public void TestAlternativeMatrix()
{
double[][] from_l1 = new double[][]
{
new double[] { -30, 20, 20 },
new double[] { 10, -20, -20 }
};
double[][] from_l2 = new double[][]
{
new double[] { -10, 20, 20 }
};
double[][][] weights = new double[][][]
{
from_l1,
from_l2
};
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 2, 1 });
n.SetWeightMatrix(weights);
double[] from_l1_expected = new double[] { -30, 20, 20, 10, -20, -20 };
double[] from_l2_expected = new double[] { -10, 20, 20 };
double[][] weights_expected = new double[][]
{
from_l1_expected,
from_l2_expected
};
Assert.AreEqual(weights_expected, n.GetWeightMatrix());
}
private void buttonCreateNetwork_Click(object sender, EventArgs e)
{
String[] layers_string = textLayers.Text.Split(";".ToCharArray());
int[] layers = new int[layers_string.Length];
for (int i = 0; i < layers_string.Length; i++)
{
layers[i] = int.Parse(layers_string[i]);
}
if (radioHyperbolic.Checked)
{
network = NNetwork.HyperbolicNetwork(layers);
is_hyperbolic = true;
is_sigmoid = false;
}
if (radioSigmoid.Checked)
{
network = NNetwork.SigmoidNetwork(layers);
is_hyperbolic = false;
is_sigmoid = true;
}
if (radioCombined.Checked)
{
network = NNetwork.CombinedNetwork(layers);
is_hyperbolic = false;
is_sigmoid = true;
}
bool two_steps = network.OutputCount() >= 2;
bool three_steps = network.OutputCount() >= 3;
checkTrain2.Enabled = two_steps;
checkTest2.Enabled = two_steps;
checkTrain3.Enabled = three_steps;
checkTest3.Enabled = three_steps;
groupWeights.Enabled = true;
}
public void TestDimensions()
{
int[] neurons_in_layers = new int[] { 3, 4, 2, 1 };
NNetwork network = NNetwork.SigmoidNetwork(neurons_in_layers);
Assert.AreEqual(network.LayerCount, neurons_in_layers.Length);
Assert.AreEqual(network.NeuronsInLayersWithoutBias, neurons_in_layers);
}
public void TrainPrediction()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 5, 2, 2 });
NetworkTrainer trainer = new NetworkTrainer(network);
double[] train_set = new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 };
trainer.TrainPrediction(train_set);
//todo
}
public void TestInputEqualsOutput()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1 });
n.SetWeightMatrix(new double[][]
{
new double[] { 1, 1 }
});
n.SetInput(new double[] { 9 });
Assert.AreEqual(n.GetOutput()[0], 9);
}
public void CanApplyTrainingForWholeNetwork()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1, 2, 2, 1 });
n.SetInput(new double[] { 0.3 });
n.SetAnswers(new double[] { 0.8 });
n.BackPropagate();
var output_before = n.GetOutput();
n.ApplyTraining();
var output_after = n.GetOutput();
Assert.AreNotEqual(output_after, output_before);
}
public void TestWeightMatrix()
{
double[] from_l1 = new double[] { -30, 20, 20, 10, -20, -20 };
double[] from_l2 = new double[] { -10, 20, 20 };
double[][] weights = new double[][]
{
from_l1,
from_l2
};
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 2, 1 });
n.SetWeightMatrix(weights);
Assert.AreEqual(n.GetWeightMatrix(), weights);
}
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 static NNetwork XorNetwork()
{
double[] from_l1 = new double[] { -30, 20, 20, 10, -20, -20 };
double[] from_l2 = new double[] { -10, 20, 20 };
double[][] weights = new double[][]
{
from_l1,
from_l2
};
NNetwork xor_network = NNetwork.SigmoidNetwork(new int[] { 2, 2, 1 });
xor_network.SetWeightMatrix(weights);
return(xor_network);
}
