russell_binary_similarity#

skfp.distances.russell_binary_similarity(vec_a: ndarray | csr_array, vec_b: ndarray | csr_array) → float#

Russell similarity for vectors of binary values.

Computes the Russell similarity [1] [2] [3] for binary data between two input arrays or sparse matrices, using the formula:

\[sim(x, y) = \frac{a}{n}\]

where

  • \(a\) - common “on” bits

  • \(n\) - length of passed vectors

The calculated similarity falls within the range \([0, 1]\). Passing all-zero vectors to this function results in a similarity of 0.

Parameters:

  • vec_a ({ndarray, sparse matrix}) – First binary input array or sparse matrix.

  • vec_b ({ndarray, sparse matrix}) – Second binary input array or sparse matrix.

Returns:

similarity – Russell similarity between vec_a and vec_b.

Return type:

float

References

Examples

>>> from skfp.distances import russell_binary_similarity
>>> import numpy as np
>>> vec_a = np.array([1, 1, 1, 1])
>>> vec_b = np.array([1, 1, 0, 0])
>>> sim = russell_binary_similarity(vec_a, vec_b)
>>> sim
0.5

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[1, 1, 1, 1]])
>>> vec_b = csr_array([[1, 1, 0, 0]])
>>> sim = russell_binary_similarity(vec_a, vec_b)
>>> sim
0.5
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