GPyOpt.objective_examples package

Submodules

GPyOpt.objective_examples.experiments1d module

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)
class GPyOpt.objective_examples.experiments1d.function1d

This is a benchmark of unidimensional functions interesting to optimize. :param bounds: the box constraints to define the domain in which the function is optimized.

plot(bounds=None)

GPyOpt.objective_examples.experiments2d module

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)
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)
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)
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)
class GPyOpt.objective_examples.experiments2d.eggholder(bounds=None, sd=None)
f(X)
class GPyOpt.objective_examples.experiments2d.function2d

This is a benchmark of bi-dimensional functions interesting to optimize.

plot()
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)
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)
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)
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)
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

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)
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)
class GPyOpt.objective_examples.experimentsNd.alpine2(input_dim, bounds=None, sd=None)

Alpine2 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)
class GPyOpt.objective_examples.experimentsNd.gSobol(a, bounds=None, sd=None)

gSolbol function

Parameters:
  • a – one-dimensional array containing the coefficients of the function.
  • sd – standard deviation, to generate noisy evaluations of the function.
f(X)

Module contents