Numeric Production Mechanism

    ml-distance
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    4.0.0 • Public • Published

    ml-distance

    NPM version build status npm download

    Distance functions to compare vectors.

    Installation

    $ npm i ml-distance

    Methods

    Distances

    • euclidean(p, q)

    Returns the euclidean distance between vectors p and q

    $d(p,q)=\sqrt{\sum\limits_{i=1}^{n}(p_i-q_i)^2}$

    • manhattan(p, q)

    Returns the city block distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}$

    • 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

    $d(p,q)=\max\limits_i(|p_i-q_i|)$

    • sorensen(p, q)

    Returns the Sørensen distance between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{\sum\limits_{i=1}^{n}{p_i+q_i}}$

    • gower(p, q)

    Returns the Gower distance between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{n}$

    • soergel(p, q)

    Returns the Soergel distance between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{max(p_i,q_i)}$

    • kulczynski(p, q)

    Returns the Kulczynski distance between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}}{min(p_i,q_i)}$

    • canberra(p, q)

    Returns the Canberra distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}\frac{\left|{p_i-q_i}\right|}{p_i+q_i}$

    • lorentzian(p, q)

    Returns the Lorentzian distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}\ln(\left|{p_i-q_i}\right|+1)$

    • intersection(p, q)

    Returns the Intersection distance between vectors p and q

    $d(p,q)=1-\sum\limits_{i=1}^{n}min(p_i,q_i)$

    • waveHedges(p, q)

    Returns the Wave Hedges distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}\left(1-\frac{min(p_i,q_i)}{max(p_i,q_i)}\right)$

    • czekanowski(p, q)

    Returns the Czekanowski distance between vectors p and q

    $d(p,q)=1-\frac{2\sum\limits_{i=1}^{n}{min(p_i,q_i)}}{\sum\limits_{i=1}^{n}{p_i+q_i}}$

    • motyka(p, q)

    Returns the Motyka distance between vectors p and q

    $d(p,q)=1-\frac{\sum\limits_{i=1}^{n}{min(p_i,q_i)}}{\sum\limits_{i=1}^{n}{p_i+q_i}}$

    Note: distance between 2 identical vectors is 0.5 !

    • ruzicka(p, q)

    Returns the Ruzicka similarity between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{max(p_i,q_i)}}{\sum\limits_{i=1}^{n}{min(p_i,q_i)}}$

    • 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

    $s(p,q)=\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}$

    • harmonicMean(p, q)

    Returns the Harmonic mean similarity between vectors p and q

    $d(p,q)=2\sum\limits_{i=1}^{n}\frac{p_i\cdot{q_i}}{p_i+q_i}$

    • cosine(p, q)

    Returns the Cosine similarity between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^2}\sum\limits_{i=1}^{n}{q_i^2}}$

    • kumarHassebrook(p, q)

    Returns the Kumar-Hassebrook similarity between vectors p and q

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}-\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}$

    • jaccard(p, q)

    Returns the Jaccard distance between vectors p and q

    $d(p,q)=1-\frac{\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}-\sum\limits_{i=1}^{n}{p_i\cdot{q_i}}}$

    • dice(p,q)

    Returns the Dice distance between vectors p and q

    $d(p,q)=1-\frac{\sum\limits_{i=1}^{n}{(p_i-q_i)^2}}{\sum\limits_{i=1}^{n}{p_i^2}+\sum\limits_{i=1}^{n}{q_i^2}}$

    • fidelity(p, q)

    Returns the Fidelity similarity between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\sqrt{p_i\cdot{q_i}}}$

    • bhattacharyya(p, q)

    Returns the Bhattacharyya distance between vectors p and q

    $d(p,q)=-\ln\left(\sum\limits_{i=1}^{n}{\sqrt{p_i\cdot{q_i}}}\right)$

    • hellinger(p, q)

    Returns the Hellinger distance between vectors p and q

    $d(p,q)=2\cdot\sqrt{1-\sum\limits_{i=1}^{n}{\sqrt{p_i\cdot{q_i}}}}$

    • matusita(p, q)

