJC8LIQ1ySpcAbgcmqOhlYBKwFLgI2V9VKYHP7TZIT2/yTgNXApUkWtdVdBqwHVrbP6hHuyqzKgeIkSdIQjety6mLg0CSLgWcCDwFrgI1t/kbgzDa9Bri2qnZX1f3ANuDUJMcBh1fVrVVVwFV9bcbGc4GSJGkURh7iquqrwO8DDwA7gMeq6hPAsVW1oy2zAzimNVkKPNi3iu2ttrRNT69LkiTNe+O4nHokvbNrK4DnAYclecNsTWao1Sz1mba5PslUkqmdO3c+3S5LkiTNOeO4nPozwP1VtbOq/hW4AXg58HC7REr7fqQtvx04vq/9MnqXX7e36en1p6iqy6tqsqomJyYmBrozkiRJ4zCOEPcAcFqSZ7anSU8H7gE2AevaMuuAG9v0JmBtkiVJVtB7gOH2dsl1V5LT2nrO6WsjSZI0ry0e9Qar6rYk1wOfBfYAnwMuB54FXJfkXHpB7+y2/NYk1wF3t+UvqKrH2+rOB64EDgVuah9JkqR5b+QhDqCq3ga8bVp5N72zcjMtfzFw8Qz1KeDkgXdwAMoxRiRJ0hD5xoYBc4QRSZI0CoY4SZKkDjLESZIkdZAhTpIkqYMMcZIkSR1kiJMkSeogQ5wkSVIHGeKGxFHiJEnSMB1QiEvyoQOpCQeKkyRJI3GgZ+JO6v+RZBGwavDdkSRJ0oGYNcQl2ZBkF/CSJN9sn13AI/iyeUmSpLGZNcRV1e9V1bOBd1XV4e3z7Kp6blVtGFEfJUmSNM2BXk79SJLDAJK8IckfJnn+EPslSZKkWRx
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
2021-06-27 14:09:23 +02:00
{
"name": "stdout",
"output_type": "stream",
"text": [
"MBGD:\n"
]
},
{
"data": {
2021-06-27 20:23:26 +02:00
"image/png": "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
2021-06-27 18:45:49 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2021-06-27 20:23:26 +02:00
"image/png": "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
2021-06-27 14:09:23 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"SGD:\n"
]
},
2021-06-24 13:03:10 +02:00
{
"data": {
2021-06-27 20:23:26 +02:00
"image/png": "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
2021-06-27 18:45:49 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2021-06-27 20:23:26 +02:00
"image/png": "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
2021-06-27 14:09:23 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Momentum:\n"
]
},
{
"data": {
2021-06-27 17:34:20 +02:00
"image/png": "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
2021-06-24 13:03:10 +02:00
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
2021-06-27 18:45:49 +02:00
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Metoda najszybszego spadku:\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAEzCAYAAADzdE1rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA0Z0lEQVR4nO3deXxU1f3/8ddJSDAhGiUiIJCEr6IWjHWJuCsaWxVFEJeiURFtU5d+xV9bURut0jatX20ttS3a1A11XLC4oWjV1N1WBFzCIhWQIILKomFJDCFzfn+cCVmYJJPkztzJzPv5eOTBzF0Pj6vw5pxzP8dYaxERERGR7kvxuwEiIiIiiULBSkRERMQjClYiIiIiHlGwEhEREfGIgpWIiIiIRxSsRERERDzSYbAyxgwxxrxqjFlijFlkjJkc2n6LMeZzY8wHoZ/Rzc65wRizzBiz1BhzSjR/AyIiIiLxwnRUx8oYMxAYaK1dYIzZFZgPjAPOA7ZYa3/f6vjhwKPASGBv4BVgP2ttg/fNFxEREYkfHfZYWWvXWmsXhD5vBpYAg9o5ZSzwmLW2zlr7KbAMF7JEREREElqn5lgZY/KBQ4B3Q5t+Yoz5yBhznzFmj9C2QcBnzU5bTftBTERERCQh9Ir0QGNMFjALuMZau8kYcxfwa8CGfv0DcClgwpy+03ijMaYEKAHo06fPYQcccEDnWy8iIklv/pr53l3MwNDdh9I3o2/L7Z98Aps2Qb9+kJvr3f2kR5o/f/56a22/cPsiClbGmDRcqApYa58EsNZ+2Wz/34HnQl9XA0OanT4YWNP6mtbacqAcoLCw0M6bNy+SpoiIiLSQPy2fquoqT65lMKy4eUXLjS++CKedBtnZsGiRC1eS1Iwxbf4HF8lbgQa4F1hirb2j2faBzQ47C1gY+vwsMMEY09sYMxQYBsztSsNFREQ6UlZURmZapifXys1u6o0KVAbY5w95LLr4NAAWXDZaoUo6FMkcq2OAi4CTWpVWuM0YU2mM+Qg4Efh/ANbaRcBMYDHwInCV3ggUEZFoKS4opnxMOTkZOWH390nrE9F1MtMyKSsqA1yoKpldwumvrGLEOli+BxRlP02gMuBZuyUxdVhuIRY0FCgiIl4IVAYorShlVfUqcrNzKSsqo7iguMPhwrzsvB3Hghte3Lqmik/uhN3rYOwEePYAd9zKa1bG6Hcj8coYM99aWxhuX8ST10VEROJdcUHxjnDUXFlRGRc+eWHYc3IycnYKS6uqV/G3CheqXtwHnt2/abtIe7SkjYiI9DiBygD50/JJmZpC/rT8DofoiguKyUrPCrtvc93mna51enV/LlsA9SlwzanseN+9+RwskXA0FCgiIj1K4/ynmvqanfblZOTwp9P+BMDkFyazoXYD4OZZba3fGtH1+6Rm8NHDu/E/S7/k9qNhyvfd9sy0