116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
# Copyright 2010-2014 Google
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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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"""Simple unit tests for python/linear_solver.swig. Not exhaustive."""
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import types
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from ortools.linear_solver import linear_solver_pb2
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from ortools.linear_solver import pywraplp
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from google.protobuf import text_format
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def compute_sum(arg):
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if type(arg) is types.GeneratorType:
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arg = [x for x in arg]
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s = 0
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for i in arg:
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s += i
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print 'sum(%s) = %d' % (str(arg), s)
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def test_sum_no_brackets():
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compute_sum(x for x in range(10) if x % 2 == 0)
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compute_sum([x for x in range(10) if x % 2 == 0])
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text_model = """
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solver_type:CBC_MIXED_INTEGER_PROGRAMMING
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model <
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maximize:true
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variable < lower_bound:1 upper_bound:10 objective_coefficient:2 >
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variable < lower_bound:1 upper_bound:10 objective_coefficient:1 >
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constraint < lower_bound:-10000 upper_bound:4
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var_index:0
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var_index:1
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coefficient:1
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coefficient:2
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>
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>
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"""
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def test_proto():
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input_proto = linear_solver_pb2.MPModelRequest()
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text_format.Merge(text_model, input_proto)
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solver = pywraplp.Solver('solveFromProto',
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pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
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print input_proto
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# For now, create the model from the proto by parsing the proto
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errors = solver.LoadModelFromProto(input_proto.model)
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assert not errors
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solver.EnableOutput()
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solver.Solve()
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# Fill solution
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solution = linear_solver_pb2.MPSolutionResponse()
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solver.FillSolutionResponseProto(solution)
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print solution
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def test_external_api():
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solver = pywraplp.Solver('TestExternalAPI',
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pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
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infinity = solver.Infinity()
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infinity2 = solver.infinity()
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assert infinity == infinity2
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# x1, x2 and x3 are continuous non-negative variables.
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x1 = solver.NumVar(0.0, infinity, 'x1')
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x2 = solver.NumVar(0.0, infinity, 'x2')
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x3 = solver.NumVar(0.0, infinity, 'x3')
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assert x1.Lb() == 0
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assert x1.Ub() == infinity
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assert not x1.Integer()
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solver.Maximize(10 * x1 + 6 * x2 + 4 * x3 + 5)
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assert solver.Objective().Offset() == 5
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c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')
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c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
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sum_of_vars = sum([x1, x2, x3])
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solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
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assert c1.Lb() == -infinity
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assert c1.Ub() == 300
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c1.SetLb(-100000)
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assert c1.Lb() == -100000
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c1.SetUb(301)
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assert c1.Ub() == 301
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solver.SetTimeLimit(10000)
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result_status = solver.Solve()
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# The problem has an optimal solution.
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assert result_status == pywraplp.Solver.OPTIMAL
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print 'Problem solved in %f milliseconds' % solver.WallTime()
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print 'Problem solved in %d iterations' % solver.Iterations()
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print x1.ReducedCost()
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print c0.DualValue()
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def main():
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test_sum_no_brackets()
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# TODO(user): Support the proto API in or-tools. When this happens, re-enable
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# the test below:
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# test_proto()
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test_external_api()
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if __name__ == '__main__':
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main()
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