Package org.carrot2.matrix.factorization

Examples of org.carrot2.matrix.factorization.KMeansMatrixFactorization

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    /**
     * Creates a {@link SpriteBuilder} with the provided parameters and log.
     */
    public SpriteBuilder(SmartSpritesParameters parameters, MessageLog messageLog)
    {
        this(parameters, messageLog, new FileSystemResourceHandler(
            parameters.getDocumentRootDir(), parameters.getCssFileEncoding(), messageLog));
    }
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    /**
     * Creates a {@link SpriteBuilder} with the provided parameters and log.
     */
    public SpriteBuilder(SmartSpritesParameters parameters, MessageLog messageLog)
    {
        this(parameters, messageLog, new FileSystemResourceHandler(
            parameters.getDocumentRootDir(), parameters.getCssFileEncoding(), messageLog));
    }
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    @Before
    public void prepare()
    {
        spriteDirectiveOccurrenceCollector = new SpriteDirectiveOccurrenceCollector(
            messageLog, new FileSystemResourceHandler(null,
                SmartSpritesParameters.DEFAULT_CSS_FILE_ENCODING, messageLog));
    }
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        this.maxIterations = maxIterations;
    }

    public void seed(DoubleMatrix2D A, DoubleMatrix2D U, DoubleMatrix2D V)
    {
        KMeansMatrixFactorization kMeansMatrixFactorization = new KMeansMatrixFactorization(
                A);
        kMeansMatrixFactorization.setK(U.columns());
        kMeansMatrixFactorization.setMaxIterations(maxIterations);
        kMeansMatrixFactorization.compute();

        U.assign(kMeansMatrixFactorization.getU());
        for (int r = 0; r < U.rows(); r++)
        {
            for (int c = 0; c < U.columns(); c++)
            {
                if (U.getQuick(r, c) < 0.001)
                {
                    U.setQuick(r, c, 0.05);
                }
            }
        }

        V.assign(kMeansMatrixFactorization.getV());
        for (int r = 0; r < V.rows(); r++)
        {
            for (int c = 0; c < V.columns(); c++)
            {
                if (V.getQuick(r, c) == 0)
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       */
    case ARABIC:
      // Intentional fall-through.

    default:
      return new ExtendedWhitespaceTokenizer();
    }
  }
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    static ITokenizer createTokenizer() {
      try {
        return new ChineseTokenizer();
      } catch (Throwable e) {
        return new ExtendedWhitespaceTokenizer();
      }
    }
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    }
    return solrStopWords.get(fieldName);
  }

  public ILexicalData getLexicalData(LanguageCode languageCode) {
    final ILexicalData carrot2LexicalData = carrot2LexicalDataFactory
        .getLexicalData(languageCode);

    return new ILexicalData() {
      public boolean isStopLabel(CharSequence word) {
        // Nothing in Solr maps to the concept of a stop label,
        // so return Carrot2's default here.
        return carrot2LexicalData.isStopLabel(word);
      }

      public boolean isCommonWord(MutableCharArray word) {
        // Loop over the fields involved in clustering first
        for (String fieldName : fieldNames) {
          for (CharArraySet stopWords : getSolrStopWordsForField(fieldName)) {
            if (stopWords.contains(word)) {
              return true;
            }
          }
        }
        // Check default Carrot2 stop words too
        return carrot2LexicalData.isCommonWord(word);
      }
    };
  }
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      return;
    }

    // Test with Maltese so that the English clustering performed in other tests
    // is not affected by the test stopwords and stoplabels.
    ILexicalData lexicalData = preprocessing.lexicalDataFactory
        .getLexicalData(LanguageCode.MALTESE);

    for (String word : wordsToCheck.split(",")) {
      if (!lexicalData.isCommonWord(new MutableCharArray(word))
          && !lexicalData.isStopLabel(word)) {
        clusters.add(new Cluster(word));
      }
    }
  }
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     * one <code>language</code>.
     */
    private void cluster(LanguageCode language)
    {
        // Preprocessing of documents
        final PreprocessingContext context = preprocessingPipeline.preprocess(documents,
            query, language);

        // Further processing only if there are words to process
        clusters = Lists.newArrayList();
        if (context.hasLabels())
        {
            // Term-document matrix building and reduction
            final VectorSpaceModelContext vsmContext = new VectorSpaceModelContext(
                context);
            final ReducedVectorSpaceModelContext reducedVsmContext = new ReducedVectorSpaceModelContext(
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public class SimpleLabelAssigner implements ILabelAssigner
{
    public void assignLabels(LingoProcessingContext context, DoubleMatrix2D stemCos,
        IntIntOpenHashMap filteredRowToStemIndex, DoubleMatrix2D phraseCos)
    {
        final PreprocessingContext preprocessingContext = context.preprocessingContext;
        final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
        final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
        final int [] mostFrequentOriginalWordIndex = preprocessingContext.allStems.mostFrequentOriginalWordIndex;
        final int desiredClusterCount = stemCos.columns();
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Related Classes of org.carrot2.matrix.factorization.KMeansMatrixFactorization

  • com.carrotsearch.hppc.BitSet
  • com.carrotsearch.hppc.IntArrayList
  • com.carrotsearch.hppc.IntIntOpenHashMap
  • com.carrotsearch.hppc.IntStack
  • com.tamingtext.carrot2.Carrot2ExampleTest
  • org.apache.http.message.BasicNameValuePair
  • org.apache.mahout.math.matrix.DoubleMatrix2D
  • org.apache.mahout.math.matrix.impl.DenseDoubleMatrix2D
  • org.carrot2.clustering.kmeans.BisectingKMeansClusteringAlgorithm
  • org.carrot2.clustering.kmeans.BisectingKMeansClusteringAlgorithmTest
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