TMrHlIftEZPkoqFAERFJGKUVpWFDFcCG2g1hh/wiDVUA4+fX8j9La6ndM5sZY3bD1K1uMV9LpD0KViIi0qNEc57Trt/CbS+7zxl/uJOFF18ctXtJYtIcKxER6VGiOc/ppjdgwFZYkJcOF4af7C7SHgUrERHpUfbtu29Urrv/OrjmPxA08OWtN0KK/oqUztN/NSIi0mMEKgP869N/eX9hC396EdKCsOKsUZw24Sbv7yFJQcFKRER6jNKKUizev83+g+W7cMpyIDubfe+e6fn1JXkoWImISI8RjYnrvevh1jnb3Jdf/UrrAUq3KFiJiEiPEY2J69e9DfkbgzBiBFx5pefXl+SiYCUiIj1CoDKwo+CnV/bZADe86T6fcPgi8v+yrxZalm5RHSsREYl7gcoAlz5zKdsatnl3UQt/mQO7NMCM78Ib+UB1FSWzSwBUDFS6RD1WIiISlwKVAfa8bU/MVMOFT17obagCzlkMpy6Hr3eBa7/XtL2mvobSilJP7yXJQz1WIiLiu0BlgNKKUlZVr6JvRl++3f5tp5ah6aysOpj2ovt8QxGsa7U+czSru0tiU7ASERFfXfn8ldw97+4dZRS8nkcVzi2vwaDN8O4g+PthO++PZnV3SWwaChQREd8EKgMtQlUsFHwBk/8DDQauOB2Crf4mzEzLpKyoLGbtkcSiYCUiIr6JVsHPtpgg3PU89LLw18Ph/b1b7k81qZSPKQcgf1o+KVNTyJ+WrzcFJWIaChQRkZhqPp8qlqEKYNIHcMxnsDYLbjpp5/1BGwSgZHYJNfU1AFTpTUHpBPVYiYhITDS+5XfhkxdSVV0V81CVsxVue9l9/ukpsGmXnY/Jzc6ltKJ0R6hqpDcFJVIKViIiEnWBygAls0tiMjG9Lb+rgJxaeGUoPHbgzvsb51a19Uag3hSUSChYiYhI1IXrBYqlo1bBjxbAthS46nTAtNzfOLequKC4zTcC9aagRELBSkREos7P3p607fD32e7zbcfAf/dsuT89NZ0ZZ83YMX+qrKiMzLTMFsfoTUGJlIKViIhEXd+Mvr7d+7q3YcQ6+G9f+M3xLfflZORw39j7WkxKLy4opnxMOXnZeRgMedl5O3qzRDqitwJFRCSqApUBNtVt8uXe+6+DG99wn0vGQF2a+5xqUtn+y+1tnldcUKwgJV2iYCUiIlETqAww8amJNNiGmN/bBKF8NvRugHsPgdeHNu0rOawk5u2R5KBgJSIiUXHygydT8WmFb/e/7H04fhV82aflIstFQ4uYfvp039oliU1zrERExHNXPn+lr6FqwGa4/SX3+erT4OvQXPSioUUs27hMFdUlatRjJSIiniufX+7r/e98AXavg+eGwcwRTdubhz1VVJdoUI+ViIh4zo85VY3GfAznLoYtaXBlmJpVzamiunhNwUpERDyXalJ9ue+u38Jf57jPpUXw2e4dn6OK6uIlBSsREfGcX2/dlf0LhmyCuXvDX0ZGdo4qqouXFKxERMRTgcoAcz6ZE/P7HvEZXDUXthv40ZkQjPBvuNHDRke3YZJUNHldREQ807jYcqzXBexdD/c943oL/u8Y+GhA5Of6EQIlcanHSkREPOPXYss3vw7D18PHOTD1hM6dW1VdFZ1GSVJSsBIREc/4MRH8sM/h2rchCFw6tmnZmkgZjOpZiWcUrERExDOxXmw5fTvc/wz0svDHo+DfXZiHbrEquSCeUbASEZEOBSoD5E/L77Bi+bfbv41pu258Awq+gk/6wk0ndv06KrkgXtHkdRERaVfrCenNK5aDm1flxzylg9fCDW82DQHWprd/fGZaJhm9MthQu2GnfSq5IF5RsBIRkXaFm5BeU1/D5BcmU7u91pfJ6mnb4f6n3RDgnSPhrbz2j8/JyOFPp/0JYKe3FjPTMikrKotiayWZaChQRETa1dYw2YbaDb6EKoDr34KDv4Tle8ANJ3d8fO32WsCtCVg+ppy87DwMhrzsPMrHlGutQPGMsdb63QYKCwvtvHnz/G6GiIiEkT8tP65KEhR8AfPLIS0IoybC60MjOy8vO4+V16yMatskORhj5ltrC8PtU4+ViIi0q6yojMy0zBbbMtMyycnIiXlbejW4twDTgjC9MPJQBZqgLrGhYCUiIu1qPnwGru5TTX1N2Eng0TblbThsLazMhuu+17lzNUFdYkHBSkREwmpeYqG0opTRw0bTK6UXFn+mkBy8Fm55zX3+4ZmwpXfk52qCusSKgpWIiOykscRCVXUVFktVdRV3z7ub7cHtvrSndz08+JQbAvzzSKjYp+NzcjJyNEFdYq7DcgvGmCHAg8AAXLmQcmvtn4wxfYHHgXxgJXCetfbr0Dk3AJcBDcDV1tp/RqX1IiISFeFKLPjVUwXw61ddIdClOXBdBG8BAmSlZ7F+yvroNkyklUh6rLYDP7PWfgc4ErjKGDMcuB6osNYOAypC3wntmwCMAE4FphtjUqPReBERiY54muh93Er42TvQYODiszouBNoonn4Pkjw6DFbW2rXW2gWhz5uBJcAgYCwwI3TYDGBc6PNY4DFrbZ219lNgGTDS43aLiEgUxctE76w6mPG0+8vqt8fB3MGRnxsvvwdJLp2aY2WMyQcOAd4F+ltr14ILX8BeocMGAZ81O211aFv
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
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}
],
"source": [
"def plot_polynomial_regression(theta):\n",
" fig = plt.figure(figsize=(10,5))\n",
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" Y_plot = polynomial_regression(theta, X_plot).tolist()\n",
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" chart = fig.add_subplot()\n",
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" chart.plot(data[\"Height\"], Y ,\"go\")\n",
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" chart.plot(X_plot, Y_plot, color=\"red\", lw=2, label=f\"degree {len(theta)}\")\n",
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" plt.ylim([0,250])\n",
