1426 lines
57 KiB
Python
1426 lines
57 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2022 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for ortools.sat.python.cp_model."""
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from absl.testing import absltest
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from ortools.sat.python import cp_model
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class SolutionCounter(cp_model.CpSolverSolutionCallback):
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"""Count solutions."""
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def __init__(self):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__solution_count = 0
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def OnSolutionCallback(self):
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self.__solution_count += 1
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def SolutionCount(self):
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return self.__solution_count
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class SolutionSum(cp_model.CpSolverSolutionCallback):
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"""Record the sum of variables in the solution."""
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def __init__(self, variables):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__sum = 0
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self.__vars = variables
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def OnSolutionCallback(self):
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self.__sum = sum(self.Value(x) for x in self.__vars)
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def Sum(self):
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return self.__sum
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class SolutionObjective(cp_model.CpSolverSolutionCallback):
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"""Record the objective value of the solution."""
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def __init__(self):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__obj = 0
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def OnSolutionCallback(self):
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self.__obj = self.ObjectiveValue()
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def Obj(self):
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return self.__obj
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class LogToString(object):
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"""Record log in a string."""
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def __init__(self):
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self.__log = ""
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def NewMessage(self, message: str):
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self.__log += message
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self.__log += "\n"
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def Log(self):
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return self.__log
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class CpModelTest(absltest.TestCase):
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def testCreateIntegerVariable(self):
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print("testCreateIntegerVariable")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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self.assertEqual("x", str(x))
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self.assertEqual("x(-10..10)", repr(x))
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y = model.NewIntVarFromDomain(cp_model.Domain.FromIntervals([[2, 4], [7]]), "y")
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self.assertEqual("y", str(y))
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self.assertEqual("y(2..4, 7)", repr(y))
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z = model.NewIntVarFromDomain(cp_model.Domain.FromValues([2, 3, 4, 7]), "z")
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self.assertEqual("z", str(z))
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self.assertEqual("z(2..4, 7)", repr(z))
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t = model.NewIntVarFromDomain(
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cp_model.Domain.FromFlatIntervals([2, 4, 7, 7]), "t"
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)
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self.assertEqual("t", str(t))
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self.assertEqual("t(2..4, 7)", repr(t))
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cst = model.NewConstant(5)
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self.assertEqual("5", str(cst))
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def testNegation(self):
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print("testNegation")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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b = model.NewBoolVar("b")
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nb = b.Not()
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self.assertEqual(b.Not(), nb)
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self.assertEqual(b.Not().Not(), b)
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self.assertEqual(nb.Index(), -b.Index() - 1)
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self.assertRaises(TypeError, x.Not)
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def testEqualityOverload(self):
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print("testEqualityOverload")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(0, 5, "y")
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self.assertEqual(x, x)
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self.assertNotEqual(x, y)
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def testLinear(self):
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print("testLinear")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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model.AddLinearConstraint(x + 2 * y, 0, 10)
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model.Minimize(y)
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solver = cp_model.CpSolver()
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self.assertEqual(cp_model.OPTIMAL, solver.Solve(model))
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self.assertEqual(10, solver.Value(x))
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self.assertEqual(-5, solver.Value(y))
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def testLinearNonEqual(self):
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print("testLinearNonEqual")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(-x + y != 3).Proto()
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self.assertLen(ct.linear.domain, 4)
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(2, ct.linear.domain[1])
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self.assertEqual(4, ct.linear.domain[2])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[3])
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def testEq(self):
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print("testEq")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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ct = model.Add(x == 2).Proto()
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self.assertLen(ct.linear.vars, 1)
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self.assertLen(ct.linear.coeffs, 1)
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(2, ct.linear.domain[0])
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self.assertEqual(2, ct.linear.domain[1])
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def testGe(self):
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print("testGe")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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ct = model.Add(x >= 2).Proto()
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self.assertLen(ct.linear.vars, 1)
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self.assertLen(ct.linear.coeffs, 1)
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(2, ct.linear.domain[0])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[1])
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def testGt(self):
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print("testGt")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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ct = model.Add(x > 2).Proto()
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self.assertLen(ct.linear.vars, 1)
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self.assertLen(ct.linear.coeffs, 1)
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(3, ct.linear.domain[0])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[1])
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def testLe(self):
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print("testLe")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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ct = model.Add(x <= 2).Proto()
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self.assertLen(ct.linear.vars, 1)
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self.assertLen(ct.linear.coeffs, 1)
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(2, ct.linear.domain[1])
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def testLt(self):
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print("testLt")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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ct = model.Add(x < 2).Proto()
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self.assertLen(ct.linear.vars, 1)
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self.assertLen(ct.linear.coeffs, 1)
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(1, ct.linear.domain[1])
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def testEqVar(self):
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print("testEqVar")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x == y + 2).Proto()
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self.assertLen(ct.linear.vars, 2)
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self.assertEqual(1, ct.linear.vars[0] + ct.linear.vars[1])
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self.assertLen(ct.linear.coeffs, 2)
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self.assertEqual(0, ct.linear.coeffs[0] + ct.linear.coeffs[1])
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(2, ct.linear.domain[0])
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self.assertEqual(2, ct.linear.domain[1])
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def testGeVar(self):
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print("testGeVar")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x >= 1 - y).Proto()
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self.assertLen(ct.linear.vars, 2)
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self.assertEqual(1, ct.linear.vars[0] + ct.linear.vars[1])
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self.assertLen(ct.linear.coeffs, 2)
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self.assertEqual(1, ct.linear.coeffs[0])
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self.assertEqual(1, ct.linear.coeffs[1])
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(1, ct.linear.domain[0])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[1])
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def testGtVar(self):
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print("testGeVar")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x > 1 - y).Proto()
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self.assertLen(ct.linear.vars, 2)
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self.assertEqual(1, ct.linear.vars[0] + ct.linear.vars[1])
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self.assertLen(ct.linear.coeffs, 2)
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self.assertEqual(1, ct.linear.coeffs[0])
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self.assertEqual(1, ct.linear.coeffs[1])
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(2, ct.linear.domain[0])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[1])
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def testLeVar(self):
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print("testLeVar")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x <= 1 - y).Proto()
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self.assertLen(ct.linear.vars, 2)
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self.assertEqual(1, ct.linear.vars[0] + ct.linear.vars[1])
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self.assertLen(ct.linear.coeffs, 2)
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self.assertEqual(1, ct.linear.coeffs[0])
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self.assertEqual(1, ct.linear.coeffs[1])
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(1, ct.linear.domain[1])
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def testLtVar(self):
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print("testLtVar")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x < 1 - y).Proto()
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self.assertLen(ct.linear.vars, 2)
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self.assertEqual(1, ct.linear.vars[0] + ct.linear.vars[1])
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self.assertLen(ct.linear.coeffs, 2)
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self.assertEqual(1, ct.linear.coeffs[0])
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self.assertEqual(1, ct.linear.coeffs[1])
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self.assertLen(ct.linear.domain, 2)
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(0, ct.linear.domain[1])
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def testSimplification1(self):
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print("testSimplification1")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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prod = (x * 2) * 2
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self.assertEqual(x, prod.Expression())
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self.assertEqual(4, prod.Coefficient())
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def testSimplification2(self):
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print("testSimplification2")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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prod = 2 * (x * 2)
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self.assertEqual(x, prod.Expression())
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self.assertEqual(4, prod.Coefficient())
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def testSimplification3(self):
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print("testSimplification3")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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prod = (2 * x) * 2
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self.assertEqual(x, prod.Expression())
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self.assertEqual(4, prod.Coefficient())
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def testSimplification4(self):
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print("testSimplification4")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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prod = 2 * (2 * x)
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self.assertEqual(x, prod.Expression())
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self.assertEqual(4, prod.Coefficient())
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def testLinearNonEqualWithConstant(self):
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print("testLinearNonEqualWithConstant")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.Add(x + y + 5 != 3).Proto()
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self.assertLen(ct.linear.domain, 4)
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# Checks that saturated arithmetics worked.
