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Interface Summary  

Distance<T>  A measure for computing how far away two objects are from each other. The Proximity of a distance measure is most likely the negative
distance. 
Proximity<T>  A measure for computing the proximity of two objects of same class. 
Similarity<T>  A measure for computing how similar two objects are. 
Class Summary  

CosineSimilarity  A Similarity measure for Vector s.The Cosine Similarity of two vectors is defined as the cosine of the angle between them. It is calculated as the dot product of the both vectors divided by the multiplied euclidean norms of both. The resulting similarity of a vector a and a vector b is between 1 (if a equals b * k) and 1 (if a equals b * k), with k being a positive rational number (and thus not zero). 
DotProduct  The Dot Product of two vectors is defined as the sum of the
pairwise products of the coordinate values of the vectors. This is not a real proximity measure. 
EuclideanDistance  A Distance measure for Vector s.The Euclidean Distance of two points is defined as the length of the segment between them. 
ManhattanDistance  A Distance measure for Vector s.The Manhattan Distance of two points is defined as the sum of the lengths of the projections of the segment between them onto the coordinate system axis. 
TestCosineSimilarity  
TestEuclideanDistance  
TestManhattanDistance  
TestVectorProximityCommon 


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