Last data update: 2014.03.03

 R: k-Nearest Neighbour Classification
knnR Documentation

k-Nearest Neighbour Classification

Description

k-nearest neighbour classification for test set from training set. For each row of the test set, the k nearest (in Euclidean distance) training set vectors are found, and the classification is decided by majority vote, with ties broken at random. If there are ties for the kth nearest vector, all candidates are included in the vote.

Usage

  knn(train, test, cl, k = 1, prob = FALSE, algorithm=c("kd_tree", "cover_tree", "brute"))

Arguments

train

matrix or data frame of training set cases.

test

matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case.

cl

factor of true classifications of training set.

k

number of neighbours considered.

prob

if this is true, the proportion of the votes for the winning class are returned as attribute prob.

algorithm

nearest neighbor search algorithm.

Value

factor of classifications of test set. doubt will be returned as NA.

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: shli@stat.wvu.edu.

References

B.D. Ripley (1996). Pattern Recognition and Neural Networks. Cambridge.

M.N. Venables and B.D. Ripley (2002). Modern Applied Statistics with S. Fourth edition. Springer.

See Also

ownn, knn.cv and knn in class.

Examples

    data(iris3)
    train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
    test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
    cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
    knn(train, test, cl, k = 3, prob=TRUE)
    attributes(.Last.value)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(FNN)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FNN/knn.Rd_%03d_medium.png", width=480, height=480)
> ### Name: knn
> ### Title: k-Nearest Neighbour Classification
> ### Aliases: knn
> ### Keywords: classif nonparametric
> 
> ### ** Examples
> 
>     data(iris3)
>     train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
>     test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
>     cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
>     knn(train, test, cl, k = 3, prob=TRUE)
 [1] s s s s s s s s s s s s s s s s s s s s s s s s s c c v c c c c c v c c c c
[39] c c c c c c c c c c c c v c c v v v v v c v v v v c v v v v v v v v v v v
attr(,"prob")
 [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
 [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667
[29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000
[36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[50] 1.0000000 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000
[57] 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000
[64] 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[71] 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667
attr(,"nn.index")
      [,1] [,2] [,3]
 [1,]   10    2   13
 [2,]   24    8   18
 [3,]    1   18    8
 [4,]    1   18    8
 [5,]    4   12   10
 [6,]   10    4   13
 [7,]   21   18   11
 [8,]   20   11   17
 [9,]   16   17   15
[10,]   10    2   13
[11,]    2    3   10
[12,]   11    1   21
[13,]    5    1    8
[14,]    9   14    4
[15,]    8    1   18
[16,]   18    1    5
[17,]    9   14   13
[18,]    4    3    7
[19,]   24   22   18
[20,]    6   22   20
[21,]    2   13    3
[22,]   20   22    5
[23,]    4    3    7
[24,]   11   20   22
[25,]    8    1   18
[26,]   41   34   50
[27,]   34   28   30
[28,]   28   61   74
[29,]   39   37   31
[30,]   45   40   43
[31,]   45   29   35
[32,]   45   29   43
[33,]   43   45   47
[34,]   52   74   48
[35,]   42   31   37
[36,]   32   46   27
