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Interface Summary
Interface
Description
AxisSplitRule
Selects which axis the data should be split along when given a list of variances.
AxisSplitter <P>
Selects which dimension the set of points should be split by, which point is used to split the lists, and splits
the lists into two sets.
KdTreeDistance <P>
Computes the distance between two points.
KdTreeSearch1 <P>
Interface for searching a single tree for the nearest-neighbor
KdTreeSearchN <P>
Interface for searching a single tree for the N nearest-neighbors.
Class Summary
Class
Description
AxisSplitRuleMax
Selects the axis with the largest variance to split.
AxisSplitRuleRandomK
Randomly selects the larger variances.
AxisSplitterMedian <P>
Splits the points in K-D Tree node by selecting the axis with the largest variance.
ExhaustiveNeighbor <P>
Exhaustively finds the nearest-neighbor to a n-dimensional point by considering every possibility.
KdTree
K-D Tree is short for k-dimensional tree and is a binary tree data structure used for quickly finding the
nearest-neighbor of a k-dimensional point in a set.
KdTree.Node
Data type for each node in the binary tree.
KdTreeConstructor <P>
Creates a new
KD-Tree
from a list of points and (optional) associated data.
KdTreeMemory <P>
Memory management for recycling KdTree data structures.
KdTreeResult
Storage for the results of a K-D Tree search.
VpTree
Vantage point tree implementation for nearest neighbor search.