public override double TestOnDataSet(SamplesSet testSet)
{
for (int i = 0; i < testSet.Count; i++)
{
var sample = testSet.samples[i];
Calculate(ref sample);
sample.processOutput();
}
return(testSet.ErrorsCount());
}
public SamplesSet GenerateSet()
{
SamplesSet res = new SamplesSet();
for (int i = 0; i < Config.current.figures.Count; i++)
{
var sel = Config.current.figures[i];
int take = Math.Min(files[sel].Length, Config.current.training_size);
//foreach(var el in files[sel].OrderBy(x => rand.Next()).Take(take))
Parallel.ForEach(files[sel].OrderBy(x => rand.Next()).Take(take), el =>
{
res.AddSample(process_output(fillImage(new Bitmap(el)), i));
});
}
res.samples = res.samples.OrderBy(x => rand.Next()).ToList();
return(res);
}
public override double TrainOnDataSet(SamplesSet samplesSet, int epochs_count, double acceptable_erorr, bool parallel = true)
{
double error = 0;
stopWatch.Restart();
for (int i = 0; i < epochs_count; i++)
{
error = 0;
for (int j = 0; j < samplesSet.Count; j++)
{
var sample = samplesSet[j];
Calculate(ref sample, parallel);
sample.processOutput();
error += sample.EstimatedError();
BackwardError(sample, parallel);
}
if (error < acceptable_erorr)
{
break;
}
}
return(error);
}
public abstract double TestOnDataSet(SamplesSet testSet);
public abstract double TrainOnDataSet(SamplesSet samplesSet, int epochs_count, double acceptable_erorr, bool parallel = true);
