opytimizer.optimizers.misc.cem¶
Cross-Entropy Method.
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class
opytimizer.optimizers.misc.cem.
CEM
(params: Optional[Dict[str, Any]] = None)¶ A CEM class, inherited from Optimizer.
This is the designed class to define CEM-related variables and methods.
References
R. Y. Rubinstein. Optimization of Computer simulation Models with Rare Events. European Journal of Operations Research (1997).
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__init__
(params: Optional[Dict[str, Any]] = None) → None¶ Initialization method.
Parameters: params – Contains key-value parameters to the meta-heuristics.
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n_updates
¶ Number of positions to employ in update formulae.
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alpha
¶ Learning rate.
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mean
¶ Array of means.
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std
¶ Array of standard deviations.
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compile
(space: opytimizer.core.space.Space) → None¶ Compiles additional information that is used by this optimizer.
Parameters: space – A Space object containing meta-information.
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_create_new_samples
(agents: List[opytimizer.core.agent.Agent], function: opytimizer.core.function.Function) → None¶ Creates new agents based on current mean and standard deviation.
Parameters: - agents (list) – List of agents.
- function – A Function object that will be used as the objective function.
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_update_mean
(updates: numpy.ndarray) → numpy.ndarray¶ Calculates and updates mean.
Parameters: updates – An array of updates’ positions. Returns: The new mean values. Return type: (np.ndarray)
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_update_std
(updates: numpy.ndarray) → numpy.ndarray¶ Calculates and updates standard deviation.
Parameters: updates – An array of updates’ positions. Returns: The new standard deviation values. Return type: (np.ndarray)
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update
(space: opytimizer.core.space.Space, function: opytimizer.core.function.Function) → None¶ Wraps Cross-Entropy Method over all agents and variables.
Parameters: - space – Space containing agents and update-related information.
- function – A Function object that will be used as the objective function.
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