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

        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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    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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   * @return The intermediate results of the current layer.
   */
  protected Vector forward(int fromLayer, Vector intermediateOutput) {
    Matrix weightMatrix = this.weightMatrixList.get(fromLayer);


    Vector vec = weightMatrix.times(intermediateOutput);
    vec = vec.assign(NeuralNetworkFunctions.getDoubleFunction(this.squashingFunctionList.get(fromLayer)));


    // add bias
    Vector vecWithBias = new DenseVector(vec.size() + 1);
    vecWithBias.set(0, 1);

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


    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 DistributedRowMatrix saveToFs(final Matrix m, Path baseTmpDirPath) throws IOException {

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    double beta = u.norm(2);
    if (beta > 0) {
      u = u.divide(beta);
    }


    Vector v = transposedA.times(u);
    int m = A.numRows();
    int n = A.numCols();


    int minDim = Math.min(m, n);
    if (iterationLimit == -1) {

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        // store data for local-reorthogonalization of V
        if (localOrtho) {
          localVEnqueue(v);
        }
        v = transposedA.times(u).minus(v.times(beta));
        // local-reorthogonalization of V
        if (localOrtho) {
          v = localVOrtho(v);
        }
        alpha = v.norm(2);

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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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      new double[]{0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000}};
    Matrix x = new DenseMatrix(m);
    EigenDecomposition eig = new EigenDecomposition(x, true);
    Matrix d = eig.getD();
    Matrix v = eig.getV();
    check("EigenvalueDecomposition (evil)...", x.times(v), v.times(d));
  }


  @Test
  public void testDeficientRank() {
    Matrix a = new DenseMatrix(10, 3).assign(new DoubleFunction() {

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