braun_blanquet_binary_similarity

skfp.distances.braun_blanquet_binary_similarity(vec_a: ndarray | csr_array, vec_b: ndarray | csr_array) → float

Braun-Blanquet similarity for vectors of binary values.

Computes the Braun-Blanquet similarity [1] [2] [3] for binary data between two input arrays or sparse matrices using the formula:

\[sim(a, b) = \frac{|a \cap b|}{\max(|a|, |b|)}\]

The calculated similarity falls within the range \([0, 1]\). Passing all-zero vectors to this function results in a similarity of 1.

Parameters:

• vec_a ({ndarray, sparse matrix}) – First binary input array or sparse matrix.

• vec_b ({ndarray, sparse matrix}) – Second binary input array or sparse matrix.

Returns:

similarity – Braun-Blanquet similarity between vec_a and vec_b.

Return type:

float

References

[1]

Braun-Blanquet, J. “Plant sociology. The study of plant communities. First ed.” McGraw-Hill Book Co., Inc., New York and London, 1932.

[2]

Deza M.M., Deza E. “Encyclopedia of Distances.” Springer, Berlin, Heidelberg, 2009.

[3]

RDKit documentation

Examples

>>> from skfp.distances import braun_blanquet_binary_similarity
>>> import numpy as np
>>> vec_a = np.array([1, 0, 1])
>>> vec_b = np.array([1, 0, 1])
>>> sim = braun_blanquet_binary_similarity(vec_a, vec_b)
>>> sim
1.0

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[1, 0, 1]])
>>> vec_b = csr_array([[0, 0, 1]])
>>> sim = braun_blanquet_binary_similarity(vec_a, vec_b)
>>> sim
0.5