HiddenMarkovModel

class pyopenms.HiddenMarkovModel

Bases: object

Cython implementation of _HiddenMarkovModel

Original C++ documentation is available here

__init__()

Overload:

__init__(self) → None

Hidden Markov Model implementation of PILIS

Overload:

__init__(self, in_0: HiddenMarkovModel) → None

Methods

__init__	Overload:
addNewState	Overload:
addSynonymTransition(self, name1, name2, ...)	Add a new synonym transition to the given state names
clear(self)	Clears all data
clearInitialTransitionProbabilities(self)	Clears the initial probabilities
clearTrainingEmissionProbabilities(self)	Clear the emission probabilities
disableTransition(self, s1, s2)	Disables the transition; deletes the nodes from the predecessor/successor list respectively
disableTransitions(self)	Disables all transitions
dump(self)	Writes some stats to cerr
enableTransition(self, s1, s2)	Enables a transition; adds s1 to the predecessor list of s2 and s2 to the successor list of s1
estimateUntrainedTransitions(self)	Estimates the transition probabilities of not trained transitions by averages similar trained ones
evaluate(self)	Evaluate the HMM, estimates the transition probabilities from the training
forwardDump(self)	Writes some info of the forward "matrix" to cerr
getNumberOfStates(self)	Returns the number of states
getPseudoCounts(self)	Returns the pseudo counts
getState(self, name)	Returns the state with the given name
getTransitionProbability(self, s1, s2)	Returns the transition probability of the given state names
setInitialTransitionProbability(self, state, ...)	Sets the initial transition probability of the given state to prob
setPseudoCounts(self, pseudo_counts)	Sets the pseudo count that are added instead of zero
setTrainingEmissionProbability(self, state, prob)	Sets the emission probability of the given state to prob
setTransitionProbability(self, s1, s2, prob)	Sets the transition probability of the given state names to prob
setVariableModifications(self, modifications)
train(self)	Trains the HMM.
writeGraphMLFile(self, filename)	Writes the HMM into a file in GraphML format

addNewState()

Overload:

addNewState(self, state: HMMState) → None

Registers a new state to the HMM

Overload:

addNewState(self, name: Union[bytes, str, String]) → None

Registers a new state to the HMM

addSynonymTransition(self, name1: Union[bytes, str, String], name2: Union[bytes, str, String], synonym1: Union[bytes, str, String], synonym2: Union[bytes, str, String]) → None

Add a new synonym transition to the given state names

clear(self) → None

Clears all data

clearInitialTransitionProbabilities(self) → None

Clears the initial probabilities

clearTrainingEmissionProbabilities(self) → None

Clear the emission probabilities

disableTransition(self, s1: Union[bytes, str, String], s2: Union[bytes, str, String]) → None

Disables the transition; deletes the nodes from the predecessor/successor list respectively

disableTransitions(self) → None

Disables all transitions

dump(self) → None

Writes some stats to cerr

enableTransition(self, s1: Union[bytes, str, String], s2: Union[bytes, str, String]) → None

Enables a transition; adds s1 to the predecessor list of s2 and s2 to the successor list of s1

estimateUntrainedTransitions(self) → None

Estimates the transition probabilities of not trained transitions by averages similar trained ones

evaluate(self) → None

Evaluate the HMM, estimates the transition probabilities from the training

forwardDump(self) → None

Writes some info of the forward “matrix” to cerr

getNumberOfStates(self) → int

Returns the number of states

getPseudoCounts(self) → float

Returns the pseudo counts

getState(self, name: Union[bytes, str, String]) → HMMState

Returns the state with the given name

getTransitionProbability(self, s1: Union[bytes, str, String], s2: Union[bytes, str, String]) → float

Returns the transition probability of the given state names

setInitialTransitionProbability(self, state: Union[bytes, str, String], prob: float) → None

Sets the initial transition probability of the given state to prob

setPseudoCounts(self, pseudo_counts: float) → None

Sets the pseudo count that are added instead of zero

setTrainingEmissionProbability(self, state: Union[bytes, str, String], prob: float) → None

Sets the emission probability of the given state to prob

setTransitionProbability(self, s1: Union[bytes, str, String], s2: Union[bytes, str, String], prob: float) → None

Sets the transition probability of the given state names to prob

setVariableModifications(self, modifications: List[bytes]) → None

train(self) → None

Trains the HMM. Initial probabilities and emission probabilities of the emitting states should be set

writeGraphMLFile(self, filename: Union[bytes, str, String]) → None

Writes the HMM into a file in GraphML format