rbfopt_test_functions module¶
Test functions.
This module implements several known mathematical functions, that can be used to test RBFOpt.
Licensed under Revised BSD license, see LICENSE. (C) Copyright Singapore University of Technology and Design 2014. (C) Copyright International Business Machines Corporation 2017.
- class rbfopt_test_functions.TestBlackBox(name)[source]¶
Bases:
RbfoptBlackBox
A black-box constructed from a known test function.
- Parameters
- namestring
The name of the function to be implemented.
- evaluate(point)[source]¶
Evaluate the black-box function.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- float
Value of the function at x.
- evaluate_noisy(point)[source]¶
Evaluate a fast approximation of the black-box function.
Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- 1D numpy.ndarray[float]
A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.
- get_dimension()[source]¶
Return the dimension of the problem.
- Returns
- int
The dimension of the problem.
- get_var_lower()[source]¶
Return the array of lower bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Lower bounds of the decision variables.
- get_var_type()[source]¶
Return the type of each variable.
- Returns
- 1D numpy.ndarray[char]
An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.
- get_var_upper()[source]¶
Return the array of upper bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Upper bounds of the decision variables.
- has_evaluate_noisy()[source]¶
Indicate whether evaluate_noisy is available.
Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.
- Returns
- bool
Is evaluate_noisy available?
- class rbfopt_test_functions.TestEnlargedBlackBox(name, dimension_multiplier=1)[source]¶
Bases:
RbfoptBlackBox
A black-box constructed increasing the size of a test function.
Construct a black box function from a given function, increasing its dimension by a given factor. The new function is put together from several independent copies of the original function, plus a coupling term. If the dimension muldiplier is d and the original function has dimension n, the new function has dimension n*d and is computed as:
where a_j are random weights that add up to 0.6, and g_1 through g_n are linear functions of a random subset of the variables. These linear function are appropriately scaled and clipped so that we do not exceed the original function bounds. The optimum of the new function stays the same. Finally, all variables are randomly permuted.
- Parameters
- namestring
The name of the function to be implemented.
- dimension_multiplierint
Dimension multiplier
- evaluate(point)[source]¶
Evaluate the black-box function.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- float
Value of the function at x.
- evaluate_noisy(point)[source]¶
Evaluate a fast approximation of the black-box function.
Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- 1D numpy.ndarray[float]
A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.
- get_dimension()[source]¶
Return the dimension of the problem.
- Returns
- int
The dimension of the problem.
- get_var_lower()[source]¶
Return the array of lower bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Lower bounds of the decision variables.
- get_var_type()[source]¶
Return the type of each variable.
- Returns
- 1D numpy.ndarray[char]
An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.
- get_var_upper()[source]¶
Return the array of upper bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Upper bounds of the decision variables.
- has_evaluate_noisy()[source]¶
Indicate whether evaluate_noisy is available.
Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.
- Returns
- bool
Is evaluate_noisy available?
- class rbfopt_test_functions.TestNoisyBlackBox(blackbox, max_rel_error=0.1, max_abs_error=0.1)[source]¶
Bases:
RbfoptBlackBox
A noisy black-box constructed from a given black-box function.
- Parameters
- blackboxRbfoptBlackBox
The black box function to which noise is added.
- max_rel_error: float
Maximum relative error.
- max_abs_error: float
Maximum absolute error.
- evaluate(point)[source]¶
Evaluate the black-box function.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- float
Value of the function at x.
- evaluate_noisy(point)[source]¶
Evaluate a fast approximation of the black-box function.
Returns an approximation of the value of evaluate(), hopefully much more quickly, and provides error bounds on the evaluation. If has_evaluate_noisy() returns False, this function will never be queried and therefore it does not have to return any value.
- Parameters
- x1D numpy.ndarray[float]
Value of the decision variables.
- Returns
- 1D numpy.ndarray[float]
A numpy array with three floats (value, lower, upper) containing the approximate value of the function at x, the lower error bound, and the upper error bound, such that the true function value is contained between value + lower and value + upper. Hence, lower should be <= 0 while upper should be >= 0.
- get_dimension()[source]¶
Return the dimension of the problem.
- Returns
- int
The dimension of the problem.
- get_var_lower()[source]¶
Return the array of lower bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Lower bounds of the decision variables.
- get_var_type()[source]¶
Return the type of each variable.
- Returns
- 1D numpy.ndarray[char]
An array of length equal to dimension, specifying the type of each variable. Possible types are ‘R’ for real (continuous) variables, ‘I’ for integer (discrete) variables, ‘C’ for categorical (discrete, unordered). Bounds for categorical variables are interpreted the same way as for integer variables, but categorical variables are handled differently by the optimization algorithm; e.g., a categorical variable with bounds [2, 4] can take the value 2, 3 or 4.
- get_var_upper()[source]¶
Return the array of upper bounds on the variables.
- Returns
- 1D numpy.ndarray[float]
Upper bounds of the decision variables.
- has_evaluate_noisy()[source]¶
Indicate whether evaluate_noisy is available.
