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How to use the nevergrad.optimization.oneshot.SamplingSearch function in nevergrad

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github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
SmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True).with_name("SmallScrHaltonSearchPlusMiddlePoint", register=True)
HammersleySearch = SamplingSearch(sampler="Hammersley").with_name("HammersleySearch", register=True)
HammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True).with_name("HammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True).with_name("LargeHammersleySearchPlusMiddlePoint", register=True)
SmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallHammersleySearchPlusMiddlePoint", register=True)
LargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True).with_name("LargeScrHammersleySearchPlusMiddlePoint", register=True)
SmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallScrHammersleySearchPlusMiddlePoint", register=True)
ScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True).with_name("ScrHammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley").with_name("LargeHammersleySearch", register=True)
LargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True).with_name("LargeScrHammersleySearch", register=True)
ScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True).with_name("ScrHammersleySearch", register=True)
QOScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                       opposition_mode="quasi").with_name("QOScrHammersleySearch", register=True)
OScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                      opposition_mode="opposite").with_name("OScrHammersleySearch", register=True)
RescaleScrHammersleySearch = SamplingSearch(
    sampler="Hammersley", scrambled=True, rescaled=True).with_name("RescaleScrHammersleySearch", register=True)
CauchyScrHammersleySearch = SamplingSearch(
    cauchy=True, sampler="Hammersley", scrambled=True).with_name("CauchyScrHammersleySearch", register=True)
LHSSearch = SamplingSearch(sampler="LHS").with_name("LHSSearch", register=True)
CauchyLHSSearch = SamplingSearch(sampler="LHS", cauchy=True).with_name("CauchyLHSSearch", register=True)


AvgHaltonSearch = SamplingSearch(recommendation_rule="average_of_best").with_name("AvgHaltonSearch", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
recommendation_rule: str = "pessimistic") -> None:
        # keep all parameters and set initialize superclass for print
        self.sampler = sampler
        self.opposition_mode = opposition_mode
        self.middle_point = middle_point
        self.scrambled = scrambled
        self.cauchy = cauchy
        self.autorescale = autorescale
        self.scale = scale
        self.rescaled = rescaled
        self.recommendation_rule = recommendation_rule
        super().__init__()


