Accord.Statistics.Kernels.Sparse.SparseLaplacian.Function C# (CSharp) Méthode

        public override double Function(double[] x, double[] y)
        {
            // Optimization in case x and y are
            // exactly the same object reference.

            if (x == y)
                return 1.0;

#pragma warning disable 0618

            double norm = SparseLinear.SquaredEuclidean(x, y);

            return Math.Exp(-gamma * Math.Sqrt(norm));
        }

        public void FunctionTest()
        {
            Laplacian dense = new Laplacian(3.6);
            SparseLaplacian target = new SparseLaplacian(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.Function(dx, dy);
            actual = target.Function(sx, sy);
            Assert.AreEqual(expected, actual, 1e-10);
            Assert.AreEqual(0.88711080792421337, actual);
            Assert.IsFalse(double.IsNaN(actual));

            expected = dense.Function(dx, dz);
            actual = target.Function(sx, sz);
            Assert.AreEqual(expected, actual, 1e-10);
            Assert.AreEqual(0.87918038010432886, actual, 1e-10);
            Assert.IsFalse(double.IsNaN(actual));

            expected = dense.Function(dy, dz);
            actual = target.Function(sy, sz);
            Assert.AreEqual(expected, actual, 1e-10);
            Assert.AreEqual(0.91349716289496652, actual);
            Assert.IsFalse(double.IsNaN(actual));
        }