def test_add_non_cplex_conform_variable(self):
var = self.interface.Variable('12x!!@#5_3', lb=-666, ub=666)
self.assertEqual(var._index, None)
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(var.name, glp_get_col_name(self.model.problem, var._index))
self.assertEqual(self.model.variables['12x!!@#5_3'].lb, -666)
self.assertEqual(self.model.variables['12x!!@#5_3'].ub, 666)
repickled = pickle.loads(pickle.dumps(self.model))
var_from_pickle = repickled.variables['12x!!@#5_3']
self.assertEqual(var_from_pickle.name, glp_get_col_name(repickled.problem, var_from_pickle._index))
def test_add_non_cplex_conform_variable(self):
var = Variable('12x!!@#5_3', lb=-666, ub=666)
self.assertEqual(var._index, None)
self.model.add(var)
self.assertTrue(var in self.model.variables.values())
self.assertEqual(var.name,
glp_get_col_name(self.model.problem, var._index))
self.assertEqual(self.model.variables['12x!!@#5_3'].lb, -666)
self.assertEqual(self.model.variables['12x!!@#5_3'].ub, 666)
repickled = pickle.loads(pickle.dumps(self.model))
var_from_pickle = repickled.variables['12x!!@#5_3']
self.assertEqual(
var_from_pickle.name,
glp_get_col_name(repickled.problem, var_from_pickle._index))
def set_linear_coefficients(self, coefficients):
if self.problem is not None:
problem = self.problem.problem
num_cols = glp_get_num_cols(problem)
ia = intArray(num_cols + 1)
va = doubleArray(num_cols + 1)
num_rows = glp_get_mat_row(self.problem.problem, self._index, ia, va)
variables_and_coefficients = {var.name: coeff for var, coeff in six.iteritems(coefficients)}
final_variables_and_coefficients = {
glp_get_col_name(problem, ia[i]): va[i] for i in range(1, num_rows + 1)
}
final_variables_and_coefficients.update(variables_and_coefficients)
ia = intArray(num_cols + 1)
va = doubleArray(num_cols + 1)
for i, (name, coeff) in enumerate(six.iteritems(final_variables_and_coefficients)):
ia[i + 1] = self.problem._variables[name]._index
va[i + 1] = coeff
glp_set_mat_row(problem, self._index, len(final_variables_and_coefficients), ia, va)
else:
raise Exception("Can't change coefficients if constraint is not associated with a model.")
def set_linear_coefficients(self, coefficients):
if self.problem is not None:
problem = self.problem.problem
self.problem.update()
num_cols = glp_get_num_cols(problem)
ia = intArray(num_cols + 1)
va = doubleArray(num_cols + 1)
num_rows = glp_get_mat_row(self.problem.problem, self._index, ia, va)
variables_and_coefficients = {var.name: coeff for var, coeff in six.iteritems(coefficients)}
final_variables_and_coefficients = {
glp_get_col_name(problem, ia[i]): va[i] for i in range(1, num_rows + 1)
}
final_variables_and_coefficients.update(variables_and_coefficients)
ia = intArray(num_cols + 1)
va = doubleArray(num_cols + 1)
for i, (name, coeff) in enumerate(six.iteritems(final_variables_and_coefficients)):
ia[i + 1] = self.problem._variables[name]._index
va[i + 1] = float(coeff)
glp_set_mat_row(problem, self._index, len(final_variables_and_coefficients), ia, va)
else:
raise Exception("Can't change coefficients if constraint is not associated with a model.")
def test_changing_variable_names_is_reflected_in_the_solver(self):
model = Model(problem=glpk_read_cplex(TESTMODELPATH))
for i, variable in enumerate(model.variables):
variable.name = "var" + str(i)
self.assertEqual(variable.name, "var" + str(i))
self.assertEqual(glp_get_col_name(model.problem, variable._index),
"var" + str(i))
def test_init_from_existing_problem(self):
inner_prob = self.model.problem
self.assertEqual(len(self.model.variables), glp_get_num_cols(inner_prob))
self.assertEqual(len(self.model.constraints), glp_get_num_rows(inner_prob))
self.assertEqual(self.model.variables.keys(),
[glp_get_col_name(inner_prob, i) for i in range(1, glp_get_num_cols(inner_prob) + 1)])
self.assertEqual(self.model.constraints.keys(),
[glp_get_row_name(inner_prob, j) for j in range(1, glp_get_num_rows(inner_prob) + 1)])
def get_clocks(problem: SwigPyObject) -> Iterable[Tuple[str, float]]:
n_recipes = 0
for j in range(1, 1 + lp.glp_get_num_cols(problem)):
clock = lp.glp_mip_col_val(problem, j)
if clock:
name = lp.glp_get_col_name(problem, j)
yield name, clock
n_recipes += 1
logger.info(f'{n_recipes} recipes in rate solution.')
