public float[] GetValues(FilterParameters frame)
{
// training
trainingRuns++;
if (trainingRuns < 10000)
{
float target = trainingFilter.GetValues(frame)[(int)BroadbandTransientFilter.Outputs.Edge];
if (target < 0.95f)
{
target = 0f;
}
output[(int)Outputs.TrainingTarget] = target;
neuralNetwork.LearningRate = target > 0.5f ? 0.05f : 0.01f;
avgError = avgError * 0.99f + 0.01f * TrainNetwork(trainingContext, frame.SpectrumDB, target);
output[(int)Outputs.Error] = avgError * 4.0f;
}
else
{
output[(int)Outputs.TrainingTarget] = 0.5f;
output[(int)Outputs.Error] = 0f;
}
// fill context with current spectrum frame
runContext.Set(frame.SpectrumDB.Take(runContext.InputCount));
neuralNetwork.Run(runContext);
output[(int)Outputs.Output] = runContext.Outputs.First();
return(output);
}
private float TrainNetwork(INetworkRunContext context, IEnumerable <float> inputs, float target)
{
context.Set(inputs.Take(context.InputCount));
context.Target[0] = target;
neuralNetwork.Train(context);
neuralNetwork.Update();
return(context.TotalError);
}