Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex3-008/mlclass-ex3/ex3.m

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+%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
+
+%  Instructions
+%  ------------
+% 
+%  This file contains code that helps you get started on the
+%  linear exercise. You will need to complete the following functions 
+%  in this exericse:
+%
+%     lrCostFunction.m (logistic regression cost function)
+%     oneVsAll.m
+%     predictOneVsAll.m
+%     predict.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
+
+%% Setup the parameters you will use for this part of the exercise
+input_layer_size  = 400;  % 20x20 Input Images of Digits
+num_labels = 10;          % 10 labels, from 1 to 10   
+                          % (note that we have mapped "0" to label 10)
+
+%% =========== Part 1: Loading and Visualizing Data =============
+%  We start the exercise by first loading and visualizing the dataset. 
+%  You will be working with a dataset that contains handwritten digits.
+%
+
+% Load Training Data
+fprintf('Loading and Visualizing Data ...\n')
+
+load('ex3data1.mat'); % training data stored in arrays X, y
+m = size(X, 1);
+
+% Randomly select 100 data points to display
+rand_indices = randperm(m);
+sel = X(rand_indices(1:100), :);
+
+%displayData(sel);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ============ Part 2: Vectorize Logistic Regression ============
+%  In this part of the exercise, you will reuse your logistic regression
+%  code from the last exercise. You task here is to make sure that your
+%  regularized logistic regression implementation is vectorized. After
+%  that, you will implement one-vs-all classification for the handwritten
+%  digit dataset.
+%
+
+fprintf('\nTraining One-vs-All Logistic Regression...\n')
+
+lambda = 0.1;
+[all_theta] = oneVsAll(X, y, num_labels, lambda);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% ================ Part 3: Predict for One-Vs-All ================
+%  After ...
+pred = predictOneVsAll(all_theta, X);
+
+fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
+