Examples of SparseRowMatrix

mikera.matrixx.impl.SparseRowMatrix
Matrix stored as a sparse collection of sparse row vectors. This format is especially efficient for: - innerProduct() with another matrix, especially one with efficient column access like SparseColumnMatrix - access via getRow() operation - transpose into SparseColumnMatrix @author Mike
org.apache.mahout.math.SparseRowMatrix
sparse matrix with general element values whose rows are accessible quickly. Implemented as a row array of either SequentialAccessSparseVectors or RandomAccessSparseVectors.

Examples of org.apache.mahout.math.SparseRowMatrix

    numRows++;
    Vector[] rowVectors = new Vector[numRows];
    for (Pair<Integer, Vector> pair : rowList) {
      rowVectors[pair.getFirst()] = pair.getSecond();
    }
    return new SparseRowMatrix(numRows, numCols, rowVectors, true, !sequentialAccess);


  }

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Examples of org.apache.mahout.math.SparseRowMatrix

  public static Matrix randomSequentialAccessSparseMatrix(int numRows,
                                                          int nonNullRows,
                                                          int numCols,
                                                          int entriesPerRow,
                                                          double entryMean) {
    Matrix m = new SparseRowMatrix(numRows, numCols);
    //double n = 0;
    Random r = RandomUtils.getRandom();
    for (int i = 0; i < nonNullRows; i++) {
      Vector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.viewRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;

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Examples of org.apache.mahout.math.SparseRowMatrix

   * </pre>
   */
  private void explicitExample(int numThreads) throws Exception {


    Double na = Double.NaN;
    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, na }),
        new DenseVector(new double[] { 2.0, na,  3.0, 5.0 }),
        new DenseVector(new double[] { na,  5.0, na,  3.0 }),
        new DenseVector(new double[] { 3.0, na,  na,  5.0 }) });


    writeLines(inputFile, preferencesAsText(preferences));


    ParallelALSFactorizationJob alsFactorization = new ParallelALSFactorizationJob();
    alsFactorization.setConf(conf);


    int numFeatures = 3;
    int numIterations = 5;
    double lambda = 0.065;


    alsFactorization.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "--tempDir", tmpDir.getAbsolutePath(), "--lambda", String.valueOf(lambda),
        "--numFeatures", String.valueOf(numFeatures), "--numIterations", String.valueOf(numIterations),
        "--numThreadsPerSolver", String.valueOf(numThreads) });


    Matrix u = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "U/part-m-00000"),
        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "M/part-m-00000"),
        preferences.numCols(), numFeatures);


    StringBuilder info = new StringBuilder();
    info.append("\nA - users x items\n\n");
    info.append(MathHelper.nice(preferences));
    info.append("\nU - users x features\n\n");

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Examples of org.apache.mahout.math.SparseRowMatrix

  public void completeJobImplicitToyExampleMultithreaded() throws Exception {
    implicitExample(2);
  }


  public void implicitExample(int numThreads) throws Exception {
    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });


    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });

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Examples of org.apache.mahout.math.SparseRowMatrix

    }
  }


  public static Matrix sampledCorpus(Matrix matrix, Random random,
      int numDocs, int numSamples, int numTopicsPerDoc) {
    Matrix corpus = new SparseRowMatrix(numDocs, matrix.numCols());
    LDASampler modelSampler = new LDASampler(matrix, random);
    Vector topicVector = new DenseVector(matrix.numRows());
    for (int i = 0; i < numTopicsPerDoc; i++) {
      int topic = random.nextInt(topicVector.size());
      topicVector.set(topic, topicVector.get(topic) + 1);
    }
    for (int docId = 0; docId < numDocs; docId++) {
      for (int sample : modelSampler.sample(topicVector, numSamples)) {
        corpus.set(docId, sample, corpus.get(docId, sample) + 1);
      }
    }
    return corpus;
  }

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Examples of org.apache.mahout.math.SparseRowMatrix

    File outputDir = getTestTempDir("output");
    outputDir.delete();
    File tmpDir = getTestTempDir("tmp");


    Double na = Double.NaN;
    Matrix preferences = new SparseRowMatrix(new int[] { 4, 4 }, new Vector[] {
        new DenseVector(new double[] {5.0, 5.0, 2.0,  na }),
        new DenseVector(new double[] {2.0,  na, 3.0, 5.0 }),
        new DenseVector(new double[] { na, 5.0,  na, 3.0 }),
        new DenseVector(new double[] {3.0,  na,  na, 5.0 }) });


    StringBuilder prefsAsText = new StringBuilder();
    String separator = "";
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
        if (!Double.isNaN(e.get())) {
          prefsAsText.append(separator).append(slice.index()).append(',').append(e.index()).append(',').append(e.get());
          separator = "\n";
        }
      }
    }
    logger.info("Input matrix:\n" + prefsAsText);
    writeLines(inputFile, prefsAsText.toString());


    ParallelALSFactorizationJob alsFactorization = new ParallelALSFactorizationJob();


    Configuration conf = new Configuration();
    conf.set("mapred.input.dir", inputFile.getAbsolutePath());
    conf.set("mapred.output.dir", outputDir.getAbsolutePath());
    conf.setBoolean("mapred.output.compress", false);


    alsFactorization.setConf(conf);
    int numFeatures = 3;
    int numIterations = 5;
    double lambda = 0.065;
    alsFactorization.run(new String[] { "--tempDir", tmpDir.getAbsolutePath(), "--lambda", String.valueOf(lambda),
        "--numFeatures", String.valueOf(numFeatures), "--numIterations", String.valueOf(numIterations) });


    Matrix u = MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "U/part-r-00000"),
        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "M/part-r-00000"),
      preferences.numCols(), numFeatures);


    RunningAverage avg = new FullRunningAverage();
    sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();

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Examples of org.apache.mahout.math.SparseRowMatrix

    if (inMemory) {
      List<Vector> eigenVectors = new ArrayList<Vector>();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(new int[] {eigenVectors.size(), eigenVectors.get(0).size()},
                                           eigenVectors.toArray(new Vector[eigenVectors.size()]),
                                           true,
                                           true);


    } else {

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Examples of org.apache.mahout.math.SparseRowMatrix

  public static Matrix randomSequentialAccessSparseMatrix(int numRows,
                                                          int nonNullRows,
                                                          int numCols,
                                                          int entriesPerRow,
                                                          double entryMean) {
    SparseRowMatrix m = new SparseRowMatrix(new int[]{numRows, numCols});
    //double n = 0;
    //Random r = RandomUtils.getRandom();
    Random r = new Random(1234L);
    for (int i = 0; i < nonNullRows; i++) {
      SequentialAccessSparseVector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.getRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;

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Examples of org.apache.mahout.math.SparseRowMatrix

    if (inMemory) {
      List<Vector> eigenVectors = Lists.newArrayList();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(eigenVectors.size(), eigenVectors.get(0).size(),
          eigenVectors.toArray(new Vector[eigenVectors.size()]), true, true);


    } else {
      eigensToVerify = eigens;
    }

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Examples of org.apache.mahout.math.SparseRowMatrix

  }


  @Test
  public void toyExampleImplicit() throws Exception {


    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });


    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });


    double alpha = 20;


    ALSWRFactorizer factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 5, true, alpha);


    SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);


    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector().all()) {


        long userID = slice.index() + 1;

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