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136 lines
3.6 KiB
Matlab
136 lines
3.6 KiB
Matlab
%% Machine Learning Online Class - Exercise 2: Logistic Regression
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the logistic
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% regression exercise. You will need to complete the following functions
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% in this exericse:
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%
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% sigmoid.m
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% costFunction.m
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% predict.m
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% costFunctionReg.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% Load Data
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% The first two columns contains the exam scores and the third column
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% contains the label.
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data = load('ex2data1.txt');
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X = data(:, [1, 2]); y = data(:, 3);
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%% ==================== Part 1: Plotting ====================
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% We start the exercise by first plotting the data to understand the
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% the problem we are working with.
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fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
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'indicating (y = 0) examples.\n']);
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plotData(X, y);
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% Put some labels
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hold on;
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% Labels and Legend
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xlabel('Exam 1 score')
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ylabel('Exam 2 score')
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% Specified in plot order
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legend('Admitted', 'Not admitted')
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hold off;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============ Part 2: Compute Cost and Gradient ============
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% In this part of the exercise, you will implement the cost and gradient
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% for logistic regression. You neeed to complete the code in
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% costFunction.m
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% Setup the data matrix appropriately, and add ones for the intercept term
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[m, n] = size(X);
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% Add intercept term to x and X_test
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X = [ones(m, 1) X];
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% Initialize fitting parameters
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initial_theta = zeros(n + 1, 1);
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% Compute and display initial cost and gradient
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[cost, grad] = costFunction(initial_theta, X, y);
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fprintf('Cost at initial theta (zeros): %f\n', cost);
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fprintf('Gradient at initial theta (zeros): \n');
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fprintf(' %f \n', grad);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============= Part 3: Optimizing using fminunc =============
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% In this exercise, you will use a built-in function (fminunc) to find the
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% optimal parameters theta.
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% Set options for fminunc
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options = optimset('GradObj', 'on', 'MaxIter', 400);
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% Run fminunc to obtain the optimal theta
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% This function will return theta and the cost
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[theta, cost] = ...
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fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
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% Print theta to screen
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fprintf('Cost at theta found by fminunc: %f\n', cost);
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fprintf('theta: \n');
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fprintf(' %f \n', theta);
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% Plot Boundary
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plotDecisionBoundary(theta, X, y);
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% Put some labels
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hold on;
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% Labels and Legend
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xlabel('Exam 1 score')
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ylabel('Exam 2 score')
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% Specified in plot order
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legend('Admitted', 'Not admitted')
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hold off;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============== Part 4: Predict and Accuracies ==============
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% After learning the parameters, you'll like to use it to predict the outcomes
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% on unseen data. In this part, you will use the logistic regression model
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% to predict the probability that a student with score 45 on exam 1 and
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% score 85 on exam 2 will be admitted.
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%
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% Furthermore, you will compute the training and test set accuracies of
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% our model.
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%
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% Your task is to complete the code in predict.m
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% Predict probability for a student with score 45 on exam 1
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% and score 85 on exam 2
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prob = sigmoid([1 45 85] * theta);
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fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
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'probability of %f\n\n'], prob);
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% Compute accuracy on our training set
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p = predict(theta, X);
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fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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