def search_knn(self, point, k, dist=None):
""" Return the k nearest neighbors of point and their distances
point must be an actual point, not a node.
k is the number of results to return. The actual results can be less
(if there aren't more nodes to return) or more in case of equal
distances.
dist is a distance function, expecting two points and returning a
distance value. Distance values can be any compareable type.
The result is an ordered list of (node, distance) tuples.
"""
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 search_knn(self, point, k, dist=None):
"""
k is the number of results to return. The actual results can be less
(if there aren't more nodes to return) or more in case of equal distances.
dist is a distance function, expecting two points and returning a distance value.
The result is an ordered list of (node, distance) tuples.
"""
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)
BY_VALUE = lambda kv: kv[1]
return sorted(results.items(), key=BY_VALUE)
def get_test_bpq(self):
bound = 5
bpq = BoundedPriorityQueue(bound)
for n in self.get_test_nodes():
bpq.add(n)
return bpq
