rcpp_ksNN {ksNN} | R Documentation |
This function calculates the prediction value of k* nearest neighbors algorithm.
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_Y