[DEBUG] Query

kdtree.pyx

@@ -520,7 +520,7 @@ cdef cypclass QueryActor:
 
     __init__(
         self,
-        D_t * query_points_ptr,
+        D_t * query_points,
         KDTree tree,
         NeighborsHeaps heaps,
         I_t idx_worker,
@@ -528,6 +528,68 @@ cdef cypclass QueryActor:
         I_t idx_end,
     ):
         printf("QueryActor: (%d, %d, %d)\n", idx_worker, idx_start, idx_end)
+        cdef I_t idx_pt
+        cdef D_t * query_point
+        cdef D_t reduced_dist_LB
+
+        for idx_pt in range(idx_start, idx_end):
+            printf("query %d\n", idx_pt)
+            query_point = query_points + idx_pt * tree._n_features
+            reduced_dist_LB = tree.min_rdist(0, query_point)
+            self._query_single_depthfirst(tree, 0, query_points, idx_pt, heaps, reduced_dist_LB)
+
+    int _query_single_depthfirst(self,
+        KDTree tree,
+        I_t idx_node,
+        D_t * query_points,
+        I_t idx_pt,
+        NeighborsHeaps heaps,
+        D_t reduced_dist_LB,
+    ) except -1:
+        """Recursive Single-tree k-neighbors query, depth-first approach"""
+        cdef NodeData_t node_info = tree._node_data_ptr[idx_node]
+
+        cdef D_t sq_dist, reduced_dist_LB_1, reduced_dist_LB_2
+        cdef I_t i, idx_left_node, idx_right_node
+
+        cdef D_t * query_point = query_points + idx_pt * tree._n_features
+
+        #------------------------------------------------------------
+        # Case 1: query point is outside node radius:
+        #         trim it from the query
+        cdef D_t largest = heaps.largest(idx_pt)
+
+        if reduced_dist_LB > largest:
+            pass
+
+        #------------------------------------------------------------
+        # Case 2: this is a leaf node.  Update set of nearby points
+        elif node_info.is_leaf:
+            for i in range(node_info.idx_start, node_info.idx_end):
+                sq_dist = sqeuclidean_dist(
+                    x1=query_point,
+                    x2=tree._data_ptr + tree._indices_ptr[i] * tree._n_features,
+                    k=tree._n_features,
+                )
+                heaps.push(idx_pt, sq_dist, tree._indices_ptr[i])
+
+        #------------------------------------------------------------
+        # Case 3: Node is not a leaf.  Recursively query subnodes
+        #         starting with the closest
+        else:
+            idx_left_node = 2 * idx_node + 1
+            idx_right_node = idx_left_node + 1
+            reduced_dist_LB_1 = tree.min_rdist(idx_left_node, query_point)
+            reduced_dist_LB_2 = tree.min_rdist(idx_right_node, query_point)
+
+            # recursively query subnodes
+            if reduced_dist_LB_1 <= reduced_dist_LB_2:
+                self._query_single_depthfirst(tree, idx_left_node, query_points, idx_pt, heaps, reduced_dist_LB_1)
+                self._query_single_depthfirst(tree, idx_right_node, query_points, idx_pt, heaps, reduced_dist_LB_2)
+            else:
+                self._query_single_depthfirst(tree, idx_right_node, query_points, idx_pt, heaps, reduced_dist_LB_2)
+                self._query_single_depthfirst(tree, idx_left_node, query_points, idx_pt, heaps, reduced_dist_LB_1)
+        return 0
 
 
 cdef cypclass KDTree:
@@ -638,59 +700,6 @@ cdef cypclass KDTree:
         free(self._node_data_ptr)
         free(self._node_bounds_ptr)
 
