def test_associativity(self):
x = parameter()
y = parameter()
pyomo.kernel.pprint(
y + x*data_expression(data_expression(x*y)))
pyomo.kernel.pprint(
y + data_expression(data_expression(x*y))*x)
def test_build_linking_constraints(self):
c = _build_linking_constraints([], [])
self.assertIs(type(c), constraint_tuple)
self.assertEqual(len(c), 0)
v = [1, data_expression(), variable(), expression(expr=1.0)]
vaux = [variable(), variable(), variable(), variable()]
c = _build_linking_constraints(v, vaux)
self.assertIs(type(c), constraint_tuple)
self.assertEqual(len(c), 4)
self.assertIs(type(c[0]), linear_constraint)
self.assertEqual(c[0].rhs, 1)
self.assertEqual(len(list(c[0].terms)), 1)
self.assertIs(list(c[0].terms)[0][0], vaux[0])
self.assertEqual(list(c[0].terms)[0][1], 1)
self.assertIs(type(c[1]), linear_constraint)
self.assertIs(c[1].rhs, v[1])
self.assertEqual(len(list(c[1].terms)), 1)
self.assertIs(list(c[1].terms)[0][0], vaux[1])
self.assertEqual(list(c[1].terms)[0][1], 1)
self.assertIs(type(c[2]), linear_constraint)
self.assertEqual(c[2].rhs, 0)
self.assertEqual(len(list(c[2].terms)), 2)
self.assertIs(list(c[2].terms)[0][0], vaux[2])
self.assertEqual(list(c[2].terms)[0][1], 1)
self.assertIs(list(c[2].terms)[1][0], v[2])
self.assertEqual(list(c[2].terms)[1][1], -1)
self.assertIs(type(c[3]), constraint)
self.assertEqual(c[3].rhs, 0)
from pyomo.repn import generate_standard_repn
repn = generate_standard_repn(c[3].body)
self.assertEqual(len(repn.linear_vars), 1)
self.assertIs(repn.linear_vars[0], vaux[3])
self.assertEqual(repn.linear_coefs[0], 1)
self.assertEqual(repn.constant, -1)
def test_bad_alpha_type(self):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=parameter())
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=data_expression())
with self.assertRaises(TypeError):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=variable())
with self.assertRaises(TypeError):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=expression())
def test_bad_alpha_type(self):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=parameter())
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=data_expression())
with self.assertRaises(TypeError):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=variable())
with self.assertRaises(TypeError):
c = dual_power(
r1=variable(lb=0),
r2=variable(lb=0),
x=[variable(),
variable()],
alpha=expression())
def test_init_NumericValue(self):
v = variable()
p = parameter()
e = expression()
d = data_expression()
o = objective()
for obj in (v, v + 1, v**2, p, p + 1, p**2, e, e + 1, e**2, d, d + 1,
d**2, o, o + 1, o**2):
self.assertTrue(isinstance(noclone(obj), NumericValue))
self.assertTrue(isinstance(noclone(obj), IIdentityExpression))
self.assertTrue(isinstance(noclone(obj), noclone))
self.assertIs(noclone(obj).expr, obj)
def testis_potentially_variable(self):
e = noclone(variable())
self.assertEqual(e.is_potentially_variable(), True)
self.assertEqual(is_potentially_variable(e), True)
e = noclone(parameter())
self.assertEqual(e.is_potentially_variable(), False)
self.assertEqual(is_potentially_variable(e), False)
e = noclone(expression())
self.assertEqual(e.is_potentially_variable(), True)
self.assertEqual(is_potentially_variable(e), True)
e = noclone(data_expression())
self.assertEqual(e.is_potentially_variable(), False)
self.assertEqual(is_potentially_variable(e), False)
def testis_potentially_variable(self):
e = noclone(variable())
self.assertEqual(e.is_potentially_variable(), True)
self.assertEqual(is_potentially_variable(e), True)
e = noclone(parameter())
self.assertEqual(e.is_potentially_variable(), False)
