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brendan
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doc string updats
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bayes_opt/bayesian_optimization.py

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@@ -62,8 +62,6 @@ def dispatch(self, event):
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callback(event, self)
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class BayesianOptimization(Observable):
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"""
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This class takes the function to optimize as well as the parameters bounds
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bounds_transformer: DomainTransformer, optional(default=None)
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If provided, the transformation is applied to the bounds.
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allow_duplicate_points: bool, optional (default=False)
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If True, the optimizer will allow duplicate points to be registered.
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This behavior may be desired in high noise situations where repeatedly probing
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the same point will give different answers. In other situations, the acquisition
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may occasionaly generate a duplicate point.
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Methods
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-------
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probe()
@@ -271,6 +275,9 @@ def maximize(self,
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acquisition_function: object, optional
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An instance of bayes_opt.util.UtilityFunction.
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If nothing is passed, a default using ucb is used
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All other parameters are unused, and are only available to ensure backwards compatability - these
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will be removed in a future release
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"""
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self._prime_subscriptions()
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self.dispatch(Events.OPTIMIZATION_START)

bayes_opt/target_space.py

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@@ -40,6 +40,12 @@ def __init__(self, target_func, pbounds, constraint=None, random_state=None,
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random_state : int, RandomState, or None
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optionally specify a seed for a random number generator
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allow_duplicate_points: bool, optional (default=False)
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If True, the optimizer will allow duplicate points to be registered.
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This behavior may be desired in high noise situations where repeatedly probing
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the same point will give different answers. In other situations, the acquisition
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may occasionaly generate a duplicate point.
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"""
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self.random_state = ensure_rng(random_state)
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self._allow_duplicate_points = allow_duplicate_points

examples/test.py

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