ENH remove redundant expr in min_max_dist (scikit-learn#25262)

neurodata · Dec 31, 2022 · 96a0bc8 · 96a0bc8
1 parent a7728f6
commit 96a0bc8
Showing 1 changed file with 2 additions and 4 deletions.
diff --git a/sklearn/neighbors/_kd_tree.pyx b/sklearn/neighbors/_kd_tree.pyx
@@ -159,10 +159,8 @@ cdef inline int min_max_dist(BinaryTree tree, ITYPE_t i_node, DTYPE_t* pt,
  d_hi = pt[j] - tree.node_bounds[1, i_node, j]
  d = (d_lo + fabs(d_lo)) + (d_hi + fabs(d_hi))
  min_dist[0] = fmax(min_dist[0], 0.5 * d)
- max_dist[0] = fmax(max_dist[0],
- fabs(pt[j] - tree.node_bounds[0, i_node, j]))
- max_dist[0] = fmax(max_dist[0],
- fabs(pt[j] - tree.node_bounds[1, i_node, j]))
+ max_dist[0] = fmax(max_dist[0], fabs(d_lo))
+ max_dist[0] = fmax(max_dist[0], fabs(d_hi))
  else:
  # as above, use the fact that x + abs(x) = 2 * max(x, 0)
  for j in range(n_features):