# pylint: disable=line-too-long
HaltonSearch = SamplingSearch().with_name("HaltonSearch", register=True)
HaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True).with_name("HaltonSearchPlusMiddlePoint", register=True)
LargeHaltonSearch = SamplingSearch(scale=100.).with_name("LargeHaltonSearch", register=True)
LargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True).with_name("LargeScrHaltonSearch", register=True)
LargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True).with_name("LargeHaltonSearchPlusMiddlePoint", register=True)
SmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True).with_name("SmallHaltonSearchPlusMiddlePoint", register=True)
ScrHaltonSearch = SamplingSearch(scrambled=True).with_name("ScrHaltonSearch", register=True)
ScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True).with_name("ScrHaltonSearchPlusMiddlePoint", register=True)
LargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True).with_name("LargeScrHaltonSearchPlusMiddlePoint", register=True)
SmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True).with_name("SmallScrHaltonSearchPlusMiddlePoint", register=True)
HammersleySearch = SamplingSearch(sampler="Hammersley").with_name("HammersleySearch", register=True)
HammersleySearchPlusMiddlePoint = SamplingSearch(
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgHammersleySearchPlusMiddlePoint", register=True)
AvgLargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearchPlusMiddlePoint", register=True)
AvgSmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHammersleySearchPlusMiddlePoint", register=True)
AvgLargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHammersleySearchPlusMiddlePoint", register=True)
AvgSmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallScrHammersleySearchPlusMiddlePoint", register=True)
AvgScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgScrHammersleySearchPlusMiddlePoint", register=True)
AvgLargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley",
                                          recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearch", register=True)
AvgLargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHammersleySearch", register=True)
AvgScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                        recommendation_rule="average_of_best").with_name("AvgScrHammersleySearch", register=True)
AvgRescaleScrHammersleySearch = SamplingSearch(
    sampler="Hammersley", scrambled=True, rescaled=True, recommendation_rule="average_of_best").with_name("AvgRescaleScrHammersleySearch", register=True)
AvgCauchyScrHammersleySearch = SamplingSearch(
    cauchy=True, sampler="Hammersley", scrambled=True, recommendation_rule="average_of_best").with_name("AvgCauchyScrHammersleySearch", register=True)
AvgLHSSearch = SamplingSearch(sampler="LHS", recommendation_rule="average_of_best").with_name("AvgLHSSearch", register=True)
AvgCauchyLHSSearch = SamplingSearch(sampler="LHS", cauchy=True, recommendation_rule="average_of_best").with_name(
    "AvgCauchyLHSSearch", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
HaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True).with_name("HaltonSearchPlusMiddlePoint", register=True)
LargeHaltonSearch = SamplingSearch(scale=100.).with_name("LargeHaltonSearch", register=True)
LargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True).with_name("LargeScrHaltonSearch", register=True)
LargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True).with_name("LargeHaltonSearchPlusMiddlePoint", register=True)
SmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True).with_name("SmallHaltonSearchPlusMiddlePoint", register=True)
ScrHaltonSearch = SamplingSearch(scrambled=True).with_name("ScrHaltonSearch", register=True)
ScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True).with_name("ScrHaltonSearchPlusMiddlePoint", register=True)
LargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True).with_name("LargeScrHaltonSearchPlusMiddlePoint", register=True)
SmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True).with_name("SmallScrHaltonSearchPlusMiddlePoint", register=True)
HammersleySearch = SamplingSearch(sampler="Hammersley").with_name("HammersleySearch", register=True)
HammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True).with_name("HammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True).with_name("LargeHammersleySearchPlusMiddlePoint", register=True)
SmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallHammersleySearchPlusMiddlePoint", register=True)
LargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True).with_name("LargeScrHammersleySearchPlusMiddlePoint", register=True)
SmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallScrHammersleySearchPlusMiddlePoint", register=True)
ScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True).with_name("ScrHammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley").with_name("LargeHammersleySearch", register=True)
LargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True).with_name("LargeScrHammersleySearch", register=True)
ScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True).with_name("ScrHammersleySearch", register=True)
QOScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearchPlusMiddlePoint", register=True)
AvgSmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHammersleySearchPlusMiddlePoint", register=True)
AvgLargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHammersleySearchPlusMiddlePoint", register=True)
AvgSmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallScrHammersleySearchPlusMiddlePoint", register=True)
AvgScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgScrHammersleySearchPlusMiddlePoint", register=True)
AvgLargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley",
                                          recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearch", register=True)
AvgLargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHammersleySearch", register=True)
AvgScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                        recommendation_rule="average_of_best").with_name("AvgScrHammersleySearch", register=True)
AvgRescaleScrHammersleySearch = SamplingSearch(
    sampler="Hammersley", scrambled=True, rescaled=True, recommendation_rule="average_of_best").with_name("AvgRescaleScrHammersleySearch", register=True)
AvgCauchyScrHammersleySearch = SamplingSearch(
    cauchy=True, sampler="Hammersley", scrambled=True, recommendation_rule="average_of_best").with_name("AvgCauchyScrHammersleySearch", register=True)
AvgLHSSearch = SamplingSearch(sampler="LHS", recommendation_rule="average_of_best").with_name("AvgLHSSearch", register=True)
AvgCauchyLHSSearch = SamplingSearch(sampler="LHS", cauchy=True, recommendation_rule="average_of_best").with_name(
    "AvgCauchyLHSSearch", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
HaltonSearch = SamplingSearch().with_name("HaltonSearch", register=True)
HaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True).with_name("HaltonSearchPlusMiddlePoint", register=True)
LargeHaltonSearch = SamplingSearch(scale=100.).with_name("LargeHaltonSearch", register=True)
LargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True).with_name("LargeScrHaltonSearch", register=True)
LargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True).with_name("LargeHaltonSearchPlusMiddlePoint", register=True)
SmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True).with_name("SmallHaltonSearchPlusMiddlePoint", register=True)
ScrHaltonSearch = SamplingSearch(scrambled=True).with_name("ScrHaltonSearch", register=True)
ScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True).with_name("ScrHaltonSearchPlusMiddlePoint", register=True)
LargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True).with_name("LargeScrHaltonSearchPlusMiddlePoint", register=True)
SmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True).with_name("SmallScrHaltonSearchPlusMiddlePoint", register=True)
HammersleySearch = SamplingSearch(sampler="Hammersley").with_name("HammersleySearch", register=True)
HammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True).with_name("HammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True).with_name("LargeHammersleySearchPlusMiddlePoint", register=True)
SmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallHammersleySearchPlusMiddlePoint", register=True)
LargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True).with_name("LargeScrHammersleySearchPlusMiddlePoint", register=True)
SmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallScrHammersleySearchPlusMiddlePoint", register=True)
ScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True).with_name("ScrHammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley").with_name("LargeHammersleySearch", register=True)
LargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True).with_name("LargeScrHammersleySearch", register=True)
ScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True).with_name("ScrHammersleySearch", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
self.scale = scale
        self.rescaled = rescaled
        self.recommendation_rule = recommendation_rule
        super().__init__()


