REAL RandomNormalREAL(REAL Mu, REAL Sigma)
{
REAL x,fx;
do {
x = RandomEqualREAL(Mu-3*Sigma, Mu+3*Sigma);
fx = (1 / (sqrt(2*PI)*Sigma)) * exp(-sqr(x-Mu) / (2*sqr(Sigma)));
} while (fx < RandomEqualREAL(0, 1));
return x;
}
void RandomWeights(NET* Net)
{
INT i,j;
for (i=1; i<=Net->OutputLayer->Units; i++) {
for (j=0; j<=Net->InputLayer->Units; j++) {
Net->OutputLayer->Weight[i][j] = RandomEqualREAL(-0.5, 0.5);
}
}
}
void InitializePole(POLE* Pole)
{
do {
Pole->x = 0;
Pole->xDot = 0;
Pole->w = RandomEqualREAL(-30, 30);
Pole->wDot = 0;
Pole->F = 0;
} while (Pole->w == 0);
}
void RandomWeights(NET* Net)
{
INT l,i,j;
for (l=1; l<NUM_LAYERS; l++) {
for (i=1; i<=Net->Layer[l]->Units; i++) {
for (j=0; j<=Net->Layer[l-1]->Units; j++) {
Net->Layer[l]->Weight[i][j] = RandomEqualREAL();
}
}
}
}
void RandomWeights(NET* Net)
{
INT i,j;
for (i=0; i<Net->KohonenLayer->Units; i++) {
for (j=0; j<Net->InputLayer->Units; j++) {
Net->KohonenLayer->Weight[i][j] = RandomEqualREAL(-30, 30);
}
}
for (i=0; i<Net->OutputLayer->Units; i++) {
for (j=0; j<Net->KohonenLayer->Units; j++) {
Net->OutputLayer->Weight[i][j] = 0;
}
}
}
void MultiLayerPerceptron::RandomWeights()
{
int i,j,k;
for( i = 1; i < nNumLayers; i++ )
{
for( j = 0; j < pLayers[i].nNumNeurons; j++ )
{
for ( k = 0; k < pLayers[i-1].nNumNeurons; k++ )
{
pLayers[i].pNeurons[j].w [k] = RandomEqualREAL(-0.5, 0.5);
pLayers[i].pNeurons[j].dw[k] = 0.0;
pLayers[i].pNeurons[j].wsave[k] = 0.0;
}
}
}
}
