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

org.apache.mahout.math.Matrix.times()
Return a new matrix containing the product of each value of the recipient and the argument @param x a double argument @return a new Matrix

    Vector v = new RandomAccessSparseVector(50);
    v.assign(1.0);
    Matrix m = SolverTest.randomSequentialAccessSparseMatrix(100, 90, 50, 20, 1.0);
    DistributedRowMatrix dm = randomDistributedMatrix(100, 90, 50, 20, 1.0, false);


    Vector expected = m.times(v);
    Vector actual = dm.times(v);
    assertEquals(0.0, expected.getDistanceSquared(actual), 1.0e-9);
  }


  public void testMatrixTimesSquaredVector() throws Exception {

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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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        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
  }
  
  public Matrix getInverseCovarianceMatrix() {
    return inverseCovarianceMatrix;
  }

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        sInv.set(i, i, 1 / diagElem);
      } else {
        throw new IllegalStateException("Eigen Value equals to 0 found.");
      }
    }
    inverseCovarianceMatrix = svd.getU().times(sInv.times(svd.getU().transpose()));
    Preconditions.checkArgument(inverseCovarianceMatrix != null, "inverseCovarianceMatrix not initialized");
  }


  public Matrix getInverseCovarianceMatrix() {
    return inverseCovarianceMatrix;

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    Vector v = new RandomAccessSparseVector(50);
    v.assign(1.0);
    Matrix m = SolverTest.randomSequentialAccessSparseMatrix(100, 90, 50, 20, 1.0);
    DistributedRowMatrix dm = randomDistributedMatrix(100, 90, 50, 20, 1.0, false);


    Vector expected = m.times(v);
    Vector actual = dm.times(v);
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }


  @Test

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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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      }
      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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    Vector v = new RandomAccessSparseVector(50);
    v.assign(1.0);
    Matrix m = SolverTest.randomSequentialAccessSparseMatrix(100, 90, 50, 20, 1.0);
    DistributedRowMatrix dm = randomDistributedMatrix(100, 90, 50, 20, 1.0, false);


    Vector expected = m.times(v);
    Vector actual = dm.times(v);
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }


  @Test

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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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    Vector x = new DenseVector(new double[]{5, -120, 630, -1120, 630});


    Vector b = new DenseVector(5);
    b.assign(1);


    assertEquals(0, m.times(x).minus(b).norm(2), 1.0e-9);


    LSMR r = new LSMR();
    Vector x1 = r.solve(m, b);


    // the ideal solution is  [5  -120   630 -1120   630] but the 5x5 hilbert matrix

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