[37,]   28   41   34
[38,]   44   48   38
[39,]   37   42   31
[40,]   29   45   35
[41,]   31   43   29
[42,]   39   49   27
[43,]   43   45   47
[44,]   33   36   35
[45,]   31   43   45
[46,]   37   31   43
[47,]   37   31   43
[48,]   50   47   37
[49,]   33   36   40
[50,]   43   37   31
[51,]   53   58   71
[52,]   74   39   48
[53,]   46   74   32
[54,]   55   54   62
[55,]   53   58   63
[56,]   58   53   56
[57,]   68   56   60
[58,]   55   54   62
[59,]   48   74   39
[60,]   54   62   67
[61,]   56   53   58
[62,]   66   51   75
[63,]   67   54   62
[64,]   46   74   39
[65,]   63   71   75
[66,]   71   63   55
[67,]   63   61   66
[68,]   52   64   72
[69,]   71   75   55
[70,]   71   75   55
[71,]   63   66   61
[72,]   74   62   48
[73,]   61   62   67
[74,]   66   61   75
[75,]   52   46   72
attr(,"nn.dist")
           [,1]      [,2]      [,3]
 [1,] 0.2000000 0.2236068 0.3000000
 [2,] 0.2000000 0.2236068 0.2645751
 [3,] 0.1414214 0.1732051 0.2236068
 [4,] 0.1414214 0.1732051 0.2236068
 [5,] 0.1732051 0.2236068 0.2645751
 [6,] 0.1732051 0.2236068 0.2449490
 [7,] 0.2828427 0.3464102 0.3605551
 [8,] 0.3741657 0.4582576 0.4582576
 [9,] 0.3605551 0.3872983 0.4123106
[10,] 0.1000000 0.1414214 0.2000000
[11,] 0.3000000 0.3162278 0.3464102
[12,] 0.3000000 0.4123106 0.4242641
[13,] 0.1414214 0.2449490 0.2645751
[14,] 0.1414214 0.2449490 0.3000000
[15,] 0.1000000 0.1414214 0.1732051
[16,] 0.1414214 0.1732051 0.1732051
[17,] 0.6244998 0.7810250 0.7937254
[18,] 0.3000000 0.3000000 0.3162278
[19,] 0.2645751 0.3162278 0.3741657
[20,] 0.3741657 0.4123106 0.4123106
[21,] 0.1414214 0.2000000 0.2645751
[22,] 0.1414214 0.2449490 0.3000000
[23,] 0.1414214 0.1414214 0.2236068
[24,] 0.1000000 0.2449490 0.2828427
[25,] 0.1414214 0.2236068 0.2449490
[26,] 0.1414214 0.2449490 0.2645751
[27,] 0.3162278 0.3464102 0.3741657
[28,] 0.3162278 0.4242641 0.5196152
[29,] 0.2449490 0.3316625 0.3741657
[30,] 0.4358899 0.4472136 0.6164414
[31,] 0.1732051 0.3000000 0.5291503
[32,] 0.2645751 0.4358899 0.5830952
[33,] 0.2828427 0.3000000 0.3464102
[34,] 0.3605551 0.4123106 0.4242641
[35,] 0.2000000 0.4123106 0.5830952
[36,] 0.3741657 0.4242641 0.4582576
[37,] 0.2828427 0.3162278 0.3162278
[38,] 0.2645751 0.5744563 0.5916080
[39,] 0.3741657 0.4472136 0.4582576
[40,] 0.2000000 0.2449490 0.3872983
[41,] 0.3162278 0.4795832 0.5099020
[42,] 0.1414214 0.3000000 0.3872983
[43,] 0.2449490 0.2645751 0.3741657
[44,] 0.1414214 0.3605551 0.8485281
[45,] 0.3316625 0.3741657 0.4123106
[46,] 0.3605551 0.3741657 0.3872983
[47,] 0.3000000 0.3162278 0.3872983
[48,] 0.2000000 0.3316625 0.3872983
[49,] 0.3872983 0.7211103 0.9000000
[50,] 0.3316625 0.3605551 0.4000000
[51,] 0.3872983 0.4358899 0.6557439
[52,] 0.1732051 0.4358899 0.4472136
[53,] 0.3000000 0.3605551 0.4582576
[54,] 0.3162278 0.3316625 0.3741657
[55,] 0.5196152 0.5567764 0.7071068
[56,] 0.2645751 0.4582576 0.6082763
[57,] 0.4123106 0.8831761 0.9327379
[58,] 0.3000000 0.4242641 0.4358899
[59,] 0.3605551 0.3741657 0.4690416
[60,] 0.5385165 0.6633250 0.7000000
[61,] 0.5477226 0.6633250 0.6782330
[62,] 0.3872983 0.4242641 0.5196152
[63,] 0.1414214 0.2449490 0.4582576
[64,] 0.2236068 0.4358899 0.4358899
[65,] 0.1732051 0.3741657 0.4123106
[66,] 0.2645751 0.3464102 0.3605551
[67,] 0.4690416 0.5099020 0.5477226
[68,] 0.0000000 0.2645751 0.3162278
[69,] 0.2236068 0.3162278 0.3872983
[70,] 0.3000000 0.4000000 0.4795832
[71,] 0.3741657 0.3741657 0.4242641
[72,] 0.2449490 0.3741657 0.4123106
[73,] 0.2236068 0.3464102 0.3605551
[74,] 0.3000000 0.5567764 0.6244998
[75,] 0.3316625 0.3605551 0.4582576
Levels: c s v
>     attributes(.Last.value)
$levels
[1] "c" "s" "v"

