/// <summary>
/// Projects an input point into feature space.
/// </summary>
///
/// <param name="input">The input point to be projected into feature space.</param>
///
/// <returns>
/// The feature space representation of the given <paramref name="input"/> point.
/// </returns>
///
public double[] Transform(double[] input)
{
switch (degree)
{
case 1:
return(Linear.Transform(input, constant));
case 2:
return(Quadratic.Transform(input, constant));
default:
return(Transform(input, degree, constant));
}
}
public void ExpandDistanceTest()
{
Linear kernel = new Linear(42);
var x = new double[] { 0.5, 2.0 };
var y = new double[] { 1.3, -0.2 };
var phi_x = kernel.Transform(x);
var phi_y = kernel.Transform(y);
double phi_d = Distance.SquareEuclidean(phi_x, phi_y);
double d = kernel.Distance(x, y);
Assert.AreEqual(phi_d, d);
}
public void ExpandReverseDistanceTest()
{
Linear kernel = new Linear(42);
var x = new double[] { 0.5, 2.0 };
var y = new double[] { 1.3, -0.2 };
var phi_x = kernel.Transform(x);
var phi_y = kernel.Transform(y);
double d = Distance.SquareEuclidean(x, y);
double phi_d = kernel.ReverseDistance(phi_x, phi_y);
Assert.AreEqual(phi_d, d, 1e-10);
Assert.IsFalse(double.IsNaN(phi_d));
Assert.IsFalse(double.IsNaN(d));
}
public void TransformTest_Linear()
{
double[][] data =
{
new double[] { 5.1, 3.5, 1.4, 0.2 },
new double[] { 5.0, 3.6, 1.4, 0.2 },
new double[] { 4.9, 3.0, 1.4, 0.2 },
new double[] { 5.8, 4.0, 1.2, 0.2 },
new double[] { 4.7, 3.2, 1.3, 0.2 },
};
var target = new Polynomial(1);
var linear = new Linear(constant: 1);
Assert.AreEqual(target.Constant, linear.Constant);
double[][] expected = data.Apply(x => linear.Transform(x));
double[][] actual = data.Apply(target.Transform);
Assert.IsTrue(expected.IsEqual(actual, 1e-10));
}
