rogot_goldberg_binary_similarity#

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

Rogot-Goldberg similarity for vectors of binary values.

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

\[sim(x, y) = \frac{a}{2 * (2a + b + c)} + \frac{d}{2 * (2d + b + c)}\]

where \(a\), \(b\), \(c\) and \(d\) correspond to the number of bit relations between the two vectors:

  • \(a\) - both are 1 (\(|x \cap y|\), common “on” bits)

  • \(b\) - \(x\) is 1, \(y\) is 0

  • \(c\) - \(x\) is 0, \(y\) is 1

  • \(d\) - both are 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:

similarity – Rogot-Goldberg similarity between vec_a and vec_b.

Return type:

float

References

Examples

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