Package org.ddogleg.nn.alg

  • 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.