Examples of SparseMatrix

ch.akuhn.hapax.linalg.SparseMatrix
ch.akuhn.matrix.SparseMatrix
com.rapidminer.data.SparseMatrix
edu.ucla.sspace.matrix.SparseMatrix
An interface for sparse {@code Matrix} implementations whose backing datastorage permits accessing rows and columns with {@link SparseVector} objects. @see Matrix @see SparseDoubleVector @author DavidJurgens
org.apache.mahout.math.SparseMatrix
Doubly sparse matrix. Implemented as a Map of RandomAccessSparseVector rows
org.ar.redoexperiments.tools.SparseMatrix
org.fnlp.ml.types.sv.SparseMatrix
类似于matlab的多维稀疏矩阵列向量优先 @author xpqiu @version 1.0SparseMatrix package edu.fudan.ml.types

Examples of edu.ucla.sspace.matrix.SparseMatrix

        }


        Properties props = System.getProperties();
        Assignments assignments = null;
        if (sparseVectors.size() > 0) {
            SparseMatrix matrix = Matrices.asSparseMatrix(sparseVectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);

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Examples of edu.ucla.sspace.matrix.SparseMatrix


        /**
         * {@inheritDoc}
         */
        public void processSpace(Properties properties) {
            SparseMatrix cleanedMatrix = (SparseMatrix) transform.transform(
                    cooccurrenceMatrix);
            for (String term : basis.keySet()) {
                int index = basis.getDimension(term);
                SparseDoubleVector sdv = cleanedMatrix.getRowVector(index);


                double score = 0;
                for (int i : sdv.getNonZeroIndices())
                    score += sdv.get(i);

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Examples of edu.ucla.sspace.matrix.SparseMatrix


        /**
         * {@inheritDoc}
         */
        public void processSpace(Properties properties) {
            SparseMatrix cleanedMatrix = (SparseMatrix) transform.transform(
                    cooccurrenceMatrix);
            for (String term : basis.keySet()) {
                int index = basis.getDimension(term);
                SparseDoubleVector sdv = cleanedMatrix.getRowVector(index);


                double score = 0;
                for (int i : sdv.getNonZeroIndices())
                    score += sdv.get(i);

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Examples of org.apache.mahout.math.SparseMatrix

    reader.close();
    
    // read the class matrix
    reader = new SequenceFile.Reader(fs, classVectorPath, conf);
    IntWritable label = new IntWritable();
    Matrix matrix = new SparseMatrix(new int[] {labelCount, featureCount});
    while (reader.next(label, value)) {
      matrix.assignRow(label.get(), value.get());
    }
    reader.close();
    
    model.setWeightMatrix(matrix);

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Examples of org.apache.mahout.math.SparseMatrix

      alphaI = in.readFloat();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = VectorWritable.readVector(in);
      perLabelThetaNormalizer = VectorWritable.readVector(in);


      weightsPerLabelAndFeature = new SparseMatrix(weightsPerLabel.size(), weightsPerFeature.size() );
      for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
        weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
      }
    } finally {
      Closeables.closeQuietly(in);

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Examples of org.apache.mahout.math.SparseMatrix

    }


    Preconditions.checkNotNull(scoresPerFeature);
    Preconditions.checkNotNull(scoresPerLabel);


    Matrix scoresPerLabelAndFeature = new SparseMatrix(scoresPerLabel.size(), scoresPerFeature.size());
    for (Pair<IntWritable,VectorWritable> entry : new SequenceFileDirIterable<IntWritable,VectorWritable>(
        new Path(base, TrainNaiveBayesJob.SUMMED_OBSERVATIONS), PathType.LIST, PathFilters.partFilter(), conf)) {
      scoresPerLabelAndFeature.assignRow(entry.getFirst().get(), entry.getSecond().get());
    }


    Vector perlabelThetaNormalizer = null;
    for (Pair<Text,VectorWritable> entry : new SequenceFileDirIterable<Text,VectorWritable>(
        new Path(base, TrainNaiveBayesJob.THETAS), PathType.LIST, PathFilters.partFilter(), conf)) {

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Examples of org.apache.mahout.math.SparseMatrix

    Matrix em = model.getEmissionMatrix();
    Matrix tr = model.getTransitionMatrix();
    // allocate the sparse data structures
    RandomAccessSparseVector sparseIp = new RandomAccessSparseVector(model
        .getNrOfHiddenStates());
    SparseMatrix sparseEm = new SparseMatrix(new int[]{
        model.getNrOfHiddenStates(), model.getNrOfOutputStates()});
    SparseMatrix sparseTr = new SparseMatrix(new int[]{
        model.getNrOfHiddenStates(), model.getNrOfHiddenStates()});
    // now transfer the values
    for (int i = 0; i < model.getNrOfHiddenStates(); ++i) {
      double value = ip.getQuick(i);
      if (value > threshold) {
        sparseIp.setQuick(i, value);
      }
      for (int j = 0; j < model.getNrOfHiddenStates(); ++j) {
        value = tr.getQuick(i, j);
        if (value > threshold) {
          sparseTr.setQuick(i, j, value);
        }
      }


      for (int j = 0; j < model.getNrOfOutputStates(); ++j) {
        value = em.getQuick(i, j);

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Examples of org.apache.mahout.math.SparseMatrix

      }
    }
  }


  public Matrix buildPreferenceVectorForUser(long realId) throws TasteException {
    Matrix ids = new SparseMatrix(1, dataModel.getNumItems());
    for (Preference pref : dataModel.getPreferencesFromUser(realId)) {
      int itemidx = itemIndex(pref.getItemID());
      ids.setQuick(0, itemidx, pref.getValue());
    }
    return ids;
  }

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Examples of org.apache.mahout.math.SparseMatrix

    return ids;
  }


  private Matrix buildConfidenceMatrixForItem(long itemId) throws TasteException {
    PreferenceArray prefs = dataModel.getPreferencesForItem(itemId);
    Matrix confidenceMatrix = new SparseMatrix(dataModel.getNumUsers(), dataModel.getNumUsers());
    for (Preference pref : prefs) {
      long userId = pref.getUserID();
      int userIdx = userIndex(userId);
      confidenceMatrix.setQuick(userIdx, userIdx, 1);
    }
    return new DiagonalMatrix(confidenceMatrix);
  }

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Examples of org.apache.mahout.math.SparseMatrix

    return new DiagonalMatrix(confidenceMatrix);
  }


  private Matrix buildConfidenceMatrixForUser(long userId) throws TasteException {
    PreferenceArray prefs = dataModel.getPreferencesFromUser(userId);
    Matrix confidenceMatrix = new SparseMatrix(dataModel.getNumItems(), dataModel.getNumItems());
    for (Preference pref : prefs) {
      long itemId = pref.getItemID();
      int itemIdx = itemIndex(itemId);
      confidenceMatrix.setQuick(itemIdx, itemIdx, 1);
    }
    return new DiagonalMatrix(confidenceMatrix);
  }

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