The Mahalanobis distance, which is essentially a stretched Euclidean distance. More...
Public Member Functions  
MahalanobisDistance ()  
Initialize the Mahalanobis distance with the empty matrix as covariance. More...  
MahalanobisDistance (const size_t dimensionality)  
Initialize the Mahalanobis distance with the identity matrix of the given dimensionality. More...  
MahalanobisDistance (arma::mat covariance)  
Initialize the Mahalanobis distance with the given covariance matrix. More...  
const arma::mat &  Covariance () const 
Access the covariance matrix. More...  
arma::mat &  Covariance () 
Modify the covariance matrix. More...  
template < typename VecTypeA , typename VecTypeB >  
double  Evaluate (const VecTypeA &a, const VecTypeB &b) 
Evaluate the distance between the two given points using this Mahalanobis distance. More...  
template < typename Archive >  
void  serialize (Archive &ar, const unsigned int version) 
Serialize the Mahalanobis distance. More...  
The Mahalanobis distance, which is essentially a stretched Euclidean distance.
Given a square covariance matrix of size x , where is the dimensionality of the points it will be evaluating, and given two vectors and also of dimensionality ,
where Q is the covariance matrix.
Because each evaluation multiplies (x_1  x_2) by the covariance matrix, it is typically much quicker to use an LMetric and simply stretch the actual dataset itself before performing any evaluations. However, this class is provided for convenience.
If you wish to use the KNN class or other treebased algorithms with this distance, it is recommended to instead stretch the dataset first, by decomposing Q = L^T L (perhaps via a Cholesky decomposition), and then multiply the data by L. If you still wish to use the KNN class with a custom distance anyway, you will need to use a different tree type than the default KDTree, which only works with the LMetric class.
Similar to the LMetric class, this offers a template parameter TakeRoot which, when set to false, will instead evaluate the distance
which is faster to evaluate.
TakeRoot  If true, takes the root of the output. It is slightly faster to leave this at the default of false, but this means the metric may not satisfy the triangle inequality and may not be usable for methods that expect a true metric. 
Definition at line 60 of file mahalanobis_distance.hpp.

inline 
Initialize the Mahalanobis distance with the empty matrix as covariance.
Don't call Evaluate() until you set the covariance with Covariance()!
Definition at line 67 of file mahalanobis_distance.hpp.

inline 
Initialize the Mahalanobis distance with the identity matrix of the given dimensionality.
dimensionality  Dimesnsionality of the covariance matrix. 
Definition at line 75 of file mahalanobis_distance.hpp.

inline 
Initialize the Mahalanobis distance with the given covariance matrix.
The given covariance matrix will be copied (this is not optimal).
covariance  The covariance matrix to use for this distance. 
Definition at line 84 of file mahalanobis_distance.hpp.
References MahalanobisDistance< TakeRoot >::Evaluate().

inline 
Access the covariance matrix.
Definition at line 104 of file mahalanobis_distance.hpp.

inline 
Modify the covariance matrix.
Definition at line 111 of file mahalanobis_distance.hpp.
References MahalanobisDistance< TakeRoot >::serialize().
double Evaluate  (  const VecTypeA &  a, 
const VecTypeB &  b  
) 
Evaluate the distance between the two given points using this Mahalanobis distance.
If the covariance matrix has not been set (i.e. if you used the empty constructor and did not later modify the covariance matrix), calling this method will probably result in a crash.
a  First vector. 
b  Second vector. 
Referenced by MahalanobisDistance< TakeRoot >::MahalanobisDistance().
void serialize  (  Archive &  ar, 
const unsigned int  version  
) 
Serialize the Mahalanobis distance.
Referenced by MahalanobisDistance< TakeRoot >::Covariance().