tanimoto_count_similarity

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

Tanimoto similarity for vectors of count values.

Computes the Tanimoto similarity [1] for count data between two input arrays or sparse matrices, using the formula:

\[sim(a, b) = \frac{a \cdot b}{\|a\|^2 + \|b\|^2 - a \cdot b}\]

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

Parameters:

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

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

Returns:

similarity – Tanimoto similarity between vec_a and vec_b.

Return type:

float

References

[1]

Bajusz, D., Rácz, A. & Héberger, K. “Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?” J Cheminform, 7, 20 (2015).

Examples

>>> from skfp.distances import tanimoto_count_similarity
>>> import numpy as np
>>> vec_a = np.array([7, 1, 1])
>>> vec_b = np.array([7, 1, 2])
>>> sim = tanimoto_count_similarity(vec_a, vec_b)
>>> sim
0.9811320754716981

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[7, 1, 1]])
>>> vec_b = csr_array([[7, 1, 2]])
>>> sim = tanimoto_count_similarity(vec_a, vec_b)
>>> sim
0.9811320754716981