Package org.apache.mahout.math

Examples of org.apache.mahout.math.Matrix.numRows()

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    ratings.setQuick(3, 3.0);
    ratings.setQuick(5, 5.0);

    Matrix riIiMaybeTransposed = AlternatingLeastSquaresSolver.createRiIiMaybeTransposed(ratings);
    assertEquals(1, riIiMaybeTransposed.numCols(), 1);
    assertEquals(3, riIiMaybeTransposed.numRows(), 3);

    assertEquals(1.0, riIiMaybeTransposed.getQuick(0, 0), EPSILON);
    assertEquals(3.0, riIiMaybeTransposed.getQuick(1, 0), EPSILON);
    assertEquals(5.0, riIiMaybeTransposed.getQuick(2, 0), EPSILON);
  }
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    // which specifically details the case of covariance matrix inversion
    // Complexity: O(min(nm2,mn2))
    SingularValueDecomposition svd = new SingularValueDecomposition(m);
    Matrix sInv = svd.getS();
    // Inverse Diagonal Elems
    for (int i = 0; i < sInv.numRows(); i++) {
      double diagElem = sInv.get(i,i);
      if (diagElem > 0.0) {
        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
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            .stepOffset(10)
            .decayExponent(0.7)
            .lambda(1 * 1.0e-3)
            .learningRate(5);
    int k = 0;
    int[] ordering = permute(gen, data.numRows());
    for (int epoch = 0; epoch < 100; epoch++) {
      for (int row : ordering) {
        lr.train(row, (int) data.get(row, 9), data.viewRow(row));
        System.out.printf("%d,%d,%.3f\n", epoch, k++, lr.auc());
      }
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    for(int row = 0; row < eigenVectors.numRows(); row++) {
      Vector oldEigen = eigenVectors.getRow(row);
      if(oldEigen == null) {
        break;
      }
      for(int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.getRow(newRow);
        if(newEigen != null) {
          if(oldEigen.dot(newEigen) > 0.9) {
            oldEigensFound.add(row);
            break;
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    SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, svdData, IntWritable.class, VectorWritable.class);
    try {
      IntWritable key = new IntWritable();
      VectorWritable value = new VectorWritable();

      for (int row = 0; row < sData.numRows(); row++) {
        key.set(row);
        value.set(sData.getRow(row));
        writer.append(key, value);
      }
    } finally {
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    ratings.setQuick(3, 3.0);
    ratings.setQuick(5, 5.0);

    Matrix riIiMaybeTransposed = solver.createRiIiMaybeTransposed(ratings);
    assertEquals(1, riIiMaybeTransposed.numCols(), 1);
    assertEquals(3, riIiMaybeTransposed.numRows(), 3);

    assertEquals(1.0, riIiMaybeTransposed.getQuick(0, 0), EPSILON);
    assertEquals(3.0, riIiMaybeTransposed.getQuick(1, 0), EPSILON);
    assertEquals(5.0, riIiMaybeTransposed.getQuick(2, 0), EPSILON);
  }
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    Matrix similarityMatrix =
      MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "part-r-00000"), 3, 3);
   
    assertNotNull(similarityMatrix);
    assertEquals(3, similarityMatrix.numCols());
    assertEquals(3, similarityMatrix.numRows());

    assertEquals(1.0, similarityMatrix.get(0, 0), EPSILON);
    assertEquals(1.0, similarityMatrix.get(1, 1), EPSILON);
    assertEquals(1.0, similarityMatrix.get(2, 2), EPSILON);
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    Matrix similarityMatrix =
        MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "part-r-00000"), 3, 3);
   
    assertNotNull(similarityMatrix);
    assertEquals(3, similarityMatrix.numCols());
    assertEquals(3, similarityMatrix.numRows());

    assertEquals(0.0, similarityMatrix.get(0, 0), EPSILON);
    assertEquals(0.5, similarityMatrix.get(0, 1), EPSILON);
    assertEquals(0.0, similarityMatrix.get(0, 2), EPSILON);
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    Pair<List<List<WeightedThing<Vector>>>, Long> reference = getResultsAndRuntime(bruteSearcher, queries);

    Pair<List<WeightedThing<Vector>>, Long> referenceSearchFirst =
        getResultsAndRuntimeSearchFirst(bruteSearcher, queries);

    double bruteSearchAvgTime = reference.getSecond() / (queries.numRows() * 1.0);
    System.out.printf("BruteSearch: avg_time(1 query) %f[s]\n", bruteSearchAvgTime);

    return Arrays.asList(new Object[][]{
        // NUM_PROJECTIONS = 3
        // SEARCH_SIZE = 10
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    Matrix similarityMatrix = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "part-r-00000"), 3, 3);

    assertNotNull(similarityMatrix);
    assertEquals(3, similarityMatrix.numCols());
    assertEquals(3, similarityMatrix.numRows());

    assertEquals(1.0, similarityMatrix.get(0, 0), EPSILON);
    assertEquals(1.0, similarityMatrix.get(1, 1), EPSILON);
    assertEquals(1.0, similarityMatrix.get(2, 2), EPSILON);
    assertEquals(0.0, similarityMatrix.get(2, 0), EPSILON);
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