apollon.hmm.poisson module¶
poisson_hmm.py – HMM with Poisson-distributed state dependent process. Copyright (C) 2018 Michael Blaß <mblass@posteo.net>
- Functions:
to_txt Serializes model to text file. to_json JSON serialization.
is_tpm Check weter array is stochastic matrix. _check_poisson_intput Check wheter input is suitable for PoissonHMM.
- Classes:
PoissonHMM HMM with univariat Poisson-distributed states.
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class
apollon.hmm.poisson.
Params
(lambda_, gamma_, delta_)¶ Bases:
object
Easy access to estimated HMM parameters and quality measures.
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class
apollon.hmm.poisson.
PoissonHmm
(X: numpy.ndarray, m_states: int, init_lambda: Union[numpy.ndarray, str] = 'quantile', init_gamma: Union[numpy.ndarray, str] = 'uniform', init_delta: Union[numpy.ndarray, str] = 'stationary', g_dirichlet: Optional[Iterable] = None, d_dirichlet: Optional[Iterable] = None, fill_diag: float = 0.8, verbose: bool = True)¶ Bases:
object
Hidden-Markov Model with univariate Poisson-distributed states.
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decoding
¶
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fit
(X: numpy.ndarray) → bool¶ Fit the initialized PoissonHMM to the input data set.
- Parameters
X (np.ndarray) –
- Returns
(int) True on success else False.
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hyper_params
¶
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init_params
¶
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params
¶
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quality
¶
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score
(X: numpy.ndarray)¶ Compute the log-likelihood of X under this HMM.
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success
¶
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to_dict
()¶ Returns HMM parameters as dict.
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training_date
¶
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verbose
¶
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version
¶
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class
apollon.hmm.poisson.
QualityMeasures
(aic, bic, nll, n_iter)¶ Bases:
object
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apollon.hmm.poisson.
assert_poisson_input_data
(X: numpy.ndarray)¶ Raise if X is not a array of integers.
- Parameters
X (np.ndarray) –
- Raises
ValueError –