public void TestSimplestConnection()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 1, 1 });
n.SetWeightMatrix(new double[][]
{
new double[] { 1, 1 },
new double[] { 1, 1 }
});
n.SetInput(new double[] { 1 });
var output = n.GetOutput()[0];
var desired = 1 / (1 + Math.Pow(Math.E, -2));
MyAssert.CloseTo(output, desired);
}
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());
}
public void TestNetworkSetAnswerAndGetDelta()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 3, 2 });
n.SetInput(new double[] { 0, 0 });
double[] outputs = n.GetOutput();
double[] answers = new double[] { 0.1, 0.9 };
n.SetAnswers(answers);
n.BackPropagate();
double[] deltas = n.GetDeltasForLayer(3);
for (int i = 0; i < answers.Length; i++)
{
MyAssert.CloseTo(deltas[i], answers[i] - outputs[i]);
}
}
public void TestRandomInit()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 3, 2 });
n.RandomizeWeights(seed: 0);
var first = n.GetWeightMatrix();
n.RandomizeWeights(seed: 0);
var equal = n.GetWeightMatrix();
Assert.AreEqual(first, equal);
n.RandomizeWeights(seed: 1);
var not_equal = n.GetWeightMatrix();
Assert.AreNotEqual(first, not_equal);
}
public void TestDerivative()
{
//Fails with square error function
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 2, 1 });
n.RandomizeWeights(-1, 10);
Random random = new Random();
double x;
double y;
double z;
x = random.NextDouble();
y = random.NextDouble();
z = some_function(x, y);
n.SetInput(new double[] { x, y });
n.SetAnswers(new double[] { z });
n.BackPropagate();
double[] ders = n.Derivatives();
double[] ests = n.Estimation(0.0001);
for (int i = 0; i < ders.Length; i++)
{
MyAssert.CloseTo(ders[i], ests[i], 0.0001);
}
}
// public static void Main()
// {
// TrainPrediction();
//// Sinus();
//// TestTanhLearningOnSinus();
//// TestTanhDerivative();
//
// }
public static void TrainPrediction()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 5, 1 });
network.RandomizeWeights(-1, 20);
NetworkTrainer trainer = new NetworkTrainer(network);
List <double> tr = new List <double>();
for (double i = 0; i <= 1; i = i + 0.05)
{
tr.Add(i);
}
double[] train_set = tr.ToArray();//new double[] { 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 };
double error = 1;
double delta = 1;
int j = 0;
for (; error > 0.01 && !(delta <= 0.00001) || j == 1; j++)
{
trainer.TrainPrediction(train_set, 0.0001, 0.2);
double new_cost = trainer.GetError();
delta = error - new_cost;
error = new_cost;
}
Console.Out.WriteLine(j + ": " + error);
for (double i = 0; i <= 0.5; i = i + 0.05)
{
network.SetInput(new double[] { i + 0.0, i + 0.1, i + 0.2, i + 0.3, i + 0.4 });
Show(new double[]
{
i + 0.5,
network.GetOutput()[0],
// network.GetOutput()[1]
});
}
}
public void CanNotGetDeltasForFirstLayer()
{
NNetwork n = NNetwork.SigmoidNetwork(new int[] { 2, 3, 2 });
Assert.Throws(typeof(CannotGetDeltasForThisLayer), () => n.GetDeltasForLayer(1));
}
public void IllegalDimensionsTest()
{
Assert.Throws(typeof(InvalidDimensionException), () => NNetwork.SigmoidNetwork(new int[] {}));
Assert.Throws(typeof(InvalidDimensionException), () => NNetwork.SigmoidNetwork(new int[] { 1, 0 }));
}
public void TestNetworkCreation()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 1, 1, 1 });
}
public void CreateTrainer()
{
NNetwork network = NNetwork.SigmoidNetwork(new int[] { 1, 2, 1 });
NetworkTrainer trainer = new NetworkTrainer(network);
}