public void FunctionTest()
{
Linear dense = new Linear(1);
SparseLinear target = new SparseLinear(1);
double[] sx = { 1, -0.555556, 2, +0.250000, 3, -0.864407, 4, -0.916667 };
double[] sy = { 1, -0.666667, 2, -0.166667, 3, -0.864407, 4, -0.916667 };
double[] sz = { 1, -0.944444, 3, -0.898305, 4, -0.916667 };
double[] dx = { -0.555556, +0.250000, -0.864407, -0.916667 };
double[] dy = { -0.666667, -0.166667, -0.864407, -0.916667 };
double[] dz = { -0.944444, +0.000000, -0.898305, -0.916667 };
double expected, actual;
expected = dense.Function(dx, dy);
actual = target.Function(sx, sy);
Assert.AreEqual(expected, actual, 1e-10);
expected = dense.Function(dx, dz);
actual = target.Function(sx, sz);
Assert.AreEqual(expected, actual, 1e-10);
expected = dense.Function(dy, dz);
actual = target.Function(sy, sz);
Assert.AreEqual(expected, actual, 1e-10);
}
public void FunctionTest()
{
IKernel linear = new Linear(1);
double[] x = { 1, 1 };
double[] y = { 1, 1 };
double actual = linear.Function(x, y);
double expected = 3;
Assert.AreEqual(expected, actual);
linear = new Linear(11.5);
x = new double[] { 0.2, 5 };
y = new double[] { 3, 0.7 };
actual = linear.Function(x, y);
expected = 15.6;
Assert.AreEqual(expected, actual);
}
/// <summary>
/// Gaussian Kernel function.
/// </summary>
///
/// <param name="x">Vector <c>x</c> in input space.</param>
/// <param name="y">Vector <c>y</c> in input space.</param>
///
/// <returns>Dot product in feature (kernel) space.</returns>
///
public double Function(double[] x, double[] y)
{
return(linear.Function(x, Transform(y)));
}
public void FunctionTest2()
{
double constant = 0.1;
Linear target = new Linear(constant);
double[] x = { 2.0, 3.1, 4.0 };
double[] y = { 2.0, 3.1, 4.0 };
double expected = Matrix.InnerProduct(x, y) + constant;
double actual;
actual = target.Function(x, y);
Assert.AreEqual(expected, actual);
actual = target.Function(x, x);
Assert.AreEqual(expected, actual);
actual = target.Function(y, y);
Assert.AreEqual(expected, actual);
}
