Examples of HMM

aima.core.probability.hmm.impl.HMM
Default implementation of the HiddenMarkovModel interface. @author Ciaran O'Reilly @author Ravi Mohan
com.nr.ci.HMM
hidden Markov models Copyright (C) Numerical Recipes Software 1986-2007 Java translation Copyright (C) Huang Wen Hui 2012 @author hwh
edu.cmu.sphinx.linguist.acoustic.HMM
Represents a hidden-markov-model. An HMM consists of a unit (context dependent or independent), a transition matrix from state to state, and a sequence of senones associated with each state. This representation of an HMM is a specialized left-to-right markov model. No backward transitions are allowed.
edu.umd.hooka.alignment.hmm.HMM
Represents an HMM that applies to a single sentence pair, which is derived from the parameters stored in a TTable and an ATable object. @author redpony
org.wiigee.logic.HMM
This is a Hidden Markov Model implementation which internally provides the basic algorithms for training and recognition (forward and backward algorithm). Since a regular Hidden Markov Model doesn't provide a possibility to train multiple sequences, this implementation has been optimized for this purposes using some state-of-the-art technologies described in several papers. @author Benjamin 'BePo' Poppinga

Examples of edu.cmu.sphinx.linguist.acoustic.HMM


            // Create tmat
            float[][] transitionMatrix = matrixPool.get(unitCount);
            SenoneSequence ss = getSenoneSequence(stid);


            HMM hmm = new SenoneHMM(unit, ss, transitionMatrix, HMMPosition.lookup(position));
            hmmManager.put(hmm);
            unitCount++;
        }


        // If we want to use this code to load sizes/create models for

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Examples of edu.cmu.sphinx.linguist.acoustic.HMM

        if (s instanceof WordSearchState) {
            numWords++;
        } else if (s instanceof UnitSearchState) {
            numUnits++;
        } else if (s instanceof HMMSearchState) {
            HMM hmm = ((HMMSearchState) s).getHMMState().getHMM();
            switch (hmm.getPosition()) {
                case BEGIN: numHMMBegin++; break;
                case END: numHMMEnd++; break;
                case SINGLE: numHMMSingle++; break;
                case INTERNAL: numHMMInternal++; break;
                default: break;

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Examples of edu.cmu.sphinx.linguist.acoustic.HMM

            } else {
                // if it's not a phone, and it's not silence, it's a
                // dummy node, and we don't care.
                continue;
            }
            HMM hmm =
                acousticModel.lookupNearestHMM(unit, HMMPosition.UNDEFINED, false);
            Graph modelGraph = buildModelGraph((SenoneHMM)hmm);
            modelGraph.validate();
            hmmGraph.insertGraph(modelGraph, node);
        }

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Examples of edu.umd.hooka.alignment.hmm.HMM

        else
          ttable = new TTable_monolithic_IFAs(
              ttfs, ltp, true);
        ATable atable = loadATable(job.getATablePath(), job);
        if (!useNullWord)
          trainer = new HMM(ttable, atable);
        else
          trainer = new HMM_NullWord(ttable, atable, job.getHMMp0());
      } else
        throw new RuntimeException("Don't understand initialization stategy: " + trainerType);
    }

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Examples of org.wiigee.logic.HMM

      BufferedWriter out = new BufferedWriter(new FileWriter(name+".txt"));
      int numStates = m.getNumStates();
      int numObservations = m.getNumObservations();
      double defaultProbability = m.getDefaultProbability();
      Quantizer quantizer = m.getQuantizer();
      HMM hmm = m.getHMM();
      
      out.write("# numStates:");
      out.newLine();
      out.write(Integer.toString(numStates));
      out.newLine();
      
      out.write("# numObservations:");
      out.newLine();
      out.write(Integer.toString(numObservations));
      out.newLine();
      
      out.write("# defaultProbability:");
      out.newLine();
      out.write(Double.toString(defaultProbability));
      out.newLine();
      
      out.write("# Quantizer: Radius");
      out.newLine();
      out.write(Double.toString(quantizer.getRadius()));
      out.newLine();
      out.write("# Quantizer: MAP");
      out.newLine();
      double[][] map = quantizer.getHashMap();
      for(int v=0; v<map.length; v++) {
        double[] d = map[v];
        out.write(Double.toString(d[0])+", "+Double.toString(d[1])+", "+Double.toString(d[2]));
        out.newLine();
      }
      
      out.write("# HMM: PI");
      out.newLine();
      double[] pi = hmm.getPi();
      for (int i=0; i<numStates; i++) {
        if(i==numStates-1) {
          out.write(Double.toString(pi[i]));
          out.newLine();
        } else {
          out.write(Double.toString(pi[i])+", ");
        }
      }
      
      out.write("# HMM: A");
      out.newLine();
      double[][] a = hmm.getA();
      for(int i=0; i<numStates; i++) {
        for(int j=0; j<numStates; j++) {
          if(j==numStates-1) {
            out.write(Double.toString(a[i][j]));
            out.newLine();
          } else {
            out.write(Double.toString(a[i][j])+", ");
          }
        }
      }
      
      out.write("# HMM: B");
      out.newLine();
      double[][] b = hmm.getB();
      for(int i=0; i<numStates; i++) {
        for(int j=0; j<numObservations; j++) {
          if(j==numObservations-1) {
            out.write(Double.toString(b[i][j]));
            out.newLine();

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Examples of org.wiigee.logic.HMM

      
      Quantizer quantizer = new Quantizer(numStates);
      quantizer.setUpManually(map, radius);
      ret.setQuantizer(quantizer);
      
      HMM hmm = new HMM(numStates, numObservations);
      hmm.setPi(pi);
      hmm.setA(a);
      hmm.setB(b);
      ret.setHMM(hmm);
      
      return ret;
    } catch (Exception e) {
      System.out.println("Error: Read from File!");

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