implementation help of Gaussian RBM in matlab

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

[Greg Heath]

"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?

Answer 1

[Greg Heath]

doc zscore
help zscore

doc mapstd
help mapstd

Hope this helps.

• Thank you for formally accepting my answer*

Greg

[subha]

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]

 [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]

thanks.