private double mahalanobisMethod(List <double> midValues, List <double> elementValues)
{
//var mainWindow = (MainWindow)Application.Current.MainWindow;
//var format = new NumberFormatInfo();
//format.NegativeSign = "-";
//int i = 0;
//double[,] table = new double[mainWindow.gridData.Rows.Count, mainWindow.gridData.Columns.Count - 1];
//foreach (DataRow row in mainWindow.gridData.Rows)
//{
// for (int j = 0; j < mainWindow.gridData.Columns.Count - 1; j++)
// {
// var value = mainWindow.gridData.Rows[i][j].ToString();
// if (value.StartsWith(",") || value.StartsWith("."))
// {
// value = "0" + value;
// }
// table[i, j] = Convert.ToDouble(value, format);
// }
// i++;
//}
//double[] d = new double[mainWindow.gridData.Columns.Count - 1];
//double[,] diff = new double[1, mainWindow.gridData.Columns.Count - 1];
//double[,] covarMatrix = table.Covariance();
//double[,] inverseCovarMatrix = null;
//try
//{
// inverseCovarMatrix = covarMatrix.Inverse();
//}
//catch (Exception exp)
//{
// inverseCovarMatrix = covarMatrix.PseudoInverse();
//}
//var mahalanobis = Mahalanobis.FromPrecisionMatrix(inverseCovarMatrix);
double result = mahalanobis.Distance(midValues.ToArray(), elementValues.ToArray());
if (Double.IsNaN(result))
{
return(0);
}
else
{
return(result);
}
}
public void doc_mahalanobis()
{
#region doc_mahalanobis_3
// Let's say we would like to compute the Mahalanobis
// distance between the two vectors x and y below:
double[] x = { 2, 5, 1 };
double[] y = { 4, 2, 2 };
// Using the covariance
double[,] covariance =
{
{ 4, 3, 0 },
{ 3, 5, 2 },
{ 0, 2, 6 }
};
// There are multiple ways to create a Mahalanobis
// distance. The easiest method by far is by using:
var mahalanobis = Mahalanobis.FromCovarianceMatrix(covariance);
// Then, you can compute the distance using:
double distance = mahalanobis.Distance(x, y);
// However, if you need more control over how the covariance matrix
// should be inverted, or if you have the precision matrix instead of
// the covariance, you can use any of the alternative methods:
var fromCholesky = new Mahalanobis(new CholeskyDecomposition(covariance));
var fromSVD = new Mahalanobis(new SingularValueDecomposition(covariance));
var fromPrecision1 = new Mahalanobis(covariance.Inverse());
var fromPrecision2 = Mahalanobis.FromPrecisionMatrix(covariance.Inverse());
// They all should produce equivalent results:
double a = fromCholesky.Distance(x, y);
double b = fromSVD.Distance(x, y);
double c = fromPrecision1.Distance(x, y);
double d = fromPrecision2.Distance(x, y);
#endregion
double expected = Distance.Mahalanobis(x, y, new CholeskyDecomposition(covariance));
Assert.AreEqual(3.5185224171518357, expected, 1e-10);
Assert.AreEqual(expected, distance, 1e-10);
Assert.AreEqual(distance, a, 1e-10);
Assert.AreEqual(distance, b, 1e-10);
Assert.AreEqual(distance, c, 1e-10);
Assert.AreEqual(distance, d, 1e-10);
}