# pylint: disable=line-too-long
HaltonSearch = SamplingSearch().with_name("HaltonSearch", register=True)
HaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True).with_name("HaltonSearchPlusMiddlePoint", register=True)
LargeHaltonSearch = SamplingSearch(scale=100.).with_name("LargeHaltonSearch", register=True)
LargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True).with_name("LargeScrHaltonSearch", register=True)
LargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True).with_name("LargeHaltonSearchPlusMiddlePoint", register=True)
SmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True).with_name("SmallHaltonSearchPlusMiddlePoint", register=True)
ScrHaltonSearch = SamplingSearch(scrambled=True).with_name("ScrHaltonSearch", register=True)
ScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True).with_name("ScrHaltonSearchPlusMiddlePoint", register=True)
LargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True).with_name("LargeScrHaltonSearchPlusMiddlePoint", register=True)
SmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True).with_name("SmallScrHaltonSearchPlusMiddlePoint", register=True)
HammersleySearch = SamplingSearch(sampler="Hammersley").with_name("HammersleySearch", register=True)
HammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True).with_name("HammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True).with_name("LargeHammersleySearchPlusMiddlePoint", register=True)
SmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallHammersleySearchPlusMiddlePoint", register=True)
LargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True).with_name("LargeScrHammersleySearchPlusMiddlePoint", register=True)
SmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
sampler="Hammersley", scrambled=True, rescaled=True).with_name("RescaleScrHammersleySearch", register=True)
CauchyScrHammersleySearch = SamplingSearch(
    cauchy=True, sampler="Hammersley", scrambled=True).with_name("CauchyScrHammersleySearch", register=True)
LHSSearch = SamplingSearch(sampler="LHS").with_name("LHSSearch", register=True)
CauchyLHSSearch = SamplingSearch(sampler="LHS", cauchy=True).with_name("CauchyLHSSearch", register=True)


AvgHaltonSearch = SamplingSearch(recommendation_rule="average_of_best").with_name("AvgHaltonSearch", register=True)
AvgHaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True, recommendation_rule="average_of_best").with_name(
    "AvgHaltonSearchPlusMiddlePoint", register=True)
AvgLargeHaltonSearch = SamplingSearch(scale=100., recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearch", register=True)
AvgLargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True, recommendation_rule="average_of_best").with_name(
    "AvgLargeScrHaltonSearch", register=True)
AvgLargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearchPlusMiddlePoint", register=True)
AvgSmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHaltonSearchPlusMiddlePoint", register=True)
AvgScrHaltonSearch = SamplingSearch(scrambled=True, recommendation_rule="average_of_best").with_name("AvgScrHaltonSearch", register=True)
AvgScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgScrHaltonSearchPlusMiddlePoint", register=True)
AvgLargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHaltonSearchPlusMiddlePoint", register=True)
AvgSmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgSmallScrHaltonSearchPlusMiddlePoint", register=True)
AvgHammersleySearch = SamplingSearch(sampler="Hammersley", recommendation_rule="average_of_best").with_name(
    "AvgHammersleySearch", register=True)
AvgHammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgHammersleySearchPlusMiddlePoint", register=True)
AvgLargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearchPlusMiddlePoint", register=True)
AvgSmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHammersleySearchPlusMiddlePoint", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
scrambled=True, scale=100., sampler="Hammersley", middle_point=True).with_name("LargeScrHammersleySearchPlusMiddlePoint", register=True)
SmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True).with_name("SmallScrHammersleySearchPlusMiddlePoint", register=True)
ScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, sampler="Hammersley", middle_point=True).with_name("ScrHammersleySearchPlusMiddlePoint", register=True)
LargeHammersleySearch = SamplingSearch(scale=100., sampler="Hammersley").with_name("LargeHammersleySearch", register=True)
LargeScrHammersleySearch = SamplingSearch(
    scale=100., sampler="Hammersley", scrambled=True).with_name("LargeScrHammersleySearch", register=True)
ScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True).with_name("ScrHammersleySearch", register=True)
QOScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                       opposition_mode="quasi").with_name("QOScrHammersleySearch", register=True)
OScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True,
                                      opposition_mode="opposite").with_name("OScrHammersleySearch", register=True)
RescaleScrHammersleySearch = SamplingSearch(
    sampler="Hammersley", scrambled=True, rescaled=True).with_name("RescaleScrHammersleySearch", register=True)
CauchyScrHammersleySearch = SamplingSearch(
    cauchy=True, sampler="Hammersley", scrambled=True).with_name("CauchyScrHammersleySearch", register=True)
LHSSearch = SamplingSearch(sampler="LHS").with_name("LHSSearch", register=True)
CauchyLHSSearch = SamplingSearch(sampler="LHS", cauchy=True).with_name("CauchyLHSSearch", register=True)


