我正在尝试将nxm数组的行连接到1xm个别组件中的n数组，或将1xn*m数组的切片连接到{{1} 1xm个别组件中的数组。然后，原始n或nxm数组将用作优化参数。问题是当我这样做时，组件似乎有一些严重的问题。我要么得到明显错误的答案或大小不匹配错误。
我已经能够通过使用1xn*m传递n 1xm数组来使程序集正常工作，但我更喜欢先前解释的方法。如果有人能告诉我如何以正确的方式完成这项工作，我将非常感激。
我提供了一个简单的代码示例，说明哪些有效，哪些代码可以执行。首先展示了我想要使用的方法，然后是我已经开始使用的方法，但是非常不喜欢。
exec() from openmdao.main.api import Assembly, Component
from openmdao.lib.datatypes.api import Float, Array, List
from openmdao.lib.drivers.api import DOEdriver, SLSQPdriver, COBYLAdriver, CaseIteratorDriver
from pyopt_driver.pyopt_driver import pyOptDriver
import numpy as np
class component1(Component):
x = Float(iotype='in')
y = Float(iotype='in')
term1 = Float(iotype='out')
a = Float(iotype='in', default_value=1)
def execute(self):
x = self.x
a = self.a
term1 = a*x**2
self.term1 = term1
print "In comp1", self.name, self.a, self.x, self.term1
def list_deriv_vars(self):
return ('x',), ('term1',)
def provideJ(self):
x = self.x
a = self.a
dterm1_dx = 2.*a*x
J = np.array([[dterm1_dx]])
print 'In comp1, J = %s' % J
return J
class component2(Component):
x = Float(iotype='in')
y = Float(iotype='in')
term1 = Float(iotype='in')
f = Float(iotype='out')
q = Array(np.zeros(2), iotype='in', dtype='float')
def execute(self):
y = self.y + self.q[0]
x = self.x + self.q[1]
term1 = self.term1
f = term1 + x + y**2
print 'q = %s' % self.q
self.f = f
print "In comp2", self.name, self.x, self.y, self.term1, self.f
class summer(Component):
total = Float(iotype='out', desc='sum of all f values')
def __init__(self, size):
super(summer, self).__init__()
self.size = size
self.add('fs', Array(np.zeros(size), iotype='in', desc='f values from all cases'))
def execute(self):
self.total = sum(self.fs)
print 'In summer, fs = %s and total = %s' % (self.fs, self.total)
class assembly(Assembly):
x = Float(iotype='in')
y = Float(iotype='in')
total = Float(iotype='out')
def __init__(self, size):
super(assembly, self).__init__()
self.size = size
self.add('a_vals', Array(np.zeros(size), iotype='in', dtype='float'))
self.add('q', Array(np.zeros((size, 2)), iotype='in', dtype='float'))
self.add('fs', Array(np.zeros(size), iotype='out', dtype='float'))
print 'in init a_vals = %s, fs = %s' % (self.a_vals, self.fs)
def configure(self):
self.add('driver', SLSQPdriver())
# self.add('driver', pyOptDriver())
# self.driver.optimizer = 'SNOPT'
# self.driver.pyopt_diff = True
#create this first, so we can connect to it
self.add('summer', summer(size=len(self.a_vals)))
self.connect('summer.total', 'total')
print 'in configure a_vals = %s' % self.a_vals
# create instances of components
for i in range(0, self.size):
c1 = self.add('comp1_%d' % i, component1())
c1.missing_deriv_policy = 'assume_zero'
c2 = self.add('comp2_%d'%i, component2())
self.connect('a_vals[%d]' % i, 'comp1_%d.a' % i)
self.connect('x', ['comp1_%d.x' % i, 'comp2_%d.x' % i])
self.connect('y', ['comp1_%d.y' % i, 'comp2_%d.y' % i])
self.connect('comp1_%d.term1' % i, 'comp2_%d.term1' % i)
self.connect('q[%d, :]' % i, 'comp2_%d.q' % i)
self.connect('comp2_%d.f' % i, 'summer.fs[%d]' % i)
self.driver.workflow.add(['comp1_%d' % i, 'comp2_%d' % i])
# self.connect('summer.fs[:]', 'fs[:]')
self.driver.workflow.add(['summer'])
# set up main driver (optimizer)
self.driver.iprint = 1
self.driver.maxiter = 100
self.driver.accuracy = 1.0e-6
self.driver.add_parameter('x', low=-5., high=5.)
