Examples of DiscreteDistribution

edu.cmu.graphchi.walks.distributions.DiscreteDistribution
Presents a map from integers to frequencies. Special distributions for avoidance can be used to exclude certain ids from the distributions in merges. @author Aapo Kyrola, akyrola@cs.cmu.edu
org.encog.ml.hmm.distributions.DiscreteDistribution
A discrete distribution is a distribution with a finite set of states that it can be in.
stallone.stat.DiscreteDistribution
@author noe
weka.classifiers.misc.monotone.DiscreteDistribution
This class represents a discrete probability distribution over a finite number of values.
In the present implementation, objects of type DiscreteDistribution are in fact immutable, so all one can do is create objects and retrieve information, such as median and mean, from them.

This implementation is part of the master's thesis: "Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken", Stijn Lievens, Ghent University, 2004.
@author Stijn Lievens (stijn.lievens@ugent.be) @version $Revision: 1.2 $

Examples of edu.cmu.graphchi.walks.distributions.DiscreteDistribution

        }
    }


    @Test
    public void testTopWithOnes() {
        DiscreteDistribution d1 = new DiscreteDistribution(new int[] {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20,21,22,23,200,298});
        DiscreteDistribution avoidDist = DiscreteDistribution.createAvoidanceDistribution(new int[]{12, 15, 20});
        IdCount[] top = d1.getTop(10);
        assertEquals(10, top.length);


        DiscreteDistribution mergedWithAvoid = DiscreteDistribution.merge(d1, avoidDist);
        IdCount[] top2 = mergedWithAvoid.getTop(10);
        assertEquals(10, top2.length);
    }

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Examples of edu.cmu.graphchi.walks.distributions.DiscreteDistribution

        assertEquals(10, top2.length);
    }


    @Test
    public void testFiltering() {
        DiscreteDistribution d1 = new DiscreteDistribution(new int[] {1,1,1,8,8,8,9,22,22,22,22,22,333,333,333,333,333,333,333,333});
        assertEquals(3, d1.getCount(1));
        assertEquals(3, d1.getCount(8));
        assertEquals(1, d1.getCount(9));
        assertEquals(5, d1.getCount(22));
        assertEquals(8, d1.getCount(333));


        DiscreteDistribution notReallyFiltered = d1.filteredAndShift(1);
        assertTrue(notReallyFiltered == d1);


        DiscreteDistribution filtered = d1.filteredAndShift(4);
        assertEquals(0, filtered.getCount(1));
        assertEquals(0, filtered.getCount(8));
        assertEquals(0, filtered.getCount(9));
        assertEquals(5 - 3, filtered.getCount(22));
        assertEquals(8 - 3, filtered.getCount(333));
        assertEquals(2, filtered.size());


        // Check that avoided ones are not filtered
        DiscreteDistribution filteredWithAnAvoid = DiscreteDistribution.merge(d1,
                DiscreteDistribution.createAvoidanceDistribution(new int[]{99, 108})).filteredAndShift(4);


        assertEquals(0, filteredWithAnAvoid.getCount(1));
        assertEquals(0, filteredWithAnAvoid.getCount(8));
        assertEquals(0, filteredWithAnAvoid.getCount(9));
        assertEquals(5 - 3, filteredWithAnAvoid.getCount(22));
        assertEquals(8 - 3, filteredWithAnAvoid.getCount(333));
        assertEquals(-1, filteredWithAnAvoid.getCount(99));
        assertEquals(-1, filteredWithAnAvoid.getCount(108));


        DiscreteDistribution filteredAll = d1.filteredAndShift(100);
        DiscreteDistribution filteredAll2 = filtered.filteredAndShift(100);


        assertEquals(0, filteredAll.getCount(1));
        assertEquals(0, filteredAll.getCount(8));
        assertEquals(0, filteredAll.getCount(9));
        assertEquals(0, filteredAll.getCount(22));
        assertEquals(0, filteredAll.getCount(333));
        assertEquals(0, filteredAll2.getCount(1));
        assertEquals(0, filteredAll2.getCount(8));
        assertEquals(0, filteredAll2.getCount(9));
        assertEquals(0, filteredAll2.getCount(22));
        assertEquals(0, filteredAll2.getCount(333));


        assertEquals(0, filteredAll.size());
        assertEquals(0, filteredAll2.size());
    }