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self.assertEqual(cp_model.INT_MIN, ct.linear.domain[0])
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self.assertEqual(-3, ct.linear.domain[1])
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self.assertEqual(-1, ct.linear.domain[2])
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self.assertEqual(cp_model.INT_MAX, ct.linear.domain[3])
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def testLinearWithEnforcement(self):
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print("testLinearWithEnforcement")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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b = model.NewBoolVar("b")
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model.AddLinearConstraint(x + 2 * y, 0, 10).OnlyEnforceIf(b.Not())
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model.Minimize(y)
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self.assertLen(model.Proto().constraints, 1)
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self.assertEqual(-3, model.Proto().constraints[0].enforcement_literal[0])
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c = model.NewBoolVar("c")
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model.AddLinearConstraint(x + 4 * y, 0, 10).OnlyEnforceIf([b, c])
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self.assertLen(model.Proto().constraints, 2)
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self.assertEqual(2, model.Proto().constraints[1].enforcement_literal[0])
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self.assertEqual(3, model.Proto().constraints[1].enforcement_literal[1])
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model.AddLinearConstraint(x + 5 * y, 0, 10).OnlyEnforceIf(c.Not(), b)
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self.assertLen(model.Proto().constraints, 3)
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self.assertEqual(-4, model.Proto().constraints[2].enforcement_literal[0])
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self.assertEqual(2, model.Proto().constraints[2].enforcement_literal[1])
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def testConstraintWithName(self):
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print("testConstraintWithName")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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ct = model.AddLinearConstraint(x + 2 * y, 0, 10).WithName("test_constraint")
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self.assertEqual("test_constraint", ct.Name())
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def testNaturalApiMinimize(self):
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print("testNaturalApiMinimize")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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model.Add(x * 2 - 1 * y == 1)
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model.Minimize(x * 1 - 2 * y + 3)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(5, solver.Value(x))
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self.assertEqual(15, solver.Value(x * 3))
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self.assertEqual(6, solver.Value(1 + x))
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self.assertEqual(-10.0, solver.ObjectiveValue())
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def testNaturalApiMaximizeFloat(self):
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print("testNaturalApiMaximizeFloat")
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model = cp_model.CpModel()
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x = model.NewBoolVar("x")
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y = model.NewIntVar(0, 10, "y")
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model.Maximize(x.Not() * 3.5 + x.Not() - y + 2 * y + 1.6)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertFalse(solver.BooleanValue(x))
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self.assertTrue(solver.BooleanValue(x.Not()))
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self.assertEqual(-10, solver.Value(-y))
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self.assertEqual(16.1, solver.ObjectiveValue())
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def testNaturalApiMaximizeComplex(self):
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print("testNaturalApiMaximizeFloat")
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model = cp_model.CpModel()
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x1 = model.NewBoolVar("x1")
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x2 = model.NewBoolVar("x1")
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x3 = model.NewBoolVar("x1")
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x4 = model.NewBoolVar("x1")
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model.Maximize(
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cp_model.LinearExpr.Sum([x1, x2])
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+ cp_model.LinearExpr.WeightedSum([x3, x4.Not()], [2, 4])
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)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(5, solver.Value(3 + 2 * x1))
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self.assertEqual(3, solver.Value(x1 + x2 + x3))
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self.assertEqual(1, solver.Value(cp_model.LinearExpr.Sum([x1, x2, x3, 0, -2])))
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self.assertEqual(
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7,
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solver.Value(
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cp_model.LinearExpr.WeightedSum([x1, x2, x4, 3], [2, 2, 2, 1])
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),
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)
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self.assertEqual(5, solver.Value(5 * x4.Not()))
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self.assertEqual(8, solver.ObjectiveValue())
