(1) How to comput the Cost function in Univirate/Multivariate Linear Regression;
(2) How to comput the Batch Gradient Descent function in Univirate/Multivariate Linear Regression;
(3) How to scale features by mean value and standard deviation;
(4) How to calculate Theta by normal equaltion;
Data1
6.1101,17.592 5.5277,9.1302 8.5186,13.662 7.0032,11.854 5.8598,6.8233 8.3829,11.886 7.4764,4.3483 8.5781,12 6.4862,6.5987 5.0546,3.8166 5.7107,3.2522 14.164,15.505 5.734,3.1551 8.4084,7.2258 5.6407,0.71618 5.3794,3.5129 6.3654,5.3048 5.1301,0.56077 6.4296,3.6518 7.0708,5.3893 6.1891,3.1386 20.27,21.767 5.4901,4.263 6.3261,5.1875 5.5649,3.0825 18.945,22.638 12.828,13.501 10.957,7.0467 13.176,14.692 22.203,24.147 5.2524,-1.22 6.5894,5.9966 9.2482,12.134 5.8918,1.8495 8.2111,6.5426 7.9334,4.5623 8.0959,4.1164 5.6063,3.3928 12.836,10.117 6.3534,5.4974 5.4069,0.55657 6.8825,3.9115 11.708,5.3854 5.7737,2.4406 7.8247,6.7318 7.0931,1.0463 5.0702,5.1337 5.8014,1.844 11.7,8.0043 5.5416,1.0179 7.5402,6.7504 5.3077,1.8396 7.4239,4.2885 7.6031,4.9981 6.3328,1.4233 6.3589,-1.4211 6.2742,2.4756 5.6397,4.6042 9.3102,3.9624 9.4536,5.4141 8.8254,5.1694 5.1793,-0.74279 21.279,17.929 14.908,12.054 18.959,17.054 7.2182,4.8852 8.2951,5.7442 10.236,7.7754 5.4994,1.0173 20.341,20.992 10.136,6.6799 7.3345,4.0259 6.0062,1.2784 7.2259,3.3411 5.0269,-2.6807 6.5479,0.29678 7.5386,3.8845 5.0365,5.7014 10.274,6.7526 5.1077,2.0576 5.7292,0.47953 5.1884,0.20421 6.3557,0.67861 9.7687,7.5435 6.5159,5.3436 8.5172,4.2415 9.1802,6.7981 6.002,0.92695 5.5204,0.152 5.0594,2.8214 5.7077,1.8451 7.6366,4.2959 5.8707,7.2029 5.3054,1.9869 8.2934,0.14454 13.394,9.0551 5.4369,0.61705
1. ex1.m
1 %% Machine Learning Online Class - Exercise 1: Linear Regression 2 3 % Instructions 4 % ------------ 5 % 6 % This file contains code that helps you get started on the 7 % linear exercise. You will need to complete the following functions 8 % in this exericse: 9 % 10 % warmUpExercise.m 11 % plotData.m 12 % gradientDescent.m 13 % computeCost.m 14 % gradientDescentMulti.m 15 % computeCostMulti.m 16 % featureNormalize.m 17 % normalEqn.m 18 % 19 % For this exercise, you will not need to change any code in this file, 20 % or any other files other than those mentioned above. 21 % 22 % x refers to the population size in 10,000s 23 % y refers to the profit in $10,000s 24 % 25 26 %% Initialization 27 clear ; close all; clc 28 29 %% ==================== Part 1: Basic Function ==================== 30 % Complete warmUpExercise.m 31 fprintf(‘Running warmUpExercise ... \n‘); 32 fprintf(‘5x5 Identity Matrix: \n‘); 33 warmUpExercise() 34 35 fprintf(‘Program paused. Press enter to continue.\n‘); 36 pause; 37 38 39 %% ======================= Part 2: Plotting ======================= 40 fprintf(‘Plotting Data ...\n‘) 41 data = load(‘ex1data1.txt‘); 42 X = data(:, 1); y = data(:, 2); 43 m = length(y); % number of training examples 44 45 % Plot Data 46 % Note: You have to complete the code in plotData.m 47 plotData(X, y); 48 49 fprintf(‘Program paused. Press enter to continue.\n‘); 50 pause; 51 52 %% =================== Part 3: Gradient descent =================== 53 fprintf(‘Running Gradient Descent ...\n‘) 54 55 X = [ones(m, 1), data(:,1)]; % Add a column of ones to x 56 theta = zeros(2, 1); % initialize fitting parameters 57 58 % Some gradient descent settings 59 iterations = 1500; 60 alpha = 0.01; 61 62 % compute and display initial cost 63 computeCost(X, y, theta) 64 65 % run gradient descent 66 theta = gradientDescent(X, y, theta, alpha, iterations); 67 68 % print theta to screen 69 fprintf(‘Theta found by gradient descent: ‘); 70 fprintf(‘%f %f \n‘, theta(1), theta(2)); 71 72 % Plot the linear fit 73 hold on; % keep previous plot visible 74 plot(X(:,2), X*theta, ‘-‘) 75 legend(‘Training data‘, ‘Linear regression‘) 76 hold off % don‘t overlay any more plots on this figure 77 78 % Predict values for population sizes of 35,000 and 70,000 79 predict1 = [1, 3.5] *theta; 80 fprintf(‘For population = 35,000, we predict a profit of %f\n‘,... 81 predict1*10000); 82 predict2 = [1, 7] * theta; 83 fprintf(‘For population = 70,000, we predict a profit of %f\n‘,... 