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

org.apache.mahout.math.Matrix.assign()
Assign the value to all elements of the receiver @param value a double value @return the modified receiver


  @Test
  public void testGivensQR() throws Exception {
    // DenseMatrix m = new DenseMatrix(dims<<2,dims);
    Matrix m = new DenseMatrix(3, 3);
    m.assign(new DoubleFunction() {
      private final Random rnd = RandomUtils.getRandom();
      @Override
      public double apply(double arg0) {
        return rnd.nextDouble() * SCALE;
      }

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      Matrix emissionMatrix = iteration.getEmissionMatrix();
      Matrix transitionMatrix = iteration.getTransitionMatrix();


      // first, assign the pseudo count
      emissionMatrix.assign(pseudoCount);
      transitionMatrix.assign(pseudoCount);


      // now count the transitions
      countTransitions(transitionMatrix, emissionMatrix, observedSequence,
          viterbiPath);

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      Matrix emissionMatrix = iteration.getEmissionMatrix();
      Matrix transitionMatrix = iteration.getTransitionMatrix();


      // first, assign the pseudo count
      emissionMatrix.assign(pseudoCount);
      transitionMatrix.assign(pseudoCount);


      // now count the transitions
      countTransitions(transitionMatrix, emissionMatrix, observedSequence,
          viterbiPath);

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        bbtSquare.assign(mC, Functions.MINUS);
        bbtSquare.assign(mC.transpose(), Functions.MINUS);


        Matrix outerSq = sq.cross(sq);
        outerSq.assign(Functions.mult(xisquaredlen));
        bbtSquare.assign(outerSq, Functions.PLUS);


      }


      EigenSolverWrapper eigenWrapper = new EigenSolverWrapper(SSVDHelper.extractRawData(bbtSquare));

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   * @param vectorSize initial vector size
   * @return a projection matrix
   */
  public static Matrix generateBasisNormal(int projectedVectorSize, int vectorSize) {
    Matrix basisMatrix = new DenseMatrix(projectedVectorSize, vectorSize);
    basisMatrix.assign(new Normal());
    for (MatrixSlice row : basisMatrix) {
      row.vector().assign(row.normalize());
    }
    return basisMatrix;
  }

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  @Test
  public void testGivensQR() throws Exception {
    // DenseMatrix m = new DenseMatrix(dims<<2,dims);
    Matrix m = new DenseMatrix(3, 3);
    m.assign(new DoubleFunction() {
      private final Random rnd = RandomUtils.getRandom();


      @Override
      public double apply(double arg0) {
        return rnd.nextDouble() * SCALE;

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   *
   * @return D
   */
  public Matrix getD() {
    Matrix x = new DenseMatrix(n, n);
    x.assign(0);
    x.viewDiagonal().assign(d);
    for (int i = 0; i < n; i++) {
      double v = e.getQuick(i);
      if (v > 0) {
        x.setQuick(i, i + 1, v);

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          bTmp.readFields(in);
        } finally {
          in.close();
        }


        b2.assign(bTmp.get().times(bTmp.get().transpose()), Functions.PLUS);
      }
    }
    l2 = new CholeskyDecomposition(b2);
    svd = new SingularValueDecomposition(l2.getL());
  }

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  @Test
  public void testNormal() {
    Matrix testData = new DenseMatrix(100000, 10);
    final Normal gen = new Normal();
    testData.assign(gen);


    final EuclideanDistanceMeasure distance = new EuclideanDistanceMeasure();
    BruteSearch ref = new BruteSearch(distance);
    ref.addAllMatrixSlicesAsWeightedVectors(testData);

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  @Test
  public void testDotCorrelation() {
    final Normal gen = new Normal();


    Matrix projection = new DenseMatrix(64, 10);
    projection.assign(gen);


    Vector query = new DenseVector(10);
    query.assign(gen);
    long qhash = HashedVector.computeHash64(query, projection);

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