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def testNaturalApiMaximize(self):
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print("testNaturalApiMaximize")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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y = model.NewIntVar(-10, 10, "y")
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model.Add(2 * x - y == 1)
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model.Maximize(x - 2 * y + 3)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(-4, solver.Value(x))
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self.assertEqual(-9, solver.Value(y))
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self.assertEqual(17, solver.ObjectiveValue())
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def testMinimizeConstant(self):
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print("testMinimizeConstant")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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model.Add(x >= -1)
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model.Minimize(10)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(10, solver.ObjectiveValue())
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def testMaximizeConstant(self):
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print("testMinimizeConstant")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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model.Add(x >= -1)
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model.Maximize(5)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(5, solver.ObjectiveValue())
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def testAddTrue(self):
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print("testAddTrue")
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model = cp_model.CpModel()
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x = model.NewIntVar(-10, 10, "x")
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model.Add(3 >= -1)
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model.Minimize(x)
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solver = cp_model.CpSolver()
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self.assertEqual("OPTIMAL", solver.StatusName(solver.Solve(model)))
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self.assertEqual(-10, solver.Value(x))
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def testAddFalse(self):
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print("testAddFalse")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(-10, 10, "x")
|
|
model.Add(3 <= -1)
|
|
model.Minimize(x)
|
|
solver = cp_model.CpSolver()
|
|
self.assertEqual("INFEASIBLE", solver.StatusName(solver.Solve(model)))
|
|
|
|
def testSum(self):
|
|
print("testSum")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 2, "x%i" % i) for i in range(100)]
|
|
model.Add(sum(x) <= 1)
|
|
model.Maximize(x[99])
|
|
solver = cp_model.CpSolver()
|
|
self.assertEqual(cp_model.OPTIMAL, solver.Solve(model))
|
|
self.assertEqual(1.0, solver.ObjectiveValue())
|
|
for i in range(100):
|
|
self.assertEqual(solver.Value(x[i]), 1 if i == 99 else 0)
|
|
|
|
def testSumWithApi(self):
|
|
print("testSumWithApi")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 2, "x%i" % i) for i in range(100)]
|
|
model.Add(cp_model.LinearExpr.Sum(x) <= 1)
|
|
model.Maximize(x[99])
|
|
solver = cp_model.CpSolver()
|
|
self.assertEqual(cp_model.OPTIMAL, solver.Solve(model))
|
|
self.assertEqual(1.0, solver.ObjectiveValue())
|
|
for i in range(100):
|
|
self.assertEqual(solver.Value(x[i]), 1 if i == 99 else 0)
|
|
|
|
def testWeightedSum(self):
|
|
print("testWeightedSum")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 2, "x%i" % i) for i in range(100)]
|
|
c = [2 for i in range(100)]
|
|
model.Add(cp_model.LinearExpr.WeightedSum(x, c) <= 3)
|
|
model.Maximize(x[99])
|
|
solver = cp_model.CpSolver()
|
|
self.assertEqual(cp_model.OPTIMAL, solver.Solve(model))
|
|
self.assertEqual(1.0, solver.ObjectiveValue())
|
|
for i in range(100):
|
|
self.assertEqual(solver.Value(x[i]), 1 if i == 99 else 0)
|
|
|
|
def testAllDifferent(self):
|
|
print("testAllDifferent")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddAllDifferent(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].all_diff.exprs, 5)
|
|
|
|
def testAllDifferentGen(self):
|
|
print("testAllDifferentGen")
|
|
model = cp_model.CpModel()
|
|
model.AddAllDifferent(model.NewIntVar(0, 4, "x%i" % i) for i in range(5))
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].all_diff.exprs, 5)
|
|
|
|
def testAllDifferentList(self):
|
|
print("testAllDifferentList")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddAllDifferent(x[0], x[1], x[2], x[3], x[4])
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].all_diff.exprs, 5)
|
|
|
|
def testElement(self):
|
|
print("testElement")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddElement(x[0], [x[1], 2, 4, x[2]], x[4])
|
|
self.assertLen(model.Proto().variables, 7)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].element.vars, 4)
|
|
self.assertEqual(0, model.Proto().constraints[0].element.index)
|
|
self.assertEqual(4, model.Proto().constraints[0].element.target)
|
|
self.assertRaises(ValueError, model.AddElement, x[0], [], x[4])
|
|
|
|
def testCircuit(self):
|
|
print("testCircuit")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar(f"x{i}") for i in range(5)]
|
|
model.AddCircuit((i, i + 1, x[i]) for i in range(5))
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].circuit.heads, 5)
|
|
self.assertLen(model.Proto().constraints[0].circuit.tails, 5)
|
|
self.assertLen(model.Proto().constraints[0].circuit.literals, 5)
|
|
self.assertRaises(ValueError, model.AddCircuit, [])
|
|
|
|
def testMultipleCircuit(self):
|
|
print("testMultipleCircuit")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar(f"x{i}") for i in range(5)]
|
|
model.AddMultipleCircuit((i, i + 1, x[i]) for i in range(5))
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].routes.heads, 5)
|
|
self.assertLen(model.Proto().constraints[0].routes.tails, 5)
