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## ml-distance

3.0.0 • Public • Published

# ml-distance

Distance functions to compare vectors.

## Installation

`\$ npm i ml-distance`

## Methods

### Distances

• `euclidean(p, q)`

Returns the euclidean distance between vectors p and q

• `manhattan(p, q)`

Returns the city block distance between vectors p and q

• `minkowski(p, q, d)`

Returns the Minkowski distance between vectors p and q for order d

• `chebyshev(p, q)`

Returns the Chebyshev distance between vectors p and q

• `sorensen(p, q)`

Returns the Sørensen distance between vectors p and q

• `gower(p, q)`

Returns the Gower distance between vectors p and q

• `soergel(p, q)`

Returns the Soergel distance between vectors p and q

• `kulczynski(p, q)`

Returns the Kulczynski distance between vectors p and q

• `canberra(p, q)`

Returns the Canberra distance between vectors p and q

• `lorentzian(p, q)`

Returns the Lorentzian distance between vectors p and q

• `intersection(p, q)`

Returns the Intersection distance between vectors p and q

• `waveHedges(p, q)`

Returns the Wave Hedges distance between vectors p and q

• `czekanowski(p, q)`

Returns the Czekanowski distance between vectors p and q

• `motyka(p, q)`

Returns the Motyka distance between vectors p and q

• `ruzicka(p, q)`

Returns the Ruzicka similarity between vectors p and q

• `tanimoto(p, q, [bitVector])`

Returns the Tanimoto distance between vectors p and q, and accepts the bitVector use, see the test case for an example

• `innerProduct(p, q)`

Returns the Inner Product similarity between vectors p and q

• `harmonicMean(p, q)`

Returns the Harmonic mean similarity between vectors p and q

• `cosine(p, q)`

Returns the Cosine similarity between vectors p and q

• `kumarHassebrook(p, q)`

Returns the Kumar-Hassebrook similarity between vectors p and q

• `jaccard(p, q)`

Returns the Jaccard distance between vectors p and q

• `dice(p, q)`

Returns the Dice distance between vectors p and q

• `fidelity(p, q)`

Returns the Fidelity similarity between vectors p and q

• `bhattacharyya(p, q)`

Returns the Bhattacharyya distance between vectors p and q

• `hellinger(p, q)`

Returns the Hellinger distance between vectors p and q

• `matusita(p, q)`

Returns the Matusita distance between vectors p and q

• `squaredChord(p, q)`

Returns the Squared-chord distance between vectors p and q

• `squaredEuclidean(p, q)`

Returns the squared euclidean distance between vectors p and q

• `pearson(p, q)`

Returns the Pearson distance between vectors p and q

• `neyman(p, q)`

Returns the Neyman distance between vectors p and q

• `squared(p, q)`

Returns the Squared distance between vectors p and q

• `probabilisticSymmetric(p, q)`

Returns the Probabilistic Symmetric distance between vectors p and q

• `divergence(p, q)`

Returns the Divergence distance between vectors p and q

• `clark(p, q)`

Returns the Clark distance between vectors p and q

• `additiveSymmetric(p, q)`

Returns the Additive Symmetric distance between vectors p and q

• `kullbackLeibler(p, q)`

Returns the Kullback-Leibler distance between vectors p and q

• `jeffreys(p, q)`

Returns the Jeffreys distance between vectors p and q

• `kdivergence(p, q)`

Returns the K divergence distance between vectors p and q

• `topsoe(p, q)`

Returns the Topsøe distance between vectors p and q

• `jensenShannon(p, q)`

Returns the Jensen-Shannon distance between vectors p and q

• `jensenDifference(p, q)`

Returns the Jensen difference distance between vectors p and q

• `taneja(p, q)`

Returns the Taneja distance between vectors p and q

• `kumarJohnson(p, q)`

Returns the Kumar-Johnson distance between vectors p and q

• `avg(p, q)`

Returns the average of city block and Chebyshev distances between vectors p and q

### Similarities

• `intersection(p, q)`

Returns the Intersection similarity between vectors p and q

• `czekanowski(p, q)`

Returns the Czekanowski similarity between vectors p and q

• `motyka(p, q)`

Returns the Motyka similarity between vectors p and q

• `kulczynski(p, q)`

Returns the Kulczynski similarity between vectors p and q

• `squaredChord(p, q)`

Returns the Squared-chord similarity between vectors p and q

• `jaccard(p, q)`

Returns the Jaccard similarity between vectors p and q

• `dice(p, q)`

Returns the Dice similarity between vectors p and q

• `tanimoto(p, q, [bitVector])`

Returns the Tanimoto similarity between vectors p and q, and accepts the bitVector use, see the test case for an example

• `tree(a,b, from, to, [options])`

Refer to ml-tree-similarity

## Contributing

A new metric should normally be in its own file in the src/dist directory. There should be a corresponding test file in test/dist.
The metric should be then added in the exports of src/index.js with a relatively small but understandable name (use camelCase).
It should also be added to this README with either a link to the formula or an inline description.

MIT

## Keywords

### Install

`npm i ml-distance`

178

3.0.0

MIT

37.3 kB

59

### Homepage

github.com/mljs/distance

### Repository

github.com/mljs/distance