bulk_russell_binary_similarity#

skfp.distances.bulk_russell_binary_similarity(X: ndarray, Y: ndarray | None = None) → ndarray#

Bulk Russell similarity for binary matrices.

Computes the pairwise Russell similarity between binary matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with i-th row and j-th column in output corresponding to i-th row from first array and j-th row from second array.

See also russell_binary_similarity().

Parameters:

  • X (ndarray) – First binary input array, of shape \(m \times m\)

  • Y (ndarray, default=None) – Second binary input array, of shape \(n \times n\). If not passed, similarities are computed between rows of X.

Returns:

similarities – Array with pairwise Russell similarity values. Shape is \(m \times n\) if two arrays are passed, or \(m \times m\) otherwise.

Return type:

ndarray

See also

russell_binary_similarity()

Russell similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_russell_binary_similarity
>>> import numpy as np
>>> X = np.array([[1, 1, 1], [0, 0, 1]])
>>> Y = np.array([[1, 0, 1], [0, 1, 1]])
>>> sim = bulk_russell_binary_similarity(X, Y)
>>> sim
array([[0.66666667, 0.66666667],
       [0.33333333, 0.33333333]])