bulk_ct4_count_distance

skfp.distances.bulk_ct4_count_distance(X: ndarray, Y: ndarray | None = None) → ndarray

Bulk Consonni–Todeschini 4 distance for vectors of count values.

Computes the pairwise Consonni–Todeschini 4 distance between count matrices. If one array is passed, distances are computed between its rows. For two arrays, distances 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_distance().

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, distances are computed between rows of X.

Returns:

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

Return type:

ndarray

See also

ct4_count_distance()

Consonni–Todeschini distance function for two vectors

Examples

>>> from skfp.distances import bulk_ct4_count_distance
>>> import numpy as np
>>> X = np.array([[1, 0, 1], [1, 0, 1]])
>>> Y = np.array([[1, 0, 1], [1, 0, 1]])
>>> dist = bulk_ct4_count_distance(X, Y)
>>> dist
array([[0., 0.],
       [0., 0.]])

>>> X = np.array([[1, 0, 1], [1, 0, 1]])
>>> dist = bulk_ct4_count_distance(X)
>>> dist
array([[0., 0.],
       [0., 0.]])