knn_meanShift: K-d tree based k nearest neighbor search
In jlisic/meanShiftR: A Computationally Efficient Mean Shift Implementation

Description Usage Arguments Value Examples

View source: R/knn.R

knn_meanShift performs a search for the k nearest neighbors of a single point, where nearest is determined by the Mahalanobis distance. This search is performed through a k-d tree.

1 2	knn_meanShift(evalData, trainData, k = min(5, NROW(trainData)), weight, leafSize = 40, maxDist = Inf, transpose = FALSE)

`trainData`	A matrix or vector of potential nearest neighbors.
`k`	A scalar indicating the number neighbors to find.
`weight`	A scalar or vector of length equal to the number of columns of `trainData`. This value is used as the diagonal elements for the inverse covariance matrix of the Mahalanobis distance.
`leafSize`	A scalar used to specify the number of points to store in the leaf nodes.
`maxDist`	A vector specifying the maximum value of the Mahalanobis that will be considered.
`transpose`	A boolean determining if you should query by columns instead of the default of rows (only for sparse matricies).
`points`	n vectors stored in an n by p matrix. k nearest neighbors are found for each vector.

A list is returned containing two items: neighbors, an n by k matrix of k indexes for each of the n vectors in points, corresponding to the nearest neighbors in trainData. value, a matrix of scalars containing the k distances between the neighbors found in trainData and points.