-    int _query_single_depthfirst(self,
-        I_t idx_node,
-        D_t* query_points,
-        I_t idx_pt,
-        NeighborsHeaps heaps,
-        D_t reduced_dist_LB,
-    ) except -1:
-        """Recursive Single-tree k-neighbors query, depth-first approach"""
-        cdef NodeData_t node_info = self._node_data_ptr[idx_node]
-
-        cdef D_t sq_dist, reduced_dist_LB_1, reduced_dist_LB_2
-        cdef I_t i, idx_left_node, idx_right_node
-
-        cdef D_t * this_query_point = query_points + idx_pt * self._n_features
-
-        #------------------------------------------------------------
-        # Case 1: query point is outside node radius:
-        #         trim it from the query
-        cdef D_t largest = heaps.largest(idx_pt)
-
-        if reduced_dist_LB > largest:
-            pass
-
-        #------------------------------------------------------------
-        # Case 2: this is a leaf node.  Update set of nearby points
-        elif node_info.is_leaf:
-            for i in range(node_info.idx_start, node_info.idx_end):
-                sq_dist = sqeuclidean_dist(
-                            x1=this_query_point,
-                            x2=self._data_ptr + self._indices_ptr[i] * self._n_features,
-                            k=self._n_features,
-                        )
-                heaps.push(idx_pt, sq_dist, self._indices_ptr[i])
-
-        #------------------------------------------------------------
-        # Case 3: Node is not a leaf.  Recursively query subnodes
-        #         starting with the closest
-        else:
-            idx_left_node = 2 * idx_node + 1
-            idx_right_node = idx_left_node + 1
-            reduced_dist_LB_1 = self.min_rdist(idx_left_node, this_query_point)
-            reduced_dist_LB_2 = self.min_rdist(idx_right_node, this_query_point)
-
-            # recursively query subnodes
-            if reduced_dist_LB_1 <= reduced_dist_LB_2:
-                self._query_single_depthfirst(idx_left_node, query_points, idx_pt, heaps, reduced_dist_LB_1)
-                self._query_single_depthfirst(idx_right_node, query_points, idx_pt, heaps, reduced_dist_LB_2)
-            else:
-                self._query_single_depthfirst(idx_right_node, query_points, idx_pt, heaps, reduced_dist_LB_2)
-                self._query_single_depthfirst(idx_left_node, query_points, idx_pt, heaps, reduced_dist_LB_1)
-        return 0
-
-
     void query(self,
         np.ndarray query_points,  # IN
         np.ndarray knn_indices,   # IN/OUT
@@ -702,7 +711,7 @@ cdef cypclass KDTree:
             I_t n_query = query_points.shape[0]
             I_t n_features = query_points.shape[1]
             I_t n_neighbors = knn_indices.shape[1]
-            D_t * _query_points_ptr = <D_t *> query_points.data
+            D_t * _query_points = <D_t *> query_points.data
             D_t rdist_lower_bound
             I_t n_workers = omp_get_max_threads()
             I_t n_points_worker = <I_t> ceil(n_query / n_workers)
@@ -716,20 +725,17 @@ cdef cypclass KDTree:
             # This Counter is used as a way to implement a barrier for
             # the asynchronous construction of the tree.
             active Counter counter = consume Counter()
-
-        idx_worker = 0
-        idx_start = n_points_worker * idx_worker
-        idx_end = min(n_points_worker * (idx_worker + 1), n_query)
-
-
-        cdef QueryActor query_actor = QueryActor(
-            query_points=_query_points_ptr,
-            tree=tree,
-            heaps=heaps,
-            idx_worker=idx_worker,
-            idx_start=n_points_worker * idx_worker,
-            idx_end=min(n_points_worker * (idx_worker + 1), n_query),
-        )
+            QueryActor query_actor
+
+        for idx_worker in range(n_workers):
+            query_actor = QueryActor(
+                _query_points,
+                self,
+                heaps,
+                idx_worker,
+                n_points_worker * idx_worker,
+                min(n_points_worker * (idx_worker + 1), n_query),
+            )
 
         heaps.sort()