Package org.ddogleg.nn.alg

package org.ddogleg.nn.alg
  • Class
    Selects which axis the data should be split along when given a list of variances.
    Selects the axis with the largest variance to split.
    Randomly selects the larger variances.
    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.
    Splits the points in K-D Tree node by selecting the axis with the largest variance.
    Exhaustively finds the nearest-neighbor to a n-dimensional point by considering every possibility.
    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.
    Data type for each node in the binary tree.
    Creates a new KD-Tree from a list of points and (optional) associated data.
    Computes the distance between two points.
    Memory management for recycling KdTree data structures.
    Storage for the results of a K-D Tree search.
    Interface for searching a single tree for the nearest-neighbor
    Interface for searching a single tree for the N nearest-neighbors.
    Vantage point tree implementation for nearest neighbor search.