tanimoto_count_distance

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

Tanimoto distance for vectors of count values.

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

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

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

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.

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).

Returns:

distance – Tanimoto distance between vec_a and vec_b.

Return type:

float

Examples

>>> from skfp.distances import tanimoto_count_distance
>>> import numpy as np
>>> vec_a = np.array([7, 1, 1])
>>> vec_b = np.array([7, 1, 2])
>>> dist = tanimoto_count_distance(vec_a, vec_b)
>>> dist
0.018867924528301883

>>> from scipy.sparse import csr_array
>>> vec_a = csr_array([[7, 1, 1]])
>>> vec_b = csr_array([[7, 1, 2]])
>>> dist = tanimoto_count_distance(vec_a, vec_b)
>>> dist
0.018867924528301883