You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
2.9 KiB
Matlab
117 lines
2.9 KiB
Matlab
%% Machine Learning Online Class - Exercise 2: Logistic Regression
|
|
%
|
|
% Instructions
|
|
% ------------
|
|
%
|
|
% This file contains code that helps you get started on the second part
|
|
% of the exercise which covers regularization with logistic regression.
|
|
%
|
|
% You will need to complete the following functions in this exericse:
|
|
%
|
|
% sigmoid.m
|
|
% costFunction.m
|
|
% predict.m
|
|
% costFunctionReg.m
|
|
%
|
|
% For this exercise, you will not need to change any code in this file,
|
|
% or any other files other than those mentioned above.
|
|
%
|
|
|
|
%% Initialization
|
|
clear ; close all; clc
|
|
|
|
%% Load Data
|
|
% The first two columns contains the X values and the third column
|
|
% contains the label (y).
|
|
|
|
data = load('ex2data2.txt');
|
|
X = data(:, [1, 2]); y = data(:, 3);
|
|
|
|
plotData(X, y);
|
|
|
|
% Put some labels
|
|
hold on;
|
|
|
|
% Labels and Legend
|
|
xlabel('Microchip Test 1')
|
|
ylabel('Microchip Test 2')
|
|
|
|
% Specified in plot order
|
|
legend('y = 1', 'y = 0')
|
|
hold off;
|
|
|
|
|
|
%% =========== Part 1: Regularized Logistic Regression ============
|
|
% In this part, you are given a dataset with data points that are not
|
|
% linearly separable. However, you would still like to use logistic
|
|
% regression to classify the data points.
|
|
%
|
|
% To do so, you introduce more features to use -- in particular, you add
|
|
% polynomial features to our data matrix (similar to polynomial
|
|
% regression).
|
|
%
|
|
|
|
% Add Polynomial Features
|
|
|
|
% Note that mapFeature also adds a column of ones for us, so the intercept
|
|
% term is handled
|
|
X = mapFeature(X(:,1), X(:,2));
|
|
|
|
% Initialize fitting parameters
|
|
initial_theta = zeros(size(X, 2), 1);
|
|
|
|
% Set regularization parameter lambda to 1
|
|
lambda = 1;
|
|
|
|
% Compute and display initial cost and gradient for regularized logistic
|
|
% regression
|
|
[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
|
|
|
|
fprintf('Cost at initial theta (zeros): %f\n', cost);
|
|
|
|
fprintf('\nProgram paused. Press enter to continue.\n');
|
|
pause;
|
|
|
|
%% ============= Part 2: Regularization and Accuracies =============
|
|
% Optional Exercise:
|
|
% In this part, you will get to try different values of lambda and
|
|
% see how regularization affects the decision coundart
|
|
%
|
|
% Try the following values of lambda (0, 1, 10, 100).
|
|
%
|
|
% How does the decision boundary change when you vary lambda? How does
|
|
% the training set accuracy vary?
|
|
%
|
|
|
|
% Initialize fitting parameters
|
|
initial_theta = zeros(size(X, 2), 1);
|
|
|
|
% Set regularization parameter lambda to 1 (you should vary this)
|
|
lambda = 1;
|
|
|
|
% Set Options
|
|
options = optimset('GradObj', 'on', 'MaxIter', 400);
|
|
|
|
% Optimize
|
|
[theta, J, exit_flag] = ...
|
|
fminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);
|
|
|
|
% Plot Boundary
|
|
plotDecisionBoundary(theta, X, y);
|
|
hold on;
|
|
title(sprintf('lambda = %g', lambda))
|
|
|
|
% Labels and Legend
|
|
xlabel('Microchip Test 1')
|
|
ylabel('Microchip Test 2')
|
|
|
|
legend('y = 1', 'y = 0', 'Decision boundary')
|
|
hold off;
|
|
|
|
% Compute accuracy on our training set
|
|
p = predict(theta, X);
|
|
|
|
fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
|
|
|
|
|