Package org.apache.mahout.math

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

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                                                             boolean isSymmetric,
                                                             String baseTmpDir) throws IOException {
    baseTmpDir = TESTDATA + baseTmpDir;
    Matrix c = SolverTest.randomSequentialAccessSparseMatrix(numRows, nonNullRows, numCols, entriesPerRow, entryMean);
    if(isSymmetric) {
      c = c.times(c.transpose());
    }
    final Matrix m = c;
    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);
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                                                      boolean isSymmetric,
                                                      String baseTmpDirSuffix) throws IOException {
    Path baseTmpDirPath = getTestTempDirPath(baseTmpDirSuffix);
    Matrix c = SolverTest.randomSequentialAccessSparseMatrix(numRows, nonNullRows, numCols, entriesPerRow, entryMean);
    if(isSymmetric) {
      c = c.times(c.transpose());
    }
    return saveToFs(c, baseTmpDirPath);
  }

  private static DistributedRowMatrix saveToFs(final Matrix m, Path baseTmpDirPath) throws IOException {
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    GivensThinSolver qrSolver = new GivensThinSolver(m.rowSize(), m.columnSize());
    qrSolver.solve(m);

    Matrix qtm = new DenseMatrix(qrSolver.getThinQtTilde());

    assertOrthonormality(qtm.transpose(), false, SVD_EPSILON);

    Matrix aClone = new DenseMatrix(qrSolver.getThinQtTilde()).transpose()
        .times(qrSolver.getRTilde());

    System.out.println("aclone : " + aClone);
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      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if(symmetric) {
      return matrix.times(matrix.transpose());
    }
    return matrix;
  }

  public static Matrix randomHierarchicalSymmetricMatrix(int size) {
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                                                      boolean isSymmetric,
                                                      String baseTmpDirSuffix) throws IOException {
    Path baseTmpDirPath = getTestTempDirPath(baseTmpDirSuffix);
    Matrix c = SolverTest.randomSequentialAccessSparseMatrix(numRows, nonNullRows, numCols, entriesPerRow, entryMean);
    if(isSymmetric) {
      c = c.times(c.transpose());
    }
    final Matrix m = c;
    Configuration conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);
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    // number is that squared.  This means that we don't get the exact answer with
    // a fast iterative solution.
    // Thus, we have to check the residuals rather than testing that the answer matched
    // the ideal.
    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    assertEquals(0, m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), 1.0e-7);

    // and we need to check that the error estimates are pretty good.
    assertEquals(m.times(x1).minus(b).norm(2), r.getResidualNorm(), 1.0e-5);
    assertEquals(m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), r.getNormalEquationResidual(), 1.0e-9);
  }
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    // number is that squared.  This means that we don't get the exact answer with
    // a fast iterative solution.
    // Thus, we have to check the residuals rather than testing that the answer matched
    // the ideal.
    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    assertEquals(0, m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), 1.0e-7);

    // and we need to check that the error estimates are pretty good.
    assertEquals(m.times(x1).minus(b).norm(2), r.getResidualNorm(), 1.0e-5);
    assertEquals(m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), r.getNormalEquationResidual(), 1.0e-9);
  }
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    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    assertEquals(0, m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), 1.0e-7);

    // and we need to check that the error estimates are pretty good.
    assertEquals(m.times(x1).minus(b).norm(2), r.getResidualNorm(), 1.0e-5);
    assertEquals(m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), r.getNormalEquationResidual(), 1.0e-9);
  }
 
  @Test
  public void random() {
    Matrix m = new DenseMatrix(200, 30).assign(Functions.random());
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    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    assertEquals(0, m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), 1.0e-7);

    // and we need to check that the error estimates are pretty good.
    assertEquals(m.times(x1).minus(b).norm(2), r.getResidualNorm(), 1.0e-5);
    assertEquals(m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2), r.getNormalEquationResidual(), 1.0e-9);
  }
 
  @Test
  public void random() {
    Matrix m = new DenseMatrix(200, 30).assign(Functions.random());
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    LSMR r = new LSMR();
    Vector x1 = r.solve(m, b);

//    assertEquals(0, m.times(x1).minus(b).norm(2), 1.0e-2);
    double norm = new SingularValueDecomposition(m).getS().viewDiagonal().norm(2);
    double actual = m.transpose().times(m).times(x1).minus(m.transpose().times(b)).norm(2);
    System.out.printf("%.4f\n", actual / norm * 1.0e6);
    assertEquals(0, actual, norm * 1.0e-5);

    // and we need to check that the error estimates are pretty good.
    assertEquals(m.times(x1).minus(b).norm(2), r.getResidualNorm(), 1.0e-5);
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