self.assertEqual(is_potentially_variable(e), False)
e = noclone(expression())
self.assertEqual(e.is_potentially_variable(), True)
self.assertEqual(is_potentially_variable(e), True)
e = noclone(data_expression())
self.assertEqual(e.is_potentially_variable(), False)
self.assertEqual(is_potentially_variable(e), False)
def test_init_NumericValue(self):
v = variable()
p = parameter()
e = expression()
d = data_expression()
o = objective()
for obj in (v, v+1, v**2,
p, p+1, p**2,
e, e+1, e**2,
d, d+1, d**2,
o, o+1, o**2):
self.assertTrue(isinstance(noclone(obj), NumericValue))
self.assertTrue(isinstance(noclone(obj), IIdentityExpression))
self.assertTrue(isinstance(noclone(obj), noclone))
self.assertIs(noclone(obj).expr, obj)
def test_bad_weights(self):
v = variable()
with self.assertRaises(ValueError):
s = sos([v], weights=[v])
v.fix(1.0)
with self.assertRaises(ValueError):
s = sos([v], weights=[v])
e = expression()
with self.assertRaises(ValueError):
s = sos([v], weights=[e])
de = data_expression()
s = sos([v], weights=[de])
p = parameter()
s = sos([v], weights=[p])
def test_bad_weights(self):
v = variable()
with self.assertRaises(ValueError):
s = sos([v], weights=[v])
v.fix(1.0)
with self.assertRaises(ValueError):
s = sos([v], weights=[v])
e = expression()
with self.assertRaises(ValueError):
s = sos([v], weights=[e])
de = data_expression()
s = sos([v], weights=[de])
p = parameter()
s = sos([v], weights=[p])
def test_build_linking_constraints(self):
c = _build_linking_constraints([],[])
self.assertIs(type(c), constraint_tuple)
self.assertEqual(len(c), 0)
c = _build_linking_constraints([None],[variable()])
self.assertIs(type(c), constraint_tuple)
self.assertEqual(len(c), 0)
v = [1,
data_expression(),
variable(),
expression(expr=1.0)]
vaux = [variable(),
variable(),
variable(),
variable()]
c = _build_linking_constraints(v, vaux)
self.assertIs(type(c), constraint_tuple)
self.assertEqual(len(c), 4)
self.assertIs(type(c[0]), linear_constraint)
self.assertEqual(c[0].rhs, 1)
self.assertEqual(len(list(c[0].terms)), 1)
self.assertIs(list(c[0].terms)[0][0], vaux[0])
self.assertEqual(list(c[0].terms)[0][1], 1)
self.assertIs(type(c[1]), linear_constraint)
self.assertIs(c[1].rhs, v[1])
self.assertEqual(len(list(c[1].terms)), 1)
self.assertIs(list(c[1].terms)[0][0], vaux[1])
self.assertEqual(list(c[1].terms)[0][1], 1)
self.assertIs(type(c[2]), linear_constraint)
self.assertEqual(c[2].rhs, 0)
self.assertEqual(len(list(c[2].terms)), 2)
self.assertIs(list(c[2].terms)[0][0], vaux[2])
self.assertEqual(list(c[2].terms)[0][1], 1)
self.assertIs(list(c[2].terms)[1][0], v[2])
self.assertEqual(list(c[2].terms)[1][1], -1)
self.assertIs(type(c[3]), constraint)
self.assertEqual(c[3].rhs, 0)
from pyomo.repn import generate_standard_repn
repn = generate_standard_repn(c[3].body)
self.assertEqual(len(repn.linear_vars), 1)
self.assertIs(repn.linear_vars[0], vaux[3])
self.assertEqual(repn.linear_coefs[0], 1)
self.assertEqual(repn.constant, -1)
def test_ctype(self):
e = data_expression()
self.assertIs(e.ctype, IExpression)
self.assertIs(type(e), data_expression)
self.assertIs(type(e)._ctype, IExpression)
def test_is_parameter_type(self):
for obj in (variable(), parameter(), objective(), expression(),
data_expression()):
self.assertEqual(noclone(obj).is_parameter_type(), False)
def test_ctype(self):
e = data_expression()
self.assertIs(e.ctype, IExpression)
self.assertIs(type(e), data_expression)
self.assertIs(type(e)._ctype, IExpression)
def test_is_parameter_type(self):
for obj in (variable(), parameter(), objective(),
expression(), data_expression()):
self.assertEqual(noclone(obj).is_parameter_type(), False)