    Returns the Matusita distance between vectors p and q

    $d(p,q)=\sqrt{2-2\cdot\sum\limits_{i=1}^{n}{\sqrt{p_i\cdot{q_i}}}}$

    • squaredChord(p, q)

    Returns the Squared-chord distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{(\sqrt{p_i}-\sqrt{q_i})^2}$

    • squaredEuclidean(p, q)

    Returns the squared euclidean distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{(p_i-q_i)^2}$

    • pearson(p, q)

    Returns the Pearson distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2}{q_i}}$

    • neyman(p, q)

    Returns the Neyman distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2}{p_i}}$

    • squared(p, q)

    Returns the Squared distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2}{p_i+q_i}}$

    • probabilisticSymmetric(p, q)

    Returns the Probabilistic Symmetric distance between vectors p and q

    $d(p,q)=2\cdot\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2}{p_i+q_i}}$

    • divergence(p, q)

    Returns the Divergence distance between vectors p and q

    $d(p,q)=2\cdot\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2}{(p_i+q_i)^2}}$

    • clark(p, q)

    Returns the Clark distance between vectors p and q

    $d(p,q)=\sqrt{\sum\limits_{i=1}^{n}{\left(\frac{\left|p_i-q_i\right|}{(p_i+q_i)}\right)^2}}$

    • additiveSymmetric(p, q)

    Returns the Additive Symmetric distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\frac{(p_i-q_i)^2\cdot(p_i+q_i)}{p_i\cdot{q_i}}}$

    • kullbackLeibler(p, q)

    Returns the Kullback-Leibler distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{p_i\cdot\ln\frac{p_i}{q_i}}$

    • jeffreys(p, q)

    Returns the Jeffreys distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left((p_i-q_i)\ln\frac{p_i}{q_i}\right)}$

    • kdivergence(p, q)

    Returns the K divergence distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left(p_i\cdot\ln\frac{2p_i}{p_i+q_i}\right)}$

    • topsoe(p, q)

    Returns the Topsøe distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left(p_i\cdot\ln\frac{2p_i}{p_i+q_i}+q_i\cdot\ln\frac{2q_i}{p_i+q_i}\right)}$

    • jensenShannon(p, q)

    Returns the Jensen-Shannon distance between vectors p and q

    $d(p,q)=\frac{1}{2}\left[\sum\limits_{i=1}^{n}{p_i\cdot\ln\frac{2p_i}{p_i+q_i}}+\sum\limits_{i=1}^{n}{q_i\cdot\ln\frac{2q_i}{p_i+q_i}}\right]$

    • jensenDifference(p, q)

    Returns the Jensen difference distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left[\frac{p_i\ln{p_i}+q_i\ln{q_i}}{2}-\left(\frac{p_i+q_i}{2}\right)\ln\left(\frac{p_i+q_i}{2}\right)\right]}$

    • taneja(p, q)

    Returns the Taneja distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\left[\frac{p_i+q_i}{2}\ln\left(\frac{p_i+q_i}{2\sqrt{p_i\cdot{q_i}}}\right)\right]}$

    • kumarJohnson(p, q)

    Returns the Kumar-Johnson distance between vectors p and q

    $d(p,q)=\sum\limits_{i=1}^{n}{\frac{\left(p_i^2-q_i^2\right)^2}{2(p_i\cdot{q_i})^{3/2}}}$

    • avg(p, q)

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

    $d(p,q)=\frac{\sum\limits_{i=1}^{n}{\left|p_i-q_i\right|}+\max\limits_i(|p_i-q_i|)}{2}$

    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.

    Authors

    License

    MIT

    Install

    npm i ml-distance

    DownloadsWeekly Downloads

    767

    Version

    4.0.0

    License

    MIT

    Unpacked Size

    246 kB

    Total Files

    480

    Last publish

    Collaborators

    • stropitek
    • targos
    • lpatiny
    • mljs-bot
    • cheminfo-bot
    • maasencioh
    • jeffersonh44
    • andcastillo