| Interface | Description |
|---|---|
| Distance<T> |
An interface to calculate a distance measure between two objects.
|
| Metric<T> |
A metric function defines a distance between elements of a set.
|
| Class | Description |
|---|---|
| ChebyshevDistance |
Chebyshev distance (or Tchebychev distance), or L∞ metric
is a metric defined on a vector space where the distance between two vectors
is the greatest of their differences along any coordinate dimension.
|
| CorrelationDistance |
Correlation distance is defined as 1 - correlation coefficient.
|
| DynamicTimeWarping<T> |
Dynamic time warping is an algorithm for measuring similarity between two
sequences which may vary in time or speed.
|
| EditDistance |
The Edit distance between two strings is a metric for measuring the amount
of difference between two sequences.
|
| EuclideanDistance |
Euclidean distance.
|
| HammingDistance<T> |
In information theory, the Hamming distance between two strings of equal
length is the number of positions for which the corresponding symbols are
different.
|
| JaccardDistance<T> |
The Jaccard index, also known as the Jaccard similarity coefficient is a
statistic used for comparing the similarity and diversity of sample sets.
|
| JensenShannonDistance |
The Jensen-Shannon divergence is a popular method of measuring the
similarity between two probability distributions.
|
| LeeDistance |
In coding theory, the Lee distance is a distance between two strings
x1x2...xn and y1y2...yn
of equal length n over the q-ary alphabet {0,1,...,q-1} of size q ≥ 2, defined as
|
| MahalanobisDistance |
In statistics, Mahalanobis distance is based on correlations between
variables by which different patterns can be identified and analyzed.
|
| ManhattanDistance |
Manhattan distance, also known as L1 distance or L1
norm, is the sum of the (absolute) differences of their coordinates.
|
| MinkowskiDistance |
Minkowski distance of order p or Lp-norm, is a generalization of
Euclidean distance that is actually L2-norm.
|
| SparseChebyshevDistance |
Chebyshev distance (or Tchebychev distance), or L∞ metric
is a metric defined on a vector space where the distance between two vectors
is the greatest of their differences along any coordinate dimension.
|
| SparseEuclideanDistance |
Euclidean distance.
|
| SparseManhattanDistance |
Manhattan distance, also known as L1 distance or L1
norm, is the sum of the (absolute) differences of their coordinates.
|
| SparseMinkowskiDistance |
Minkowski distance of order p or Lp-norm, is a generalization of
Euclidean distance that is actually L2-norm.
|
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