Still more numpy 1.6 support changes.

Author: lmcinnes

hdbscan/hdbscan_.py

@@ -198,14 +198,13 @@ def _hdbscan_sparse_distance_matrix(X, min_samples=5, alpha=1.0,
 def _hdbscan_prims_kdtree(X, min_samples=5, alpha=1.0,
                           metric='minkowski', p=2, leaf_size=40,
                           gen_min_span_tree=False, **kwargs):
+    if X.dtype != np.float64:
+        X = X.astype(np.float64)
 
     # The Cython routines used require contiguous arrays
     if not X.flags['C_CONTIGUOUS']:
         X = np.array(X, dtype=np.double, order='C')
 
-    if X.dtype != np.float64:
-        X = X.astype(np.float64, order='C')
-
     tree = KDTree(X, metric=metric, leaf_size=leaf_size, **kwargs)
 
     # TO DO: Deal with p for minkowski appropriately
@@ -232,14 +231,13 @@ def _hdbscan_prims_kdtree(X, min_samples=5, alpha=1.0,
 def _hdbscan_prims_balltree(X, min_samples=5, alpha=1.0,
                             metric='minkowski', p=2, leaf_size=40,
                             gen_min_span_tree=False, **kwargs):
+    if X.dtype != np.float64:
+        X = X.astype(np.float64)
 
     # The Cython routines used require contiguous arrays
     if not X.flags['C_CONTIGUOUS']:
         X = np.array(X, dtype=np.double, order='C')
 
-    if X.dtype != np.float64:
-        X = X.astype(np.float64)
-
     tree = BallTree(X, metric=metric, leaf_size=leaf_size, **kwargs)
 
     dist_metric = DistanceMetric.get_metric(metric, **kwargs)
@@ -274,7 +272,7 @@ def _hdbscan_boruvka_kdtree(X, min_samples=5, alpha=1.0,
         core_dist_n_jobs = max(cpu_count() + 1 + core_dist_n_jobs, 1)
 
     if X.dtype != np.float64:
-        X = X.astype(np.float64, order='C')
+        X = X.astype(np.float64)
 
     tree = KDTree(X, metric=metric, leaf_size=leaf_size, **kwargs)
     alg = KDTreeBoruvkaAlgorithm(tree, min_samples, metric=metric,