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implementation help of Gaussian RBM in matlab

Asked by subha on 23 Nov 2013
Latest activity Commented on by subha on 28 Nov 2013

First i would like to know how to make visible layer to zero mean and unit variance.I have seen in few example they followed below way.but i couldnot understand

subtracting the corresponding data with its mean and divide it by standard division, my data becomes NaN.

I am new to matlab and Neural networks.

data= batchdata(:,:,batch);
mean_data=mean(data,1),data=bsxfun(data,mean_data);
std_data=std(data,[],1);
data=bsxfun(@rdivide,data,std_data);

i am not able to find the reason

can anybody help to clear this

1 Comment

Greg Heath on 23 Nov 2013

"subtracting the corresponding data with its mean and divide it by standard division, my data becomes NaN."

Did it ever occur to you to post that code?

subha

1 Answer

Answer by Greg Heath on 23 Nov 2013
Accepted answer
doc zscore
help zscore
doc mapstd
help mapstd

Hope this helps.

  • Thank you for formally accepting my answer*

Greg

3 Comments

subha on 23 Nov 2013

i greg, thanks for your answer.

But i am sure mean zero and unit variance can be achieved in that way also.But i would like to know why it didn't work.What mistake i have done when i implement it.

thanks and regards subha

Greg Heath on 25 Nov 2013
 [x, t ] = engine_dataset;
 [ I N ] = size(x)   %  2  1199
 [ O N ] = size(t)   %  2  1199
 z    = [ x; t];
 muz  = mean(z')';
 stdz = std(z')';
% [ muz stdz ] = [ 141.2  090.7
%                 1259.5  354.8
%                  754.2  548.7
%                  961.7  466.1 ]
 zn    = ( z - repmat(muz,1,N))./repmat(stdz,1,N);
 muzn  = mean(zn')';
 stdzn = std(zn')';
% [ muzn stdzn ] = [  -0.0000    1.0000
%                      0.0000    1.0000
%                     -0.0000    1.0000
%                     -0.0000    1.0000 ]
subha on 28 Nov 2013

thanks.

Greg Heath

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