Indicate if a fast but potentially noisy version of evaluate is available through the function evaluate_noisy. If True, such function will be used to try to accelerate convergence of the optimization algorithm. If False, the function evaluate_noisy will never be queried.
- Returns
- bool
Is evaluate_noisy available?
- class rbfopt_test_functions.branin[source]¶
Bases:
object
Branin function of the Dixon-Szego test set.
- additional_optima = <Mock name='mock.array()' id='140550716838352'>¶
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.397887357729739¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.branin_cat[source]¶
Bases:
object
Branin function of the Dixon-Szego test set, with categorical vars.
- additional_optima = <Mock name='mock.array()' id='140550716838352'>¶
- dimension = 3¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.397887357729739¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.camel[source]¶
Bases:
object
Six-hump Camel function of the Dixon-Szego test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1.0316284535¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.ex4_1_1[source]¶
Bases:
object
ex4_1_1 function of the GlobalLib test set.
- dimension = 1¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -7.487312360731¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.ex4_1_2[source]¶
Bases:
object
ex4_1_2 function of the GlobalLib test set.
- dimension = 1¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -663.4993631230575¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.ex8_1_1[source]¶
Bases:
object
ex8_1_1 function of the GlobalLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -2.0218067833¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.ex8_1_1_cat[source]¶
Bases:
object
ex8_1_1 function of the GlobalLib test set.
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -2.4161466378205514¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.ex8_1_4[source]¶
Bases:
object
ex8_1_4 function of the GlobalLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.gear[source]¶
Bases:
object
gear function of the MINLPLib test set.
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.gear4[source]¶
Bases:
object
gear4 function of the MINLPLib test set.
- dimension = 5¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1.6434284739¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.gear4_cat[source]¶
Bases:
object
gear4 function of the MINLPLib test set, with categorical variables
- dimension = 6¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1.6434284739¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.goldsteinprice[source]¶
Bases:
object
Goldstein & Price function of the Dixon-Szego test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 3¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.hartman3[source]¶
Bases:
object
Hartman3 function of the Dixon-Szego test set.
- a = [[3.0, 0.1, 3.0, 0.1], [10.0, 10.0, 10.0, 10.0], [30.0, 35.0, 30.0, 35.0]]¶
- c = [1.0, 1.2, 3.0, 3.2]¶
- dimension = 3¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -3.8626347486217725¶
- p = [[0.3689, 0.4699, 0.1091, 0.03815], [0.117, 0.4387, 0.8732, 0.5743], [0.2673, 0.747, 0.5547, 0.8828]]¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.hartman3_cat[source]¶
Bases:
object
Hartman3 function of the Dixon-Szego test set, with categorical vars.
- a = <Mock name='mock.array()' id='140550716838352'>¶
- c = <Mock name='mock.array()' id='140550716838352'>¶
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -4.822787424687719¶
- p = <Mock name='mock.array()' id='140550716838352'>¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.hartman6[source]¶
Bases:
object
Hartman6 function of the Dixon-Szego test set.
- a = [[10.0, 0.05, 3.0, 17.0], [3.0, 10.0, 3.5, 8.0], [17.0, 17.0, 1.7, 0.05], [3.5, 0.1, 10.0, 10.0], [1.7, 8.0, 17.0, 0.1], [8.0, 14.0, 8.0, 14.0]]¶
- c = [1.0, 1.2, 3.0, 3.2]¶
- dimension = 6¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -3.32236801141551¶
- p = [[0.1312, 0.2329, 0.2348, 0.4047], [0.1696, 0.4135, 0.1451, 0.8828], [0.5569, 0.8307, 0.3522, 0.8732], [0.0124, 0.3736, 0.2883, 0.5743], [0.8283, 0.1004, 0.3047, 0.1091], [0.5886, 0.9991, 0.665, 0.0381]]¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.hartman6_cat[source]¶
Bases:
object
Hartman6 function of the Dixon-Szego test set, with categorical vars.
- a = <Mock name='mock.array()' id='140550716838352'>¶
- c = <Mock name='mock.array()' id='140550716838352'>¶
- dimension = 7¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -3.96231691936822¶
- p = <Mock name='mock.array()' id='140550716838352'>¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.least[source]¶
Bases:
object
least function of the GlobalLib test set.
- dimension = 3¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 14085.139848928¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs02[source]¶
Bases:
object
nvs02 function of the MINLPLib test set.
- dimension = 5¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 5.92239325641¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs03[source]¶
Bases:
object
nvs03 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 16.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs04[source]¶
Bases:
object
nvs04 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.72¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs06[source]¶
Bases:
object
nvs06 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1.7703125¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs07[source]¶
Bases:
object
nvs07 function of the MINLPLib test set.
- dimension = 3¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 4.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs07_cat[source]¶
Bases:
object
nvs07 function of the MINLPLib test set, with categorical variables.
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 2.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs09[source]¶
Bases:
object
nvs09 function of the MINLPLib test set.
- dimension = 10¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -43.134336918035¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs09_cat[source]¶
Bases:
object
nvs09 function of the MINLPLib test set with categorical variables
- dimension = 11¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -53.179649471788274¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs14[source]¶
Bases:
object
nvs14 function of the MINLPLib test set.
