function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)

% theta: trained weights from the autoencoder

% visibleSize: the number of input units (probably 64) 

% hiddenSize: the number of hidden units (probably 25) 

% data: Our matrix containing the training data as columns.  So, data(:,i) is the i-th training example. 

% We first convert theta to the (W1, W2, b1, b2) matrix/vector format, so that this 

% follows the notation convention of the lecture notes. 

W1 = reshape(theta(1:hiddenSize*visibleSize), hiddenSize, visibleSize);

b1 = theta(2*hiddenSize*visibleSize+1:2*hiddenSize*visibleSize+hiddenSize);

%% ---------- YOUR CODE HERE --------------------------------------

%  Instructions: Compute the activation of the hidden layer for the Sparse Autoencoder.

z2=W1*data+repmat(b1,1,size(data,2));

activation=sigmoid(z2);

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

end

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

% Here's an implementation of the sigmoid function, which you may find useful

% in your computation of the costs and the gradients.  This inputs a (row or

% column) vector (say (z1, z2, z3)) and returns (f(z1), f(z2), f(z3)). 

function sigm = sigmoid(x)

    sigm = 1 ./ (1 + exp(-x));

end