def test_set_linear_coefficients_constraint(self):
constraint = self.model.constraints.M_atp_c
constraint.set_linear_coefficients({self.model.variables.R_Biomass_Ecoli_core_w_GAM: 666.})
num_cols = glp_get_num_cols(self.model.problem)
ia = intArray(num_cols + 1)
da = doubleArray(num_cols + 1)
index = constraint._index
num = glp_get_mat_row(self.model.problem, index, ia, da)
for i in range(1, num + 1):
col_name = glp_get_col_name(self.model.problem, ia[i])
if col_name == 'R_Biomass_Ecoli_core_w_GAM':
self.assertEqual(da[i], 666.)
def test_set_linear_coefficients_constraint(self):
constraint = self.model.constraints.M_atp_c
constraint.set_linear_coefficients(
{self.model.variables.R_Biomass_Ecoli_core_w_GAM: 666.})
num_cols = glp_get_num_cols(self.model.problem)
ia = intArray(num_cols + 1)
da = doubleArray(num_cols + 1)
index = constraint._index
num = glp_get_mat_row(self.model.problem, index, ia, da)
for i in range(1, num + 1):
col_name = glp_get_col_name(self.model.problem, ia[i])
if col_name == 'R_Biomass_Ecoli_core_w_GAM':
self.assertEqual(da[i], 666.)
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
constraint_variables = [self.problem._variables[glp_get_col_name(self.problem.problem, ia[i])] for i in
range(1, nnz + 1)]
expression = symbolics.add(
[symbolics.mul((symbolics.Real(da[i]), constraint_variables[i - 1])) for i in
range(1, nnz + 1)])
self._expression = expression
return self._expression
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self.index, ia, da)
constraint_variables = [self.problem._variables[glp_get_col_name(self.problem.problem, ia[i])] for i in
range(1, nnz + 1)]
expression = sympy.Add._from_args(
[sympy.Mul._from_args((sympy.RealNumber(da[i]), constraint_variables[i - 1])) for i in
range(1, nnz + 1)])
self._expression = expression
return self._expression
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self._index, ia, da)
constraint_variables = [self.problem._variables[glp_get_col_name(self.problem.problem, ia[i])] for i in
range(1, nnz + 1)]
expression = symbolics.add(
[symbolics.mul((symbolics.Real(da[i]), constraint_variables[i - 1])) for i in
range(1, nnz + 1)])
self._expression = expression
return self._expression
def solve_with_glpsol(glp_prob):
"""Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.
# Examples
# --------
# >>> problem = glp_create_prob()
# ... glp_read_lp(problem, None, "../tests/data/model.lp")
# ... solution = solve_with_glpsol(problem)
# ... print 'asdf'
# 'asdf'
# >>> print solution
# 0.839784
# Returns
# -------
# dict
# A dictionary containing the objective value (key ='objval')
# and variable primals.
"""
from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols
row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]
col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]
with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
tmp_file_name = tmp_file.name
glp_write_lp(glp_prob, None, tmp_file_name)
cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
term = check_output(cmd)
log.info(term)
try:
with open(tmp_file_name + '.sol') as sol_handle:
# print sol_handle.read()
solution = dict()
for i, line in enumerate(sol_handle.readlines()):
if i <= 1 or line == '\n':
pass
elif i <= len(row_ids):
solution[row_ids[i - 2]] = line.strip().split(' ')
elif i <= len(row_ids) + len(col_ids) + 1:
solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
else:
print(i)
print(line)
raise Exception("Argggh!")
finally:
os.remove(tmp_file_name + ".sol")
return solution
def solve_with_glpsol(glp_prob):
"""Solve glpk problem with glpsol commandline solver. Mainly for testing purposes.
