public void ConstructorTest1()
{
double[,] value =
{
{ 4, -2 },
{ 3, 1 },
};
var svd = new SingularValueDecomposition(value);
Assert.AreEqual(2, svd.Rank);
var target = new GramSchmidtOrthogonalization(value);
var Q = target.OrthogonalFactor;
var R = target.UpperTriangularFactor;
double[,] inv = Q.Inverse();
double[,] transpose = Q.Transpose();
double[,] m = Matrix.Multiply(Q, transpose);
Assert.IsTrue(Matrix.IsEqual(m, Matrix.Identity(2), 1e-10));
Assert.IsTrue(Matrix.IsEqual(inv, transpose, 1e-10));
}
/// <summary>
/// Generates a random <see cref="Measures.Covariance(double[], double[], bool)"/> matrix.
/// </summary>
///
/// <param name="size">The size of the square matrix.</param>
/// <param name="minValue">The minimum value for a diagonal element.</param>
/// <param name="maxValue">The maximum size for a diagonal element.</param>
///
/// <returns>A square, positive-definite matrix which
/// can be interpreted as a covariance matrix.</returns>
///
public static double[,] RandomCovariance(int size, double minValue, double maxValue)
{
double[,] A = Accord.Math.Matrix.Random(size, minValue, maxValue, symmetric: true);
var gso = new GramSchmidtOrthogonalization(A);
double[,] Q = gso.OrthogonalFactor;
double[] diagonal = Vector.Random(size, minValue, maxValue).Abs();
double[,] psd = Matrix.Dot(Q.TransposeAndDotWithDiagonal(diagonal), Q);
Accord.Diagnostics.Debug.Assert(psd.IsPositiveDefinite());
return psd;
}
