self_taught_learning.m

function acc = self_taught_learning(params)


inputSize  = 28 * 28;
numLabels  = 5;
hiddenSize = params.hiddenSize;
sparsityParam = params.sparsityParam; % desired average activation of the hidden units.
                     % (This was denoted by the Greek alphabet rho, which looks like a lower-case "p",
		             %  in the lecture notes). 
lambda = params.lambda;       % weight decay parameter       
beta = params.beta;            % weight of sparsity penalty term   
maxIter = 100;

%% ======================================================================
%  STEP 1: Load data from the MNIST database
%
%  This loads our training and test data from the MNIST database files.
%  We have sorted the data for you in this so that you will not have to
%  change it.

% Load MNIST database files
mnistData   = loadMNISTImages('train-images.idx3-ubyte');
mnistLabels = loadMNISTLabels('train-labels.idx1-ubyte');

% Set Unlabeled Set (All Images)

% Simulate a Labeled and Unlabeled set
labeledSet   = find(mnistLabels >= 0 & mnistLabels <= 4);
unlabeledSet = find(mnistLabels >= 5);

numTrain = round(numel(labeledSet)/2);
trainSet = labeledSet(1:numTrain);
testSet  = labeledSet(numTrain+1:end);

unlabeledData = mnistData(:, unlabeledSet);

trainData   = mnistData(:, trainSet);
trainLabels = mnistLabels(trainSet)' + 1; % Shift Labels to the Range 1-5

testData   = mnistData(:, testSet);
testLabels = mnistLabels(testSet)' + 1;   % Shift Labels to the Range 1-5

% Output Some Statistics
fprintf('# examples in unlabeled set: %d\n', size(unlabeledData, 2));
fprintf('# examples in supervised training set: %d\n\n', size(trainData, 2));
fprintf('# examples in supervised testing set: %d\n\n', size(testData, 2));

%% ======================================================================
%  STEP 2: Train the sparse autoencoder
%  This trains the sparse autoencoder on the unlabeled training
%  images. 

%  Randomly initialize the parameters
theta = initializeParameters(hiddenSize, inputSize);


opttheta = theta; 
addpath minFunc/
options.Method = 'lbfgs'; % Here, we use L-BFGS to optimize our cost
                          % function. Generally, for minFunc to work, you
                          % need a function pointer with two outputs: the
                          % function value and the gradient. In our problem,
                          % sparseAutoencoderCost.m satisfies this.
options.maxIter = 100;	  % Maximum number of iterations of L-BFGS to run 
options.display = 'on';
[opttheta, cost] = minFunc( @(p) sparseAutoencoderCost(p, ...
                                   inputSize, hiddenSize, ...
                                   lambda, sparsityParam, ...
                                   beta, unlabeledData), ...
                              theta, options);

%% -----------------------------------------------------
                          
% Visualize weights
W1 = reshape(opttheta(1:hiddenSize * inputSize), hiddenSize, inputSize);
% display_network(W1');

%%======================================================================
%% STEP 3: Extract Features from the Supervised Dataset


trainFeatures = feedForwardAutoencoder(opttheta, hiddenSize, inputSize, ...
                                       trainData);

testFeatures = feedForwardAutoencoder(opttheta, hiddenSize, inputSize, ...
                                       testData);

%%======================================================================
%% STEP 4: Train the softmax classifier

softmaxModel = struct;  

%  Use lambda = 1e-4 for the weight regularization for softmax

% compute softmaxModel using softmaxTrain on trainFeatures and
% trainLabels

numClasses = 5; % use a labeled dataset with the digits 1 to 5 with which to train the softmax classifier.
options.maxIter = 100;
softmaxModel = softmaxTrain(hiddenSize, numClasses, 1e-4, ...
                            trainFeatures, trainLabels, options);


%%======================================================================
%% STEP 5: Testing 


[pred] = softmaxPredict(softmaxModel, testFeatures);

%% -----------------------------------------------------

% Classification Score
acc = 100*mean(pred(:) == testLabels(:));

end