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: bytes | str | String) → None

Registers a new state to the HMM

addSynonymTransition(self, name1: bytes | str | String, name2: bytes | str | String, synonym1: bytes | str | String, synonym2: 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: bytes | str | String, s2: 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: bytes | str | String, s2: 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: bytes | str | String) → HMMState#: Returns the state with the given name

getTransitionProbability(self, s1: bytes | str | String, s2: bytes | str | String) → float#: Returns the transition probability of the given state names

setInitialTransitionProbability(self, state: 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: bytes | str | String, prob: float) → None#: Sets the emission probability of the given state to prob

setTransitionProbability(self, s1: bytes | str | String, s2: 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: bytes | str | String) → None#: Writes the HMM into a file in GraphML format