lvqinit: Initialize a LVQ Codebook

In class: Functions for Classification

Initialize a LVQ Codebook

Description

Construct an initial codebook for LVQ methods.

Usage

lvqinit(x, cl, size, prior, k = 5)

Arguments

x	a matrix or data frame of training examples, n by p.
cl	the classifications for the training examples. A vector or factor of length n.
size	the size of the codebook. Defaults to min(round(0.4*ng*(ng-1 + p/2),0), n) where ng is the number of classes.
prior	Probabilities to represent classes in the codebook. Default proportions in the training set.
k	k used for k-NN test of correct classification. Default is 5.

Details

Selects size examples from the training set without replacement with proportions proportional to the prior or the original proportions.

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.

See Also

lvq1, lvq2, lvq3, olvq1, lvqtest

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)
cd1 <- olvq1(train, cl, cd)
lvqtest(cd1, train)

class documentation built on May 3, 2023, 5:09 p.m.