public void ComputeTest3()
{
// Schölkopf KPCA toy example
double[][] inputs = scholkopf().ToJagged();
int[] output = Matrix.Expand(new int[,] { { 1 }, { 2 }, { 3 } }, new int[] { 30, 30, 30 }).GetColumn(0);
IKernel kernel = new Gaussian(0.2);
var target = new KernelDiscriminantAnalysis(inputs, output, kernel);
target.Compute();
double[][] actual = target.Transform(inputs, 2);
double[][] expected1 =
{
new double[] { 1.2785801485080475, 0.20539157505913622},
new double[] { 1.2906613255489541, 0.20704272225753775},
new double[] { 1.2978134597266808, 0.20802649628632208},
};
double[][] actual1 = actual.Submatrix(0, 2, 0, 1);
Assert.IsTrue(Matrix.IsEqual(actual1, expected1, 0.0000001));
// Assert the result equals the transformation of the input
double[][] result = target.Result.ToJagged();
double[][] projection = target.Transform(inputs);
Assert.IsTrue(Matrix.IsEqual(result, projection));
int[] actual2 = target.Classify(inputs);
Assert.IsTrue(Matrix.IsEqual(actual2, output));
int[] actual3 = new int[inputs.Length];
double[][] scores = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
actual3[i] = target.Classify(inputs[i], out scores[i]);
Assert.IsTrue(Matrix.IsEqual(actual3, output));
scores = scores.Get(0, 5, null);
double[][] expected = new double[][] {
new double[] { -6.23928931356786E-06, -5.86731829543872, -4.76988430445096 },
new double[] { -9.44593697210785E-05, -5.92312597750504, -4.82189359956088 },
new double[] { -0.000286839977573986, -5.95629842504978, -4.85283341267476 },
new double[] { -4.38986003009456E-05, -5.84990179343448, -4.75189423787298 },
new double[] { -0.000523817959022851, -5.77534144986199, -4.683120454667 }
};
//Assert.IsTrue(Matrix.IsEqual(scores, expected, 1e-6));
}
