public 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();
Assert.Less(error, 0.53);
}
public void TestCostFunctionAccumulation()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 2, 4, 3 });
NetworkTrainer trainer = new NetworkTrainer(network);
double[] train_set = new[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 };
Assert.Throws(typeof(NoErrorInfoYetException), () => trainer.GetError());
double error;
trainer.TrainPrediction(train_set);
error = trainer.GetError();
Assert.AreNotEqual(error, 0);
trainer.TrainPrediction(train_set);
Assert.AreNotEqual(error, trainer.GetError());
}
