public BPResponse BP(BPRequest bpr)
{
Utility.Verify(() => bpr!=null,"BP bpr");
Stopwatch StopWatch=new Stopwatch();
StopWatch.Start();
bool trace=this.Inspector!=null;
if(trace)
Inspector.Trace("Backpropagation start, MaxEpochs={0}, MinEpochs={1}",bpr.MinEpochs.ToString(),bpr.MaxEpochs.ToString());
BPResponseEstimate estimate= BPResponseEstimate.Unknown;
NeuronBase[] output=Structure.Output;
List<BPEpochResponse> bper=new List<BPEpochResponse>(bpr.MaxEpochs);
for(int i=0;i<bpr.MaxEpochs;i++) {
bool succ=true;
if(trace)
Inspector.Trace("Backpropagation- start epoch {0}",(i+1).ToString());
TrainingData[] ts= bpr.GetTrainingData().ToArray();
List<double> errors=new List<double>(ts.Length);
for(int j=0; j<ts.Length; j++){
bool testPass=true;
if(ts[j].TestingSets!=null && ts[j].TestingSets.Any(x => GetPulseError(x,ts[j].Output)>bpr.MaxMSE))
testPass=false;
double mse=GetPulseError(ts[j].Input,ts[j].Output);
errors.Add(mse);
if(mse>bpr.MaxMSE || !testPass) {
for(var k=0;k!=output.Length;k++)
((Neuron)output[k]).BP(ts[j].Output[k]-output[k].LastOutputSignal,LearnFactor,Momentum);
succ=false;
}
}
double eps=GetTotalEps(errors.ToArray());
if(trace)
Inspector.Trace("End of epoch {0}, total Eps={1}",(i+1).ToString(),eps);
bper.Add(new BPEpochResponse(errors.ToArray(),eps));
if(i>=bpr.MinEpochs && succ) {
estimate=BPResponseEstimate.Success;
break;
}
if(i>=bpr.MaxEpochs-1){
if(!succ)
estimate=BPResponseEstimate.Failure;
break;
}
}
StopWatch.Stop();
BPResponse response=new BPResponse(estimate,bper.ToArray(),StopWatch.Elapsed);
return response;
}
public BPResponse BP(BPRequest bpr)
{
Utility.Verify(() => bpr != null, "BP bpr");
Stopwatch StopWatch = new Stopwatch();
StopWatch.Start();
bool trace = this.Inspector != null;
if (trace)
{
Inspector.Trace("Backpropagation start, MaxEpochs={0}, MinEpochs={1}", bpr.MinEpochs.ToString(), bpr.MaxEpochs.ToString());
}
BPResponseEstimate estimate = BPResponseEstimate.Unknown;
NeuronBase[] output = Structure.Output;
List <BPEpochResponse> bper = new List <BPEpochResponse>(bpr.MaxEpochs);
for (int i = 0; i < bpr.MaxEpochs; i++)
{
bool succ = true;
if (trace)
{
Inspector.Trace("Backpropagation- start epoch {0}", (i + 1).ToString());
}
TrainingData[] ts = bpr.GetTrainingData().ToArray();
List <double> errors = new List <double>(ts.Length);
for (int j = 0; j < ts.Length; j++)
{
bool testPass = true;
if (ts[j].TestingSets != null && ts[j].TestingSets.Any(x => GetPulseError(x, ts[j].Output) > bpr.MaxMSE))
{
testPass = false;
}
double mse = GetPulseError(ts[j].Input, ts[j].Output);
errors.Add(mse);
if (mse > bpr.MaxMSE || !testPass)
{
for (var k = 0; k != output.Length; k++)
{
((Neuron)output[k]).BP(ts[j].Output[k] - output[k].LastOutputSignal, LearnFactor, Momentum);
}
succ = false;
}
}
double eps = GetTotalEps(errors.ToArray());
if (trace)
{
Inspector.Trace("End of epoch {0}, total Eps={1}", (i + 1).ToString(), eps);
}
bper.Add(new BPEpochResponse(errors.ToArray(), eps));
if (i >= bpr.MinEpochs && succ)
{
estimate = BPResponseEstimate.Success;
break;
}
if (i >= bpr.MaxEpochs - 1)
{
if (!succ)
{
estimate = BPResponseEstimate.Failure;
}
break;
}
}
StopWatch.Stop();
BPResponse response = new BPResponse(estimate, bper.ToArray(), StopWatch.Elapsed);
return(response);
}
