bulk_simpson_binary_similarity#

Bulk Simpson similarity for binary matrices.

Computes the pairwise Simpson (also known as asymmetric similarity or overlap coefficient) 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 the first array and j-th row from the second array.

See also simpson_binary_similarity().

Parameters:

X (ndarray or CSR sparse array) – First binary input array, of shape \(m \times d\).
Y (ndarray or CSR sparse array, default=None) – Second binary input array, of shape \(n \times d\). 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

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. ]])

>>> from scipy.sparse import csr_array
>>> X = csr_array([[1, 0, 1], [0, 0, 1]])
>>> Y = csr_array([[1, 0, 1], [0, 1, 1]])
>>> sim = bulk_simpson_binary_similarity(X, Y)
>>> sim
array([[1. , 0.5],
       [1. , 1. ]])