AvgHaltonSearch = SamplingSearch(recommendation_rule="average_of_best").with_name("AvgHaltonSearch", register=True)
AvgHaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True, recommendation_rule="average_of_best").with_name(
    "AvgHaltonSearchPlusMiddlePoint", register=True)
AvgLargeHaltonSearch = SamplingSearch(scale=100., recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearch", register=True)
AvgLargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True, recommendation_rule="average_of_best").with_name(
    "AvgLargeScrHaltonSearch", register=True)
AvgLargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearchPlusMiddlePoint", register=True)
AvgSmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHaltonSearchPlusMiddlePoint", register=True)
AvgScrHaltonSearch = SamplingSearch(scrambled=True, recommendation_rule="average_of_best").with_name("AvgScrHaltonSearch", register=True)
github facebookresearch / nevergrad / nevergrad / optimization / oneshot.py View on Github external
AvgHaltonSearch = SamplingSearch(recommendation_rule="average_of_best").with_name("AvgHaltonSearch", register=True)
AvgHaltonSearchPlusMiddlePoint = SamplingSearch(middle_point=True, recommendation_rule="average_of_best").with_name(
    "AvgHaltonSearchPlusMiddlePoint", register=True)
AvgLargeHaltonSearch = SamplingSearch(scale=100., recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearch", register=True)
AvgLargeScrHaltonSearch = SamplingSearch(scale=100., scrambled=True, recommendation_rule="average_of_best").with_name(
    "AvgLargeScrHaltonSearch", register=True)
AvgLargeHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHaltonSearchPlusMiddlePoint", register=True)
AvgSmallHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHaltonSearchPlusMiddlePoint", register=True)
AvgScrHaltonSearch = SamplingSearch(scrambled=True, recommendation_rule="average_of_best").with_name("AvgScrHaltonSearch", register=True)
AvgScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgScrHaltonSearchPlusMiddlePoint", register=True)
AvgLargeScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=100., middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHaltonSearchPlusMiddlePoint", register=True)
AvgSmallScrHaltonSearchPlusMiddlePoint = SamplingSearch(
    scale=.01, middle_point=True, scrambled=True, recommendation_rule="average_of_best").with_name("AvgSmallScrHaltonSearchPlusMiddlePoint", register=True)
AvgHammersleySearch = SamplingSearch(sampler="Hammersley", recommendation_rule="average_of_best").with_name(
    "AvgHammersleySearch", register=True)
AvgHammersleySearchPlusMiddlePoint = SamplingSearch(
    sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgHammersleySearchPlusMiddlePoint", register=True)
AvgLargeHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeHammersleySearchPlusMiddlePoint", register=True)
AvgSmallHammersleySearchPlusMiddlePoint = SamplingSearch(
    scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallHammersleySearchPlusMiddlePoint", register=True)
AvgLargeScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=100., sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgLargeScrHammersleySearchPlusMiddlePoint", register=True)
AvgSmallScrHammersleySearchPlusMiddlePoint = SamplingSearch(
    scrambled=True, scale=.01, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best").with_name("AvgSmallScrHammersleySearchPlusMiddlePoint", register=True)
AvgScrHammersleySearchPlusMiddlePoint = SamplingSearch(

nevergrad

A Python toolbox for performing gradient-free optimization

GitHub
MIT
Latest version published 2 months ago

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