Package org.apache.mahout.math

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

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    Matrix similarityMatrix =
        MathHelper.readEntries(fs, 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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            .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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    // 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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      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));

      weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size());
      for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
        weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
      }
    } finally {
      Closeables.close(in, true);
    }
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        new SequenceFileValueIterator<MatrixWritable>(inputPath, true, new Configuration());
    Matrix m = it.next().get();
    it.close();
    PrintStream ps = getPrintStream(outputFile);
    String[] columnLabels = getLabels(m.numCols(), m.getColumnLabelBindings(), "col");
    String[] rowLabels = getLabels(m.numRows(), m.getRowLabelBindings(), "row");
    if (doLabels) {
      ps.print("rowid,");
      ps.print(columnLabels[0]);
      for (int c = 1; c < m.numCols(); c++) {
        ps.print(',' + columnLabels[c]);
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      for (int c = 1; c < m.numCols(); c++) {
        ps.print(',' + columnLabels[c]);
      }
      ps.println();
    }
    for (int r = 0; r < m.numRows(); r++) {
      if (doLabels) {
        ps.print(rowLabels[0] + ',');
      }
      ps.print(Double.toString(m.getQuick(r,0)));
      for (int c = 1; c < m.numCols(); c++) {
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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.viewRow(row);
      if (oldEigen == null) {
        break;
      }
      for (int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.viewRow(newRow);
        if (newEigen != null && oldEigen.dot(newEigen) > 0.9) {
          oldEigensFound.add(row);
          break;
        }
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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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    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(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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