public void TestSIN()
{
var activation = new ActivationSIN();
Assert.IsTrue(activation.HasDerivative);
var clone = activation.Clone();
Assert.IsInstanceOfType(clone, typeof(ActivationSIN));
double[] input = { 0.0, Math.PI / 4, Math.PI / 2 };
activation.ActivationFunction(input, 0, 3); //it's actually Sin(2x)
Assert.AreEqual(0.0, input[0], 0.01);
Assert.AreEqual(1.0, input[1], 0.01);
Assert.AreEqual(0.0, input[2], 0.01);
// test derivative
input[0] = activation.DerivativeFunction(0, input[0]);
input[1] = activation.DerivativeFunction(Math.PI / 4, input[1]);
input[2] = activation.DerivativeFunction(Math.PI / 2, input[2]);
Assert.AreEqual(1.0, input[0], 0.01);
Assert.AreEqual(0.0, input[1], 0.01);
Assert.AreEqual(-1.0, input[2], 0.01);
}
public void TestSIN()
{
var activation = new ActivationSIN();
Assert.IsTrue(activation.HasDerivative);
var clone = (ActivationSIN) activation.Clone();
Assert.IsNotNull(clone);
double[] input = {0.0};
activation.ActivationFunction(input, 0, 1);
Assert.AreEqual(0.0, input[0], 0.1);
// test derivative, should throw an error
input[0] = activation.DerivativeFunction(input[0],input[0]);
Assert.AreEqual(1.0, input[0], 0.1);
}
public void TestSIN()
{
var activation = new ActivationSIN();
Assert.IsTrue(activation.HasDerivative());
var clone = (ActivationSIN)activation.Clone();
Assert.IsNotNull(clone);
double[] input = { 0.0 };
activation.ActivationFunction(input, 0, 1);
Assert.AreEqual(0.0, input[0], 0.1);
// test derivative, should throw an error
input[0] = activation.DerivativeFunction(input[0], input[0]);
Assert.AreEqual(1.0, input[0], 0.1);
}
void AddLayers(List<LayerConfig> gen)
{
foreach (var g in gen)
{
IActivationFunction act;
if (g.ActivationType == 0)
{
act = new ActivationBiPolar();
}
switch (g.ActivationType )
{
case 0:
act = new ActivationBiPolar();
break;
case 1:
act = new ActivationBipolarSteepenedSigmoid ();
break;
case 2:
act = new ActivationClippedLinear();
break;
case 3:
act = new ActivationCompetitive();
break;
case 4:
act = new ActivationElliott();
break;
case 5:
act = new ActivationElliottSymmetric();
break;
case 6:
act = new ActivationGaussian();
break;
case 7:
act = new ActivationLinear();
break;
case 8:
act = new ActivationLOG();
break;
case 9:
act = new ActivationRamp();
break;
case 10:
act = new ActivationRamp();
break;
case 11:
act = new ActivationSigmoid();
break;
case 12:
act = new ActivationSIN();
break;
case 13:
act = new ActivationSoftMax();
break;
case 14:
act = new ActivationSteepenedSigmoid();
break;
case 15:
act = new ActivationStep();
break;
case 16:
act = new ActivationTANH();
break;
default:
act = new ActivationSoftMax();
break;
}
network.AddLayer(new BasicLayer(act, g.hasBias, g.neurons));
}
}