Package org.apache.mahout.math

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

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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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          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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    DenseMatrix transitionMatrix = new DenseMatrix(nrOfHiddenStates, nrOfHiddenStates);
    DenseMatrix emissionMatrix = new DenseMatrix(nrOfHiddenStates, nrOfOutputStates);
    // assign a small initial probability that is larger than zero, so
    // unseen states will not get a zero probability
    transitionMatrix.assign(pseudoCount);
    emissionMatrix.assign(pseudoCount);
    // given no prior knowledge, we have to assume that all initial hidden
    // states are equally likely
    DenseVector initialProbabilities = new DenseVector(nrOfHiddenStates);
    initialProbabilities.assign(1.0 / (double) nrOfHiddenStates);
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        nrOfOutputStates);
    DenseVector initialProbabilities = new DenseVector(nrOfHiddenStates);

    // assign pseudo count to avoid zero probabilities
    transitionMatrix.assign(pseudoCount);
    emissionMatrix.assign(pseudoCount);
    initialProbabilities.assign(pseudoCount);

    // now loop over the sequences to count the number of transitions
    Iterator<int[]> hiddenSequenceIt = hiddenSequences.iterator();
    Iterator<int[]> observedSequenceIt = observedSequences.iterator();
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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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   * @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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