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" plt.show()\n",
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" \n",
"\n",
"def plot_cost_function(logs):\n",
" no_iter = no_iter = [x for x in range(1, len(logs) + 1)]\n",
" all_cost = [row[0] for row in logs]\n",
" \n",
" fig = plt.figure(figsize=(10,5)) \n",
" plt.plot(no_iter, all_cost)\n",
" plt.xlabel('No. of iterations')\n",
" plt.ylabel('Cost')\n",
" plt.show()\n",
" \n",
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"\n",
"print(\"BGD:\")\n",
"plot_polynomial_regression(final_theta_BGD)\n",
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"plot_cost_function(logs_1)\n",
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"print(\"MBGD:\")\n",
"plot_polynomial_regression(final_theta_MBGD)\n",
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"plot_cost_function(logs_2)\n",
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"print(\"SGD:\")\n",
"plot_polynomial_regression(final_theta_SGD)\n",
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"plot_cost_function(logs_3)\n",
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"print(\"Momentum:\")\n",
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"plot_polynomial_regression(final_theta_momentum)\n",
"plot_cost_function(logs_4)\n",
"print(\"Metoda najszybszego spadku:\")\n",
"plot_polynomial_regression(final_steepest_descent)\n",
"plot_cost_function(logs_5)"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## 5. Regresja wielomianowa z wykorzystaniem gotowych bibliotek"
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]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import Ridge, LinearRegression"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=4)),\n",
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" ('linearregression', LinearRegression())])"
]
},
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = make_pipeline(PolynomialFeatures(degree=degree, include_bias=True), \n",
" LinearRegression())\n",
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"model.fit(data[[\"Height\"]],Y)"
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]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"4"
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]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"Y_plot_2 = model.predict([[x] for x in X_plot])\n",
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"\n",
"fig = plt.figure(figsize=(10,5))\n",
"chart = fig.add_subplot()\n",
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"chart.plot(data[\"Height\"], Y ,\"go\")\n",
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"chart.plot(X_plot, Y_plot_2, color=\"red\", lw=2, label=f\"degree {degree}\")\n",
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"plt.ylim([0,250])\n",
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"plt.xlim([0,100])\n",
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"degree"
]
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},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"polyfit_theta = np.polyfit(data[\"Height\"]/data[\"Height\"].max(), Y, degree)\n",
"plot_polynomial_regression(polyfit_theta)"
]
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"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": "3.8.8"
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}
},
"nbformat": 4,
"nbformat_minor": 4
}
