## Package de.aitools.aq.algebra.vector.functions

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