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get_auto_slices(self: pysmile.learning.EM) -> bool

Gets whether auto slicing is enabled

get_eq_sample_size(self: pysmile.learning.EM) -> int

Gets equivalent sample size for EM

get_last_score(self: pysmile.learning.EM) -> float

Gets score after last EM run

get_randomize_parameters(self: pysmile.learning.EM) -> bool

Checks if EM should randomize parameters

get_relevance(self: pysmile.learning.EM) -> bool

Gets relevance factor for EM

get_seed(self: pysmile.learning.EM) -> int

Gets random seed for EM

get_uniformize_parameters(self: pysmile.learning.EM) -> bool

Checks if parameters should be uniformized before EM

learn(self: pysmile.learning.EM, data: pysmile.learning.DataSet, net: pysmile.Network, matching: List[pysmile.learning.DataMatch], fixed_nodes: List[int]) -> None
learn(self: pysmile.learning.EM, data: pysmile.learning.DataSet, net: pysmile.Network, matching: List[pysmile.learning.DataMatch], fixed_nodes: List[str]) -> None
learn(self: pysmile.learning.EM, data: pysmile.learning.DataSet, net: pysmile.Network, matching: List[pysmile.learning.DataMatch]) -> None

Performs EM learning

set_auto_slices(self: pysmile.learning.EM, value: bool) -> None

Enables or disables auto slicing

set_eq_sample_size(self: pysmile.learning.EM, size: int) -> None

Sets equivalent sample size for EM

set_randomize_parameters(self: pysmile.learning.EM, value: bool) -> None

Enables or disables random parameter initialization

set_relevance(self: pysmile.learning.EM, value: bool) -> None

Sets relevance factor for EM

set_seed(self: pysmile.learning.EM, seed: int) -> None

Sets random seed for EM

set_uniformize_parameters(self: pysmile.learning.EM, value: bool) -> None

Enables or disables parameter uniformization