public void Run()
{
if (_neuralNet == null)
{
return;
}
_neuralNet.Run(AddNoise(Trainigs[TestSample], TestNoise), _expectations[TestSample], out double[] outputs);
Columns.SetColumns(outputs);
}
public void Train()
{
_neuralNet = new NeuralNet(new int[] { Trainigs[0].Length, X, Y, 4 }, L, 0, 1, new Sigmoid(), new MeanSquare());
var noises = new double[2][][];
for (var j = 0; j < noises.Length; j++)
{
noises[j] = new double[Trainigs.Length][];
for (var k = 0; k < Trainigs.Length; k++)
{
noises[j][k] = AddNoise(Trainigs[k], LearnNoise);
}
}
var learnErrors = new double[E];
var testErrors = new double[E];
for (var i = 0; i < E; i++)
{
testErrors[i] = 0;
for (var j = 0; j < noises.Length; j++)
{
for (var k = 0; k < Trainigs.Length; k++)
{
testErrors[i] += _neuralNet.Run(noises[j][k], _expectations[k], out double[] outputs);
}
}
testErrors[i] /= noises.Length * Trainigs.Length;
learnErrors[i] = 0;
for (var j = 0; j < Trainigs.Length; j++)
{
learnErrors[i] += _neuralNet.Train(Trainigs[j], _expectations[j]);
}
learnErrors[i] /= Trainigs.Length;
}
Lines.SetLines(learnErrors, testErrors);
}