84 predict2*10000); 85 86 fprintf(‘Program paused. Press enter to continue.\n‘); 87 pause; 88 89 %% ============= Part 4: Visualizing J(theta_0, theta_1) ============= 90 fprintf(‘Visualizing J(theta_0, theta_1) ...\n‘) 91 92 % Grid over which we will calculate J 93 theta0_vals = linspace(-10, 10, 100); 94 theta1_vals = linspace(-1, 4, 100); 95 96 % initialize J_vals to a matrix of 0‘s 97 J_vals = zeros(length(theta0_vals), length(theta1_vals)); 98 99 % Fill out J_vals 100 for i = 1:length(theta0_vals) 101 for j = 1:length(theta1_vals) 102 t = [theta0_vals(i); theta1_vals(j)]; 103 J_vals(i,j) = computeCost(X, y, t); 104 end 105 end 106 107 108 % Because of the way meshgrids work in the surf command, we need to 109 % transpose J_vals before calling surf, or else the axes will be flipped 110 J_vals = J_vals‘; 111 % Surface plot 112 figure; 113 surf(theta0_vals, theta1_vals, J_vals) 114 xlabel(‘\theta_0‘); ylabel(‘\theta_1‘); 115 116 % Contour plot 117 figure; 118 % Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100 119 contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20)) 120 xlabel(‘\theta_0‘); ylabel(‘\theta_1‘); 121 hold on; 122 plot(theta(1), theta(2), ‘rx‘, ‘MarkerSize‘, 10, ‘LineWidth‘, 2);
2.warmUpExercise.m
1 function A = warmUpExercise() 2 %WARMUPEXERCISE Example function in octave 3 % A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix 4 5 A = []; 6 % ============= YOUR CODE HERE ============== 7 % Instructions: Return the 5x5 identity matrix 8 % In octave, we return values by defining which variables 9 % represent the return values (at the top of the file) 10 % and then set them accordingly. 11 A = eye(5); 12 13 14 15 16 17 18 % =========================================== 19 20 21 end
3. computCost.m
1 function J = computeCost(X, y, theta) 2 %COMPUTECOST Compute cost for linear regression 3 % J = COMPUTECOST(X, y, theta) computes the cost of using theta as the 4 % parameter for linear regression to fit the data points in X and y 5 6 % Initialize some useful values 7 m = length(y); % number of training examples 8 9 % You need to return the following variables correctly 10 J = 0; 11 12 % ====================== YOUR CODE HERE ====================== 13 % Instructions: Compute the cost of a particular choice of theta 14 % You should set J to the cost. 15 hypothesis = X*theta; 16 J = 1/(2*m)*(sum((hypothesis-y).^2)); 17 18 % ========================================================================= 19 20 end
4.gradientDescent.m
1 function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) 2 %GRADIENTDESCENT Performs gradient descent to learn theta 3 % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by 4 % taking num_iters gradient steps with learning rate alpha 5 6 % Initialize some useful values 7 m = length(y); % number of training examples 8 J_history = zeros(num_iters, 1); 9 10 for iter = 1:num_iters 11 12 % ====================== YOUR CODE HERE ====================== 13 % Instructions: Perform a single gradient step on the parameter vector 14 % theta. 15 % 16 % Hint: While debugging, it can be useful to print out the values 17 % of the cost function (computeCost) and gradient here. 18 % 19 hypothesis = X*theta; 20 delta = X‘*(hypothesis-y); 21 theta = theta - alpha/m*delta; 22 23 % ============================================================ 24 25 % Save the cost J in every iteration 26 J_history(iter) = computeCost(X, y, theta); 27 28 end 29 30 end
Data2
2104,3,399900 1600,3,329900 2400,3,369000 1416,2,232000 3000,4,539900 1985,4,299900 1534,3,314900 1427,3,198999 1380,3,212000 1494,3,242500 1940,4,239999 2000,3,347000 1890,3,329999 4478,5,699900 1268,3,259900 2300,4,449900 1320,2,299900 1236,3,199900 2609,4,499998 3031,4,599000 1767,3,252900 1888,2,255000 1604,3,242900 1962,4,259900 3890,3,573900 1100,3,249900 1458,3,464500 2526,3,469000 2200,3,475000 2637,3,299900 1839,2,349900 1000,1,169900 2040,4,314900 3137,3,579900 1811,4,285900 1437,3,249900 1239,3,229900 2132,4,345000 4215,4,549000 2162,4,287000 1664,2,368500 2238,3,329900 2567,4,314000 1200,3,299000 852,2,179900 1852,4,299900 1203,3,239500
0.ex1_multi.m
