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 org.apache.mahout.math.SparseMatrix

    }
    return new DiagonalMatrix(confidenceMatrix);
  }


  private Matrix buildPreferenceVectorForItem(long realId) throws TasteException {
    Matrix ids = new SparseMatrix(1, dataModel.getNumUsers());
    for (Preference pref : dataModel.getPreferencesForItem(realId)) {
      int useridx = userIndex(pref.getUserID());
      ids.setQuick(0, useridx, pref.getValue());
    }
    return ids;
  }

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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(model.getNrOfHiddenStates(), model.getNrOfOutputStates());
    SparseMatrix sparseTr = new SparseMatrix(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

    }


    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 = scoresPerLabel.like();
    /* 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

  public ClosestCentroidBenchmark(VectorBenchmarks mark) {
    this.mark = mark;
  }


  public void benchmark(DistanceMeasure measure) throws IOException {
    SparseMatrix clusterDistances = new SparseMatrix(mark.numClusters, mark.numClusters);
    for (int i = 0; i < mark.numClusters; i++) {
      for (int j = 0; j < mark.numClusters; j++) {
        double distance = Double.POSITIVE_INFINITY;
        if (i != j) {
          distance = measure.distance(mark.clusters[i], mark.clusters[j]);
        }
        clusterDistances.setQuick(i, j, distance);
      }
    }


    long distanceCalculations = 0;
    TimingStatistics stats = new TimingStatistics();
    for (int l = 0; l < mark.loop; l++) {
      TimingStatistics.Call call = stats.newCall(mark.leadTimeUsec);
      for (int i = 0; i < mark.numVectors; i++) {
        Vector vector = mark.vectors[1][mark.vIndex(i)];
        double minDistance = Double.MAX_VALUE;
        for (int k = 0; k < mark.numClusters; k++) {
          double distance = measure.distance(vector, mark.clusters[k]);
          distanceCalculations++;
          if (distance < minDistance) {
            minDistance = distance;
          }
        }
      }
      if (call.end(mark.maxTimeUsec)) {
        break;
      }
    }
    mark.printStats(stats, measure.getClass().getName(), "Closest C w/o Elkan's trick", "distanceCalculations = "
        + distanceCalculations);


    distanceCalculations = 0;
    stats = new TimingStatistics();
    Random rand = RandomUtils.getRandom();
    for (int l = 0; l < mark.loop; l++) {
      TimingStatistics.Call call = stats.newCall(mark.leadTimeUsec);
      for (int i = 0; i < mark.numVectors; i++) {
        Vector vector = mark.vectors[1][mark.vIndex(i)];
        int closestCentroid = rand.nextInt(mark.numClusters);
        double dist = measure.distance(vector, mark.clusters[closestCentroid]);
        distanceCalculations++;
        for (int k = 0; k < mark.numClusters; k++) {
          if (closestCentroid != k) {
            double centroidDist = clusterDistances.getQuick(k, closestCentroid);
            if (centroidDist < 2 * dist) {
              dist = measure.distance(vector, mark.clusters[k]);
              closestCentroid = k;
              distanceCalculations++;
            }

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Examples of org.apache.mahout.math.SparseMatrix


  @Test
  public void addLambdaTimesNuiTimesE() {
    int nui = 5;
    double lambda = 0.2;
    Matrix matrix = new SparseMatrix(5, 5);


    AlternatingLeastSquaresSolver.addLambdaTimesNuiTimesE(matrix, lambda, nui);


    for (int n = 0; n < 5; n++) {
      assertEquals(1.0, matrix.getQuick(n, n), EPSILON);
    }
  }

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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

      randomVectorValues.add(values);
      randomVectors.add(v);
    }
    vectors = new Vector[3][numVectors];
    clusters = new Vector[numClusters];
    clusterDistances = new SparseMatrix(numClusters, numClusters);
  }

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Examples of org.apache.mahout.math.SparseMatrix

    return 0;
  }


  private Matrix readMatrix(Path dir) throws IOException {


    Matrix matrix = new SparseMatrix(new int[] { Integer.MAX_VALUE, Integer.MAX_VALUE });


    FileSystem fs = dir.getFileSystem(getConf());
    for (FileStatus seqFile : fs.globStatus(new Path(dir, "part-*"))) {
      Path path = seqFile.getPath();
      SequenceFile.Reader reader = null;
      try {
        reader = new SequenceFile.Reader(fs, path, getConf());
        IntWritable key = new IntWritable();
        VectorWritable value = new VectorWritable();
        while (reader.next(key, value)) {
          int row = key.get();
          Iterator<Vector.Element> elementsIterator = value.get().iterateNonZero();
          while (elementsIterator.hasNext()) {
            Vector.Element element = elementsIterator.next();
            matrix.set(row, element.index(), element.get());
          }
        }
      } finally {
        IOUtils.quietClose(reader);
      }

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Examples of org.apache.mahout.math.SparseMatrix

        labelCount = value.get().size();
      }
    }


    // read the class matrix
    Matrix matrix = new SparseMatrix(new int[] {labelCount, featureCount});
    for (Pair<IntWritable,VectorWritable> record
         : new SequenceFileIterable<IntWritable,VectorWritable>(classVectorPath, true, conf)) {
      IntWritable label = record.getFirst();
      VectorWritable value = record.getSecond();
      matrix.assignRow(label.get(), value.get());
    }
    
    model.setWeightMatrix(matrix);


    // read theta normalizer

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Examples of org.apache.mahout.math.SparseMatrix


  @Test
  public void addLambdaTimesNuiTimesE() {
    int nui = 5;
    double lambda = 0.2;
    Matrix matrix = new SparseMatrix(new int[] { 5, 5 });


    solver.addLambdaTimesNuiTimesE(matrix, lambda, nui);


    for (int n = 0; n < 5; n++) {
      assertEquals(1.0, matrix.getQuick(n, n), EPSILON);
    }
  }

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