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Description
Interface Summary  

DivergenceFunctionContainer<FirstType,SecondType>  Interface for a class that holds a divergence function. 
Class Summary  

ChebyshevDistanceMetric  An implementation of the Chebyshev distance, which is the absolute value of the largest difference between two vectors in a single dimension. 
CosineDistanceMetric  The CosineDistanceMetric class implements a semimetric between
two vectors based on the cosine between the vectors. 
DefaultDivergenceFunctionContainer<FirstType,SecondType>  The DefaultDivergenceFunctionContainer class implements an object
that holds a divergence function. 
DivergencesEvaluator<InputType,ValueType>  Evaluates the divergence (distance) between an input and a list of values, storing the resulting divergence values in a vector. 
DivergencesEvaluator.Learner<DataType,InputType,ValueType>  A learner adapter for the DivergencesEvaluator . 
EuclideanDistanceMetric  The EuclideanDistanceMetric implements a distance metric that
computes the Euclidean distance between two points. 
EuclideanDistanceSquaredMetric  The EuclideanDistanceSquaredMetric implements a distance metric
that computes the squared Euclidean distance between two points. 
IdentityDistanceMetric  A distance metric that is 0 if two objects are equal and 1 if they are not. 
ManhattanDistanceMetric  The ManhattanDistanceMetric class implements a distance metric
between two vectors that is implemented as the sum of the absolute value of
the difference between the elements in the vectors. 
MinkowskiDistanceMetric  An implementation of the Minkowski distance metric. 
WeightedEuclideanDistanceMetric  A distance metric that weights each dimension of a vector differently before computing Euclidean distance. 
Provides distance functions. Distance functions are useful for learning algorithms such as clustering for determining the distance between two objects.


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