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Distances and divergences between distributions implemented in python.

Project description

Travis CI build SonarCloud Quality SonarCloud Maintainability Codacy Maintainability Maintainability Pypi project Pypi total project downloads

Distances and divergences between distributions implemented in python.

How do I install this package?

As usual, just download it using pip:

pip install dictances

Tests Coverage

Since some software handling coverages sometime get slightly different results, here’s three of them:

Coveralls Coverage SonarCloud Coverage Code Climate Coverate

Available metrics

A number of distances and divergences are available:

Distances

Methods

Bhattacharyya distance

bhattacharyya

Bhattacharyya coefficient

bhattacharyya_coefficient

Canberra distance

canberra

Chebyshev distance

chebyshev

Chi Square distance

chi_square

Cosine Distance

cosine

Euclidean distance

euclidean

Hamming distance

hamming

Jensen-Shannon divergence

jensen_shannon

Kullback-Leibler divergence

kullback_leibler

Mean absolute error

mae

Taxicab geometry

manhattan, cityblock, total_variation

Minkowski distance

minkowsky

Mean squared error

mse

Pearson’s distance

pearson

Squared deviations from the mean

squared_variation

Usage example

from dictances import cosine

cosine(my_first_dictionary, my_second_dictionary)

Handling nested dictionaries

If you need to compute the distance between two nested dictionaries you can use deflate_dict as follows:

from dictances import cosine
from deflate_dict import deflate

my_first_dictionary = {
    "a": 8,
    "b": {
        "c": 3,
        "d": 6
    }
}

my_second_dictionary = {
    "b": {
        "c": 8,
        "d": 1
    },
    "y": 3,

}

cosine(deflate(my_first_dictionary), deflate(my_second_dictionary))

Project details


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Source Distribution

dictances-1.5.2.tar.gz (8.6 kB view hashes)

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