simpson_binary_distance#

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

Simpson distance for vectors of binary values.

Computes the Simpson distance for binary data between two input arrays or sparse matrices by subtracting the Simpson similarity [1] [2] [3] [4] from 1, using the formula:

\[dist(a, b) = 1 - sim(a, b)\]

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

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:

distance – Simpson distance between vec_a and vec_b.

Return type:

float

References

Examples

>>> from skfp.distances import simpson_binary_distance
>>> import numpy as np
>>> vec_a = np.array([1, 0, 1])
>>> vec_b = np.array([1, 0, 1])
>>> dist = simpson_binary_distance(vec_a, vec_b)
>>> dist
0.0

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[1, 0, 1]])
>>> vec_b = csr_array([[1, 0, 1]])
>>> dist = simpson_binary_distance(vec_a, vec_b)
>>> dist
0.0

simpson_binary_distance#

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