Я использовал пример, описанный здесь (http://openmdao.readthedocs.org/en/1.5.0/usr-guide/tutorials/doe-drivers.html?highlight=driver), чтобы показать мою проблему. Я хочу использовать тот же подход для одного компонента, где «параметры» являются массивом и больше не плавают. См. пример ниже
from openmdao.api import IndepVarComp, Group, Problem, ScipyOptimizer, ExecComp, DumpRecorder, Component
from openmdao.drivers.latinhypercube_driver import LatinHypercubeDriver, OptimizedLatinHypercubeDriver
import numpy as np
class Paraboloid(Component):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """
def __init__(self):
super(Paraboloid, self).__init__()
self.add_param('x', val=0.0)
self.add_param('y', val=0.0)
self.add_output('f_xy', val=0.0)
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
"""
x = params['x']
y = params['y']
unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0
def linearize(self, params, unknowns, resids):
#""" Jacobian for our paraboloid."""
x = params['x']
y = params['y']
J = {}
J['f_xy', 'x'] = 2.0*x - 6.0 + y
J['f_xy', 'y'] = 2.0*y + 8.0 + x
return J
class ParaboloidArray(Component):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """
def __init__(self):
super(ParaboloidArray, self).__init__()
self.add_param('X', val=np.array([0., 0.]))
self.add_output('f_xy', val=0.0)
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
"""
x = params['X'][0]
y = params['y'][1]
unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
top.driver = OptimizedLatinHypercubeDriver(num_samples=4, seed=0, population=20, generations=4, norm_method=2)
top.driver.add_desvar('x', lower=-50.0, upper=50.0)
top.driver.add_desvar('y', lower=-50.0, upper=50.0)
top.driver.add_objective('f_xy')
top.setup()
top.run()
top.cleanup()
###########################
print("case float ok")
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('X', np.array([50., 50.])), promotes=['*'])
root.add('comp', ParaboloidArray(), promotes=['*'])
top.driver = OptimizedLatinHypercubeDriver(num_samples=4, seed=0, population=20, generations=4, norm_method=2)
top.driver.add_desvar('X', lower=np.array([-50., -50.]), upper=np.array([50., 50.]))
top.driver.add_objective('f_xy')
top.setup()
top.run()
top.cleanup()
Я получаю следующую ошибку:
Traceback (most recent call last):
File "C:\Program Files (x86)\Wing IDE 101 5.0\src\debug\tserver\_sandbox.py", line 102, in <module>
File "D:\tlefeb\Anaconda2\Lib\site-packages\openmdao\core\problem.py", line 1038, in run
self.driver.run(self)
File "D:\tlefeb\Anaconda2\Lib\site-packages\openmdao\drivers\predeterminedruns_driver.py", line 108, in run
for run in runlist:
File "D:\tlefeb\Anaconda2\Lib\site-packages\openmdao\drivers\latinhypercube_driver.py", line 57, in _build_runlist
design_var_buckets = self._get_buckets(bounds['lower'], bounds['upper'])
File "D:\tlefeb\Anaconda2\Lib\site-packages\openmdao\drivers\latinhypercube_driver.py", line 101, in _get_buckets
bucket_walls = np.linspace(low, high, self.num_samples + 1)
File "D:\tlefeb\Anaconda2\Lib\site-packages\numpy\core\function_base.py", line 102, in linspace
if step == 0:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
Я что-то неправильно понял в своем способе кодирования?