1 %% Machine Learning Online Class 2 % Exercise 1: Linear regression with multiple variables 3 % 4 % Instructions 5 % ------------ 6 % 7 % This file contains code that helps you get started on the 8 % linear regression exercise. 9 % 10 % You will need to complete the following functions in this 11 % exericse: 12 % 13 % warmUpExercise.m 14 % plotData.m 15 % gradientDescent.m 16 % computeCost.m 17 % gradientDescentMulti.m 18 % computeCostMulti.m 19 % featureNormalize.m 20 % normalEqn.m 21 % 22 % For this part of the exercise, you will need to change some 23 % parts of the code below for various experiments (e.g., changing 24 % learning rates). 25 % 26 27 %% Initialization 28 29 %% ================ Part 1: Feature Normalization ================ 30 31 %% Clear and Close Figures 32 clear ; close all; clc 33 34 fprintf(‘Loading data ...\n‘); 35 36 %% Load Data 37 data = load(‘ex1data2.txt‘); 38 X = data(:, 1:2); 39 y = data(:, 3); 40 m = length(y); 41 42 % Print out some data points 43 fprintf(‘First 10 examples from the dataset: \n‘); 44 fprintf(‘ x = [%.0f %.0f], y = %.0f \n‘, [X(1:10,:) y(1:10,:)]‘); 45 46 fprintf(‘Program paused. Press enter to continue.\n‘); 47 pause; 48 49 % Scale features and set them to zero mean 50 fprintf(‘Normalizing Features ...\n‘); 51 52 [X mu sigma] = featureNormalize(X); 53 54 % Add intercept term to X 55 X = [ones(m, 1) X]; 56 57 58 %% ================ Part 2: Gradient Descent ================ 59 60 % ====================== YOUR CODE HERE ====================== 61 % Instructions: We have provided you with the following starter 62 % code that runs gradient descent with a particular 63 % learning rate (alpha). 64 % 65 % Your task is to first make sure that your functions - 66 % computeCost and gradientDescent already work with 67 % this starter code and support multiple variables. 68 % 69 % After that, try running gradient descent with 70 % different values of alpha and see which one gives 71 % you the best result. 72 % 73 % Finally, you should complete the code at the end 74 % to predict the price of a 1650 sq-ft, 3 br house. 75 % 76 % Hint: By using the ‘hold on‘ command, you can plot multiple 77 % graphs on the same figure. 78 % 79 % Hint: At prediction, make sure you do the same feature normalization. 80 % 81 82 fprintf(‘Running gradient descent ...\n‘); 83 84 % Choose some alpha value 85 alpha = 0.01; 86 num_iters = 400; 87 88 % Init Theta and Run Gradient Descent 89 theta = zeros(3, 1); 90 [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters); 91 92 % Plot the convergence graph 93 figure; 94 plot(1:numel(J_history), J_history, ‘-b‘, ‘LineWidth‘, 2); 95 xlabel(‘Number of iterations‘); 96 ylabel(‘Cost J‘); 97 98 % Display gradient descent‘s result 99 fprintf(‘Theta computed from gradient descent: \n‘); 100 fprintf(‘ %f \n‘, theta); 101 fprintf(‘\n‘); 102 103 % Estimate the price of a 1650 sq-ft, 3 br house 104 % ====================== YOUR CODE HERE ====================== 105 % Recall that the first column of X is all-ones. Thus, it does 106 % not need to be normalized. 107 price = 0; % You should change this 108 109 110 % ============================================================ 111 112 fprintf([‘Predicted price of a 1650 sq-ft, 3 br house ‘ ... 113 ‘(using gradient descent):\n $%f\n‘], price); 114 115 fprintf(‘Program paused. Press enter to continue.\n‘); 116 pause; 117 118 %% ================ Part 3: Normal Equations ================ 119 120 fprintf(‘Solving with normal equations...\n‘); 121 122 % ====================== YOUR CODE HERE ====================== 123 % Instructions: The following code computes the closed form 124 % solution for linear regression using the normal 125 % equations. You should complete the code in 126 % normalEqn.m 127 % 128 % After doing so, you should complete this code 129 % to predict the price of a 1650 sq-ft, 3 br house. 130 % 131 132 %% Load Data 133 data = csvread(‘ex1data2.txt‘); 134 X = data(:, 1:2); 135 y = data(:, 3); 136 m = length(y); 137 138 % Add intercept term to X 139 X = [ones(m, 1) X]; 140 141 % Calculate the parameters from the normal equation 142 theta = normalEqn(X, y); 143 144 % Display normal equation‘s result 145 fprintf(‘Theta computed from the normal equations: \n‘); 146 fprintf(‘ %f \n‘, theta); 147 fprintf(‘\n‘); 148 149 150 % Estimate the price of a 1650 sq-ft, 3 br house 151 % ====================== YOUR CODE HERE ====================== 152 price = 0; % You should change this 153 154 155 % ============================================================ 156 157 fprintf([‘Predicted price of a 1650 sq-ft, 3 br house ‘ ... 158 ‘(using normal equations):\n $%f\n‘], price);