|
|
self.assertLen(model.Proto().constraints[0].routes.literals, 5)
|
|
self.assertRaises(ValueError, model.AddMultipleCircuit, [])
|
|
|
|
def testAllowedAssignments(self):
|
|
print("testAllowedAssignments")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddAllowedAssignments(
|
|
x, [(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0, 0)]
|
|
)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].table.vars, 5)
|
|
self.assertLen(model.Proto().constraints[0].table.values, 15)
|
|
self.assertRaises(
|
|
TypeError,
|
|
model.AddAllowedAssignments,
|
|
x,
|
|
[(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0)],
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
model.AddAllowedAssignments,
|
|
[],
|
|
[(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0)],
|
|
)
|
|
|
|
def testForbiddenAssignments(self):
|
|
print("testForbiddenAssignments")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddForbiddenAssignments(
|
|
x, [(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0, 0)]
|
|
)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].table.vars, 5)
|
|
self.assertLen(model.Proto().constraints[0].table.values, 15)
|
|
self.assertTrue(model.Proto().constraints[0].table.negated)
|
|
self.assertRaises(
|
|
TypeError,
|
|
model.AddForbiddenAssignments,
|
|
x,
|
|
[(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0)],
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
model.AddForbiddenAssignments,
|
|
[],
|
|
[(0, 1, 2, 3, 4), (4, 3, 2, 1, 1), (0, 0, 0, 0)],
|
|
)
|
|
|
|
def testAutomaton(self):
|
|
print("testAutomaton")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
model.AddAutomaton(x, 0, [2, 3], [(0, 0, 0), (0, 1, 1), (1, 2, 2), (2, 3, 3)])
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].automaton.vars, 5)
|
|
self.assertLen(model.Proto().constraints[0].automaton.transition_tail, 4)
|
|
self.assertLen(model.Proto().constraints[0].automaton.transition_head, 4)
|
|
self.assertLen(model.Proto().constraints[0].automaton.transition_label, 4)
|
|
self.assertLen(model.Proto().constraints[0].automaton.final_states, 2)
|
|
self.assertEqual(0, model.Proto().constraints[0].automaton.starting_state)
|
|
self.assertRaises(
|
|
TypeError,
|
|
model.AddAutomaton,
|
|
x,
|
|
0,
|
|
[2, 3],
|
|
[(0, 0, 0), (0, 1, 1), (2, 2), (2, 3, 3)],
|
|
)
|
|
self.assertRaises(
|
|
ValueError,
|
|
model.AddAutomaton,
|
|
[],
|
|
0,
|
|
[2, 3],
|
|
[(0, 0, 0), (0, 1, 1), (2, 3, 3)],
|
|
)
|
|
self.assertRaises(
|
|
ValueError, model.AddAutomaton, x, 0, [], [(0, 0, 0), (0, 1, 1), (2, 3, 3)]
|
|
)
|
|
self.assertRaises(ValueError, model.AddAutomaton, x, 0, [2, 3], [])
|
|
|
|
def testInverse(self):
|
|
print("testInverse")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, 4, "x%i" % i) for i in range(5)]
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddInverse(x, y)
|
|
self.assertLen(model.Proto().variables, 10)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].inverse.f_direct, 5)
|
|
self.assertLen(model.Proto().constraints[0].inverse.f_inverse, 5)
|
|
|
|
def testMaxEquality(self):
|
|
print("testMaxEquality")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMaxEquality(x, y)
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 5)
|
|
self.assertEqual(0, model.Proto().constraints[0].lin_max.target.vars[0])
|
|
self.assertEqual(1, model.Proto().constraints[0].lin_max.target.coeffs[0])
|
|
|
|
def testMinEquality(self):
|
|
print("testMinEquality")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMinEquality(x, y)
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 5)
|
|
self.assertEqual(0, model.Proto().constraints[0].lin_max.target.vars[0])
|
|
self.assertEqual(-1, model.Proto().constraints[0].lin_max.target.coeffs[0])
|
|
|
|
def testMinEqualityList(self):
|
|
print("testMinEqualityList")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMinEquality(x, [y[0], y[2], y[1], y[3]])
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 4)
|
|
self.assertEqual(0, model.Proto().constraints[0].lin_max.target.vars[0])
|
|
self.assertEqual(-1, model.Proto().constraints[0].lin_max.target.coeffs[0])
|
|
|
|
def testMinEqualityTuple(self):
|
|
print("testMinEqualityTuple")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMinEquality(x, (y[0], y[2], y[1], y[3]))
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 4)
|
|
self.assertEqual(0, model.Proto().constraints[0].lin_max.target.vars[0])
|
|
self.assertEqual(-1, model.Proto().constraints[0].lin_max.target.coeffs[0])
|
|
|
|
def testMinEqualityGenerator(self):
|
|
print("testMinEqualityGenerator")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMinEquality(x, (z for z in y))
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 5)
|
|
self.assertEqual(0, model.Proto().constraints[0].lin_max.target.vars[0])
|
|
self.assertEqual(-1, model.Proto().constraints[0].lin_max.target.coeffs[0])
|
|
|
|
def testMinEqualityWithConstant(self):
|
|
print("testMinEqualityWithConstant")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 4, "y")
|
|
model.AddMinEquality(x, [y, 3])
|
|
self.assertLen(model.Proto().variables, 2)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
lin_max = model.Proto().constraints[0].lin_max
|
|
self.assertLen(lin_max.exprs, 2)
|
|
self.assertLen(lin_max.exprs[0].vars, 1)
|
|
self.assertEqual(1, lin_max.exprs[0].vars[0])
|
|
self.assertEqual(-1, lin_max.exprs[0].coeffs[0])
|
|
self.assertEqual(0, lin_max.exprs[0].offset)
|
|
self.assertEmpty(lin_max.exprs[1].vars)
|
|
self.assertEqual(-3, lin_max.exprs[1].offset)
|
|
|
|
def testAbs(self):
|
|
print("testAbs")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(-5, 5, "y")
|
|
model.AddAbsEquality(x, y)
|
|
self.assertLen(model.Proto().variables, 2)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].lin_max.exprs, 2)
|
|
self.assertEqual(1, model.Proto().constraints[0].lin_max.exprs[0].vars[0])
|
|
self.assertEqual(1, model.Proto().constraints[0].lin_max.exprs[0].coeffs[0])
|
|
self.assertEqual(1, model.Proto().constraints[0].lin_max.exprs[1].vars[0])
|
|
self.assertEqual(-1, model.Proto().constraints[0].lin_max.exprs[1].coeffs[0])
