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/*
* Copyright 2009 David Jurgens
*
* This file is part of the S-Space package and is covered under the terms and
* conditions therein.
*
* The S-Space package is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 2 as published
* by the Free Software Foundation and distributed hereunder to you.
*
* THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
* EXPRESS OR IMPLIED ARE MADE. BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
* NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
* PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
* WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
* RIGHTS.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see
*
* - Required (at least one of):
*
*
* - {@code -d}, {@code --docFile=FILE[,FILE...]} a file where each line is
* a document. This is the preferred input format for large corpora
*
*
- {@code -f}, {@code --fileList=FILE[,FILE...]} a list of document files
* where each file is specified on its own line.
*
*
*
* - Algorithm Options:
*
*
* - {@code --dimensions=
} how many dimensions to use for the LSA
* vectors. See {@link LatentSemanticAnalysis} for default value
*
* - {@code --preprocess=
} specifies an instance of {@link
* edu.ucla.sspace.lsa.MatrixTransformer} to use in preprocessing the
* word-document matrix compiled by LSA prior to computing the SVD. See
* {@link LatentSemanticAnalysis} for default value
*
* - {@code -F}, {@code --tokenFilter=FILE[include|exclude][,FILE...]}
* specifies a list of one or more files to use for {@link
* edu.ucla.sspace.text.TokenFilter filtering} the documents. An option
* flag may be added to each file to specify how the words in the filter
* filter should be used: {@code include} if only the words in the filter
* file should be retained in the document; {@code exclude} if only the
* words not in the filter file should be retained in the
* document.
*
*
- {@code -S}, {@code --svdAlgorithm}={@link
* edu.ucla.sspace.matrix.SVD.Algorithm} species a specific {@code
* SVD.Algorithm} method to use when reducing the dimensionality in LSA.
* In general, users should not need to specify this option, as the
* default setting will choose the fastest algorithm available on the
* system. This is only provided as an advanced option for users who
* want to compare the algorithms' performance or any variations between
* the SVD results.
*
*
*
* - Program Options:
*
*
* - {@code -o}, {@code --outputFormat=}text|binary} Specifies the
* output formatting to use when generating the semantic space ({@code
* .sspace}) file. See {@link edu.ucla.sspace.common.SemanticSpaceUtils
* SemanticSpaceUtils} for format details.
*
*
- {@code -t}, {@code --threads=INT} how many threads to use when
* processing the documents. The default is one per core.
*
*
- {@code -w}, {@code --overwrite=BOOL} specifies whether to overwrite
* the existing output files. The default is {@code true}. If set to
* {@code false}, a unique integer is inserted into the file name.
*
*
- {@code -v}, {@code --verbose} specifies whether to print runtime
* information to standard out
*
*
*
*
*
*
*
* An invocation will produce one file as output {@code
* lsa-semantic-space.sspace}. If {@code overwrite} was set to {@code true},
* this file will be replaced for each new semantic space. Otherwise, a new
* output file of the format {@code lsa-semantic-space.sspace} will be
* created, where {@code } is a unique identifier for that program's
* invocation. The output file will be placed in the directory specified on the
* command line.
*
*
*
* This class is desgined to run multi-threaded and performs well with one
* thread per core, which is the default setting.
*
* @see LatentSemanticAnalysis
* @see edu.ucla.sspace.matrix.Transform Transform
*
* @author David Jurgens
*/
public class LSAMain extends GenericMain {
private BasisMapping basis;
private LSAMain() {
}
/**
* Adds all of the options to the {@link ArgOptions}.
*/
protected void addExtraOptions(ArgOptions options) {
options.addOption('n', "dimensions",
"the number of dimensions in the semantic space",
true, "INT", "Algorithm Options");
options.addOption('p', "preprocess", "a MatrixTransform class to "
+ "use for preprocessing", true, "CLASSNAME",
"Algorithm Options");
options.addOption('S', "svdAlgorithm", "a specific SVD algorithm to use"
, true, "SVD.Algorithm",
"Advanced Algorithm Options");
options.addOption('B', "saveTermBasis",
"If true, the term basis mapping will be stored " +
"to the given file name",
true, "FILE", "Optional");
}
public static void main(String[] args) throws Exception {
LSAMain lsa = new LSAMain();
lsa.run(args);
}
protected SemanticSpace getSpace() {
try {
int dimensions = argOptions.getIntOption("dimensions", 300);
Transform transform = new LogEntropyTransform();
if (argOptions.hasOption("preprocess"))
transform = ReflectionUtil.getObjectInstance(
argOptions.getStringOption("preprocess"));
String algName = argOptions.getStringOption("svdAlgorithm", "ANY");
MatrixFactorization factorization = SVD.getFactorization(
Algorithm.valueOf(algName.toUpperCase()));
basis = new StringBasisMapping();
return new LatentSemanticAnalysis(
false, dimensions, transform, factorization, false, basis);
} catch (IOException ioe) {
throw new IOError(ioe);
}
}
/**
* Returns the {@likn SSpaceFormat.BINARY binary} format as the default
* format of a {@code LatentSemanticAnalysis} space.
*/
protected SSpaceFormat getSpaceFormat() {
return SSpaceFormat.BINARY;
}
protected void postProcessing() {
if (argOptions.hasOption('B'))
SerializableUtil.save(basis, argOptions.getStringOption('B'));
}
/**
* {@inheritDoc}
*/
protected String getAlgorithmSpecifics() {
return
"The --svdAlgorithm provides a way to manually specify which " +
"algorithm should\nbe used internally. This option should not be" +
" used normally, as LSA will\nselect the fastest algorithm " +
"available. However, in the event that it\nis needed, valid" +
" options are: SVDLIBC, SVDLIBJ, MATLAB, OCTAVE, JAMA and COLT\n";
}
}