pypomp.core.results.PompTrainResult

class pypomp.core.results.PompTrainResult(method: str, execution_time: float | None, key: Array, timestamp: Timestamp = <factory>, theta: list[dict] = <factory>, traces_da: DataArray = <factory>, optimizer: Optimizer = <factory>, J: int = 0, M: int = 0, eta: LearningRate | None = None, alpha: float = 0.97, thresh: int = 0, alpha_cooling: float = 1.0)

Bases: PompEstimationTracesMixin, PompBaseResult

Result from Pomp.train() method.

Methods

CLL([average])	Return conditional log-likelihoods as a DataFrame.
ESS([average])	Return Effective Sample Size as a DataFrame.
__init__(method, execution_time, key, ...)
merge(*results)	Merge multiple result objects of the same type.
print_summary([n])	Print a summary of this result.
to_dataframe([ignore_nan])	Convert result to DataFrame using traces_da.
traces()	Return traces DataFrame using traces_da.

Attributes

J	The number of particles used for filtering.
M	The number of iterations performed.
alpha	The discount factor for the gradient moving average.
alpha_cooling	The cooling factor for the discount factor.
eta	The learning rate object.
thresh	The resampling threshold used.
traces_da	Parameter traces and log-likelihoods across iterations.
optimizer	The optimizer used for training.
theta	The list of parameter sets used for the computation.
method	The name of the method that produced this result (e.g., 'pfilter', 'mif').
execution_time	Total execution time in seconds.
key	The JAX random key used for this execution.
timestamp	The date and time when the result was created.