|
|
passed = False
|
|
error_msg = None
|
|
try:
|
|
abs(x)
|
|
except NotImplementedError as e:
|
|
error_msg = str(e)
|
|
passed = True
|
|
self.assertEqual(
|
|
"calling abs() on a linear expression is not supported, "
|
|
"please use CpModel.AddAbsEquality",
|
|
error_msg,
|
|
)
|
|
self.assertTrue(passed)
|
|
|
|
def testDivision(self):
|
|
print("testDivision")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 10, "x")
|
|
y = model.NewIntVar(0, 50, "y")
|
|
model.AddDivisionEquality(x, y, 6)
|
|
self.assertLen(model.Proto().variables, 2)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].int_div.exprs, 2)
|
|
self.assertEqual(model.Proto().constraints[0].int_div.exprs[0].vars[0], 1)
|
|
self.assertEqual(model.Proto().constraints[0].int_div.exprs[0].coeffs[0], 1)
|
|
self.assertEmpty(model.Proto().constraints[0].int_div.exprs[1].vars)
|
|
self.assertEqual(model.Proto().constraints[0].int_div.exprs[1].offset, 6)
|
|
passed = False
|
|
error_msg = None
|
|
try:
|
|
x / 3
|
|
except NotImplementedError as e:
|
|
error_msg = str(e)
|
|
passed = True
|
|
self.assertEqual(
|
|
"calling // on a linear expression is not supported, "
|
|
"please use CpModel.AddDivisionEquality",
|
|
error_msg,
|
|
)
|
|
self.assertTrue(passed)
|
|
|
|
def testModulo(self):
|
|
print("testModulo")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 10, "x")
|
|
y = model.NewIntVar(0, 50, "y")
|
|
model.AddModuloEquality(x, y, 6)
|
|
self.assertLen(model.Proto().variables, 2)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].int_mod.exprs, 2)
|
|
self.assertEqual(model.Proto().constraints[0].int_mod.exprs[0].vars[0], 1)
|
|
self.assertEqual(model.Proto().constraints[0].int_mod.exprs[0].coeffs[0], 1)
|
|
self.assertEmpty(model.Proto().constraints[0].int_mod.exprs[1].vars)
|
|
self.assertEqual(model.Proto().constraints[0].int_mod.exprs[1].offset, 6)
|
|
passed = False
|
|
error_msg = None
|
|
try:
|
|
x % 3
|
|
except NotImplementedError as e:
|
|
error_msg = str(e)
|
|
passed = True
|
|
self.assertEqual(
|
|
"calling %% on a linear expression is not supported, "
|
|
"please use CpModel.AddModuloEquality",
|
|
error_msg,
|
|
)
|
|
self.assertTrue(passed)
|
|
|
|
def testMultiplicationEquality(self):
|
|
print("testMultiplicationEquality")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = [model.NewIntVar(0, 4, "y%i" % i) for i in range(5)]
|
|
model.AddMultiplicationEquality(x, y)
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].int_prod.exprs, 5)
|
|
self.assertEqual(0, model.Proto().constraints[0].int_prod.target.vars[0])
|
|
|
|
def testImplication(self):
|
|
print("testImplication")
|
|
model = cp_model.CpModel()
|
|
x = model.NewBoolVar("x")
|
|
y = model.NewBoolVar("y")
|
|
model.AddImplication(x, y)
|
|
self.assertLen(model.Proto().variables, 2)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].bool_or.literals, 1)
|
|
self.assertLen(model.Proto().constraints[0].enforcement_literal, 1)
|
|
self.assertEqual(x.Index(), model.Proto().constraints[0].enforcement_literal[0])
|
|
self.assertEqual(y.Index(), model.Proto().constraints[0].bool_or.literals[0])
|
|
|
|
def testBoolOr(self):
|
|
print("testBoolOr")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddBoolOr(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].bool_or.literals, 5)
|
|
model.AddBoolOr([x[0], x[1], False])
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertRaises(TypeError, model.AddBoolOr, [x[2], 2])
|
|
y = model.NewIntVar(0, 4, "y")
|
|
self.assertRaises(TypeError, model.AddBoolOr, [y, False])
|
|
|
|
def testBoolOrListOrGet(self):
|
|
print("testBoolOrListOrGet")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddBoolOr(x)
|
|
model.AddBoolOr(True, x[0], x[2])
|
|
model.AddBoolOr(False, x[0])
|
|
model.AddBoolOr(x[i] for i in [0, 2, 3, 4])
|
|
self.assertLen(model.Proto().variables, 7)
|
|
self.assertLen(model.Proto().constraints, 4)
|
|
self.assertLen(model.Proto().constraints[0].bool_or.literals, 5)
|
|
self.assertLen(model.Proto().constraints[1].bool_or.literals, 3)
|
|
self.assertLen(model.Proto().constraints[2].bool_or.literals, 2)
|
|
self.assertLen(model.Proto().constraints[3].bool_or.literals, 4)
|
|
|
|
def testAtLeastOne(self):
|
|
print("testAtLeastOne")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddAtLeastOne(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].bool_or.literals, 5)
|
|
model.AddAtLeastOne([x[0], x[1], False])
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertRaises(TypeError, model.AddAtLeastOne, [x[2], 2])
|
|
y = model.NewIntVar(0, 4, "y")
|
|
self.assertRaises(TypeError, model.AddAtLeastOne, [y, False])
|
|
|
|
def testAtMostOne(self):
|
|
print("testAtMostOne")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddAtMostOne(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].at_most_one.literals, 5)
|
|
model.AddAtMostOne([x[0], x[1], False])
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertRaises(TypeError, model.AddAtMostOne, [x[2], 2])
|
|
y = model.NewIntVar(0, 4, "y")
|
|
self.assertRaises(TypeError, model.AddAtMostOne, [y, False])
|
|
|
|
def testExactlyOne(self):
|
|
print("testExactlyOne")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddExactlyOne(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].exactly_one.literals, 5)
|
|
model.AddExactlyOne([x[0], x[1], False])
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertRaises(TypeError, model.AddExactlyOne, [x[2], 2])
|
|
y = model.NewIntVar(0, 4, "y")
|
|
self.assertRaises(TypeError, model.AddExactlyOne, [y, False])
|
|
|
|
def testBoolAnd(self):
|
|
print("testBoolAnd")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddBoolAnd(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].bool_and.literals, 5)
|
|
model.AddBoolAnd([x[1], x[2].Not(), True])
|
|
self.assertEqual(1, model.Proto().constraints[1].bool_and.literals[0])
|
|
self.assertEqual(-3, model.Proto().constraints[1].bool_and.literals[1])
|
|
self.assertEqual(5, model.Proto().constraints[1].bool_and.literals[2])
|
|
|
|
def testBoolXOr(self):
|
|
print("testBoolXOr")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
