--- a
+++ b/combinedDeepLearningActiveContour/functions/mrPredict.m
@@ -0,0 +1,33 @@
+function [pred] = mrPredict(mrModel, data)
+
+% mrModel  - model trained using mrTrain
+% data - the N x M input matrix, where each column data(:, i) corresponds to
+%        a single test set
+%
+% Your code should produce the prediction matrix 
+% pred, where pred(i) is argmax_c P(y(c) | x(i)).
+ 
+% Unroll the parameters from theta
+theta = mrModel.optTheta;  % this provides a numClasses x inputSize matrix
+pred = zeros(1, size(data, 2));
+
+%% ---------- YOUR CODE HERE --------------------------------------
+%  Instructions: Compute pred using theta assuming that the labels start 
+%                from 1.
+z=theta*data;
+pred=sigmoid(z)>.5;
+
+
+% ---------------------------------------------------------------------
+
+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