Package smile.math.distance

Distance and metric measures.

See: Description

Interface Summary

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 Summary

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.

Package smile.math.distance Description

Distance and metric measures. A distance function maps pairs of points into the nonnegative reals and has to satisfy

• non-negativity: d(x, y) ≥ 0
• isolation: d(x, y) = 0 if and only if x = y
• symmetry: d(x, y) = d(x, y)

. Note that a distance function is not required to satisfy triangular inequality |x - y| + |y - z| ≥ |x - z|, which is necessary for a metric.