void TrainNet(NET* Net)
{
INT n,t;
POLE Pole;
REAL wOld, wNew, ScoreOld, ScoreNew, dScore, dScoreMean, StepSize;
REAL Input[N];
REAL Output[M];
REAL Target[M];
n = 0;
while (n<TRAIN_STEPS) {
t = 0;
InitializePole(&Pole);
fprintf(f, " Time Angle Force\n");
fprintf(f, "%4.1fs %5.1f° %5.1fN\n", t * T, Pole.w, Pole.F);
wOld = Pole.w;
ScoreOld = ScoreOfPole(&Pole);
SimulatePole(&Pole);
wNew = Pole.w;
ScoreNew = ScoreOfPole(&Pole);
while (PoleStillBalanced(&Pole) AND (t<BALANCED)) {
n++;
t++;
Net->Alpha = 0.5 * pow(0.01, (REAL) n / TRAIN_STEPS);
Net->Alpha_ = 0.5 * pow(0.01, (REAL) n / TRAIN_STEPS);
Net->Alpha__ = 0.005;
Net->Gamma = 0.05;
Net->Sigma = 6.0 * pow(0.2, (REAL) n / TRAIN_STEPS);
Input[0] = wOld;
Input[1] = wNew;
SetInput(Net, Input);
PropagateNet(Net);
GetOutput(Net, Output);
Pole.F = Output[0];
StepSize = Net->KohonenLayer->StepSize[Net->Winner];
Pole.F += StepSize * RandomNormalREAL(0, 10);
fprintf(f, "%4.1fs %5.1f° %5.1fN\n", t * T, Pole.w, Pole.F);
wOld = Pole.w;
ScoreOld = ScoreOfPole(&Pole);
SimulatePole(&Pole);
wNew = Pole.w;
ScoreNew = ScoreOfPole(&Pole);
dScore = ScoreNew - ScoreOld;
dScoreMean = Net->KohonenLayer->dScoreMean[Net->Winner];
if (dScore > dScoreMean) {
Target[0] = Pole.F;
TrainUnits(Net, Input, Target);
}
Net->KohonenLayer->dScoreMean[Net->Winner] += Net->Gamma * (dScore - dScoreMean);
}
if (PoleStillBalanced(&Pole))
fprintf(f, "Pole still balanced after %0.1fs ...\n\n", t * T);
else
fprintf(f, "Pole fallen after %0.1fs ...\n\n", (t+1) * T);
}
}
void SimulateNet(NET* Net, INT* Input, INT* Target, BOOL Training, BOOL Protocoling)
{
INT Output[M];
SetInput(Net, Input, Protocoling);
PropagateNet(Net);
GetOutput(Net, Output, Protocoling);
ComputeOutputError(Net, Target);
if (Training)
AdjustWeights(Net);
}
void SimulateNet(NET* Net, REAL* Input, REAL* Output, REAL* Target, BOOL Training)
{
SetInput(Net, Input);
PropagateNet(Net);
GetOutput(Net, Output);
ComputeOutputError(Net, Target);
if (Training) {
BackpropagateNet(Net);
AdjustWeights(Net);
}
}
