public void Inverse3x3()
{
double[,] matrix = new double[, ]
{
{ 1, 5, 3 },
{ 1, 3, 2 },
{ 2, 4, -6 }
};
double[,] inverted = MatrixMxN.Inverse(matrix);
double[,] expected = new double[, ]
{
{ -1.44444, 2.33333, 0.05556 },
{ 0.55556, -0.66667, 0.05556 },
{ -0.11111, 0.33333, -0.11111 }
};
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
Assert.AreEqual(expected[i, j], Math.Round(inverted[i, j], 5));
}
}
}
public ContinuousDistributionN(double[] mean, double[,] covariance)
{
Dimension = mean.Length;
Mean = mean;
Covariance = covariance;
Inverse = MatrixMxN.Inverse(covariance);
Determinant = MatrixMxN.Determinant(covariance);
}
public static double[,] CovarianceToCorrelation(double[,] covariance)
{
int dimensions = covariance.GetLength(0);
double[,] correlation = new double[dimensions, dimensions];
for (int j = 0; j < dimensions; j++)
{
for (int i = 0; i < dimensions; i++)
{
if (i != j)
{
continue;
}
correlation[i, j] = Math.Sqrt(covariance[i, j]);
}
}
double[,] inverse = MatrixMxN.Inverse(correlation);
return(MatrixMxN.MultiplyMatrix(MatrixMxN.MultiplyMatrix(inverse, covariance), inverse));
}
public void Inverse2x2()
{
double[,] matrix = new double[, ]
{
{ 1, 2 },
{ 3, 4 }
};
double[,] inverted = MatrixMxN.Inverse(matrix);
double[,] expected = new double[, ]
{
{ -2, 1 },
{ 1.5, -0.5 }
};
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 2; j++)
{
Assert.AreEqual(expected[i, j], Math.Round(inverted[i, j], 5));
}
}
}