--- a
+++ b/combinedDeepLearningActiveContour/functions/intersampleIMAGES.m
@@ -0,0 +1,65 @@
+% sampleIMAGES.m
+% sampling patches for learning
+function patches = sampleIMAGES(numpatches)
+% sampleIMAGES
+% Returns 10000 patches for training
+load IMAGES; % load images from disk 
+patchsize = 8; % we'll use 8x8 patches 
+%numpatches = 10000;
+% Initialize patches with zeros. Your code will fill in this matrix--one
+% column per patch, 10000 columns. 
+patches = zeros(patchsize*patchsize, numpatches);
+ 
+%% ---------- YOUR CODE HERE --------------------------------------
+% Instructions: Fill in the variable called "patches" using data 
+% from IMAGES. 
+% 
+% IMAGES is a 3D array containing 10 images
+% For instance, IMAGES(:,:,6) is a 512x512 array containing the 6th image,
+% and you can type "imagesc(IMAGES(:,:,6)), colormap gray;" to visualize
+% it. (The contrast on these images look a bit off because they have
+% been preprocessed using using "whitening." See the lecture notes for
+% more details.) As a second example, IMAGES(21:30,21:30,1) is an image
+% patch corresponding to the pixels in the block (21,21) to (30,30) of
+% Image 1
+ 
+counter = 1;
+ranimg = ceil(rand(1, numpatches) * 10);
+ranpix = ceil(rand(2, numpatches) * (512 - patchsize));
+ranpixm = ranpix + patchsize - 1;
+while(counter <= numpatches)
+whichimg = ranimg(1, counter);
+whichpix = ranpix(:, counter);
+whichpixm = ranpixm(:, counter);
+patch = IMAGES(whichpix(1):whichpixm(1), whichpix(2):whichpixm(2), whichimg);
+repatch = reshape(patch, patchsize * patchsize, 1);
+patches(:, counter) = repatch;
+counter = counter + 1;
+end
+ 
+%% ---------------------------------------------------------------
+% For the autoencoder to work well we need to normalize the data
+% Specifically, since the output of the network is bounded between [0,1]
+% (due to the sigmoid activation function), we have to make sure 
+% the range of pixel values is also bounded between [0,1]
+patches = normalizeData(patches);
+ 
+end
+
+%% ---------------------------------------------------------------
+function patches = normalizeData(patches)
+
+% Squash data to [0.1, 0.9] since we use sigmoid as the activation
+% function in the output layer
+
+% Remove DC (mean of images). 
+patches = bsxfun(@minus, patches, mean(patches));
+
+% Truncate to +/-3 standard deviations and scale to -1 to 1
+pstd = 3 * std(patches(:));
+patches = max(min(patches, pstd), -pstd) / pstd;
+
+% Rescale from [-1,1] to [0.1,0.9]
+patches = (patches + 1) * 0.4 + 0.1;
+
+end
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