From 9d4504806bffbf687a37ff802ad134b030854820 Mon Sep 17 00:00:00 2001 From: Anna Nowak Date: Fri, 25 Jun 2021 09:42:03 +0200 Subject: [PATCH] SGD, MBGD --- Polynomial Regression.ipynb | 21739 +--------------------------------- 1 file changed, 47 insertions(+), 21692 deletions(-) diff --git a/Polynomial Regression.ipynb b/Polynomial Regression.ipynb index 03f94c3..7059613 100644 --- a/Polynomial Regression.ipynb +++ b/Polynomial Regression.ipynb @@ -29,6 +29,7 @@ "outputs": [], "source": [ "import pandas as pd\n", + "import random\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "np.set_printoptions(suppress=True)" @@ -78,21688 +79,10 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[13546.918787280789, matrix([[0],\n", - " [0],\n", - " [0]])], [8345.193627588793, matrix([[16.14403568],\n", - " [13.70734956],\n", - " [11.67579352]])], [5189.87054682073, 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sum(theta * np.power(x, i) for i, theta in enumerate(theta.tolist()))\n", @@ -21768,10 +91,11 @@ " 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", + "def classic_gradient(theta, X, Y):\n", " return 1.0 / len(Y) * (X.T * (X * theta - Y)) \n", "\n", - "def gradient_descent(X, Y, theta, gradient = gradient, cost_function = mean_squared_error, alpha=0.1, eps=10**-5, max_steps = 10000000000):\n", + "# Batch gradient descent (BGD)\n", + "def BGD(X, Y, theta, gradient = classic_gradient, cost_function = mean_squared_error, alpha=0.1, eps=10**-5, max_steps = 10000000000):\n", " cost = cost_function(theta, X, Y)\n", " logs = [[cost, theta]]\n", " \n", @@ -21783,11 +107,42 @@ " break\n", " cost = next_cost\n", " return theta, logs\n", + "\n", + "# Mini-batch gradient descent (MBGD)\n", + "def MBGD(X, Y, theta, gradient = classic_gradient, cost_function = mean_squared_error, alpha=0.1, epochs=5, batch_size=16):\n", + " cost = cost_function(theta, X, Y)\n", + " logs = [[cost, theta]]\n", + " start, end = 0, batch_size\n", " \n", + " 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", + " theta = theta - alpha * gradient(theta, batch[0], batch[1])\n", + " 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", + "# Stochastic gradient descent (SGD)\n", + "def SGD(X, Y, theta, gradient = classic_gradient, cost_function = mean_squared_error, alpha=0.1, epochs=5, batch_size=16):\n", + " return MBGD(X, Y, theta, gradient, cost_function, alpha, epochs, 1)\n", + "\n", "#print(mean_squared_error([1,2,1,1],[1,2,43,1]))\n", "#mean_squared_error(polynomial_regression(initial_theta, X), Y)\n", - "final_theta, logs = gradient_descent(X, Y, initial_theta)\n", - "print(logs)" + "#final_theta, logs_1 = BGD(X, Y, initial_theta)\n", + "#final_theta, logs_2 = MBGD(X, Y, initial_theta, epochs = 30, batch_size = 16)\n", + "final_theta, logs_2 = SGD(X, Y, initial_theta, epochs = 30)" ] }, { @@ -21812,14 +167,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -21863,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -21873,7 +228,7 @@ " ('linearregression', LinearRegression())])" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -21886,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -21895,7 +250,7 @@ "2" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" },