def test_optimization():
class ExampleOS(ClassWithOptimizableVariables):
def __init__(self):
super(ExampleOS, self).__init__()
self.X = OptimizableVariable(name="X",
variable_type="variable",
value=3.0)
self.Y = OptimizableVariable(name="Y",
variable_type="variable",
value=20.0)
self.Z = OptimizableVariable(name="Z",
variable_type="pickup",
functionobject=(FunctionObject(
"f = lambda x, y: x**2 + y**2",
["f"]), "f"),
args=(self.X, self.Y))
def testmerit(s):
# let x, y being on a circle of radius 5
return (s.X()**2 + s.Y()**2 - 5.**2)**2
def testmerit2(s):
# let x**2 + y**2 + z**2 be minimized with
# the pickup constraint z = x**2 + y**2
return s.X()**2 + s.Y()**2 + s.Z()**2
os = ExampleOS()
optimi = Optimizer(os,
testmerit,
backend=ScipyBackend(method="Nelder-Mead",
options={
"maxiter": 1000,
"disp": False
},
name="scipybackend"),
name="optimizer")
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())
optimi.backend = Newton1DBackend(dx=1e-6, iterations=500)
optimi.meritfunction = testmerit
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())
def test_optimization():
class ExampleOS(ClassWithOptimizableVariables):
def __init__(self):
super(ExampleOS, self).__init__()
self.X = OptimizableVariable(name="X",
variable_type="variable",
value=3.0)
self.Y = OptimizableVariable(name="Y",
variable_type="variable",
value=20.0)
self.Z = OptimizableVariable(
name="Z",
variable_type="pickup",
functionobject=(
FunctionObject("f = lambda x, y: x**2 + y**2",
["f"]),
"f"),
args=(self.X, self.Y))
def testmerit(s):
# let x, y being on a circle of radius 5
return (s.X()**2 + s.Y()**2 - 5.**2)**2
def testmerit2(s):
# let x**2 + y**2 + z**2 be minimized with
# the pickup constraint z = x**2 + y**2
return s.X()**2 + s.Y()**2 + s.Z()**2
os = ExampleOS()
optimi = Optimizer(os, testmerit,
backend=ScipyBackend(method="Nelder-Mead",
options={"maxiter": 1000,
"disp": False},
name="scipybackend"),
name="optimizer")
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())
optimi.backend = Newton1DBackend(dx=1e-6, iterations=500)
optimi.meritfunction = testmerit
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())
def test_optimization():
class ExampleOS(ClassWithOptimizableVariables):
def __init__(self):
super(ExampleOS, self).__init__()
self.X = OptimizableVariable(name="X", status="Variable", value=3.0)
self.Y = OptimizableVariable(name="Y", status="Variable", value=20.0)
self.Z = OptimizableVariable(name="Z", status="Pickup", \
function=lambda x, y: x**2 + y**2, \
args=(self.X, self.Y))
def testmerit(s):
# let x, y being on a circle of radius 5
return (s.X()**2 + s.Y()**2 - 5.**2)**2
def testmerit2(s):
# let x**2 + y**2 + z**2 be minimized with the pickup constraint z = x**2 + y**2
return s.X()**2 + s.Y()**2 + s.Z()**2
os = ExampleOS()
optimi = Optimizer(os, testmerit, backend=ScipyBackend(method='Nelder-Mead', options={'maxiter':1000, 'disp':False}))
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())
optimi.backend = Newton1DBackend(dx=1e-6, iterations=500)
optimi.meritfunction = testmerit
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, 25.0)
optimi.meritfunction = testmerit2
optimi.run()
assert np.isclose(os.X()**2 + os.Y()**2, os.Z())