Minor changes to other cython

Author: lmcinnes

hdbscan/_hdbscan_linkage.pyx

@@ -3,15 +3,10 @@
 # Authors: Leland McInnes, Steve Astels
 # License: 3-clause BSD
 
-cimport cython
-
-import numpy as np
 cimport numpy as np
 
 from libc.float cimport DBL_MAX
 
-from scipy.spatial.distance import cdist, pdist
-from dist_metrics import DistanceMetric
 from dist_metrics cimport DistanceMetric
 
 cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core(
@@ -52,7 +47,7 @@ cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core(
 
     return result
 
-cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core_cdist(
+cpdef np.ndarray[np.double_t, ndim=2] mst_linkage_core_vector(
                                np.ndarray[np.double_t, ndim=2, mode='c'] raw_data,
                                np.ndarray[np.double_t, ndim=1, mode='c'] core_distances,
                                DistanceMetric dist_metric,

hdbscan/_hdbscan_tree.pyx

@@ -36,7 +36,7 @@ cdef list bfs_from_hierarchy(np.ndarray[np.double_t, ndim=2] hierarchy, np.intp_
     return result
 
 cpdef np.ndarray condense_tree(np.ndarray[np.double_t, ndim=2] hierarchy,
-                          np.intp_t min_cluster_size=10):
+                               np.intp_t min_cluster_size=10):
 
     cdef np.intp_t root
     cdef np.intp_t num_points

hdbscan/hdbscan_.py

@@ -29,7 +29,7 @@
 
 from ._hdbscan_linkage import (single_linkage,
                                mst_linkage_core,
-                               mst_linkage_core_cdist,
+                               mst_linkage_core_vector,
                                label)
 from ._hdbscan_tree import (condense_tree,
                             compute_stability,
@@ -123,8 +123,8 @@ def _hdbscan_prims_kdtree(X, min_samples=5, alpha=1.0,
     core_distances = tree.query(X, k=min_samples,
                                 dualtree=True,
                                 breadth_first=True)[0][:, -1].copy(order='C')
-    #Mutual reachability distance is implicite in mst_linkage_core_cdist
-    min_spanning_tree = mst_linkage_core_cdist(X, core_distances, dist_metric, alpha)
+    #Mutual reachability distance is implicite in mst_linkage_core_vector
+    min_spanning_tree = mst_linkage_core_vector(X, core_distances, dist_metric, alpha)
 
     #Sort edges of the min_spanning_tree by weight
     min_spanning_tree = min_spanning_tree[np.argsort(min_spanning_tree.T[2]), :]
@@ -161,8 +161,8 @@ def _hdbscan_prims_balltree(X, min_samples=5, alpha=1.0,
                                 dualtree=True,
                                 breadth_first=True)[0][:, -1].copy(order='C')
 
-    #Mutual reachability distance is implicite in mst_linkage_core_cdist
-    min_spanning_tree = mst_linkage_core_cdist(X, core_distances, dist_metric, alpha)
+    #Mutual reachability distance is implicite in mst_linkage_core_vector
+    min_spanning_tree = mst_linkage_core_vector(X, core_distances, dist_metric, alpha)
     #Sort edges of the min_spanning_tree by weight
     min_spanning_tree = min_spanning_tree[np.argsort(min_spanning_tree.T[2]), :]
     #Convert edge list into standard hierarchical clustering format