- dimension = 5¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -40358.1547693¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs15[source]¶
Bases:
object
nvs15 function of the MINLPLib test set.
- dimension = 3¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.nvs16[source]¶
Bases:
object
nvs16 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.703125¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.perm0_8[source]¶
Bases:
object
perm0 function of dimension 8 from Arnold Neumaier. http://www.mat.univie.ac.at/~neum/glopt/my_problems.html We use parameters (8, 100) here.
- dimension = 8¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1000.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.perm_6[source]¶
Bases:
object
perm function of dimension 6 from Arnold Neumaier. http://www.mat.univie.ac.at/~neum/glopt/my_problems.html We use parameters (6, 60) here.
- dimension = 6¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 1000.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.prob03[source]¶
Bases:
object
prob03 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 10.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.rbrock[source]¶
Bases:
object
rbrock function of the GlobalLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schaeffer_f7_12_1[source]¶
Bases:
object
Schaeffer F7 function.
- dimension = 12¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schaeffer_f7_12_1_int_cat[source]¶
Bases:
object
Schaeffer F7 function with integer and categorical variables
- dimension = 13¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schaeffer_f7_12_2[source]¶
Bases:
object
Schaeffer F7 function.
- dimension = 12¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 10¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schaeffer_f7_12_2_int_cat[source]¶
Bases:
object
Schaeffer F7 function with integer and categorical variables.
- dimension = 13¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_1[source]¶
Bases:
object
schoen function of dimension 10 with 50 stationary points.
- dimension = 10¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_1_cat[source]¶
Bases:
object
schoen function of dimension 10 with categorical variables.
- dimension = 12¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_1_int[source]¶
Bases:
object
schoen function of dimension 10 with 50 stationary points.
Mixed integer version.
- dimension = 10¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_2[source]¶
Bases:
object
schoen function of dimension 10 with 50 stationary points.
- dimension = 10¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_2_cat[source]¶
Bases:
object
schoen function of dimension 10 with categorical variables.
- dimension = 12¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_10_2_int[source]¶
Bases:
object
schoen function of dimension 10 with 50 stationary points.
Mixed integer version.
- dimension = 10¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_6_1[source]¶
Bases:
object
schoen function of dimension 6 with 50 stationary points.
- dimension = 6¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_6_1_int[source]¶
Bases:
object
schoen function of dimension 6 with 50 stationary points.
Mixed integer version.
- dimension = 6¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_6_2[source]¶
Bases:
object
schoen function of dimension 6 with 50 stationary points.
- dimension = 6¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.schoen_6_2_int[source]¶
Bases:
object
schoen function of dimension 6 with 50 stationary points.
Mixed integer version.
- dimension = 6¶
- f = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -1000¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- z = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.shekel10[source]¶
Bases:
object
Shekel10 function of the Dixon-Szego test set.
- a = [[4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 5.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 5.0, 1.0, 2.0, 3.6], [4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 3.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 3.0, 1.0, 2.0, 3.6]]¶
- c = [0.1, 0.2, 0.2, 0.4, 0.4, 0.6, 0.3, 0.7, 0.5, 0.5]¶
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10.5362837262196¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.shekel5[source]¶
Bases:
object
Shekel5 function of the Dixon-Szego test set.
- a = [[4.0, 1.0, 8.0, 6.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0], [4.0, 1.0, 8.0, 6.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0]]¶
- c = [0.1, 0.2, 0.2, 0.4, 0.4]¶
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10.153195850979¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.shekel7[source]¶
Bases:
object
Shekel7 function of the Dixon-Szego test set.
- a = [[4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 5.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 5.0], [4.0, 1.0, 8.0, 6.0, 3.0, 2.0, 3.0], [4.0, 1.0, 8.0, 6.0, 7.0, 9.0, 3.0]]¶
- c = [0.1, 0.2, 0.2, 0.4, 0.4, 0.6, 0.3]¶
- dimension = 4¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -10.4028188369303¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.sporttournament06[source]¶
Bases:
object
sporttournament06 function of the MINLPLib test set.
- dimension = 15¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -12.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.st_miqp1[source]¶
Bases:
object
st_miqp1 function of the MINLPLib test set.
- dimension = 5¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 281.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.st_miqp1_cat[source]¶
Bases:
object
st_miqp1 function of the MINLPLib test set, with categorical variables.
- c = <Mock name='mock.array()' id='140550716838352'>¶
- dimension = 6¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 186.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.st_miqp3[source]¶
Bases:
object
st_miqp3 function of the MINLPLib test set.
- dimension = 2¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = -6.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶
- class rbfopt_test_functions.st_test1[source]¶
Bases:
object
st_test1 function of the MINLPLib test set.
- dimension = 5¶
- optimum_point = <Mock name='mock.array()' id='140550716838352'>¶
- optimum_value = 0.0¶
- var_lower = <Mock name='mock.array()' id='140550716838352'>¶
- var_type = <Mock name='mock.array()' id='140550716838352'>¶
- var_upper = <Mock name='mock.array()' id='140550716838352'>¶