Practical Neural Network Recipies in C++

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Appendix

}

tot err - 0.0 ; // Total error will be cumulated here

for (tset-0 ; tset<tptr->ntrain ; tset++) { // Do all samples

dptr - tptr->data + size * tset ; // Point to this sample

trial ( dptr ) ; // Evaluate network for it

1f (outmod — 0UTM0D AUTO) { // If this 1s AUTOASSOCIATIVE

for (1-0 ; Knout ; 1++) { // then the expected outputs

d1ff - *dptr++ - outC13 ; // are just the Inputs

tot err ·+- d1ff * d1ff ;

}

}

else 1f (outmod — OUTMOD CLASSIFY) { // If this 1s Classification

tclass - (1nt) dptr[n1n] - 1 ; // class 1s stored after Inputs

for (1-0 ; Knout ; 1++) { // Recall that train added a

1f (tclass — 1) // fraction so that th