HiddenMarkovModel
- class pyopenms.HiddenMarkovModel
Bases:
objectCython implementation of _HiddenMarkovModel
Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1HiddenMarkovModel.html
- __init__()
Cython signature: void HiddenMarkovModel() Hidden Markov Model implementation of PILIS
Cython signature: void HiddenMarkovModel(HiddenMarkovModel &)
Methods
Cython signature: void HiddenMarkovModel()
Cython signature: void addNewState(HMMState * state)
Cython signature: void addSynonymTransition(const String & name1, const String & name2, const String & synonym1, const String & synonym2) Add a new synonym transition to the given state names
Cython signature: void clear() Clears all data
Cython signature: void clearInitialTransitionProbabilities() Clears the initial probabilities
Cython signature: void clearTrainingEmissionProbabilities() Clear the emission probabilities
Cython signature: void disableTransition(const String & s1, const String & s2) Disables the transition; deletes the nodes from the predecessor/successor list respectively
Cython signature: void disableTransitions() Disables all transitions
Cython signature: void dump() Writes some stats to cerr
Cython signature: void enableTransition(const String & s1, const String & s2) Enables a transition; adds s1 to the predecessor list of s2 and s2 to the successor list of s1
Cython signature: void estimateUntrainedTransitions() Estimates the transition probabilities of not trained transitions by averages similar trained ones
Cython signature: void evaluate() Evaluate the HMM, estimates the transition probabilities from the training
Cython signature: void forwardDump() Writes some info of the forward "matrix" to cerr
Cython signature: size_t getNumberOfStates() Returns the number of states
Cython signature: double getPseudoCounts() Returns the pseudo counts
Cython signature: HMMState * getState(const String & name) Returns the state with the given name
Cython signature: double getTransitionProbability(const String & s1, const String & s2) Returns the transition probability of the given state names
Cython signature: void setInitialTransitionProbability(const String & state, double prob) Sets the initial transition probability of the given state to prob
Cython signature: void setPseudoCounts(double pseudo_counts) Sets the pseudo count that are added instead of zero
Cython signature: void setTrainingEmissionProbability(const String & state, double prob) Sets the emission probability of the given state to prob
Cython signature: void setTransitionProbability(const String & s1, const String & s2, double prob) Sets the transition probability of the given state names to prob
Cython signature: void setVariableModifications(StringList & modifications)
Cython signature: void train() Trains the HMM.
Cython signature: void writeGraphMLFile(const String & filename) Writes the HMM into a file in GraphML format
- addNewState()
Cython signature: void addNewState(HMMState * state) Registers a new state to the HMM
Cython signature: void addNewState(const String & name) Registers a new state to the HMM
- addSynonymTransition()
Cython signature: void addSynonymTransition(const String & name1, const String & name2, const String & synonym1, const String & synonym2) Add a new synonym transition to the given state names
- clear()
Cython signature: void clear() Clears all data
- clearInitialTransitionProbabilities()
Cython signature: void clearInitialTransitionProbabilities() Clears the initial probabilities
- clearTrainingEmissionProbabilities()
Cython signature: void clearTrainingEmissionProbabilities() Clear the emission probabilities
- disableTransition()
Cython signature: void disableTransition(const String & s1, const String & s2) Disables the transition; deletes the nodes from the predecessor/successor list respectively
- disableTransitions()
Cython signature: void disableTransitions() Disables all transitions
- dump()
Cython signature: void dump() Writes some stats to cerr
- enableTransition()
Cython signature: void enableTransition(const String & s1, const String & s2) Enables a transition; adds s1 to the predecessor list of s2 and s2 to the successor list of s1
- estimateUntrainedTransitions()
Cython signature: void estimateUntrainedTransitions() Estimates the transition probabilities of not trained transitions by averages similar trained ones
- evaluate()
Cython signature: void evaluate() Evaluate the HMM, estimates the transition probabilities from the training
- forwardDump()
Cython signature: void forwardDump() Writes some info of the forward “matrix” to cerr
- getNumberOfStates()
Cython signature: size_t getNumberOfStates() Returns the number of states
- getPseudoCounts()
Cython signature: double getPseudoCounts() Returns the pseudo counts
- getState()
Cython signature: HMMState * getState(const String & name) Returns the state with the given name
- getTransitionProbability()
Cython signature: double getTransitionProbability(const String & s1, const String & s2) Returns the transition probability of the given state names
- setInitialTransitionProbability()
Cython signature: void setInitialTransitionProbability(const String & state, double prob) Sets the initial transition probability of the given state to prob
- setPseudoCounts()
Cython signature: void setPseudoCounts(double pseudo_counts) Sets the pseudo count that are added instead of zero
- setTrainingEmissionProbability()
Cython signature: void setTrainingEmissionProbability(const String & state, double prob) Sets the emission probability of the given state to prob
- setTransitionProbability()
Cython signature: void setTransitionProbability(const String & s1, const String & s2, double prob) Sets the transition probability of the given state names to prob
- setVariableModifications()
Cython signature: void setVariableModifications(StringList & modifications)
- train()
Cython signature: void train() Trains the HMM. Initial probabilities and emission probabilities of the emitting states should be set
- writeGraphMLFile()
Cython signature: void writeGraphMLFile(const String & filename) Writes the HMM into a file in GraphML format