

   IInniittiiaalliizzee aa LLVVQQ CCooddeebbooookk

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

   AArrgguummeennttss::

          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 code-
             book. Default proportions in the training set.

          k: k used for k-NN test of correct classification.
             Default is 5.

   DDeessccrriippttiioonn::

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

   VVaalluuee::

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

   RReeffeerreenncceess::

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

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

   SSeeee AAllssoo::

        `lvq1', `lvq2', `lvq3', `olvq1', `lvqtest'

   EExxaammpplleess::

        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)

