bulk_simpson_binary_similarity#

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

Bulk Simpson similarity for binary matrices.

Computes the pairwise Simpson [1] (also known as asymmetric similarity [2] [3] or overlap coefficient [4]) 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 simpson_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 Simpson similarity values. Shape is \(m \times n\) if two arrays are passed, or \(m \times m\) otherwise.

Return type:

ndarray

References

See also

simpson_binary_similarity()

Simpson similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_simpson_binary_similarity
>>> import numpy as np
>>> X = np.array([[1, 0, 1], [0, 0, 1]])
>>> Y = np.array([[1, 0, 1], [0, 1, 1]])
>>> sim = bulk_simpson_binary_similarity(X, Y)
>>> sim
array([[1. , 0.5],
       [1. , 1. ]])