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- structure_learning.approximators.approximator.Approximator(abc.ABC)
-
- HillClimb
class HillClimb(structure_learning.approximators.approximator.Approximator) |
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HillClimb(data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], initial_state: structure_learning.data_structures.dag.DAG = None, score: structure_learning.scores.score.Score = <class 'structure_learning.scores.bge.BGeScore'>, max_iter=100000, epsilon=0.0001, tabu_length=100, keep_particles=False, blacklist=None, whitelist=None, seed=None, probabilistic=False)
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- Method resolution order:
- HillClimb
- structure_learning.approximators.approximator.Approximator
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], initial_state: structure_learning.data_structures.dag.DAG = None, score: structure_learning.scores.score.Score = <class 'structure_learning.scores.bge.BGeScore'>, max_iter=100000, epsilon=0.0001, tabu_length=100, keep_particles=False, blacklist=None, whitelist=None, seed=None, probabilistic=False)
- Initialize self. See help(type(self)) for accurate signature.
- config(self)
- Returns the configuration of the HillClimb algorithm.
- run(self, increment=None)
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
Methods inherited from structure_learning.approximators.approximator.Approximator:
- save(self, filename: str, compression='gzip')
- Saves the Graph object to a file.
Parameters:
filename (str): Path to the output file.
Class methods inherited from structure_learning.approximators.approximator.Approximator:
- load(filename: str, compression='gzip')
- Loads a Graph object from a file.
Parameters:
filename (str): Path to the input file.
Returns:
Graph: Loaded Graph object.
Data descriptors inherited from structure_learning.approximators.approximator.Approximator:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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