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neuralnetsse.go
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/
neuralnetsse.go
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package gnubg
import "github.com/amlwwalker/go-gnubg/internal/gnubg/sigmoid"
func neuralNetEvaluateSSE(pnn *_NeuralNet, arInput []float32, arOutput *[_NUM_OUTPUTS]float32, pnState *_NNState) error {
ar := make([]float32, pnn.cHidden)
// var s string
// s += "=== neuralNetEvaluateSSE()\n"
// s += fmt.Sprintf(" arInput: %v\n", arInput)
evaluateSSE(pnn, arInput, ar, arOutput)
// s += fmt.Sprintf(" arOutput: %v\n", arOutput)
// fmt.Printf("%v", s)
// if 1 == 1 {
// os.Exit(0)
// }
return nil
}
func evaluateSSE(pnn *_NeuralNet, arInput []float32, ar []float32, arOutput *[_NUM_OUTPUTS]float32) {
var cHidden int = pnn.cHidden
/* Calculate activity at hidden nodes */
copy(ar, pnn.arHiddenThreshold)
prWeight := pnn.arHiddenWeight[:]
if pnn.cInput != 214 { /* everything but the racing net */
for i := 0; i < 200; { /* base inputs */
var ari float32 = arInput[i]
i++
/* 3 binaries, 1 float */
if ari == 0.0 {
prWeight = prWeight[cHidden:]
} else {
for j := range ar {
ar[j] += prWeight[j]
}
prWeight = prWeight[cHidden:]
}
ari = arInput[i]
i++
if ari == 0.0 {
prWeight = prWeight[cHidden:]
} else {
for j := range ar {
ar[j] += prWeight[j]
}
prWeight = prWeight[cHidden:]
}
ari = arInput[i]
i++
if ari == 0.0 {
prWeight = prWeight[cHidden:]
/* If 3rd element is 0, so is 4th. Skip it */
prWeight = prWeight[cHidden:]
i++
continue
} else {
for j := range ar {
ar[j] += prWeight[j]
}
prWeight = prWeight[cHidden:]
}
ari = arInput[i]
i++
if ari == 0.0 {
prWeight = prWeight[cHidden:]
} else {
if ari == 1.0 {
for j := range ar {
ar[j] += prWeight[j]
}
} else {
for j := range ar {
ar[j] += prWeight[j] * ari
}
}
prWeight = prWeight[cHidden:]
} /* base inputs are done */
}
if pnn.cInput == 250 { /* Pruning nets are over, contact/crashed still have 2 * 25 floats */
for i := 200; i < 250; i++ {
var ari float32 = arInput[i]
if ari == 0.0 {
prWeight = prWeight[cHidden:]
} else {
for j := range ar {
ar[j] += prWeight[j] * ari
}
prWeight = prWeight[cHidden:]
}
}
}
} else { /* racing net */
for i := 0; i < pnn.cInput; i++ {
var ari float32 = arInput[i]
if ari == 0.0 {
prWeight = prWeight[cHidden:]
} else {
if ari == 1.0 {
for j := range ar {
ar[j] += prWeight[j]
}
} else {
for j := range ar {
ar[j] += prWeight[j] * ari
}
}
prWeight = prWeight[cHidden:]
}
}
}
for i := 0; i < cHidden; i++ {
ar[i] = sigmoid.Sigmoid(-pnn.rBetaHidden * ar[i])
}
/* Calculate activity at output nodes */
prWeight = pnn.arOutputWeight
for i := 0; i < pnn.cOutput; i++ {
r := pnn.arOutputThreshold[i]
for j := 0; j < cHidden; j++ {
r += ar[j] * prWeight[0]
prWeight = prWeight[1:]
}
arOutput[i] = sigmoid.Sigmoid(-pnn.rBetaOutput * r)
}
}