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Examples of edu.cmu.graphchi.walks.distributions.DiscreteDistribution

                n = r.nextInt(10000);
            } while (countToId.containsKey(n)); // Unique count keys
            countToId.put(n, i);
            insertMultiple(workArr, i, n);
        }
        DiscreteDistribution dist = new DiscreteDistribution(toIntArray(workArr));




        /* Then insert some edges to avoid */
        int[] avoids = new int[] {0, 2, 4, 32,33, 66, 67,68,  99, 102, 184};
        DiscreteDistribution avoidDistr = DiscreteDistribution.createAvoidanceDistribution(avoids);


        // Test the merge works both ways
        DiscreteDistribution mergedL = DiscreteDistribution.merge(dist, avoidDistr);
        DiscreteDistribution mergedR = DiscreteDistribution.merge(avoidDistr, dist);


        for(int a : avoids) {
            assertEquals(-1, avoidDistr.getCount(a));
            assertEquals(-1, mergedL.getCount(a));
            assertEquals(-1, mergedR.getCount(a));
        }


        IdCount[] top = dist.getTop(10);
        int j = 0;
        HashSet<Integer> avoidSet = new HashSet<Integer>();

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

      new HiddenMarkovModel(2, 2);
    
    hmm.setPi(0, 0.95);
    hmm.setPi(1, 0.05);
    
    hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.95, 0.05 } }));
    hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.20, 0.80 } }));
    
    hmm.setTransitionProbability(0, 1, 0.05);
    hmm.setTransitionProbability(0, 0, 0.95);
    hmm.setTransitionProbability(1, 0, 0.10);
    hmm.setTransitionProbability(1, 1, 0.90);

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

      new HiddenMarkovModel(2, 2);
    
    hmm.setPi(0, 0.95);
    hmm.setPi(1, 0.05);
    
    hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.95, 0.05 } }));
    hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.20, 0.80 } }));
    
    hmm.setTransitionProbability(0, 1, 0.05);
    hmm.setTransitionProbability(0, 0, 0.95);
    hmm.setTransitionProbability(1, 0, 0.10);
    hmm.setTransitionProbability(1, 1, 0.90);

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

    HiddenMarkovModel hmm = new HiddenMarkovModel(2,2);
    
    hmm.setPi(0, 0.50);
    hmm.setPi(1, 0.50);
    
    hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.8, 0.2 } }));
    hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.1, 0.9 } }));
    
    hmm.setTransitionProbability(0, 1, 0.2);
    hmm.setTransitionProbability(0, 0, 0.8);
    hmm.setTransitionProbability(1, 0, 0.2);
    hmm.setTransitionProbability(1, 1, 0.8);

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

    this.transitionProbability = new double[theStates][theStates];
    this.stateDistributions = new StateDistribution[theStates];


    for (int i = 0; i < theStates; i++) {
      this.pi[i] = 1. / theStates;
      this.stateDistributions[i] = new DiscreteDistribution(this.items);


      for (int j = 0; j < theStates; j++) {
        this.transitionProbability[i][j] = 1.0 / theStates;
      }
    }

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution


  public StateDistribution createNewDistribution() {
    if (isContinuous()) {
      return new ContinousDistribution(getStateCount());
    } else {
      return new DiscreteDistribution(this.items);
    }
  }

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

          Matrix cova = section.parseMatrix(params, HiddenMarkovModel.TAG_COVARIANCE);
          ContinousDistribution dist = new ContinousDistribution(mean,cova.getData());
          distributions.add(dist);
        } else if( "DiscreteDistribution".equals(t) ) {
          Matrix prob = section.parseMatrix(params, HiddenMarkovModel.TAG_PROBABILITIES);
          DiscreteDistribution dist = new DiscreteDistribution(prob.getData());
          distributions.add(dist);
        }
      }
    }

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Examples of org.encog.ml.hmm.distributions.DiscreteDistribution

        ContinousDistribution cDist = (ContinousDistribution)sd;
        out.writeProperty(HiddenMarkovModel.TAG_MEAN, cDist.getMean());
        out.writeProperty(HiddenMarkovModel.TAG_COVARIANCE, cDist.getCovariance());
        
      } else if( sd instanceof DiscreteDistribution ) {
        DiscreteDistribution dDist = (DiscreteDistribution)sd;
        out.writeProperty(HiddenMarkovModel.TAG_PROBABILITIES, new Matrix(dDist.getProbabilities()));
      }
    }


    out.flush();
  }

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