pyclustering
0.10.1
pyclustring is a Python, C++ data mining library.

Represents balanced static KDtree that does not provide services to add and remove nodes after initialization. More...
Public Member Functions  
def  __init__ (self, points, payloads=None) 
Initializes balanced static KDtree. More...  
def  __len__ (self) 
Returns amount of nodes in the KDtree. More...  
def  get_root (self) 
Returns root of the tree. More...  
def  find_node_with_payload (self, point, point_payload, cur_node=None) 
Find node with specified coordinates and payload. More...  
def  find_node (self, point, cur_node=None) 
Find node with coordinates that are defined by specified point. More...  
def  find_nearest_dist_node (self, point, distance, retdistance=False) 
Find nearest neighbor in area with radius = distance. More...  
def  find_nearest_dist_nodes (self, point, distance) 
Find neighbors that are located in area that is covered by specified distance. More...  
Represents balanced static KDtree that does not provide services to add and remove nodes after initialization.
In the term KD tree, k denotes the dimensionality of the space being represented. Each data point is represented as a node in the kd tree in the form of a record of type node.
There is an example how to create KDtree:
Output result of the example above  figure 1.
def pyclustering.container.kdtree.kdtree_balanced.__init__  (  self,  
points,  
payloads = None 

) 
Initializes balanced static KDtree.
[in]  points  (array_like): Points that should be used to build KDtree. 
[in]  payloads  (array_like): Payload of each point in points . 
Reimplemented in pyclustering.container.kdtree.kdtree.
def pyclustering.container.kdtree.kdtree_balanced.__len__  (  self  ) 
def pyclustering.container.kdtree.kdtree_balanced.find_nearest_dist_node  (  self,  
point,  
distance,  
retdistance = False 

) 
Find nearest neighbor in area with radius = distance.
[in]  point  (list): Maximum distance where neighbors are searched. 
[in]  distance  (double): Maximum distance where neighbors are searched. 
[in]  retdistance  (bool): If True  returns neighbors with distances to them, otherwise only neighbors is returned. 
def pyclustering.container.kdtree.kdtree_balanced.find_nearest_dist_nodes  (  self,  
point,  
distance  
) 
Find neighbors that are located in area that is covered by specified distance.
[in]  point  (list): Coordinates that is considered as centroid for searching. 
[in]  distance  (double): Distance from the center where searching is performed. 
Definition at line 458 of file kdtree.py.
Referenced by pyclustering.container.kdtree.kdtree_balanced.find_nearest_dist_node().
def pyclustering.container.kdtree.kdtree_balanced.find_node  (  self,  
point,  
cur_node = None 

) 
Find node with coordinates that are defined by specified point.
If node with specified parameters does not exist then None will be returned, otherwise required node will be returned.
[in]  point  (list): Coordinates of the point whose node should be found. 
[in]  cur_node  (node): Node from which search should be started. 
Definition at line 415 of file kdtree.py.
Referenced by pyclustering.container.kdtree.kdtree.remove().
def pyclustering.container.kdtree.kdtree_balanced.find_node_with_payload  (  self,  
point,  
point_payload,  
cur_node = None 

) 
Find node with specified coordinates and payload.
If node with specified parameters does not exist then None will be returned, otherwise required node will be returned.
[in]  point  (list): Coordinates of the point whose node should be found. 
[in]  point_payload  (any): Payload of the node that is searched in the tree. 
[in]  cur_node  (node): Node from which search should be started. 
Definition at line 397 of file kdtree.py.
Referenced by pyclustering.container.kdtree.kdtree.remove().
def pyclustering.container.kdtree.kdtree_balanced.get_root  (  self  ) 