$class
[1] "factor"

$prob
 [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
 [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667
[29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000
[36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[50] 1.0000000 1.0000000 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000
[57] 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000
[64] 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[71] 1.0000000 0.6666667 1.0000000 1.0000000 0.6666667

$nn.index
      [,1] [,2] [,3]
 [1,]   10    2   13
 [2,]   24    8   18
 [3,]    1   18    8
 [4,]    1   18    8
 [5,]    4   12   10
 [6,]   10    4   13
 [7,]   21   18   11
 [8,]   20   11   17
 [9,]   16   17   15
[10,]   10    2   13
[11,]    2    3   10
[12,]   11    1   21
[13,]    5    1    8
[14,]    9   14    4
[15,]    8    1   18
[16,]   18    1    5
[17,]    9   14   13
[18,]    4    3    7
[19,]   24   22   18
[20,]    6   22   20
[21,]    2   13    3
[22,]   20   22    5
[23,]    4    3    7
[24,]   11   20   22
[25,]    8    1   18
[26,]   41   34   50
[27,]   34   28   30
[28,]   28   61   74
[29,]   39   37   31
[30,]   45   40   43
[31,]   45   29   35
[32,]   45   29   43
[33,]   43   45   47
[34,]   52   74   48
[35,]   42   31   37
[36,]   32   46   27
[37,]   28   41   34
[38,]   44   48   38
[39,]   37   42   31
[40,]   29   45   35
[41,]   31   43   29
[42,]   39   49   27
[43,]   43   45   47
[44,]   33   36   35
[45,]   31   43   45
[46,]   37   31   43
[47,]   37   31   43
[48,]   50   47   37
[49,]   33   36   40
[50,]   43   37   31
[51,]   53   58   71
[52,]   74   39   48
[53,]   46   74   32
[54,]   55   54   62
[55,]   53   58   63
[56,]   58   53   56
[57,]   68   56   60
[58,]   55   54   62
[59,]   48   74   39
[60,]   54   62   67
[61,]   56   53   58
[62,]   66   51   75
[63,]   67   54   62
[64,]   46   74   39
[65,]   63   71   75
[66,]   71   63   55
[67,]   63   61   66
[68,]   52   64   72
[69,]   71   75   55
[70,]   71   75   55
[71,]   63   66   61
[72,]   74   62   48
[73,]   61   62   67
[74,]   66   61   75
[75,]   52   46   72

