bulk_ct4_count_similarity

skfp.distances.bulk_ct4_count_similarity(X: ndarray, Y: ndarray | None = None) → ndarray

Bulk Consonni–Todeschini 4 similarity for count matrices.

Computes the pairwise Consonni–Todeschini 4 similarity between count 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 ct4_count_similarity().

Parameters:

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

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

Returns:

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

Return type:

ndarray

See also

ct4_count_similarity()

Consonni–Todeschini similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_ct4_count_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_ct4_count_similarity(X, Y)
>>> sim
array([[1.        , 0.5       ],
       [0.63092975, 0.63092975]])