, Shingo Sashida [aut]| Title: | K* Nearest Neighbors Algorithm |
|---|---|
| Description: | Prediction with k* nearest neighbor algorithm based on a publication by Anava and Levy (2016) <arXiv:1701.07266>. |
| Authors: | Kei Nakagawa [aut, cre]
, Shingo Sashida [aut] |
| Maintainer: | Kei Nakagawa <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.2 |
| Built: | 2025-02-22 04:09:24 UTC |
| Source: | https://github.com/cran/ksNN |
This function calculates the prediction value of k* nearest neighbors algorithm.
ksNN(Label, Distance, L_C = 1)ksNN(Label, Distance, L_C = 1)
Label |
vectors of the known labels of the samples. |
Distance |
vectors of the distance between the target sample we want to predict and the other samples. |
L_C |
parameter of k* nearest neighbors algorithm. |
the prediction value(pred) and the weight of the samples(alpha).
This algorithm is based on Anava and Levy(2017).
library(ksNN) set.seed(1) #make the nonlinear regression problem X<-runif(100) Y<-X^6-3*X^3+5*X^2+2 suffle<-order(rnorm(length(X))) X<-X[suffle] Y<-Y[suffle] test_X<-X[1] test_Y<-Y[1] train_X<-X[-1] train_Y<-Y[-1] Label<-train_Y Distance<-sqrt((test_X-train_X)^2) pred_ksNN<-ksNN(Label,Distance,L_C=1) #the predicted value with k*NN pred_ksNN$pred #the 'true' value test_Ylibrary(ksNN) set.seed(1) #make the nonlinear regression problem X<-runif(100) Y<-X^6-3*X^3+5*X^2+2 suffle<-order(rnorm(length(X))) X<-X[suffle] Y<-Y[suffle] test_X<-X[1] test_Y<-Y[1] train_X<-X[-1] train_Y<-Y[-1] Label<-train_Y Distance<-sqrt((test_X-train_X)^2) pred_ksNN<-ksNN(Label,Distance,L_C=1) #the predicted value with k*NN pred_ksNN$pred #the 'true' value test_Y
This function calculates the prediction value of k* nearest neighbors algorithm.
rcpp_ksNN(Label, Distance, L_C = 1)rcpp_ksNN(Label, Distance, L_C = 1)
Label |
vectors of the known labels of the samples. |
Distance |
vectors of the distance between the target sample we want to predict and the other samples. |
L_C |
parameter of k* nearest neighbors algorithm. |
the prediction value(pred) and the weight of the samples(alpha).
This algorithm is based on Anava and Levy(2017).
library(ksNN) set.seed(1) #make the nonlinear regression problem X<-runif(100) Y<-X^6-3*X^3+5*X^2+2 suffle<-order(rnorm(length(X))) X<-X[suffle] Y<-Y[suffle] test_X<-X[1] test_Y<-Y[1] train_X<-X[-1] train_Y<-Y[-1] Label<-train_Y Distance<-sqrt((test_X-train_X)^2) pred_ksNN<-rcpp_ksNN(Label,Distance,L_C=1) #the predicted value with k*NN pred_ksNN$pred #the 'true' value test_Ylibrary(ksNN) set.seed(1) #make the nonlinear regression problem X<-runif(100) Y<-X^6-3*X^3+5*X^2+2 suffle<-order(rnorm(length(X))) X<-X[suffle] Y<-Y[suffle] test_X<-X[1] test_Y<-Y[1] train_X<-X[-1] train_Y<-Y[-1] Label<-train_Y Distance<-sqrt((test_X-train_X)^2) pred_ksNN<-rcpp_ksNN(Label,Distance,L_C=1) #the predicted value with k*NN pred_ksNN$pred #the 'true' value test_Y