# Examples
# --------
# >>> problem = glp_create_prob()
# ... glp_read_lp(problem, None, "../tests/data/model.lp")
# ... solution = solve_with_glpsol(problem)
# ... print 'asdf'
# 'asdf'
# >>> print solution
# 0.839784
# Returns
# -------
# dict
# A dictionary containing the objective value (key ='objval')
# and variable primals.
"""
from swiglpk import glp_get_row_name, glp_get_col_name, glp_write_lp, glp_get_num_rows, glp_get_num_cols
row_ids = [glp_get_row_name(glp_prob, i) for i in range(1, glp_get_num_rows(glp_prob) + 1)]
col_ids = [glp_get_col_name(glp_prob, i) for i in range(1, glp_get_num_cols(glp_prob) + 1)]
with tempfile.NamedTemporaryFile(suffix=".lp", delete=True) as tmp_file:
tmp_file_name = tmp_file.name
glp_write_lp(glp_prob, None, tmp_file_name)
cmd = ['glpsol', '--lp', tmp_file_name, '-w', tmp_file_name + '.sol', '--log', '/dev/null']
term = check_output(cmd)
log.info(term)
try:
with open(tmp_file_name + '.sol') as sol_handle:
# print sol_handle.read()
solution = dict()
for i, line in enumerate(sol_handle.readlines()):
if i <= 1 or line == '\n':
pass
elif i <= len(row_ids):
solution[row_ids[i - 2]] = line.strip().split(' ')
elif i <= len(row_ids) + len(col_ids) + 1:
solution[col_ids[i - 2 - len(row_ids)]] = line.strip().split(' ')
else:
print(i)
print(line)
raise Exception("Argggh!")
finally:
os.remove(tmp_file_name + ".sol")
return solution
def test_init_from_existing_problem(self):
inner_prob = self.model.problem
self.assertEqual(len(self.model.variables),
glp_get_num_cols(inner_prob))
self.assertEqual(len(self.model.constraints),
glp_get_num_rows(inner_prob))
self.assertEqual(self.model.variables.keys(), [
glp_get_col_name(inner_prob, i)
for i in range(1,
glp_get_num_cols(inner_prob) + 1)
])
self.assertEqual(self.model.constraints.keys(), [
glp_get_row_name(inner_prob, j)
for j in range(1,
glp_get_num_rows(inner_prob) + 1)
])
def _get_expression(self):
if self.problem is not None:
col_num = glp_get_num_cols(self.problem.problem)
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem.problem, self.index, ia, da)
constraint_variables = [
self.problem._variables[glp_get_col_name(
self.problem.problem, ia[i])] for i in range(1, nnz + 1)
]
expression = sympy.Add._from_args([
sympy.Mul._from_args(
(sympy.RealNumber(da[i]), constraint_variables[i - 1]))
for i in range(1, nnz + 1)
])
self._expression = expression
return self._expression
def _initialize_model_from_problem(self, problem):
try:
self.problem = problem
glp_create_index(self.problem)
except TypeError:
raise TypeError("Provided problem is not a valid GLPK model.")
row_num = glp_get_num_rows(self.problem)
col_num = glp_get_num_cols(self.problem)
for i in range(1, col_num + 1):
var = Variable(
glp_get_col_name(self.problem, i),
lb=glp_get_col_lb(self.problem, i),
ub=glp_get_col_ub(self.problem, i),
problem=self,
type=_GLPK_VTYPE_TO_VTYPE[
glp_get_col_kind(self.problem, i)]
)
# This avoids adding the variable to the glpk problem
super(Model, self)._add_variables([var])
variables = self.variables
for j in range(1, row_num + 1):
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem, j, ia, da)
constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
# for i in range(1, nnz + 1)])
lhs = 0
glpk_row_type = glp_get_row_type(self.problem, j)
if glpk_row_type == GLP_FX:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = row_lb
elif glpk_row_type == GLP_LO:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = None
elif glpk_row_type == GLP_UP:
row_lb = None
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_DB:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_FR:
row_lb = None
row_ub = None
else:
raise Exception(
"Currently, optlang does not support glpk row type %s"
% str(glpk_row_type)
)
log.exception()
if isinstance(lhs, int):
lhs = symbolics.Integer(lhs)
elif isinstance(lhs, float):
lhs = symbolics.Real(lhs)
constraint_id = glp_get_row_name(self.problem, j)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(constraint_id)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([constraint_id])
super(Model, self)._add_constraints(
[Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
sloppy=True
)
term_generator = (
(glp_get_obj_coef(self.problem, index), variables[index - 1])
for index in range(1, glp_get_num_cols(problem) + 1)
)
self._objective = Objective(
symbolics.add(
[symbolics.mul((symbolics.Real(term[0]), term[1])) for term in term_generator if
term[0] != 0.]
