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| postmnmx | Examples See Also |
Postprocess data which has been preprocessed by premnmx
[p,t] = postmnmx(pn,minp,maxp,tn,mint,maxt)
[p] = postmnmx(pn,minp,maxp)
postmnmx postprocesses the network training set which was preprocessed by premnmx. It converts the data back into unnormalized units.
postmnmx takes these inputs,
PN - R x Q matrix of normalized input vectors.
minp- R x 1 vector containing minimums for each P.
maxp- R x 1 vector containing maximums for each P.
TN - S x Q matrix of normalized target vectors.
mint- S x 1 vector containing minimums for each T.
maxt- S x 1 vector containing maximums for each T.
postmnmx, and perform a linear regression between the network outputs (unnormalized) and the targets to check the quality of the network training.
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,minp,maxp,tn,mint,maxt] = premnmx(p,t);
net = newff(minmax(pn),[5 1],{'tansig' 'purelin'},'trainlm');
net = train(net,pn,tn);
an = sim(net,pn);
[a] = postmnmx(an,mint,maxt);
[m,b,r] = postreg(a,t);
p = 0.5(pn+1)*(maxp-minp) + minp;
premnmx, prepca, poststd