1.featureNormalize.m
1 function [X_norm, mu, sigma] = featureNormalize(X) 2 %FEATURENORMALIZE Normalizes the features in X 3 % FEATURENORMALIZE(X) returns a normalized version of X where 4 % the mean value of each feature is 0 and the standard deviation 5 % is 1. This is often a good preprocessing step to do when 6 % working with learning algorithms. 7 8 % You need to set these values correctly 9 X_norm = X; 10 mu = zeros(1, size(X, 2)); 11 sigma = zeros(1, size(X, 2)); 12 13 % ====================== YOUR CODE HERE ====================== 14 % Instructions: First, for each feature dimension, compute the mean 15 % of the feature and subtract it from the dataset, 16 % storing the mean value in mu. Next, compute the 17 % standard deviation of each feature and divide 18 % each feature by it‘s standard deviation, storing 19 % the standard deviation in sigma. 20 % 21 % Note that X is a matrix where each column is a 22 % feature and each row is an example. You need 23 % to perform the normalization separately for 24 % each feature. 25 % 26 % Hint: You might find the ‘mean‘ and ‘std‘ functions useful. 27 % 28 mu = mean(X); 29 sigma = std(X); 30 X_norm = (X_norm.-mu)./sigma; 31 32 % ============================================================ 33 34 end
2.computCostMulti.m
1 function J = computeCostMulti(X, y, theta) 2 %COMPUTECOSTMULTI Compute cost for linear regression with multiple variables 3 % J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the 4 % parameter for linear regression to fit the data points in X and y 5 6 % Initialize some useful values 7 m = length(y); % number of training examples 8 9 % You need to return the following variables correctly 10 J = 0; 11 12 % ====================== YOUR CODE HERE ====================== 13 % Instructions: Compute the cost of a particular choice of theta 14 % You should set J to the cost. 15 hypothesis = X*theta; 16 J = 1/(2*m)*(sum((hypothesis-y).^2)); 17 18 19 20 21 % ========================================================================= 22 23 end
3.gradientDescentMulti.m
1 function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters) 2 %GRADIENTDESCENTMULTI Performs gradient descent to learn theta 3 % theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by 4 % taking num_iters gradient steps with learning rate alpha 5 6 % Initialize some useful values 7 m = length(y); % number of training examples 8 J_history = zeros(num_iters, 1); 9 10 for iter = 1:num_iters 11 12 % ====================== YOUR CODE HERE ====================== 13 % Instructions: Perform a single gradient step on the parameter vector 14 % theta. 15 % 16 % Hint: While debugging, it can be useful to print out the values 17 % of the cost function (computeCostMulti) and gradient here. 18 % 19 hypothesis = X*theta; 20 delta = X‘*(hypothesis-y); 21 theta = theta - alpha/m*delta; 22 23 % ============================================================ 24 25 % Save the cost J in every iteration 26 J_history(iter) = computeCostMulti(X, y, theta); 27 28 end 29 30 end
4.normalEqn.m
1 function [theta] = normalEqn(X, y) 2 %NORMALEQN Computes the closed-form solution to linear regression 3 % NORMALEQN(X,y) computes the closed-form solution to linear 4 % regression using the normal equations. 5 6 theta = zeros(size(X, 2), 1); 7 8 % ====================== YOUR CODE HERE ====================== 9 % Instructions: Complete the code to compute the closed form solution 10 % to linear regression and put the result in theta. 11 % 12 13 % ---------------------- Sample Solution ---------------------- 14 15 theta = pinv(X‘*X)*X‘*y; 16 17 18 % ------------------------------------------------------------- 19 20 21 % ============================================================ 22 23 end
时间： 2024-10-06 22:05:20