main_vq.m

用Cross validation的方法建立人工神经网络的模型！

function [mse_VQ, R2_VQ, accu_VQ] = main_VQ(blocksize,nwds)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% VQ -> compressed_image

fprintf('\n');
fprintf('VQ begin...\n');
[compressed_image] = VQ_WithoutPCA(blocksize, nwds);

%filename = ['VQ_Ali_',num2str(blocksize),'_',num2str(nwds),'_2.mat'];
%fprintf('loaing ');
%fprintf(filename);
%load(filename);

fprintf('\nVQ finished...\n');

no_features = size(compressed_image{1},2);

VQ_compressed_data = [];
for i = 1: size(compressed_image, 2)
    if i <= 612
        class = 0;  % has disease
    else
        class = 1;  % no disease
    end
    data = [compressed_image{i} class];
    VQ_compressed_data = [VQ_compressed_data; data];
end
%[meanv, stdv, VQ_compressed_data] = normalize(VQ_compressed_data, [], []);

[mse_VQ, R2_VQ, accu_VQ] = classifierANN(VQ_compressed_data);