model.AddBoolXOr(x)
|
|
self.assertLen(model.Proto().variables, 5)
|
|
self.assertLen(model.Proto().constraints, 1)
|
|
self.assertLen(model.Proto().constraints[0].bool_xor.literals, 5)
|
|
|
|
def testMapDomain(self):
|
|
print("testMapDomain")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewBoolVar("x%i" % i) for i in range(5)]
|
|
y = model.NewIntVar(0, 10, "y")
|
|
model.AddMapDomain(y, x, 2)
|
|
self.assertLen(model.Proto().variables, 6)
|
|
self.assertLen(model.Proto().constraints, 10)
|
|
|
|
def testInterval(self):
|
|
print("testInterval")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
i = model.NewIntervalVar(x, 3, y, "i")
|
|
self.assertEqual(1, i.Index())
|
|
|
|
j = model.NewFixedSizeIntervalVar(x, 2, "j")
|
|
self.assertEqual(2, j.Index())
|
|
start_expr = j.StartExpr()
|
|
size_expr = j.SizeExpr()
|
|
end_expr = j.EndExpr()
|
|
self.assertEqual(x.Index(), start_expr.Index())
|
|
self.assertEqual(size_expr, 2)
|
|
self.assertEqual(str(end_expr), "(x + 2)")
|
|
|
|
def testOptionalInterval(self):
|
|
print("testOptionalInterval")
|
|
model = cp_model.CpModel()
|
|
b = model.NewBoolVar("b")
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
i = model.NewOptionalIntervalVar(x, 3, y, b, "i")
|
|
j = model.NewOptionalIntervalVar(x, y, 10, b, "j")
|
|
k = model.NewOptionalIntervalVar(x, -y, 10, b, "k")
|
|
l = model.NewOptionalIntervalVar(x, 10, -y, b, "l")
|
|
self.assertEqual(1, i.Index())
|
|
self.assertEqual(3, j.Index())
|
|
self.assertEqual(5, k.Index())
|
|
self.assertEqual(7, l.Index())
|
|
self.assertRaises(TypeError, model.NewOptionalIntervalVar, 1, 2, 3, x, "x")
|
|
self.assertRaises(TypeError, model.NewOptionalIntervalVar, b + x, 2, 3, b, "x")
|
|
self.assertRaises(
|
|
AttributeError, model.NewOptionalIntervalVar, 1, 2, 3, b + 1, "x"
|
|
)
|
|
|
|
def testNoOverlap(self):
|
|
print("testNoOverlap")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
z = model.NewIntVar(0, 3, "y")
|
|
i = model.NewIntervalVar(x, 3, y, "i")
|
|
j = model.NewIntervalVar(x, 5, z, "j")
|
|
ct = model.AddNoOverlap([i, j])
|
|
self.assertEqual(4, ct.Index())
|
|
self.assertLen(ct.Proto().no_overlap.intervals, 2)
|
|
self.assertEqual(1, ct.Proto().no_overlap.intervals[0])
|
|
self.assertEqual(3, ct.Proto().no_overlap.intervals[1])
|
|
|
|
def testNoOverlap2D(self):
|
|
print("testNoOverlap2D")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
z = model.NewIntVar(0, 3, "y")
|
|
i = model.NewIntervalVar(x, 3, y, "i")
|
|
j = model.NewIntervalVar(x, 5, z, "j")
|
|
ct = model.AddNoOverlap2D([i, j], [j, i])
|
|
self.assertEqual(4, ct.Index())
|
|
self.assertLen(ct.Proto().no_overlap_2d.x_intervals, 2)
|
|
self.assertEqual(1, ct.Proto().no_overlap_2d.x_intervals[0])
|
|
self.assertEqual(3, ct.Proto().no_overlap_2d.x_intervals[1])
|
|
self.assertLen(ct.Proto().no_overlap_2d.y_intervals, 2)
|
|
self.assertEqual(3, ct.Proto().no_overlap_2d.y_intervals[0])
|
|
self.assertEqual(1, ct.Proto().no_overlap_2d.y_intervals[1])
|
|
|
|
def testCumulative(self):
|
|
print("testCumulative")
|
|
model = cp_model.CpModel()
|
|
intervals = [
|
|
model.NewIntervalVar(
|
|
model.NewIntVar(0, 10, f"s_{i}"),
|
|
5,
|
|
model.NewIntVar(5, 15, f"e_{i}"),
|
|
f"interval[{i}]",
|
|
)
|
|
for i in range(10)
|
|
]
|
|
demands = [1, 3, 5, 2, 4, 5, 3, 4, 2, 3]
|
|
capacity = 4
|
|
ct = model.AddCumulative(intervals, demands, capacity)
|
|
self.assertEqual(20, ct.Index())
|
|
self.assertLen(ct.Proto().cumulative.intervals, 10)
|
|
self.assertRaises(TypeError, model.AddCumulative, [intervals[0], 3], [2, 3], 3)
|
|
|
|
def testGetOrMakeIndexFromConstant(self):
|
|
print("testGetOrMakeIndexFromConstant")
|
|
model = cp_model.CpModel()
|
|
self.assertEqual(0, model.GetOrMakeIndexFromConstant(3))
|
|
self.assertEqual(0, model.GetOrMakeIndexFromConstant(3))
|
|
self.assertEqual(1, model.GetOrMakeIndexFromConstant(5))
|
|
model_var = model.Proto().variables[0]
|
|
self.assertLen(model_var.domain, 2)
|
|
self.assertEqual(3, model_var.domain[0])
|
|
self.assertEqual(3, model_var.domain[1])
|
|
|
|
def testStr(self):
|
|
print("testStr")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
self.assertEqual(str(x == 2), "x == 2")
|
|
self.assertEqual(str(x >= 2), "x >= 2")
|
|
self.assertEqual(str(x <= 2), "x <= 2")
|
|
self.assertEqual(str(x > 2), "x >= 3")
|
|
self.assertEqual(str(x < 2), "x <= 1")
|
|
self.assertEqual(str(x != 2), "x != 2")
|
|
self.assertEqual(str(x * 3), "(3 * x)")
|
|
self.assertEqual(str(-x), "-x")
|
|
self.assertEqual(str(x + 3), "(x + 3)")
|
|
self.assertEqual(str(x <= cp_model.INT_MAX), "True (unbounded expr x)")
|
|
self.assertEqual(str(x != 9223372036854775807), "x <= 9223372036854775806")
|
|
self.assertEqual(str(x != -9223372036854775808), "x >= -9223372036854775807")
|
|
y = model.NewIntVar(0, 4, "y")
|
|
self.assertEqual(
|
|
str(cp_model.LinearExpr.WeightedSum([x, y + 1, 2], [1, -2, 3])),
|
|
"x - 2 * (y + 1) + 6",
|
|
)
|
|
self.assertEqual(str(cp_model.LinearExpr.Term(x, 3)), "(3 * x)")
|
|
self.assertEqual(str(x != y), "(x + -y) != 0")
|
|
self.assertEqual(
|
|
"0 <= x <= 10", str(cp_model.BoundedLinearExpression(x, [0, 10]))
|
|
)
|
|
print(str(model))
|
|
b = model.NewBoolVar("b")
|
|
self.assertEqual(str(cp_model.LinearExpr.Term(b.Not(), 3)), "(3 * not(b))")
|
|
|
|
i = model.NewIntervalVar(x, 2, y, "i")
|
|
self.assertEqual(str(i), "i")
|
|
|
|
def testRepr(self):
|
|
print("testRepr")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
z = model.NewIntVar(0, 3, "z")
|
|
self.assertEqual(repr(x), "x(0..4)")
|
|
self.assertEqual(repr(x * 2), "ProductCst(x(0..4), 2)")
|
|
self.assertEqual(repr(x + y), "Sum(x(0..4), y(0..3))")
|
|
self.assertEqual(
|
|
repr(cp_model.LinearExpr.Sum([x, y, z])),
|
|
"SumArray(x(0..4), y(0..3), z(0..3), 0)",
|
|
)
|
|
self.assertEqual(
|
|
repr(cp_model.LinearExpr.WeightedSum([x, y, 2], [1, 2, 3])),
|
|
"WeightedSum([x(0..4), y(0..3)], [1, 2], 6)",
|
|
)
|
|
i = model.NewIntervalVar(x, 2, y, "i")
|
|
self.assertEqual(repr(i), "i(start = x, size = 2, end = y)")
|
|
b = model.NewBoolVar("b")
|
|
x1 = model.NewIntVar(0, 4, "x1")
|
|
y1 = model.NewIntVar(0, 3, "y1")
