bulk_braun_blanquet_binary_distance#

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

Bulk Braun-Blanquet distance for vectors of binary values.

Computes the pairwise Braun-Blanquet distance between binary 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 braun_blanquet_binary_distance().

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, distances are computed between rows of X.

Returns:

distances – Array with pairwise Braun-Blanquet distance values. Shape is $m\times n$ if two arrays are passed, or $m\times m$ otherwise.

Return type:

ndarray

See also

braun_blanquet_binary_distance()

Braun-Blanquet distance function for two vectors

Examples

>>> from skfp.distances import bulk_braun_blanquet_binary_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_braun_blanquet_binary_distance(X, Y)
>>> dist
array([[0., 0.],
       [0., 0.]])

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