bulk_rogot_goldberg_binary_similarity#

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

Bulk Rogot-Goldberg similarity for binary matrices.

Computes the pairwise Rogot-Goldberg 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 rogot_goldberg_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 Rogot-Goldberg similarity values. Shape is \(m \times n\) if two arrays are passed, or \(m \times m\) otherwise.

Return type:

ndarray

See also

rogot_goldberg_binary_similarity()

Rogot-Goldberg similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_rogot_goldberg_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_rogot_goldberg_binary_similarity(X, Y)
>>> sim
array([[0.4       , 0.4       ],
       [0.66666667, 0.66666667]])