cnnPool.m

function pooledFeatures = cnnPool(poolDim, convolvedFeatures)
%cnnPool Pools the given convolved features
%
% Parameters:
%  poolDim - dimension of pooling region
%  convolvedFeatures - convolved features to pool (as given by cnnConvolve)
%                      convolvedFeatures(imageRow, imageCol, featureNum, imageNum)
%
% Returns:
%  pooledFeatures - matrix of pooled features in the form
%                   pooledFeatures(poolRow, poolCol, featureNum, imageNum)
%     

numImages = size(convolvedFeatures, 4);
numFilters = size(convolvedFeatures, 3);
convolvedDim = size(convolvedFeatures, 1);

pooledFeatures = zeros(convolvedDim / poolDim, ...
        convolvedDim / poolDim, numFilters, numImages);

% Instructions:
%   Now pool the convolved features in regions of poolDim x poolDim,
%   to obtain the 
%   (convolvedDim/poolDim) x (convolvedDim/poolDim) x numFeatures x numImages 
%   matrix pooledFeatures, such that
%   pooledFeatures(poolRow, poolCol, featureNum, imageNum) is the 
%   value of the featureNum feature for the imageNum image pooled over the
%   corresponding (poolRow, poolCol) pooling region. 
%   
%   Use mean pooling here.

%%% YOUR CODE HERE %%%
for imageNum = 1 : numImages
    for filterNum = 1 : numFilters
        
        pooledImage = zeros(convolvedDim / poolDim, convolvedDim / poolDim);
        convolvedImage = squeeze(convolvedFeatures(:, :, filterNum, imageNum));
        x = (floor(size(convolvedImage,1)/poolDim)) * poolDim;
        convolvedImage = convolvedImage(1:x,1:x);
        for i = 1:size(pooledImage,1)
            for j = 1:size(pooledImage,2)
                
                pooledImage(i,j) = sum (sum (convolvedImage((((i-1)*poolDim)+1):((((i-1)*poolDim)+1)+poolDim-1), (((j-1)*poolDim)+1):((((j-1)*poolDim)+1)+poolDim-1))));
                
            end
        end
        
        pooledImage = pooledImage ./ (poolDim*poolDim);
        
        pooledFeatures(:, :, filterNum, imageNum) = pooledImage;
        
    end
end 
      
end