Package org.apache.mahout.math.function

Examples of org.apache.mahout.math.function.DoubleFunction

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  }

  @Override
  public Vector sample() {
    Vector v = new DenseVector(dimension).assign(
      new DoubleFunction() {
        @Override
        public double apply(double ignored) {
          return gen.nextGaussian();
        }
      }
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   * @param projectedVectorSize final projected size of a vector (number of projection vectors)
   * @param vectorSize initial vector size
   * @return a list of projection vectors
   */
  public static List<Vector> generateVectorBasis(int projectedVectorSize, int vectorSize) {
    DoubleFunction random = new Normal();
    List<Vector> basisVectors = Lists.newArrayList();
    for (int i = 0; i < projectedVectorSize; ++i) {
      Vector basisVector = new DenseVector(vectorSize);
      basisVector.assign(random);
      basisVector.normalize();
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      public double apply(Vector column) {
        return column.minValue();
      }
    }).minValue();

    source.assign(new DoubleFunction() {
      @Override
      public double apply(double value) {
        return (value - min) * range / (max - min);
      }
    });
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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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    for (int i=0; i<terms.length; i++) {
      terms[i] = String.valueOf((char) (i + 'a'));
    }
    int numGeneratingTopics = 3;
    int numTerms = 26;
    Matrix matrix = ClusteringTestUtils.randomStructuredModel(numGeneratingTopics, numTerms, new DoubleFunction() {
      @Override public double apply(double d) {
        return 1.0 / Math.pow(d + 1.0, 2);
      }
    });
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  @Test
  public void testRandomStructuredModelViaMR() throws Exception {
    int numGeneratingTopics = 3;
    int numTerms = 9;
    Matrix matrix = ClusteringTestUtils.randomStructuredModel(numGeneratingTopics, numTerms, new DoubleFunction() {
      @Override
      public double apply(double d) {
        return 1.0 / Math.pow(d + 1.0, 3);
      }
    });
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          private final Random random = RandomUtils.getRandom();
          @Override
          public Vector apply(Integer dummy) {
            Vector result =
                type == VectorType.SPARSE ? new RandomAccessSparseVector(numItems) : new DenseVector(numItems);
            result.assign(new DoubleFunction() {
              @Override
              public double apply(double ignored) {
                return random.nextDouble();
              }
            });
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    check("EigenvalueDecomposition (evil)...", x.times(v), v.times(d));
  }

  @Test
  public void testDeficientRank() {
    Matrix a = new DenseMatrix(10, 3).assign(new DoubleFunction() {
      private final Random gen = RandomUtils.getRandom();
      @Override
      public double apply(double arg1) {
        return gen.nextGaussian();
      }
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    }
  }

  private static Vector randomVector(final Random gen, int n) {
    Vector x = new DenseVector(n);
    x.assign(new DoubleFunction() {
      @Override
      public double apply(double v) {
        return gen.nextGaussian();
      }
    });
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    updateSteps.assign(other.updateSteps);
    updateCounts.assign(other.updateCounts);
  }

  public boolean validModel() {
    double k = beta.aggregate(Functions.PLUS, new DoubleFunction() {
      @Override
      public double apply(double v) {
        return Double.isNaN(v) || Double.isInfinite(v) ? 1 : 0;
      }
    });
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