Cursera: machine learning ex7.

machine_learning/mlclass-ex7-008/mlclass-ex7/ex7.m

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+%% Machine Learning Online Class
+%  Exercise 7 | Principle Component Analysis and K-Means Clustering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     pca.m
+%     projectData.m
+%     recoverData.m
+%     computeCentroids.m
+%     findClosestCentroids.m
+%     kMeansInitCentroids.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
+
+%% ================= Part 1: Find Closest Centroids ====================
+%  To help you implement K-Means, we have divided the learning algorithm 
+%  into two functions -- findClosestCentroids and computeCentroids. In this
+%  part, you shoudl complete the code in the findClosestCentroids function. 
+%
+fprintf('Finding closest centroids.\n\n');
+
+% Load an example dataset that we will be using
+load('ex7data2.mat');
+
+% Select an initial set of centroids
+K = 3; % 3 Centroids
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Find the closest centroids for the examples using the
+% initial_centroids
+idx = findClosestCentroids(X, initial_centroids);
+
+fprintf('Closest centroids for the first 3 examples: \n')
+fprintf(' %d', idx(1:3));
+fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ===================== Part 2: Compute Means =========================
+%  After implementing the closest centroids function, you should now
+%  complete the computeCentroids function.
+%
+fprintf('\nComputing centroids means.\n\n');
+
+%  Compute means based on the closest centroids found in the previous part.
+centroids = computeCentroids(X, idx, K);
+
+fprintf('Centroids computed after initial finding of closest centroids: \n')
+fprintf(' %f %f \n' , centroids');
+fprintf('\n(the centroids should be\n');
+fprintf('   [ 2.428301 3.157924 ]\n');
+fprintf('   [ 5.813503 2.633656 ]\n');
+fprintf('   [ 7.119387 3.616684 ]\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% =================== Part 3: K-Means Clustering ======================
+%  After you have completed the two functions computeCentroids and
+%  findClosestCentroids, you have all the necessary pieces to run the
+%  kMeans algorithm. In this part, you will run the K-Means algorithm on
+%  the example dataset we have provided. 
+%
+fprintf('\nRunning K-Means clustering on example dataset.\n\n');
+
+% Load an example dataset
+load('ex7data2.mat');
+
+% Settings for running K-Means
+K = 3;
+max_iters = 10;
+
+% For consistency, here we set centroids to specific values
+% but in practice you want to generate them automatically, such as by
+% settings them to be random examples (as can be seen in
+% kMeansInitCentroids).
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Run K-Means algorithm. The 'true' at the end tells our function to plot
+% the progress of K-Means
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters, true);
+fprintf('\nK-Means Done.\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ============= Part 4: K-Means Clustering on Pixels ===============
+%  In this exercise, you will use K-Means to compress an image. To do this,
+%  you will first run K-Means on the colors of the pixels in the image and
+%  then you will map each pixel on to it's closest centroid.
+%  
+%  You should now complete the code in kMeansInitCentroids.m
+%
+
+fprintf('\nRunning K-Means clustering on pixels from an image.\n\n');
+
+%  Load an image of a bird
+A = double(imread('bird_small.png'));
+
+% If imread does not work for you, you can try instead
+%   load ('bird_small.mat');
+
+A = A / 255; % Divide by 255 so that all values are in the range 0 - 1
+
+% Size of the image
+img_size = size(A);
+
+% Reshape the image into an Nx3 matrix where N = number of pixels.
+% Each row will contain the Red, Green and Blue pixel values
+% This gives us our dataset matrix X that we will use K-Means on.
+X = reshape(A, img_size(1) * img_size(2), 3);
+
+% Run your K-Means algorithm on this data
+% You should try different values of K and max_iters here
+K = 16; 
+max_iters = 10;
+
+% When using K-Means, it is important the initialize the centroids
+% randomly. 
+% You should complete the code in kMeansInitCentroids.m before proceeding
+initial_centroids = kMeansInitCentroids(X, K);
+
+% Run K-Means
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% ================= Part 5: Image Compression ======================
+%  In this part of the exercise, you will use the clusters of K-Means to
+%  compress an image. To do this, we first find the closest clusters for
+%  each example. After that, we 
+
+fprintf('\nApplying K-Means to compress an image.\n\n');
+
+% Find closest cluster members
+idx = findClosestCentroids(X, centroids);
+
+% Essentially, now we have represented the image X as in terms of the
+% indices in idx. 
+
+% We can now recover the image from the indices (idx) by mapping each pixel
+% (specified by it's index in idx) to the centroid value
+X_recovered = centroids(idx,:);
+
+% Reshape the recovered image into proper dimensions
+X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3);
+
+% Display the original image 
+subplot(1, 2, 1);
+imagesc(A); 
+title('Original');
+
+% Display compressed image side by side
+subplot(1, 2, 2);
+imagesc(X_recovered)
+title(sprintf('Compressed, with %d colors.', K));
+
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+