92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 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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"""Implements a step function."""
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from ortools.sat.python import cp_model
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class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
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"""Print intermediate solutions."""
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def __init__(self, variables: list[cp_model.IntVar]):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__variables = variables
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def on_solution_callback(self) -> None:
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for v in self.__variables:
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print(f"{v}={self.value(v)}", end=" ")
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print()
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def step_function_sample_sat():
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"""Encode the step function."""
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# Model.
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model = cp_model.CpModel()
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# Declare our primary variable.
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x = model.new_int_var(0, 20, "x")
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# Create the expression variable and implement the step function
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# Note it is not defined for x == 2.
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#
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# - 3
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# -- -- --------- 2
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# 1
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# -- --- 0
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# 0 ================ 20
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#
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expr = model.new_int_var(0, 3, "expr")
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# expr == 0 on [5, 6] U [8, 10]
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b0 = model.new_bool_var("b0")
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model.add_linear_expression_in_domain(
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x, cp_model.Domain.from_intervals([(5, 6), (8, 10)])
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).only_enforce_if(b0)
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model.add(expr == 0).only_enforce_if(b0)
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# expr == 2 on [0, 1] U [3, 4] U [11, 20]
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b2 = model.new_bool_var("b2")
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model.add_linear_expression_in_domain(
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x, cp_model.Domain.from_intervals([(0, 1), (3, 4), (11, 20)])
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).only_enforce_if(b2)
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model.add(expr == 2).only_enforce_if(b2)
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# expr == 3 when x == 7
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b3 = model.new_bool_var("b3")
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model.add(x == 7).only_enforce_if(b3)
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model.add(expr == 3).only_enforce_if(b3)
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# At least one bi is true. (we could use an exactly one constraint).
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model.add_bool_or(b0, b2, b3)
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# Search for x values in increasing order.
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model.add_decision_strategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)
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# Create a solver and solve with a fixed search.
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solver = cp_model.CpSolver()
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# Force the solver to follow the decision strategy exactly.
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solver.parameters.search_branching = cp_model.FIXED_SEARCH
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# Enumerate all solutions.
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solver.parameters.enumerate_all_solutions = True
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# Search and print out all solutions.
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solution_printer = VarArraySolutionPrinter([x, expr])
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solver.solve(model, solution_printer)
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step_function_sample_sat()
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