File size: 663 Bytes
c289d87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | from __future__ import annotations
from typing import Iterable
import numpy as np
def _tanimoto(fp_a: np.ndarray, fp_b: np.ndarray) -> float:
inter = float(np.sum((fp_a > 0) & (fp_b > 0)))
union = float(np.sum((fp_a > 0) | (fp_b > 0)))
if union == 0:
return 0.0
return inter / union
def selection_diversity(fingerprints: Iterable[np.ndarray]) -> float:
fps = list(fingerprints)
if len(fps) < 2:
return 0.0
distances = []
for i in range(len(fps)):
for j in range(i + 1, len(fps)):
distances.append(1.0 - _tanimoto(fps[i], fps[j]))
return float(np.mean(distances)) if distances else 0.0
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