trastd (Neural Network Toolbox)

Preprocess data using a precalculated mean and standard deviation

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

trastd preprocesses the network training set using the mean and standard deviation that were previously computed by prestd. This function needs to be used when a network has been trained using data normalized by prestd. All subsequent inputs to the network need to be transformed using the same normalization.

trastd(P,T) takes these inputs,

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

meanp - R x 1 vector containing the original means for each input.

stdp - R x 1 vector containing the original standard deviations for each input.

and returns,

PN - R x Q matrix of normalized input vectors.

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);
net = newff(minmax(pn),[5 1],{'tansig' 'purelin'},'trainlm');
net = train(net,pn,tn);

If we then receive new inputs to apply to the trained network, we will use trastd to transform them first. Then the transformed inputs can be used to simulate the previously trained network. The network output must also be unnormalized using poststd.

p2 = [1.5 -0.8;0.05 -0.3];
[p2n] = trastd(p2,meanp,stdp);
an = sim(net,pn);
[a] = poststd(an,meant,stdt);

Algorithm

pn = (p-meanp)/stdp;