public static void TestTanhLearningOnSinus()
{
NNetwork network = NNetwork.HyperbolicNetwork(new int[] { 1, 2, 1 });
network.RandomizeWeights(1, 2);
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] inputs = SinusTrainSet()[0];
double[][] outputs = SinusTrainSet()[1];
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.000001) || j == 1; j++)
{
trainer.TrainClassification(inputs, outputs);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
double[][] input_test = SinusTrainSet(20)[0];
double[][] output_test = SinusTrainSet(20)[1];
trainer.IsLearning = false;
trainer.TrainClassification(input_test, output_test);
error = trainer.GetError();
Console.Out.WriteLine(error);
for (int i = 0; i < input_test.Length; i++ )
{
network.SetInput(input_test[i]);
Show(new []{input_test[i][0], network.GetOutput()[0], Math.Sin(input_test[i][0])});
}
}
public void DimensionTestCheck()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 2, 4, 3 });
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] incorrect_input = new double[1][] { new double[3] };
double[][] correct_input = new double[1][] { new double[2] };
double[][] incorrect_output = new double[1][] { new double[4] };
double[][] correct_output = new double[1][] { new double[3] };
Assert.Throws(typeof(IncorrectInputDimensionException),
() => trainer.TrainClassification(incorrect_input, correct_output));
Assert.Throws(typeof(IncorrectOutputDimensionException),
() => trainer.TrainClassification(correct_input, incorrect_output));
}
public void TestHyperbolicClassification()
{
NNetwork network = NNetwork.HyperbolicNetwork(new int[] { 1, 2, 1 });
network.RandomizeWeights(1, 2);
NetworkTrainer trainer = new NetworkTrainer(network);
double[][] inputs = SinusTrainSet()[0];
double[][] outputs = SinusTrainSet()[1];
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.000001) || j == 1; j++)
{
trainer.TrainClassification(inputs, outputs);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
double[][] input_test = SinusTrainSet(20)[0];
double[][] output_test = SinusTrainSet(20)[1];
trainer.IsLearning = false;
trainer.TrainClassification(input_test, output_test);
error = trainer.GetError();
Assert.Less(error, 0.55);
}