def test_continuous_variable_decode(self):
""" Test decode func of IntegerToBinaryConverter for continuous variables"""
try:
mdl = Model('test_continuous_varable_decode')
c = mdl.continuous_var(lb=0, ub=10.9, name='c')
x = mdl.binary_var(name='x')
mdl.maximize(c + x * x)
op = QuadraticProgram()
op.from_docplex(mdl)
converter = IntegerToBinary()
op = converter.convert(op)
admm_params = ADMMParameters()
qubo_optimizer = MinimumEigenOptimizer(NumPyMinimumEigensolver())
continuous_optimizer = CplexOptimizer()
solver = ADMMOptimizer(
qubo_optimizer=qubo_optimizer,
continuous_optimizer=continuous_optimizer,
params=admm_params,
)
result = solver.solve(op)
result = converter.interpret(result)
self.assertEqual(result.x[0], 10.9)
self.assertListEqual(result.variable_names, ['c', 'x'])
self.assertDictEqual(result.variables_dict, {'c': 10.9, 'x': 0})
except NameError as ex:
self.skipTest(str(ex))
def test_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {'x0': 1, 'x1': 2, 'x2': 1}
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
op2 = conv.convert(op)
result = OptimizationResult(x=[0, 1, 1, 1, 1],
fval=17,
variables=op2.variables)
new_result = conv.interpret(result)
np.testing.assert_array_almost_equal(new_result.x, [0, 1, 5])
self.assertEqual(new_result.fval, 17)
self.assertListEqual(new_result.variable_names, ['x0', 'x1', 'x2'])
self.assertDictEqual(new_result.variables_dict, {
'x0': 0,
'x1': 1,
'x2': 5
})
def test_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {'x0': 1, 'x1': 2, 'x2': 1}
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
_ = conv.convert(op)
new_x = conv.interpret([0, 1, 1, 1, 1])
np.testing.assert_array_almost_equal(new_x, [0, 1, 5])
def test_integer_to_binary(self):
""" Test integer to binary """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.maximize(0, linear, {})
conv = IntegerToBinary()
op2 = conv.convert(op)
for x in op2.variables:
self.assertEqual(x.vartype, Variable.Type.BINARY)
dct = op2.objective.linear.to_dict()
self.assertEqual(dct[2], 3)
self.assertEqual(dct[3], 6)
self.assertEqual(dct[4], 6)
def test_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {}
linear['x0'] = 1
linear['x1'] = 2
linear['x2'] = 1
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
_ = conv.convert(op)
result = OptimizationResult(x=[0, 1, 1, 1, 1], fval=17)
new_result = conv.interpret(result)
self.assertListEqual(new_result.x, [0, 1, 5])
self.assertEqual(new_result.fval, 17)