public void CovarianceTest6()
{
double[][] matrix = new double[, ]
{
{ 4.0, 2.0, 0.60 },
{ 4.2, 2.1, 0.59 },
{ 3.9, 2.0, 0.58 },
{ 4.3, 2.1, 0.62 },
{ 4.1, 2.2, 0.63 }
}.ToArray();
double[,] expected = new double[, ]
{
{ 0.02500, 0.00750, 0.00175 },
{ 0.00750, 0.00700, 0.00135 },
{ 0.00175, 0.00135, 0.00043 },
};
double[] weights = { 1, 1, 1, 1, 1 };
double[,] actual = Tools.WeightedCovariance(matrix, weights, 0);
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.0001));
matrix = matrix.Transpose();
actual = Tools.WeightedCovariance(matrix, weights, 1);
Assert.IsTrue(Matrix.IsEqual(expected, actual, 0.0001));
}
/// <summary>
/// Calculates the scatter matrix of a sample matrix.
/// </summary>
///
/// <remarks>
/// By dividing the Scatter matrix by the sample size, we get the population
/// Covariance matrix. By dividing by the sample size minus one, we get the
/// sample Covariance matrix.
/// </remarks>
///
/// <param name="weights">The number of times each sample should be repeated.</param>
/// <param name="matrix">A number multi-dimensional array containing the matrix values.</param>
/// <param name="dimension">
/// Pass 0 to if mean vector is a row vector, 1 otherwise. Default value is 0.
/// </param>
///
/// <returns>The covariance matrix.</returns>
///
public static double[,] WeightedCovariance(double[][] matrix, int[] weights, int dimension = 0)
{
double[] mean = Tools.WeightedMean(matrix, weights, dimension);
return(Tools.WeightedCovariance(matrix, weights, mean, dimension));
}
/// <summary>
/// Calculates the scatter matrix of a sample matrix.
/// </summary>
///
/// <remarks>
/// By dividing the Scatter matrix by the sample size, we get the population
/// Covariance matrix. By dividing by the sample size minus one, we get the
/// sample Covariance matrix.
/// </remarks>
///
/// <param name="matrix">A number multi-dimensional array containing the matrix values.</param>
/// <param name="weights">An unit vector containing the importance of each sample
/// in <see param="values"/>. The sum of this array elements should add up to 1.</param>
/// <param name="means">The mean value of the given values, if already known.</param>
///
/// <returns>The covariance matrix.</returns>
///
public static double[,] WeightedCovariance(double[][] matrix, double[] weights, double[] means)
{
return(Tools.WeightedCovariance(matrix, weights, means, dimension: 0));
}
