SquareMahalanobis Structure 
Namespace: Accord.Math.Distances
[SerializableAttribute] public struct SquareMahalanobis : IMetric<double[]>, IDistance<double[]>, IDistance<double[], double[]>
The SquareMahalanobis type exposes the following members.
Name  Description  

SquareMahalanobis(Double) 
Initializes a new instance of the Mahalanobis class.
 
SquareMahalanobis(CholeskyDecomposition) 
Initializes a new instance of the Mahalanobis class.
 
SquareMahalanobis(SingularValueDecomposition) 
Initializes a new instance of the Mahalanobis class.

Name  Description  

Distance 
Computes the distance d(x,y) between points
x and y.
 
Equals  Indicates whether this instance and a specified object are equal. (Inherited from ValueType.)  
FromCovarianceMatrix 
Creates a new SquareMahalanobis distance from a covariance matrix.
 
FromPrecisionMatrix 
Creates a new SquareMahalanobis distance from a precision matrix.
 
GetHashCode  Returns the hash code for this instance. (Inherited from ValueType.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
ToString  Returns the fully qualified type name of this instance. (Inherited from ValueType.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual 
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.)  
To(Type)  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 
// Let's say we would like to compute the Squared // Mahalanobis distance between the vectors 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 = SquareMahalanobis.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 SquareMahalanobis(new CholeskyDecomposition(covariance)); var fromSVD = new SquareMahalanobis(new SingularValueDecomposition(covariance)); var fromPrecision1 = new SquareMahalanobis(covariance.Inverse()); var fromPrecision2 = SquareMahalanobis.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);