# lvq2: Learning Vector Quantization 2.1 In class: Functions for Classification

## Description

Moves examples in a codebook to better represent the training set.

## Usage

 ```1 2``` ```lvq2(x, cl, codebk, niter = 100 * nrow(codebk\$x), alpha = 0.03, win = 0.3) ```

## Arguments

 `x` a matrix or data frame of examples `cl` a vector or factor of classifications for the examples `codebk` a codebook `niter` number of iterations `alpha` constant for training `win` a tolerance for the closeness of the two nearest vectors.

## Details

Selects `niter` examples at random with replacement, and adjusts the nearest two examples in the codebook if one is correct and the other incorrect.

## Value

A codebook, represented as a list with components `x` and `cl` giving the examples and classes.

## References

Kohonen, T. (1990) The self-organizing map. Proc. IEEE 78, 1464–1480.

Kohonen, T. (1995) Self-Organizing Maps. Springer, Berlin.

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

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

`lvqinit`, `lvq1`, `olvq1`, `lvq3`, `lvqtest`

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```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))) cd <- lvqinit(train, cl, 10) lvqtest(cd, train) cd0 <- olvq1(train, cl, cd) lvqtest(cd0, train) cd2 <- lvq2(train, cl, cd0) lvqtest(cd2, train) ```

### Example output

``` [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 c c c c c c c c c c c
[39] c c c c c c c c c c c c v c v v v v c v v v v v v c c v v v v c v c v c v
Levels: c s v
[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 c c c c c c c c c c c
[39] c c c c c c c c c c c c v v v v v v c v v v v v v v v v v v v v v v v v v
Levels: c s v
[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 c c c c c c c c c c c
[39] c c c c c c c c c c c c v v v v v v c v v v v v v v v v v v v v v v v v v
Levels: c s v
```

class documentation built on Jan. 13, 2022, 9:07 a.m.