public void DistanceTest()
{
Gaussian dense = new Gaussian(3.6);
SparseGaussian target = new SparseGaussian(3.6);
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.Distance(dx, dy);
actual = target.Distance(sx, sy);
Assert.AreEqual(expected, actual);
expected = dense.Distance(dx, dz);
actual = target.Distance(sx, sz);
Assert.AreEqual(expected, actual);
expected = dense.Distance(dy, dz);
actual = target.Distance(sy, sz);
Assert.AreEqual(expected, actual);
}
public void GaussianReverseDistanceTest()
{
var gaussian = new Gaussian(4.2);
var x = new double[] { 0.2, 0.5 };
var y = new double[] { 0.3, -0.7 };
double expected = Distance.SquareEuclidean(x, y);
double df = gaussian.Distance(x, y);
double actual = gaussian.ReverseDistance(df);
Assert.AreEqual(expected, actual, 1e-10);
}
public void doc_test_generic()
{
#region doc_distance_generic
// Let's say we would like to create a composite Gaussian
// kernel based on a Hamming distance defined for strings:
var gaussian = new Gaussian <Hamming, string>(new Hamming(), sigma: 4.2);
string x = "abba";
string y = "aaab";
// Now, we can compute the kernel function as:
double k = gaussian.Function(x, y); // 0.8436074263840595
// We can also obtain the distance in kernel space as:
double d = gaussian.Distance(x, y); // 0.312785147231881
#endregion
Assert.AreEqual(0.8436074263840595, k, 1e-10);
Assert.AreEqual(0.312785147231881, d, 1e-10);
}
public void doc_test_generic()
{
#region doc_kernel_generic
// Let's say we would like to create a composite Gaussian
// kernel based on the String Subsequence Kernel (SSK):
var gaussian = new Gaussian <StringSubsequence, string>(
new StringSubsequence(k: 2), sigma: 4.2);
string x = "abba";
string y = "aaab";
// Now, we can compute the kernel function as:
double k = gaussian.Function(x, y); // 0.97588765410825273
// We can also obtain the distance in kernel space as:
double d = gaussian.Distance(x, y); // 0.048224691783494533
#endregion
Assert.AreEqual(0.97588765410825273, k, 1e-10);
Assert.AreEqual(0.048224691783494533, d, 1e-10);
}
public void GaussianDistanceTest()
{
var gaussian = new Gaussian(1);
double[] x = { 1, 1 };
double[] y = { 1, 1 };
double actual = gaussian.Distance(x, y);
double expected = 0;
Assert.AreEqual(expected, actual);
gaussian = new Gaussian(11.5);
x = new double[] { 0.2, 0.5 };
y = new double[] { 0.3, -0.7 };
actual = gaussian.Distance(x, y);
expected = Accord.Statistics.Tools.Distance(gaussian, x, y);
Assert.AreEqual(expected, actual, 1e-10);
}
public void GaussianDistanceTest()
{
IDistance gaussian = new Gaussian(1);
double[] x = { 1, 1 };
double[] y = { 1, 1 };
double actual = gaussian.Distance(x, y);
double expected = 0;
Assert.AreEqual(expected, actual);
gaussian = new Gaussian(11.5);
x = new double[] { 0.2, 0.5 };
y = new double[] { 0.3, -0.7 };
actual = gaussian.Distance(x, y);
expected = 341.46531595796711;
Assert.AreEqual(expected, actual, 1e-10);
}
public void doc_test()
{
#region doc_kernel
// Let's say we would like to create a composite Gaussian kernel
// based on an underlying Polynomial kernel of the second degree
var gaussian = new Gaussian <Polynomial>(new Polynomial(degree: 2), sigma: 4.2);
double[] x = { 2, 4 };
double[] y = { 1, 3 };
// Now, we can compute the kernel function as:
double k = gaussian.Function(x, y); // 0.041810692337960746
// We can also obtain the distance in kernel space as:
double d = gaussian.Distance(x, y); // 1.9163786153240785
// Whereas the Euclidean distance in input space would be:
double e = Distance.Euclidean(x, y); // 1.4142135623730952
#endregion
Assert.AreEqual(0.041810692337960746, k, 1e-10);
Assert.AreEqual(1.9163786153240785, d, 1e-10);
Assert.AreEqual(1.4142135623730952, e, 1e-10);
}
public void doc_test()
{
#region doc_distance
// Let's say we would like to create a composite
// Gaussian kernel based on an Euclidean distance:
var gaussian = new Gaussian <Euclidean>(new Euclidean(), sigma: 4.2);
double[] x = { 2, 4 };
double[] y = { 1, 3 };
// Now, we can compute the kernel function as:
double k = gaussian.Function(x, y); // 0.96070737352744806
// We can also obtain the distance in kernel space as:
double d = gaussian.Distance(x, y); // 0.078585252945103878
// Whereas the Euclidean distance in input space would be:
double e = Distance.Euclidean(x, y); // 1.4142135623730952
#endregion
Assert.AreEqual(0.96070737352744806, k, 1e-10);
Assert.AreEqual(0.078585252945103878, d, 1e-10);
Assert.AreEqual(1.4142135623730952, e, 1e-10);
}
