inst/tests/CheckKNN.R

In matloff/qeML: Quick and Easy Machine Learning Tools

checkKNN <- function() 
{

   set.seed(9999)
   
   exampleDF <-
   data.frame(x=matrix(runif(14),ncol=2),y=c('a','a','b','b','b','b','b'))
   exampleDF$y <- as.factor(exampleDF$y)
   
   exampleDF
   #         x.1        x.2 y
   # 1 0.8608396 0.81819992 a
   # 2 0.6602416 0.99815678 a
   # 3 0.8004162 0.74069639 b
   # 4 0.2076460 0.81829430 b
   # 5 0.6894843 0.02551977 b
   # 6 0.8448705 0.16775453 b
   # 7 0.2197564 0.76513326 b
   
   w <- pdist(exampleDF[,1:2],exampleDF[,1:2])
   matrix(w@dist,ncol=7)
   sqrt(sum((exampleDF[1,1:2]-exampleDF[2,1:2])^2))
   #           [,1]      [,2]      [,3]       [,4]      [,5]      [,6]       [,7]
   # [1,] 0.0000000 0.2694885 0.0982740 0.65319359 0.8109898 0.6506414 0.64327574
   # [2,] 0.2694885 0.0000000 0.2931464 0.48702493 0.9730765 0.8506796 0.49832430
   # [3,] 0.0982740 0.2931464 0.0000000 0.59782773 0.7237288 0.5746639 0.58117378
   # [4,] 0.6531936 0.4870249 0.5978277 0.00000000 0.9277174 0.9106356 0.05452301
   # [5,] 0.8109898 0.9730765 0.7237288 0.92771739 0.0000000 0.2106552 0.87616915
   # [6,] 0.6506414 0.8506796 0.5746639 0.91063559 0.2106552 0.0000000 0.86465538
   # [7,] 0.6432757 0.4983243 0.5811738 0.05452301 0.8761691 0.8646554 0.00000000
   # [1] 0.2694885
   
   knnOut <- qeKNN(exampleDF,'y',k=2,holdout=NULL,scaleX=FALSE)
   
   knnOut$whichClosest
   #      [,1] [,2]
   # [1,]    1    3
   # [2,]    2    1
   # [3,]    3    1
   # [4,]    4    7
   # [5,]    5    6
   # [6,]    6    5
   # [7,]    7    4
   
   knnOut$regests
   # [1] 0.5 1.0 0.5 0.0 0.0 0.0 0.0
   
   predict(knnOut,exampleDF[,1:2])
   # $predClasses
   # [1] "b" "a" "b" "b" "b" "b" "b"
   
   # $probs
   # [1] 0.5 1.0 0.5 0.0 0.0 0.0 0.0
   
   predict(knnOut,exampleDF[,1:2],newxK=2)
   # $predClasses
   # [1] "b" "a" "b" "b" "b" "b" "b"
   
   # $probs
   # [1] 0.50 0.75 0.50 0.00 0.00 0.00 0.00

}