private void DualPerceptron(List <Tuple <double[], double> > data)
{
Kernel kernel = new LinearKernel();
foreach (var item in netMLObject.Options)
{
if (item == "linearkernel")
{
kernel = new LinearKernel();
}
else if (item == "gaussiankernel")
{
kernel = new GaussianKernel(1.0);
}
else if (item == "polynomialkernel")
{
kernel = new PolynomialKernel(1);
}
else if (item == "logitkernel")
{
kernel = new LogitKernel();
}
else if (item == "tanhkernel")
{
kernel = new TanhKernel();
}
}
classification = new DualPerceptronClassifier(data, kernel);
}
public void TanhPrimeTest()
{
var a = new Matrix(2, 2);
a.InRandomize();
var b = a.Duplicate();
a = new TanhKernel().Backward(a);
b.InMap((x) => 1 - Math.Pow((Math.Exp(x) - Math.Exp(-x)) / (Math.Exp(x) + Math.Exp(-x)), 2));
Assert.IsTrue(a == b, "Tanh Derivative successful");
}
public void TanhTest()
{
var a = new Matrix(2, 2);
a.InRandomize();
var b = a.Duplicate();
a = new TanhKernel().Forward(a);
b.InMap((x) => (Math.Exp(x) - Math.Exp(-x)) / (Math.Exp(x) + Math.Exp(-x)));
Assert.IsTrue(a == b, "Tanh Activation successful");
}
private void SupportVectorMachine(List <Tuple <double[], double> > data)
{
Kernel kernel = new LinearKernel();
double n = 0.0;
double C = 0.0;
bool nAndCSet = false;
foreach (var item in netMLObject.Options)
{
if (item == "linearkernel")
{
kernel = new LinearKernel();
}
else if (item == "gaussiankernel")
{
kernel = new GaussianKernel(1.0);
}
else if (item == "polynomialkernel")
{
kernel = new PolynomialKernel(1);
}
else if (item == "logitkernel")
{
kernel = new LogitKernel();
}
else if (item == "tanhkernel")
{
kernel = new TanhKernel();
}
}
foreach (var value in netMLObject.DoubleValues)
{
if (value.Key == "n")
{
n = value.Value;
nAndCSet = true;
}
else if (value.Key == "c")
{
C = value.Value;
nAndCSet = true;
}
}
if (nAndCSet)
{
classification = new SVMClassifier(data, kernel, n, C);
}
else
{
classification = new SVMClassifier(data, kernel);
}
}