def search_knn(self, point, k, dist=None):
if dist is None:
get_dist = lambda n: n.dist(point)
else:
get_dist = lambda n: dist(n.data, point)
results = BoundedPriorityQueue(k)
self._search_node(point, k, results, get_dist)
# We sort the final result by the distance in the tuple
# (<KdNode>, distance)
BY_VALUE = lambda kv: kv[1]
return sorted(results.items(), key=BY_VALUE)
def knn_search(root, query_point, k_neighbors, k_dimensions, distance_func):
bpq = BoundedPriorityQueue(k_neighbors)
visited_tree_paths = set([])
root_tree_path = ''
num_points_visited = {'count': 1}
def knn_search_rec(current_kdnode, parent_kdnode, depth, tree_path):
if current_kdnode is None:
return
# determine in which half of the hyperplane-splitted region
# in which the query point lies
split_point = current_kdnode.point[:k_dimensions]
axis = depth % k_dimensions
splitting_hyperplane = split_point[axis]
if query_point[axis] < splitting_hyperplane:
knn_search_rec(current_kdnode.left, current_kdnode,\
depth + 1, tree_path + '0')
else:
knn_search_rec(current_kdnode.right, current_kdnode,\
depth + 1, tree_path + '1')
# keep track of the K nearest distances and corresponding points
d = distance_func(split_point, query_point)
bpq.maxheap_insert(d, current_kdnode.point)
# if the hypersphere radius intersects the hyperplane of the
# current axis, then it is possible that a nearer neighbor lies
# on the other half of the hyperplane
knn_distances = bpq.get_priorities()
hypersphere_radius = max(knn_distances)
hyperplane_distance = abs(splitting_hyperplane - query_point[axis])
hypersphere_will_cross_hyperplane =\
(hypersphere_radius > hyperplane_distance)
if (current_kdnode is root) or (tree_path in visited_tree_paths):
return
else:
# mark kdnode as visited_tree_paths
visited_tree_paths.add(tree_path)
num_points_visited['count'] += 1
if current_kdnode is parent_kdnode.left:
subtree_on_other_side_of_hyperplane = parent_kdnode.right
other_tree_path = tree_path[:-1] + '1'
else:
subtree_on_other_side_of_hyperplane = parent_kdnode.left
other_tree_path = tree_path[:-1] + '0'
if not bpq.is_full() or hypersphere_will_cross_hyperplane:
if other_tree_path not in visited_tree_paths:
knn_search_rec(subtree_on_other_side_of_hyperplane, \
parent_kdnode, depth, other_tree_path)
else:
visited_tree_paths.add(other_tree_path)
knn_search_rec(root, parent_kdnode=None, depth=0, tree_path=root_tree_path)
nearest_points = bpq.get_elements()
distances = bpq.get_priorities()
return nearest_points, distances, num_points_visited['count']
def knn_search(root, query_point, k_neighbors, k_dimensions,
distance_func):
bpq = BoundedPriorityQueue(k_neighbors)
visited_tree_paths = set([])
root_tree_path = ''
num_points_visited = {'count': 1}
def knn_search_rec(current_kdnode, parent_kdnode, depth, tree_path):
if current_kdnode is None:
return
# determine in which half of the hyperplane-splitted region
# in which the query point lies
split_point = current_kdnode.point[:k_dimensions]
axis = depth % k_dimensions
splitting_hyperplane = split_point[axis]
if query_point[axis] < splitting_hyperplane:
knn_search_rec(current_kdnode.left, current_kdnode,\
depth + 1, tree_path + '0')
else:
knn_search_rec(current_kdnode.right, current_kdnode,\
depth + 1, tree_path + '1')
# keep track of the K nearest distances and corresponding points
d = distance_func(split_point, query_point)
bpq.maxheap_insert(d, current_kdnode.point)
# if the hypersphere radius intersects the hyperplane of the
# current axis, then it is possible that a nearer neighbor lies
# on the other half of the hyperplane
knn_distances = bpq.get_priorities()
hypersphere_radius = max(knn_distances)
hyperplane_distance = abs(splitting_hyperplane - query_point[axis])
hypersphere_will_cross_hyperplane =\
(hypersphere_radius > hyperplane_distance)
if (current_kdnode is root) or (tree_path in visited_tree_paths):
return
else:
# mark kdnode as visited_tree_paths
visited_tree_paths.add(tree_path)
num_points_visited['count'] += 1
if current_kdnode is parent_kdnode.left:
subtree_on_other_side_of_hyperplane = parent_kdnode.right
other_tree_path = tree_path[:-1] + '1'
else:
subtree_on_other_side_of_hyperplane = parent_kdnode.left
other_tree_path = tree_path[:-1] + '0'
if not bpq.is_full() or hypersphere_will_cross_hyperplane:
if other_tree_path not in visited_tree_paths:
knn_search_rec(subtree_on_other_side_of_hyperplane, \
parent_kdnode, depth, other_tree_path)
else:
visited_tree_paths.add(other_tree_path)
knn_search_rec(root,
parent_kdnode=None,
depth=0,
tree_path=root_tree_path)
nearest_points = bpq.get_elements()
distances = bpq.get_priorities()
return nearest_points, distances, num_points_visited['count']