Source code for openmdao.examples.krig_sin
from __future__ import print_function
import numpy as np
from openmdao.api import Group, Problem, MetaModel, \
KrigingSurrogate, FloatKrigingSurrogate
[docs]class TrigMM(Group):
''' FloatKriging gives responses as floats '''
def __init__(self):
super(TrigMM, self).__init__()
# Create meta_model for f_x as the response
sin_mm = self.add("sin_mm", MetaModel())
sin_mm.add_param('x', val=0.)
sin_mm.add_output('f_x:float', val=0., surrogate=FloatKrigingSurrogate())
sin_mm.add_output('f_x:norm_dist', val=(0.,0.), surrogate=KrigingSurrogate(eval_rmse=True))
if __name__ == '__main__':
prob = Problem()
prob.root = TrigMM()
prob.setup()
#traning data is just set manually. No connected input needed, since
# we're assuming the data is pre-existing
prob['sin_mm.train:x'] = np.linspace(0,10,20)
prob['sin_mm.train:f_x:float'] = np.sin(prob['sin_mm.train:x'])
prob['sin_mm.train:f_x:norm_dist'] = np.cos(prob['sin_mm.train:x'])
prob['sin_mm.x'] = 2.1 #prediction happens at this value
prob.run()
print('float predicted:', '%3.4f'%prob['sin_mm.f_x:float']) #predicted value
print('float actual: ', '%3.4f'%np.sin(2.1))
print('norm_dist predicted:', '%3.4f,'%prob['sin_mm.f_x:norm_dist'][0], '%3.4e'%prob['sin_mm.f_x:norm_dist'][1]) #predicted value
print('norm_dist actual: ', '%3.4f'%np.cos(2.1))