self.driver.add_parameter('y', low=-5., high=5.)
self.driver.add_parameter('q', low=0., high=5.)
self.driver.add_objective('summer.total')
if __name__ == "__main__":
""" the result should be -1 at (x, y) = (-0.5, 0) """
import time
from openmdao.main.api import set_as_top
a_vals = np.array([1., 1., 1., 1.])
test = set_as_top(assembly(size=len(a_vals)))
test.a_vals = a_vals
print 'in main, test.a_vals = %s, test.fs = %s' % (test.a_vals, test.fs)
test.x = 2.
test.y = -5
test.q = np.tile(np.arange(0., 2.), (4, 1))
print test.q
tt = time.time()
test.run()
print "Elapsed time: ", time.time()-tt, "seconds"
print 'result = ', test.summer.total
print '(x, y) = (%s, %s)' % (test.x, test.y)
print 'fs = %s' % test.fs
print test.fs
----------
答案 0 :(得分：1)
我可以通过对q和comp2_.q的连接进行少量更改来解决设置错误。
我来自：
self.connect('q[%d, :]' % i, 'comp2_%d.q' % i)
到：
self.connect('q[%d]' % i, 'comp2_%d.q' % i)
然后问题贯穿其第一次评估。不幸的是，它在衍生物计算中做了某些事情。即使我打开SNOPT并使用pyopt_diff = True，也会发生这种情况。所以这个玩具问题还有其他问题。但删除额外的:会让您超越连接错误。
答案 1 :(得分：1)
所以，我也看了你的模特，你肯定没有做错任何事。在为最佳优化问题组装网络图时，模型设置中存在一个错误。它似乎从输入中丢失了q变量，并且从未在用于求解总导数的向量中为其分配空间。我认为它对q感到困惑，因为它与任何东西没有直接的完全连接，只是将连接切片到编号为comp2s。
您的第一个解决方法可能是最好的解决方法。但是，我还发现了另一个。我创建了一个名为fakefake的虚拟组件;除了允许您将完整的q向量直接连接到某个东西之外，此组件不执行任何操作。然后，我获取其输出fakefake.out并在约束中使用它。由于该输出永远不会改变，因此总是满足约束。此解决方法有效，因为完整的q连接可防止在修剪期间错误地从图表中删除输入。
通过这些更改，我能够让它运行。我不确定答案是否正确，因为我不知道它们应该是什么。我在下面提供了我的代码。请注意，我还添加了component2和summer的衍生产品。
from openmdao.main.api import Assembly, Component
from openmdao.lib.datatypes.api import Float, Array, List
from openmdao.lib.drivers.api import DOEdriver, SLSQPdriver, COBYLAdriver, CaseIteratorDriver
from pyopt_driver.pyopt_driver import pyOptDriver
import numpy as np
class component1(Component):
x = Float(iotype='in')
y = Float(iotype='in')
term1 = Float(iotype='out')
a = Float(iotype='in', default_value=1)
def execute(self):
x = self.x
a = self.a
term1 = a*x**2
self.term1 = term1
print "In comp1", self.name, self.a, self.x, self.term1
def list_deriv_vars(self):
return ('x',), ('term1',)
def provideJ(self):
x = self.x
a = self.a
dterm1_dx = 2.*a*x
J = np.array([[dterm1_dx]])
print 'In comp1, J = %s' % J
return J
class component2(Component):
x = Float(iotype='in')
y = Float(iotype='in')
term1 = Float(iotype='in')
q = Array(np.zeros(2), iotype='in', dtype='float')
f = Float(iotype='out')
def execute(self):
y = self.y + self.q[0]
x = self.x + self.q[1]
term1 = self.term1
f = term1 + x + y**2
print 'q = %s' % self.q
self.f = f
print "In comp2", self.name, self.x, self.y, self.term1, self.f
def list_deriv_vars(self):
return ('x', 'y', 'term1', 'q'), ('f',)
def provideJ(self):
# f = (y+q0)**2 + x + q1 + term1
df_dx = 1.0
df_dy = 2.0*self.y + 2.0*self.q[0]
df_dterm1 = 1.0
df_dq = np.array([2.0*self.q[0] + 2.0*self.y, 1.0])
J = np.array([[df_dx, df_dy, df_dterm1, df_dq[0], df_dq[1]]])
return J
class summer(Component):
total = Float(iotype='out', desc='sum of all f values')
def __init__(self, size):
super(summer, self).__init__()
self.size = size
self.add('fs', Array(np.zeros(size), iotype='in', desc='f values from all cases'))
def execute(self):
self.total = sum(self.fs)
