internal void Test(out double fullEror, out List <double> realOut, out List <double> learnOut, double learningSetSizePart)
{
int lernSetSize = (int)(learningSet.Count * learningSetSizePart);
fullEror = 0;
realOut = new List <double>();
learnOut = new List <double>();
var testSet = learningSet.GetRange(learningSet.Count - lernSetSize, lernSetSize);
List <double> realInput = new List <double>();
List <double> fullOutputs = new List <double>();
fullOutputs.AddRange(learningSet[0].Value);
for (int i = 0; i < learningSet[learningSet.Count - lernSetSize].Value.Count; i++)
{
realInput.Add(learningSet[learningSet.Count - lernSetSize].Value[i]);
}
double error = 0;
foreach (var item in testSet)
{
var output = neuralNetwork.CalculateOutputs(realInput.ToArray());
realOut.AddRange(output);
learnOut.AddRange(item.Key);
fullOutputs.AddRange(output);
for (int i = 0; i < output.Length; i++)
{
error += Math.Pow((output[i] - item.Value[i]), 2);
}
realInput.RemoveRange(0, neuralNetwork.OutputLayerSize);
realInput.AddRange(output);
}
fullEror = Math.Sqrt(error);
}
//обучение на одном классе
private double[] LearningOnOneClass(out double error, double[] nextValues, double[] inputVector)
{
neuroNet.CalculateOutputs(inputVector);
double[] iLOutput = neuroNet.InputLayerOutput;
double[] hLOutput = neuroNet.HiddenLayerOutput;
double[] oLOutput = neuroNet.OutputLayerOutput;
double[] hLInput = neuroNet.HiddenLayerInput;
double[] oLInput = neuroNet.OutputLayerInput;
//Ошибка на выходе сети
double[] errorOnOutput = neuroNet.TrainOutputLayer(oLOutput, learnOutput: nextValues, layerInput: oLInput).ToArray();
//Величины корректировки каждой связи межд вых и скр слоем
double[][] corOutToHidden = GetWeightCorrect(errorOnOutput, hLOutput);
//Ошибка между вых и скрытым слоем
double[] errorOnHidden = neuroNet.TrainHiddenLayer(errorOnOutput, hLInput);
//Величины корректировки каждой связи межд скр и вх слоем
double[][] corHiddenToInput = GetWeightCorrect(errorOnHidden, iLOutput);
neuroNet.CorrectWeights(corOutToHidden, corHiddenToInput, moment);
error = 0;
for (int i = 0; i < oLOutput.Length; i++)
{
error += Math.Pow((oLOutput[i] - nextValues[i]), 2);
}
error = Math.Sqrt(error);
return(oLOutput);
}
