FIX Updated loop structure in hdbscan _tree.pyx to avoid error (sci…

…kit-learn#26547)

sklearn/cluster/_hdbscan/_tree.pyx

@@ -36,6 +36,9 @@ import cython
 
 import numpy as np
 
+cdef extern from "numpy/arrayobject.h":
+ intp_t * PyArray_SHAPE(cnp.PyArrayObject *)
+
 cdef cnp.float64_t INFTY = np.inf
 cdef cnp.intp_t NOISE = -1
 
@@ -240,7 +243,7 @@ cdef dict _compute_stability(
  cnp.float64_t[::1] result, births
  cnp.intp_t[:] parents = condensed_tree['parent']
 
- cnp.intp_t parent, cluster_size, result_index
+ cnp.intp_t parent, cluster_size, result_index, idx
  cnp.float64_t lambda_val
  CONDENSED_t condensed_node
  cnp.float64_t[:, :] result_pre_dict
@@ -251,14 +254,15 @@ cdef dict _compute_stability(
  largest_child = max(largest_child, smallest_cluster)
  births = np.full(largest_child + 1, np.nan, dtype=np.float64)
 
- births = np.full(largest_child + 1, np.nan, dtype=np.float64)
- for condensed_node in condensed_tree:
+ for idx in range(PyArray_SHAPE(<cnp.PyArrayObject*> condensed_tree)[0]):
+  condensed_node = condensed_tree[idx]
  births[condensed_node.child] = condensed_node.value
 
  births[smallest_cluster] = 0.0
 
  result = np.zeros(num_clusters, dtype=np.float64)
- for condensed_node in condensed_tree:
+ for idx in range(PyArray_SHAPE(<cnp.PyArrayObject*> condensed_tree)[0]):
+ condensed_node = condensed_tree[idx]
  parent = condensed_node.parent
  lambda_val = condensed_node.value
  cluster_size = condensed_node.cluster_size