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):
""" 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.
"""
prev = None
current = self
# If a distance function is provided, use it, otherwise use the default Euclidean distance (Pow((x2-x1),2) + ...)
if dist is None:
get_dist = lambda n: n.dist(point)
else:
get_dist = lambda n: dist(n.data, point)
# the nodes do not keep a reference to their parents
parents = {current: None}
# go down the tree as we would for inserting
# JO: Similar to if (current == null)
# I think here we search for the test point.
######while current:
###### if point[current.axis] < current.data[current.axis]:
###### # left side
###### parents[current.left] = current
###### prev = current
###### current = current.left
###### else:
###### # right side
###### parents[current.right] = current
###### prev = current
###### current = current.right
######
######if not prev:
###### return []
examined = set()
results = BoundedPriorityQueue(k)
# Go up the tree, looking for better solutions
####current = prev
####while current:
# search node and update results
current._search_node(point, k, results, examined, get_dist)
#current = parents[current]
# We sort the final result by the distance in the tuple (<KdNode>, distance)
BY_VALUE = lambda kv: kv[1]
print ("Visited " + str(len(examined)) + " nodes.")
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
def get_test_bpq(self):
bound = 5
bpq = BoundedPriorityQueue(bound)
for n in self.get_test_nodes():
bpq.add(n)
return bpq
