Distances and divergences between distributions implemented in python.
Project description
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:
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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