|
|
j = model.NewOptionalIntervalVar(x1, 2, y1, b, "j")
|
|
self.assertEqual(repr(j), "j(start = x1, size = 2, end = y1, is_present = b)")
|
|
x2 = model.NewIntVar(0, 4, "x2")
|
|
y2 = model.NewIntVar(0, 3, "y2")
|
|
k = model.NewOptionalIntervalVar(x2, 2, y2, b.Not(), "k")
|
|
self.assertEqual(
|
|
repr(k), "k(start = x2, size = 2, end = y2, is_present = Not(b))"
|
|
)
|
|
|
|
def testDisplayBounds(self):
|
|
print("testDisplayBounds")
|
|
self.assertEqual("10..20", cp_model.DisplayBounds([10, 20]))
|
|
self.assertEqual("10", cp_model.DisplayBounds([10, 10]))
|
|
self.assertEqual("10..15, 20..30", cp_model.DisplayBounds([10, 15, 20, 30]))
|
|
|
|
def testShortName(self):
|
|
print("testShortName")
|
|
model = cp_model.CpModel()
|
|
v = model.Proto().variables.add()
|
|
v.domain.extend([5, 10])
|
|
self.assertEqual("[5..10]", cp_model.ShortName(model.Proto(), 0))
|
|
|
|
def testIntegerExpressionErrors(self):
|
|
print("testIntegerExpressionErrors")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 1, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
self.assertRaises(TypeError, x.__mul__, y)
|
|
self.assertRaises(NotImplementedError, x.__div__, y)
|
|
self.assertRaises(NotImplementedError, x.__truediv__, y)
|
|
self.assertRaises(NotImplementedError, x.__mod__, y)
|
|
self.assertRaises(NotImplementedError, x.__pow__, y)
|
|
self.assertRaises(NotImplementedError, x.__lshift__, y)
|
|
self.assertRaises(NotImplementedError, x.__rshift__, y)
|
|
self.assertRaises(NotImplementedError, x.__and__, y)
|
|
self.assertRaises(NotImplementedError, x.__or__, y)
|
|
self.assertRaises(NotImplementedError, x.__xor__, y)
|
|
self.assertRaises(ArithmeticError, x.__lt__, cp_model.INT_MIN)
|
|
self.assertRaises(ArithmeticError, x.__gt__, cp_model.INT_MAX)
|
|
self.assertRaises(TypeError, x.__add__, "dummy")
|
|
self.assertRaises(TypeError, x.__mul__, "dummy")
|
|
|
|
def testModelErrors(self):
|
|
print("testModelErrors")
|
|
model = cp_model.CpModel()
|
|
self.assertRaises(TypeError, model.Add, "dummy")
|
|
self.assertRaises(TypeError, model.GetOrMakeIndex, "dummy")
|
|
self.assertRaises(TypeError, model.Minimize, "dummy")
|
|
|
|
def testSolverErrors(self):
|
|
print("testSolverErrors")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 1, "x")
|
|
y = model.NewIntVar(-10, 10, "y")
|
|
model.AddLinearConstraint(x + 2 * y, 0, 10)
|
|
model.Minimize(y)
|
|
solver = cp_model.CpSolver()
|
|
self.assertRaises(RuntimeError, solver.Value, x)
|
|
solver.Solve(model)
|
|
self.assertRaises(TypeError, solver.Value, "not_a_variable")
|
|
self.assertRaises(TypeError, model.AddBoolOr, [x, y])
|
|
|
|
def testHasObjectiveMinimize(self):
|
|
print("testHasObjectiveMinimizs")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 1, "x")
|
|
y = model.NewIntVar(-10, 10, "y")
|
|
model.AddLinearConstraint(x + 2 * y, 0, 10)
|
|
self.assertFalse(model.HasObjective())
|
|
model.Minimize(y)
|
|
self.assertTrue(model.HasObjective())
|
|
|
|
def testHasObjectiveMaximize(self):
|
|
print("testHasObjectiveMaximizs")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 1, "x")
|
|
y = model.NewIntVar(-10, 10, "y")
|
|
model.AddLinearConstraint(x + 2 * y, 0, 10)
|
|
self.assertFalse(model.HasObjective())
|
|
model.Maximize(y)
|
|
self.assertTrue(model.HasObjective())
|
|
|
|
def testSearchForAllSolutions(self):
|
|
print("testSearchForAllSolutions")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
|
|
solver = cp_model.CpSolver()
|
|
solution_counter = SolutionCounter()
|
|
status = solver.SearchForAllSolutions(model, solution_counter)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(5, solution_counter.SolutionCount())
|
|
model.Minimize(x)
|
|
self.assertRaises(
|
|
TypeError, solver.SearchForAllSolutions, model, solution_counter
|
|
)
|
|
|
|
def testSolveWithSolutionCallback(self):
|
|
print("testSolveWithSolutionCallback")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
|
|
solver = cp_model.CpSolver()
|
|
solution_sum = SolutionSum([x, y])
|
|
self.assertRaises(RuntimeError, solution_sum.Value, x)
|
|
status = solver.SolveWithSolutionCallback(model, solution_sum)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(6, solution_sum.Sum())
|
|
|
|
def testValue(self):
|
|
print("testValue")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 10, "x")
|
|
y = model.NewIntVar(0, 10, "y")
|
|
model.Add(x + 2 * y == 29)
|
|
solver = cp_model.CpSolver()
|
|
status = solver.Solve(model)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(solver.Value(x), 9)
|
|
self.assertEqual(solver.Value(y), 10)
|
|
self.assertEqual(solver.Value(2), 2)
|
|
|
|
def testBooleanValue(self):
|
|
print("testBooleanValue")
|
|
model = cp_model.CpModel()
|
|
x = model.NewBoolVar("x")
|
|
y = model.NewBoolVar("y")
|
|
z = model.NewBoolVar("z")
|
|
model.AddBoolOr([x, z.Not()])
|
|
model.AddBoolOr([x, z])
|
|
model.AddBoolOr([x.Not(), y.Not()])
|
|
solver = cp_model.CpSolver()
|
|
status = solver.Solve(model)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(solver.BooleanValue(x), True)
|
|
self.assertEqual(solver.Value(x), 1 - solver.Value(x.Not()))
|
|
self.assertEqual(solver.Value(y), 1 - solver.Value(y.Not()))
|
|
self.assertEqual(solver.Value(z), 1 - solver.Value(z.Not()))
|
|
self.assertEqual(solver.BooleanValue(y), False)
|
|
self.assertEqual(solver.BooleanValue(True), True)
|
|
self.assertEqual(solver.BooleanValue(False), False)
|
|
self.assertEqual(solver.BooleanValue(2), True)
|
|
self.assertEqual(solver.BooleanValue(0), False)
|
|
|
|
def testUnsupportedOperators(self):
|
|
print("testUnsupportedOperators")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 10, "x")
|
|
y = model.NewIntVar(0, 10, "y")
|
|
z = model.NewIntVar(0, 10, "z")
|
|
|
|
with self.assertRaises(NotImplementedError):
|
|
model.Add(x == min(y, z))
|
|
with self.assertRaises(NotImplementedError):
|
|
if x > y:
|
|
print("passed1")
|
|
with self.assertRaises(NotImplementedError):
|
|
if x == 2:
|
|
print("passed2")
|
|
|
|
def testIsLiteralTrueFalse(self):
|
|
print("testIsLiteralTrueFalse")
|
|
model = cp_model.CpModel()
|
|
x = model.NewConstant(0)
|
