prestd (Neural Network Toolbox)

Preprocess data so that its mean is 0 and the standard deviation is 1

[pn,meanp,stdp,tn,meant,stdt] = prestd(p,t)

[pn,meanp,stdp] = prestd(p)

prestd preprocesses the network training set by normalizing the inputs and targets so that they have means of zero and standard deviations of 1.

prestd(p,t) takes these inputs,

p - R x Q matrix of input (column) vectors.

t - S x Q matrix of target vectors.

and returns,

pn - R x Q matrix of normalized input vectors.

meanp - R x 1 vector containing standard deviations for each P.

stdp - R x 1 vector containing standard deviations for each P.

tn - S x Q matrix of normalized target vectors.

meant - S x 1 vector containing standard deviations for each T.

stdt - S x 1 vector containing standard deviations for each T.

Examples

Here is the code to normalize a given data set so that the inputs and targets will have means of zero and standard deviations of 1.

p = [-0.92 0.73 -0.47 0.74 0.29; -0.08 0.86 -0.67 -0.52 0.93];
t = [-0.08 3.4 -0.82 0.69 3.1];
[pn,meanp,stdp,tn,meant,stdt] = prestd(p,t);

If you just want to normalize the input,

[pn,meanp,stdp] = prestd(p);

pn = (p-meanp)/stdp;