),
problem=self,
direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
glp_scale_prob(self.problem, GLP_SF_AUTO)
def __init__(self, problem=None, *args, **kwargs):
super(Model, self).__init__(*args, **kwargs)
self.configuration = Configuration()
if problem is None:
self.problem = glp_create_prob()
glp_create_index(self.problem)
if self.name is not None:
glp_set_prob_name(self.problem, str(self.name))
else:
try:
self.problem = problem
glp_create_index(self.problem)
except TypeError:
raise TypeError("Provided problem is not a valid GLPK model.")
row_num = glp_get_num_rows(self.problem)
col_num = glp_get_num_cols(self.problem)
for i in range(1, col_num + 1):
var = Variable(
glp_get_col_name(self.problem, i),
lb=glp_get_col_lb(self.problem, i),
ub=glp_get_col_ub(self.problem, i),
problem=self,
type=_GLPK_VTYPE_TO_VTYPE[
glp_get_col_kind(self.problem, i)]
)
# This avoids adding the variable to the glpk problem
super(Model, self)._add_variables([var])
variables = self.variables
for j in range(1, row_num + 1):
ia = intArray(col_num + 1)
da = doubleArray(col_num + 1)
nnz = glp_get_mat_row(self.problem, j, ia, da)
constraint_variables = [variables[ia[i] - 1] for i in range(1, nnz + 1)]
# Since constraint expressions are lazily retrieved from the solver they don't have to be built here
# lhs = _unevaluated_Add(*[da[i] * constraint_variables[i - 1]
# for i in range(1, nnz + 1)])
lhs = 0
glpk_row_type = glp_get_row_type(self.problem, j)
if glpk_row_type == GLP_FX:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = row_lb
elif glpk_row_type == GLP_LO:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = None
elif glpk_row_type == GLP_UP:
row_lb = None
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_DB:
row_lb = glp_get_row_lb(self.problem, j)
row_ub = glp_get_row_ub(self.problem, j)
elif glpk_row_type == GLP_FR:
row_lb = None
row_ub = None
else:
raise Exception(
"Currently, optlang does not support glpk row type %s"
% str(glpk_row_type)
)
log.exception()
if isinstance(lhs, int):
lhs = sympy.Integer(lhs)
elif isinstance(lhs, float):
lhs = sympy.RealNumber(lhs)
constraint_id = glp_get_row_name(self.problem, j)
for variable in constraint_variables:
try:
self._variables_to_constraints_mapping[variable.name].add(constraint_id)
except KeyError:
self._variables_to_constraints_mapping[variable.name] = set([constraint_id])
super(Model, self)._add_constraints(
[Constraint(lhs, lb=row_lb, ub=row_ub, name=constraint_id, problem=self, sloppy=True)],
sloppy=True
)
term_generator = (
(glp_get_obj_coef(self.problem, index), variables[index - 1])
for index in range(1, glp_get_num_cols(problem) + 1)
)
self._objective = Objective(
_unevaluated_Add(
*[_unevaluated_Mul(sympy.RealNumber(term[0]), term[1]) for term in term_generator if
term[0] != 0.]),
problem=self,
direction={GLP_MIN: 'min', GLP_MAX: 'max'}[glp_get_obj_dir(self.problem)])
glp_scale_prob(self.problem, GLP_SF_AUTO)
def test_changing_variable_names_is_reflected_in_the_solver(self):
model = self.interface.Model(problem=glpk_read_cplex(TESTMODELPATH))
for i, variable in enumerate(model.variables):
variable.name = "var" + str(i)
self.assertEqual(variable.name, "var" + str(i))
self.assertEqual(glp_get_col_name(model.problem, variable._index), "var" + str(i))
def test_add_constraints(self):
x = self.interface.Variable('x', lb=0, ub=1, type='binary')
y = self.interface.Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = self.interface.Variable('z', lb=0., ub=10., type='integer')
constr1 = self.interface.Constraint(0.3 * x + 0.4 * y + 66. * z, lb=-100, ub=0., name='test')
constr2 = self.interface.Constraint(2.333 * x + y + 3.333, ub=100.33, name='test2')
constr3 = self.interface.Constraint(2.333 * x + y + z, lb=-300)
constr4 = self.interface.Constraint(x, lb=-300, ub=-300)
constr5 = self.interface.Constraint(3 * x)
self.model.add(constr1)
self.model.add(constr2)
self.model.add(constr3)
self.model.add([constr4, constr5])
self.assertIn(constr1.name, self.model.constraints)
self.assertIn(constr2.name, self.model.constraints)
self.assertIn(constr3.name, self.model.constraints)
self.assertIn(constr4.name, self.model.constraints)
self.assertIn(constr5.name, self.model.constraints)
# constr1
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr1._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 0.3, 'y': 0.4, 'z': 66.})
self.assertEqual(glp_get_row_type(self.model.problem, constr1._index), GLP_DB)
