problem module¶
Scalable problem  Problem¶
Classes:

The scalable problem from Tedford and Martins, 2010, builds a list of strongly coupled models ( 
Functions:
seed(self, seed=None) 


Random values in a given shape. 
 class gemseo.problems.scalable.parametric.core.problem.TMProblem(n_shared=1, n_local=None, n_coupling=None, full_coupling=True, noised_coupling=False, seed=1)[source]¶
Bases:
object
The scalable problem from Tedford and Martins, 2010, builds a list of strongly coupled models (
TMSubModel
) completed by a main model (TMMainModel
) computing the objective function and the constraints.These disciplines are defined on an unit design space whose parameters comprised in [0, 1] (
TMDesignSpace
). This problem is defined by the number of shared design parameters, the number of local design parameters per discipline and the number of output coupling variables per discipline. The strongly coupled disciplines can be either fully coupled (one discipline depends on all the others) or circularly coupled (one discipline depends only on the previous one and the first discipline depends only on the last one).Constructor.
 Parameters
n_shared (int) –
size of the shared design parameters. Default: 1.
By default it is set to 1.
n_local (list(int)) –
sizes of the local design parameters for the different disciplines. Same length as n_coupling. If None, use [1, 1]. Default: None.
By default it is set to None.
n_coupling (list(int)) –
sizes of the coupling parameters for the different disciplines. Same length as n_local. If None, use [1, 1]. Default: None.
By default it is set to None.
full_coupling (bool) –
if True, fully couple the disciplines. Otherwise, use circular coupling. Default: True.
By default it is set to True.
noised_coupling (bool) –
if True, add a noise component u_local_i on the ith discipline output.
By default it is set to False.
seed (int) –
seed for replicability.
By default it is set to 1.
Methods:
get_default_inputs
([names])Get default input values.
Get the TM design space.
Reset the TM design space.
 get_default_inputs(names=None)[source]¶
Get default input values.
 Parameters
names (list(str)) –
names of the inputs.
By default it is set to None.
 Returns
name and values of the inputs.
 Return type
dict
 gemseo.problems.scalable.parametric.core.problem.npseed()¶
seed(self, seed=None)
Reseed a legacy MT19937 BitGenerator
Notes
This is a convenience, legacy function.
The best practice is to not reseed a BitGenerator, rather to recreate a new one. This method is here for legacy reasons. This example demonstrates best practice.
>>> from numpy.random import MT19937 >>> from numpy.random import RandomState, SeedSequence >>> rs = RandomState(MT19937(SeedSequence(123456789))) # Later, you want to restart the stream >>> rs = RandomState(MT19937(SeedSequence(987654321)))
 gemseo.problems.scalable.parametric.core.problem.rand(d0, d1, ..., dn)¶
Random values in a given shape.
Note
This is a convenience function for users porting code from Matlab, and wraps random_sample. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones.
Create an array of the given shape and populate it with random samples from a uniform distribution over
[0, 1)
. Parameters
d0 (int, optional) – The dimensions of the returned array, must be nonnegative. If no argument is given a single Python float is returned.
d1 (int, optional) – The dimensions of the returned array, must be nonnegative. If no argument is given a single Python float is returned.
... (int, optional) – The dimensions of the returned array, must be nonnegative. If no argument is given a single Python float is returned.
dn (int, optional) – The dimensions of the returned array, must be nonnegative. If no argument is given a single Python float is returned.
 Returns
out – Random values.
 Return type
ndarray, shape
(d0, d1, ..., dn)
See also
random
Examples
>>> np.random.rand(3,2) array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random