--- a
+++ b/combinedDeepLearningActiveContour/functions/stackedAEPredict.m
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+function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data)
+
+% stackedAEPredict: Takes a trained theta and a test data set,
+% and returns the predicted labels for each example.
+
+% theta: trained weights from the autoencoder
+% visibleSize: the number of input units
+% hiddenSize:  the number of hidden units *at the 2nd layer*
+% numClasses:  the number of categories
+% data: Our matrix containing the training data as columns.  So, data(:,i) is the i-th training example.
+
+% Your code should produce the prediction matrix
+% pred, where pred(i) is argmax_c P(y(c) | x(i)).
+
+%% Unroll theta parameter
+
+% We first extract the part which compute the softmax gradient
+mrTheta = reshape(theta(1:hiddenSize*numClasses), numClasses, hiddenSize);
+
+% Extract out the "stack"
+stack = params2stack(theta(hiddenSize*numClasses+1:end), netconfig);
+
+%% ---------- YOUR CODE HERE --------------------------------------
+%  Instructions: Compute pred using theta assuming that the labels start
+%                from 1.
+
+depth = numel(stack);
+z = cell(depth+1,1);
+a = cell(depth+1, 1);
+a{1} = data;
+
+for layer = (1:depth)
+  z{layer+1} = stack{layer}.w * a{layer} + repmat(stack{layer}.b, [1, size(a{layer},2)]);
+  a{layer+1} = sigmoid(z{layer+1});
+end
+
+z_mr = mrTheta * a{depth+1};
+pred=sigmoid(z_mr)>.5;
+
+% -----------------------------------------------------------
+
+end
+
+
+% You might find this useful
+function sigm = sigmoid(x)
+    sigm = 1 ./ (1 + exp(-x));
+end
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