# lvq1 {class}

### Description

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

### Usage

lvq1(x, cl, codebk, niter = 100 * nrow(codebk$x), alpha = 0.03)

### 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

### Details

Selects `niter`

examples at random with replacement, and adjusts the nearest example in the codebook for each.

### Values

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.

### Examples

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) cd1 <- lvq1(train, cl, cd0) lvqtest(cd1, train)

Documentation reproduced from package class, version 7.3-14. License: GPL-2 | GPL-3