self.assertEqual(glp_get_row_lb(self.model.problem, constr1._index), -100)
self.assertEqual(glp_get_row_ub(self.model.problem, constr1._index), 0)
# constr2
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr2._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr2._index), GLP_UP)
self.assertEqual(glp_get_row_lb(self.model.problem, constr2._index), -1.7976931348623157e+308)
self.assertEqual(glp_get_row_ub(self.model.problem, constr2._index), 96.997)
# constr3
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr3._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1., 'z': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr3._index), GLP_LO)
self.assertEqual(glp_get_row_lb(self.model.problem, constr3._index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr3._index), 1.7976931348623157e+308)
# constr4
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr4._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 1})
self.assertEqual(glp_get_row_type(self.model.problem, constr4._index), GLP_FX)
self.assertEqual(glp_get_row_lb(self.model.problem, constr4._index), -300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr4._index), -300)
# constr5
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr5._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 3})
self.assertEqual(glp_get_row_type(self.model.problem, constr5._index), GLP_FR)
self.assertLess(glp_get_row_lb(self.model.problem, constr5._index), -1e30)
self.assertGreater(glp_get_row_ub(self.model.problem, constr5._index), 1e30)
def test_add_constraints(self):
x = Variable('x', lb=0, ub=1, type='binary')
y = Variable('y', lb=-181133.3, ub=12000., type='continuous')
z = Variable('z', lb=0., ub=10., type='integer')
constr1 = Constraint(0.3 * x + 0.4 * y + 66. * z,
lb=-100,
ub=0.,
name='test')
constr2 = Constraint(2.333 * x + y + 3.333, ub=100.33, name='test2')
constr3 = Constraint(2.333 * x + y + z, lb=-300)
constr4 = Constraint(x, lb=-300, ub=-300)
constr5 = Constraint(3 * x)
self.model.add(constr1)
self.model.add(constr2)
self.model.add(constr3)
self.model.add([constr4, constr5])
self.assertIn(constr1.name, self.model.constraints)
self.assertIn(constr2.name, self.model.constraints)
self.assertIn(constr3.name, self.model.constraints)
self.assertIn(constr4.name, self.model.constraints)
self.assertIn(constr5.name, self.model.constraints)
# constr1
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr1._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 0.3, 'y': 0.4, 'z': 66.})
self.assertEqual(glp_get_row_type(self.model.problem, constr1._index),
GLP_DB)
self.assertEqual(glp_get_row_lb(self.model.problem, constr1._index),
-100)
self.assertEqual(glp_get_row_ub(self.model.problem, constr1._index), 0)
# constr2
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr2._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr2._index),
GLP_UP)
self.assertEqual(glp_get_row_lb(self.model.problem, constr2._index),
-1.7976931348623157e+308)
self.assertEqual(glp_get_row_ub(self.model.problem, constr2._index),
96.997)
# constr3
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr3._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 2.333, 'y': 1., 'z': 1.})
self.assertEqual(glp_get_row_type(self.model.problem, constr3._index),
GLP_LO)
self.assertEqual(glp_get_row_lb(self.model.problem, constr3._index),
-300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr3._index),
1.7976931348623157e+308)
# constr4
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr4._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 1})
self.assertEqual(glp_get_row_type(self.model.problem, constr4._index),
GLP_FX)
self.assertEqual(glp_get_row_lb(self.model.problem, constr4._index),
-300)
self.assertEqual(glp_get_row_ub(self.model.problem, constr4._index),
-300)
# constr5
ia = intArray(glp_get_num_rows(self.model.problem) + 1)
da = doubleArray(glp_get_num_rows(self.model.problem) + 1)
nnz = glp_get_mat_row(self.model.problem, constr5._index, ia, da)
coeff_dict = dict()
for i in range(1, nnz + 1):
coeff_dict[glp_get_col_name(self.model.problem, ia[i])] = da[i]
self.assertDictEqual(coeff_dict, {'x': 3})
self.assertEqual(glp_get_row_type(self.model.problem, constr5._index),
GLP_FR)
self.assertLess(glp_get_row_lb(self.model.problem, constr5._index),
-1e30)
self.assertGreater(glp_get_row_ub(self.model.problem, constr5._index),
1e30)