bulk_mcconnaughey_binary_similarity#

skfp.distances.bulk_mcconnaughey_binary_similarity(X: ndarray, Y: ndarray | None = None, normalized: bool = False) → ndarray#

Bulk McConnaughey similarity for binary matrices.

Computes the pairwise McConnaughey 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 mcconnaughey_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.

  • normalized (bool, default=False) – Whether to normalize the values inside the result matrix to range [0, 1] by adding one and dividing the result by 2.

Returns:

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

Return type:

ndarray

See also

mcconnaughey_binary_similarity()

McConnaughey similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_mcconnaughey_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_mcconnaughey_binary_similarity(X, Y)
>>> sim
array([[0.66666667, 0.5       ],
       [0.5       , 0.5       ]])