--- a
+++ b/combinedDeepLearningActiveContour/functions/mrCost.m
@@ -0,0 +1,47 @@
+% softmaxCost.m
+function [cost, grad] = mrCost(theta, numOuts, inputSize, lambda, data, labels)
+ 
+% numOuts - the number of number of outputs 
+% inputSize - the size N of the input vector
+% lambda - weight decay parameter
+% data - the N x M input matrix, where each column data(:, i) corresponds to
+% a single test set
+% labels - an M x 1 matrix containing the labels corresponding for the input data
+%
+ 
+% Unroll the parameters from theta
+theta = reshape(theta, numOuts, inputSize);
+ 
+
+numCases = size(data, 2);
+ 
+%groundTruth = full(sparse(labels, 1:numCases, 1));
+cost = 0;
+ 
+thetagrad = zeros(numOuts, inputSize);
+ 
+%% ---------- YOUR CODE HERE --------------------------------------
+% Instructions: Compute the cost and gradient for softmax regression.
+% You need to compute thetagrad and cost.
+% The groundTruth matrix might come in handy.
+ 
+[nfeatures, nsamples] = size(data);
+
+zi = theta * data;
+%zi=zi+.0005*randn(size(zi));
+hzi = 1./(1+exp(-zi));
+temp1 = labels .* log(hzi);
+temp2 = (1-labels) .* log(1-hzi);
+
+cost = - sum(sum(temp1+temp2)) ./ nsamples;
+cost = cost + sum(sum(theta .^ 2)) .* lambda ./ 2;
+ 
+temp3 = labels - hzi;
+temp4 = temp3 * data';
+thetagrad = - temp4 ./ nsamples;
+thetagrad = thetagrad + lambda .* theta;
+ 
+% ------------------------------------------------------------------
+% Unroll the gradient matrices into a vector for minFunc
+grad = [thetagrad(:)];
+end
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