print 'In summer, fs = %s and total = %s' % (self.fs, self.total)
def list_deriv_vars(self):
return ('fs',), ('total',)
def provideJ(self):
J = np.ones((1.0, len(self.fs)))
return J
class fakefake(Component):
out = Float(0.0, iotype='out')
def __init__(self, size):
super(fakefake, self).__init__()
self.size = size
self.add('q', Array(np.zeros(size), iotype='in', dtype='float'))
def execute(self):
pass
def list_deriv_vars(self):
return ('q',), ('out',)
def provideJ(self):
J = np.zeros((1.0, 2.0*len(self.q)))
return J
class assembly(Assembly):
x = Float(iotype='in')
y = Float(iotype='in')
total = Float(iotype='out')
def __init__(self, size):
super(assembly, self).__init__()
self.size = size
self.add('a_vals', Array(np.zeros(size), iotype='in', dtype='float'))
self.add('q', Array(np.zeros((size, 2)), iotype='in', dtype='float'))
self.add('fs', Array(np.zeros(size), iotype='out', dtype='float'))
print 'in init a_vals = %s, fs = %s' % (self.a_vals, self.fs)
def configure(self):
self.add('driver', SLSQPdriver())
# self.add('driver', pyOptDriver())
# self.driver.optimizer = 'SNOPT'
# self.driver.pyopt_diff = True
#create this first, so we can connect to it
self.add('summer', summer(size=len(self.a_vals)))
self.connect('summer.total', 'total')
# Trying something...
self.add('fakefake', fakefake(self.size))
self.connect('q', 'fakefake.q')
print 'in configure a_vals = %s' % self.a_vals
# create instances of components
for i in range(0, self.size):
c1 = self.add('comp1_%d' % i, component1())
c1.missing_deriv_policy = 'assume_zero'
c2 = self.add('comp2_%d'%i, component2())
self.connect('a_vals[%d]' % i, 'comp1_%d.a' % i)
self.connect('x', ['comp1_%d.x' % i, 'comp2_%d.x' % i])
self.connect('y', ['comp1_%d.y' % i, 'comp2_%d.y' % i])
self.connect('comp1_%d.term1' % i, 'comp2_%d.term1' % i)
#self.connect('q[%d, :]' % i, 'comp2_%d.q' % i)
#self.connect('q[%d]' % i, 'comp2_%d.q' % i)
self.connect('comp2_%d.f' % i, 'summer.fs[%d]' % i)
self.driver.workflow.add(['comp1_%d' % i, 'comp2_%d' % i])
# self.connect('summer.fs[:]', 'fs[:]')
self.driver.workflow.add(['summer'])
# set up main driver (optimizer)
self.driver.iprint = 1
self.driver.maxiter = 100
self.driver.accuracy = 1.0e-6
self.driver.add_parameter('x', low=-5., high=5.)
self.driver.add_parameter('y', low=-5., high=5.)
self.driver.add_parameter('q', low=0., high=5.)
#for i in range(0, self.size):
# self.driver.add_parameter('comp2_%d.q' % i, low=0., high=5.)
self.driver.add_objective('summer.total')
self.driver.add_constraint('fakefake.out < 1000')
if __name__ == "__main__":
""" the result should be -1 at (x, y) = (-0.5, 0) """
import time
from openmdao.main.api import set_as_top
a_vals = np.array([1., 1., 1., 1.])
test = set_as_top(assembly(size=len(a_vals)))
test.a_vals = a_vals
print 'in main, test.a_vals = %s, test.fs = %s' % (test.a_vals, test.fs)
test.x = 2.
test.y = -5
test.q = np.tile(np.arange(0., 2.), (4, 1))
print test.q
tt = time.time()
test.run()
print "Elapsed time: ", time.time()-tt, "seconds"
print 'result = ', test.summer.total
print '(x, y) = (%s, %s)' % (test.x, test.y)
print 'fs = %s' % test.fs
print test.fs