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gla - train codebooks for vector quantization using the generalized
Lloyd algorithm
gla [-i initialcodebookfile] [-s codebooksize]
[-I iterations] [-t threshold] trainingfile codebookfile
gla
implements the generalized Lloyd algorithm for training codebooks to
be used in vector quantization (see vqencode(1)
and vqdecode(1)
). trainingfile
is an input data file in DAT format which contains training vectors.
codebookfile is the output file in CBK format which contains the codewords
which form the codebook. Initial codewords are chosen at random,
unless initialcodebookfile is specified. The distortion measure which
gla attempts to minimize is the squared error.
- -i initialcodebookfile
- String. Initial codebook. Note: one (and only one) of -i and -s must be
given.
- -s codebooksize
- Integer. Number of codewords to create for random
initial codebook. Note: one (and only one) of -i and -s must be given.
- -I iterations
- Integer. Traininging stops after iterations iterations
of the generalized Lloyd algorithm through the training data. Note: one
(and only one) of -t and -I must be given.
- -t threshold
- Float. Stop the
training process when the average distortion changes by less than threshold
from one iteration to the next; that is, stop if (D(i-1) - D(i))/D(i) < threshold,
where D(i) is the average distortion for the ith iteration and is averaged
over the entire training set. Note: one (and only one) of -t and -I must
be given.
vqencode(1)
, vqdecode(1)
, QccPackVQ(3)
, QccPack(3)
A. Gersho and R. Gray, Vector Quantization and Signal Compression. Norwell,
MA: Kluwer Academic Publishers, 1992.
Copyright (C) 1997-2021
James E. Fowler
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