| Example Program HMM Silent States Hidden Markov Model with silent states code example A tutorial about HMMs with silent states
| 1 | #include <iostream>
| | 2 | #include <fstream>
| | 3 | #include <seqan/graph_algorithms.h>
| | 4 | #include <seqan/basic/basic_logvalue.h>
| | 5 |
| | 6 | using namespace seqan;
| | 7 |
| | 8 | int main() {
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| 9 | typedef LogProb<> TProbability;
| | 10 | typedef Dna TAlphabet;
| | 11 | typedef Size<TAlphabet>::Type TSize;
| | 12 | typedef Graph<Hmm<TAlphabet, TProbability> > THmm;
| | 13 | typedef VertexDescriptor<THmm>::Type TVertexDescriptor;
| | 14 | typedef EdgeDescriptor<THmm>::Type TEdgeDescriptor;
| | 15 |
| | 16 | Dna dnaA = Dna('A');
| | 17 | Dna dnaC = Dna('C');
| | 18 | Dna dnaG = Dna('G');
| | 19 | Dna dnaT = Dna('T');
| | 20 |
| | 21 | THmm hmm;
| | 22 |
|
| 23 | TVertexDescriptor begState = addVertex(hmm);
| | 24 | assignBeginState(hmm, begState);
| | 25 |
|
| 26 | TVertexDescriptor emitState1 = addVertex(hmm);
| | 27 | emissionProbability(hmm, emitState1, dnaA) = 0.0;
| | 28 | emissionProbability(hmm, emitState1, dnaC) = 0.8;
| | 29 | emissionProbability(hmm, emitState1, dnaG) = 0.1;
| | 30 | emissionProbability(hmm, emitState1, dnaT) = 0.1;
| | 31 |
| | 32 | TVertexDescriptor emitState2 = addVertex(hmm);
| | 33 | emissionProbability(hmm, emitState2, dnaA) = 0.0;
| | 34 | emissionProbability(hmm, emitState2, dnaC) = 0.2;
| | 35 | emissionProbability(hmm, emitState2, dnaG) = 0.2;
| | 36 | emissionProbability(hmm, emitState2, dnaT) = 0.6;
| | 37 |
| | 38 | TVertexDescriptor emitState3 = addVertex(hmm);
| | 39 | emissionProbability(hmm, emitState3, dnaA) = 0.7;
| | 40 | emissionProbability(hmm, emitState3, dnaC) = 0.1;
| | 41 | emissionProbability(hmm, emitState3, dnaG) = 0.1;
| | 42 | emissionProbability(hmm, emitState3, dnaT) = 0.1;
| | 43 |
| | 44 | TVertexDescriptor emitState4 = addVertex(hmm);
| | 45 | emissionProbability(hmm, emitState4, dnaA) = 0.25;
| | 46 | emissionProbability(hmm, emitState4, dnaC) = 0.25;
| | 47 | emissionProbability(hmm, emitState4, dnaG) = 0.25;
| | 48 | emissionProbability(hmm, emitState4, dnaT) = 0.25;
| | 49 |
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| 50 | TVertexDescriptor delState1 = addVertex(hmm, true);
| | 51 | TVertexDescriptor delState2 = addVertex(hmm, true);
| | 52 | TVertexDescriptor delState3 = addVertex(hmm, true);
| | 53 | TVertexDescriptor delState4 = addVertex(hmm, true);
| | 54 |
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| 55 | TVertexDescriptor eState = addVertex(hmm);
| | 56 | assignEndState(hmm, eState);
| | 57 |
|
| 58 | addEdge(hmm, begState, emitState1, 0.5);
| | 59 | addEdge(hmm, begState, delState1, 0.5);
| | 60 | addEdge(hmm, emitState1, emitState2, 0.5);
| | 61 | addEdge(hmm, emitState1, delState2, 0.5);
| | 62 | addEdge(hmm, delState1, emitState2, 0.5);
| | 63 | addEdge(hmm, delState1, delState2, 0.5);
| | 64 | addEdge(hmm, emitState2, emitState3, 0.5);
| | 65 | addEdge(hmm, emitState2, delState3, 0.5);
| | 66 | addEdge(hmm, delState2, emitState3, 0.5);
| | 67 | addEdge(hmm, delState2, delState3, 0.5);
| | 68 | addEdge(hmm, emitState3, emitState4, 0.5);
| | 69 | addEdge(hmm, emitState3, delState4, 0.5);
| | 70 | addEdge(hmm, delState3, emitState4, 0.5);
| | 71 | addEdge(hmm, delState3, delState4, 0.5);
| | 72 | addEdge(hmm, emitState4, eState, 1.0);
| | 73 | addEdge(hmm, delState4, eState, 1.0);
| | 74 |
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| 75 | ::std::cout << hmm << ::std::endl;
| | 76 |
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| 77 | String<Dna> sequence = "CA";
| | 78 | String<TVertexDescriptor> path;
| | 79 | TProbability p = viterbiAlgorithm(hmm, sequence, path);
| | 80 | ::std::cout << "Viterbi algorithm" << ::std::endl;
| | 81 | ::std::cout << "Probability of the best path: " << p << ::std::endl;
| | 82 | ::std::cout << "Sequence: " << ::std::endl;
| | 83 | for(TSize i = 0; i<length(sequence); ++i) ::std::cout << sequence[i] << ',';
| | 84 | ::std::cout << ::std::endl;
| | 85 | ::std::cout << "State path: " << ::std::endl;
| | 86 | for(TSize i = 0; i<length(path); ++i) {
| | 87 | ::std::cout << path[i];
| | 88 | if (isSilent(hmm, path[i])) ::std::cout << " (Silent)";
| | 89 | if (i < length(path) - 1) ::std::cout << ',';
| | 90 | }
| | 91 | ::std::cout << ::std::endl;
| | 92 |
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| 93 | ::std::cout << "Forward algorithm" << ::std::endl;
| | 94 | p = forwardAlgorithm(hmm, sequence);
| | 95 | ::std::cout << "Probability that the HMM generated the sequence: " << p << ::std::endl;
| | 96 |
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| 97 | ::std::cout << "Backward algorithm" << ::std::endl;
| | 98 | p = backwardAlgorithm(hmm, sequence);
| | 99 | ::std::cout << "Probability that the HMM generated the sequence: " << p << ::std::endl;
| | 100 |
| | 101 | return 0;
| | 102 | }
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