|
self.assertFalse(cp_model.ObjectIsATrueLiteral(x))
|
|
self.assertTrue(cp_model.ObjectIsAFalseLiteral(x))
|
|
self.assertTrue(cp_model.ObjectIsATrueLiteral(x.Not()))
|
|
self.assertFalse(cp_model.ObjectIsAFalseLiteral(x.Not()))
|
|
self.assertTrue(cp_model.ObjectIsATrueLiteral(True))
|
|
self.assertTrue(cp_model.ObjectIsAFalseLiteral(False))
|
|
self.assertFalse(cp_model.ObjectIsATrueLiteral(False))
|
|
self.assertFalse(cp_model.ObjectIsAFalseLiteral(True))
|
|
|
|
def testSolveMinimizeWithSolutionCallback(self):
|
|
print("testSolveMinimizeWithSolutionCallback")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
model.Maximize(x + 2 * y)
|
|
|
|
solver = cp_model.CpSolver()
|
|
solution_obj = SolutionObjective()
|
|
status = solver.SolveWithSolutionCallback(model, solution_obj)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
print("obj = ", solution_obj.Obj())
|
|
self.assertEqual(11, solution_obj.Obj())
|
|
|
|
def testSolutionHinting(self):
|
|
print("testSolutionHinting")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
model.AddHint(x, 2)
|
|
model.AddHint(y, 4)
|
|
solver = cp_model.CpSolver()
|
|
solver.parameters.cp_model_presolve = False
|
|
status = solver.Solve(model)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(2, solver.Value(x))
|
|
self.assertEqual(4, solver.Value(y))
|
|
|
|
def testStats(self):
|
|
print("testStats")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 4, 6)
|
|
model.AddLinearConstraint(2 * x + y, 0, 10)
|
|
model.Maximize(x + 2 * y)
|
|
|
|
solver = cp_model.CpSolver()
|
|
status = solver.Solve(model)
|
|
self.assertEqual(cp_model.OPTIMAL, status)
|
|
self.assertEqual(solver.NumBooleans(), 0)
|
|
self.assertEqual(solver.NumConflicts(), 0)
|
|
self.assertEqual(solver.NumBranches(), 0)
|
|
self.assertGreater(solver.WallTime(), 0.0)
|
|
|
|
def testSearchStrategy(self):
|
|
print("testSearchStrategy")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddDecisionStrategy(
|
|
[y, x], cp_model.CHOOSE_MIN_DOMAIN_SIZE, cp_model.SELECT_MAX_VALUE
|
|
)
|
|
self.assertLen(model.Proto().search_strategy, 1)
|
|
strategy = model.Proto().search_strategy[0]
|
|
self.assertLen(strategy.variables, 2)
|
|
self.assertEqual(y.Index(), strategy.variables[0])
|
|
self.assertEqual(x.Index(), strategy.variables[1])
|
|
self.assertEqual(
|
|
cp_model.CHOOSE_MIN_DOMAIN_SIZE, strategy.variable_selection_strategy
|
|
)
|
|
self.assertEqual(cp_model.SELECT_MAX_VALUE, strategy.domain_reduction_strategy)
|
|
|
|
def testModelAndResponseStats(self):
|
|
print("testStats")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
model.Maximize(x + 2 * y)
|
|
self.assertTrue(model.ModelStats())
|
|
|
|
solver = cp_model.CpSolver()
|
|
solver.Solve(model)
|
|
self.assertTrue(solver.ResponseStats())
|
|
|
|
def testValidateModel(self):
|
|
print("testValidateModel")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, 5, "x")
|
|
y = model.NewIntVar(0, 5, "y")
|
|
model.AddLinearConstraint(x + y, 6, 6)
|
|
model.Maximize(x + 2 * y)
|
|
self.assertFalse(model.Validate())
|
|
|
|
def testValidateModelWithOverflow(self):
|
|
print("testValidateModel")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(0, cp_model.INT_MAX, "x")
|
|
y = model.NewIntVar(0, 10, "y")
|
|
model.AddLinearConstraint(x + y, 6, cp_model.INT_MAX)
|
|
model.Maximize(x + 2 * y)
|
|
self.assertTrue(model.Validate())
|
|
|
|
def testCopyModel(self):
|
|
print("testCopyModel")
|
|
model = cp_model.CpModel()
|
|
b = model.NewBoolVar("b")
|
|
x = model.NewIntVar(0, 4, "x")
|
|
y = model.NewIntVar(0, 3, "y")
|
|
i = model.NewOptionalIntervalVar(x, 12, y, b, "i")
|
|
lin = model.Add(x + y <= 10)
|
|
|
|
new_model = cp_model.CpModel()
|
|
new_model.CopyFrom(model)
|
|
copy_b = new_model.GetBoolVarFromProtoIndex(b.Index())
|
|
copy_x = new_model.GetIntVarFromProtoIndex(x.Index())
|
|
copy_y = new_model.GetIntVarFromProtoIndex(y.Index())
|
|
copy_i = new_model.GetIntervalVarFromProtoIndex(i.Index())
|
|
|
|
self.assertEqual(b.Index(), copy_b.Index())
|
|
self.assertEqual(x.Index(), copy_x.Index())
|
|
self.assertEqual(y.Index(), copy_y.Index())
|
|
self.assertEqual(i.Index(), copy_i.Index())
|
|
|
|
with self.assertRaises(ValueError):
|
|
new_model.GetBoolVarFromProtoIndex(-1)
|
|
|
|
with self.assertRaises(ValueError):
|
|
new_model.GetIntVarFromProtoIndex(-1)
|
|
|
|
with self.assertRaises(ValueError):
|
|
new_model.GetIntervalVarFromProtoIndex(-1)
|
|
|
|
with self.assertRaises(ValueError):
|
|
new_model.GetBoolVarFromProtoIndex(x.Index())
|
|
|
|
with self.assertRaises(ValueError):
|
|
new_model.GetIntervalVarFromProtoIndex(lin.Index())
|
|
|
|
interval_ct = new_model.Proto().constraints[copy_i.Index()].interval
|
|
self.assertEqual(12, interval_ct.size.offset)
|
|
|
|
def testCustomLog(self):
|
|
print("testCustomLog")
|
|
model = cp_model.CpModel()
|
|
x = model.NewIntVar(-10, 10, "x")
|
|
y = model.NewIntVar(-10, 10, "y")
|
|
model.AddLinearConstraint(x + 2 * y, 0, 10)
|
|
model.Minimize(y)
|
|
solver = cp_model.CpSolver()
|
|
solver.parameters.log_search_progress = True
|
|
solver.parameters.log_to_stdout = False
|
|
log_callback = LogToString()
|
|
solver.log_callback = log_callback.NewMessage
|
|
|
|
self.assertEqual(cp_model.OPTIMAL, solver.Solve(model))
|
|
self.assertEqual(10, solver.Value(x))
|
|
self.assertEqual(-5, solver.Value(y))
|
|
|
|
self.assertRegex(log_callback.Log(), "Parameters.*log_to_stdout.*")
|
|
|
|
def testIssue2762(self):
|
|
print("testIssue2762")
|
|
model = cp_model.CpModel()
|
|
|
|
x = [model.NewBoolVar("a"), model.NewBoolVar("b")]
|
|
with self.assertRaises(NotImplementedError):
|
|
model.Add((x[0] != 0) or (x[1] != 0))
|
|
|
|
def testModelError(self):
|
|
print("TestModelError")
|
|
model = cp_model.CpModel()
|
|
x = [model.NewIntVar(0, -2, "x%i" % i) for i in range(100)]
|
|
model.Add(sum(x) <= 1)
|
|
solver = cp_model.CpSolver()
|
|
solver.parameters.log_search_progress = True
|
|
self.assertEqual(cp_model.MODEL_INVALID, solver.Solve(model))
|
|
self.assertEqual(solver.SolutionInfo(), 'var #0 has no domain(): name: "x0"')
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main()
|