bulk_tanimoto_count_similarity#

skfp.distances.bulk_tanimoto_count_similarity(X: ndarray, Y: ndarray | None = None) → ndarray#

Bulk Tanimoto similarity for count matrices.

Computes the pairwise Tanimoto similarity between count matrices. If one array is passed, similarities are computed between its rows. For two arrays, similarities are between their respective rows, with i-th row and j-th column in output corresponding to i-th row from first array and j-th row from second array.

See also tanimoto_count_similarity().

Parameters:

  • X (ndarray) – First count input array, of shape \(m \times m\)

  • Y (ndarray, default=None) – Second count input array, of shape \(n \times n\). If not passed, similarities are computed between rows of X.

Returns:

similarities – Array with pairwise Tanimoto similarity values. Shape is \(m \times n\) if two arrays are passed, or \(m \times m\) otherwise.

Return type:

ndarray

See also

tanimoto_count_similarity()

Tanimoto similarity function for two vectors.

Examples

>>> from skfp.distances import bulk_tanimoto_count_similarity
>>> import numpy as np
>>> X = np.array([[1, 0, 1], [0, 0, 1]])
>>> Y = np.array([[1, 0, 1], [0, 1, 1]])
>>> sim = bulk_tanimoto_count_similarity(X, Y)
>>> sim
array([[1.        , 0.33333333],
       [0.5       , 0.5       ]])