GPyOpt.objective_examples package¶
Submodules¶
GPyOpt.objective_examples.experiments1d module¶
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class
GPyOpt.objective_examples.experiments1d.
forrester
(sd=None)¶ Bases:
GPyOpt.objective_examples.experiments1d.function1d
Forrester function.
Parameters: sd – standard deviation, to generate noisy evaluations of the function. -
f
(X)¶
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GPyOpt.objective_examples.experiments2d module¶
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class
GPyOpt.objective_examples.experiments2d.
beale
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Cosines function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
branin
(bounds=None, a=None, b=None, c=None, r=None, s=None, t=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Branin function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
cosines
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Cosines function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
dropwave
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Cosines function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
function2d
¶ This is a benchmark of bi-dimensional functions interesting to optimize.
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plot
()¶
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class
GPyOpt.objective_examples.experiments2d.
goldstein
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Goldstein function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
mccormick
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Mccormick function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(x)¶
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class
GPyOpt.objective_examples.experiments2d.
powers
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Powers function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(x)¶
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class
GPyOpt.objective_examples.experiments2d.
rosenbrock
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Cosines function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶
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class
GPyOpt.objective_examples.experiments2d.
sixhumpcamel
(bounds=None, sd=None)¶ Bases:
GPyOpt.objective_examples.experiments2d.function2d
Six hump camel function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(x)¶
GPyOpt.objective_examples.experimentsNd module¶
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class
GPyOpt.objective_examples.experimentsNd.
ackley
(input_dim, bounds=None, sd=None)¶ Ackley function
Parameters: sd – standard deviation, to generate noisy evaluations of the function. -
f
(X)¶
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class
GPyOpt.objective_examples.experimentsNd.
alpine1
(input_dim, bounds=None, sd=None)¶ Alpine1 function
Parameters: - bounds – the box constraints to define the domain in which the function is optimized.
- sd – standard deviation, to generate noisy evaluations of the function.
-
f
(X)¶