$nn.dist
           [,1]      [,2]      [,3]
 [1,] 0.2000000 0.2236068 0.3000000
 [2,] 0.2000000 0.2236068 0.2645751
 [3,] 0.1414214 0.1732051 0.2236068
 [4,] 0.1414214 0.1732051 0.2236068
 [5,] 0.1732051 0.2236068 0.2645751
 [6,] 0.1732051 0.2236068 0.2449490
 [7,] 0.2828427 0.3464102 0.3605551
 [8,] 0.3741657 0.4582576 0.4582576
 [9,] 0.3605551 0.3872983 0.4123106
[10,] 0.1000000 0.1414214 0.2000000
[11,] 0.3000000 0.3162278 0.3464102
[12,] 0.3000000 0.4123106 0.4242641
[13,] 0.1414214 0.2449490 0.2645751
[14,] 0.1414214 0.2449490 0.3000000
[15,] 0.1000000 0.1414214 0.1732051
[16,] 0.1414214 0.1732051 0.1732051
[17,] 0.6244998 0.7810250 0.7937254
[18,] 0.3000000 0.3000000 0.3162278
[19,] 0.2645751 0.3162278 0.3741657
[20,] 0.3741657 0.4123106 0.4123106
[21,] 0.1414214 0.2000000 0.2645751
[22,] 0.1414214 0.2449490 0.3000000
[23,] 0.1414214 0.1414214 0.2236068
[24,] 0.1000000 0.2449490 0.2828427
[25,] 0.1414214 0.2236068 0.2449490
[26,] 0.1414214 0.2449490 0.2645751
[27,] 0.3162278 0.3464102 0.3741657
[28,] 0.3162278 0.4242641 0.5196152
[29,] 0.2449490 0.3316625 0.3741657
[30,] 0.4358899 0.4472136 0.6164414
[31,] 0.1732051 0.3000000 0.5291503
[32,] 0.2645751 0.4358899 0.5830952
[33,] 0.2828427 0.3000000 0.3464102
[34,] 0.3605551 0.4123106 0.4242641
[35,] 0.2000000 0.4123106 0.5830952
[36,] 0.3741657 0.4242641 0.4582576
[37,] 0.2828427 0.3162278 0.3162278
[38,] 0.2645751 0.5744563 0.5916080
[39,] 0.3741657 0.4472136 0.4582576
[40,] 0.2000000 0.2449490 0.3872983
[41,] 0.3162278 0.4795832 0.5099020
[42,] 0.1414214 0.3000000 0.3872983
[43,] 0.2449490 0.2645751 0.3741657
[44,] 0.1414214 0.3605551 0.8485281
[45,] 0.3316625 0.3741657 0.4123106
[46,] 0.3605551 0.3741657 0.3872983
[47,] 0.3000000 0.3162278 0.3872983
[48,] 0.2000000 0.3316625 0.3872983
[49,] 0.3872983 0.7211103 0.9000000
[50,] 0.3316625 0.3605551 0.4000000
[51,] 0.3872983 0.4358899 0.6557439
[52,] 0.1732051 0.4358899 0.4472136
[53,] 0.3000000 0.3605551 0.4582576
[54,] 0.3162278 0.3316625 0.3741657
[55,] 0.5196152 0.5567764 0.7071068
[56,] 0.2645751 0.4582576 0.6082763
[57,] 0.4123106 0.8831761 0.9327379
[58,] 0.3000000 0.4242641 0.4358899
[59,] 0.3605551 0.3741657 0.4690416
[60,] 0.5385165 0.6633250 0.7000000
[61,] 0.5477226 0.6633250 0.6782330
[62,] 0.3872983 0.4242641 0.5196152
[63,] 0.1414214 0.2449490 0.4582576
[64,] 0.2236068 0.4358899 0.4358899
[65,] 0.1732051 0.3741657 0.4123106
[66,] 0.2645751 0.3464102 0.3605551
[67,] 0.4690416 0.5099020 0.5477226
[68,] 0.0000000 0.2645751 0.3162278
[69,] 0.2236068 0.3162278 0.3872983
[70,] 0.3000000 0.4000000 0.4795832
[71,] 0.3741657 0.3741657 0.4242641
[72,] 0.2449490 0.3741657 0.4123106
[73,] 0.2236068 0.3464102 0.3605551
[74,] 0.3000000 0.5567764 0.6244998
[75,] 0.3316625 0.3605551 0.4582576

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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