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use black on examples/python

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Laurent Perron
2023-07-01 06:06:53 +02:00
parent d65333dab0
commit 84ec414e61
41 changed files with 18801 additions and 4140 deletions
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87
examples/python/linear_programming.py
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@@ -11,14 +11,16 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Linear programming examples that show how to use the APIs."""
from ortools.linear_solver import pywraplp
def Announce(solver, api_type):
print('---- Linear programming example with ' + solver + ' (' + api_type +
') -----')
print(
"---- Linear programming example with " + solver + " (" + api_type + ") -----"
)
def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):
@@ -28,23 +30,25 @@ def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):
if not solver:
return
Announce(optimization_problem_type, 'natural language API')
Announce(optimization_problem_type, "natural language API")
infinity = solver.infinity()
# x1, x2 and x3 are continuous non-negative variables.
x1 = solver.NumVar(0.0, infinity, 'x1')
x2 = solver.NumVar(0.0, infinity, 'x2')
x3 = solver.NumVar(0.0, infinity, 'x3')
x1 = solver.NumVar(0.0, infinity, "x1")
x2 = solver.NumVar(0.0, infinity, "x2")
x3 = solver.NumVar(0.0, infinity, "x3")
solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)
c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')
c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, "ConstraintName0")
c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
sum_of_vars = sum([x1, x2, x3])
c2 = solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
c2 = solver.Add(sum_of_vars <= 100.0, "OtherConstraintName")
SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != 'PDLP')
SolveAndPrint(
solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != "PDLP"
)
# Print a linear expression's solution value.
print('Sum of vars: %s = %s' % (sum_of_vars, sum_of_vars.solution_value()))
print("Sum of vars: %s = %s" % (sum_of_vars, sum_of_vars.solution_value()))
def RunLinearExampleCppStyleAPI(optimization_problem_type):
@@ -53,13 +57,13 @@ def RunLinearExampleCppStyleAPI(optimization_problem_type):
if not solver:
return
Announce(optimization_problem_type, 'C++ style API')
Announce(optimization_problem_type, "C++ style API")
infinity = solver.infinity()
# x1, x2 and x3 are continuous non-negative variables.
x1 = solver.NumVar(0.0, infinity, 'x1')
x2 = solver.NumVar(0.0, infinity, 'x2')
x3 = solver.NumVar(0.0, infinity, 'x3')
x1 = solver.NumVar(0.0, infinity, "x1")
x2 = solver.NumVar(0.0, infinity, "x2")
x3 = solver.NumVar(0.0, infinity, "x3")
# Maximize 10 * x1 + 6 * x2 + 4 * x3.
objective = solver.Objective()
@@ -69,31 +73,32 @@ def RunLinearExampleCppStyleAPI(optimization_problem_type):
objective.SetMaximization()
# x1 + x2 + x3 <= 100.
c0 = solver.Constraint(-infinity, 100.0, 'c0')
c0 = solver.Constraint(-infinity, 100.0, "c0")
c0.SetCoefficient(x1, 1)
c0.SetCoefficient(x2, 1)
c0.SetCoefficient(x3, 1)
# 10 * x1 + 4 * x2 + 5 * x3 <= 600.
c1 = solver.Constraint(-infinity, 600.0, 'c1')
c1 = solver.Constraint(-infinity, 600.0, "c1")
c1.SetCoefficient(x1, 10)
c1.SetCoefficient(x2, 4)
c1.SetCoefficient(x3, 5)
# 2 * x1 + 2 * x2 + 6 * x3 <= 300.
c2 = solver.Constraint(-infinity, 300.0, 'c2')
c2 = solver.Constraint(-infinity, 300.0, "c2")
c2.SetCoefficient(x1, 2)
c2.SetCoefficient(x2, 2)
c2.SetCoefficient(x3, 6)
SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2],
optimization_problem_type != 'PDLP')
SolveAndPrint(
solver, [x1, x2, x3], [c0, c1, c2], optimization_problem_type != "PDLP"
)
def SolveAndPrint(solver, variable_list, constraint_list, is_precise):
"""Solve the problem and print the solution."""
print('Number of variables = %d' % solver.NumVariables())
print('Number of constraints = %d' % solver.NumConstraints())
print("Number of variables = %d" % solver.NumVariables())
print("Number of constraints = %d" % solver.NumConstraints())
result_status = solver.Solve()
@@ -105,38 +110,40 @@ def SolveAndPrint(solver, variable_list, constraint_list, is_precise):
if is_precise:
assert solver.VerifySolution(1e-7, True)
print('Problem solved in %f milliseconds' % solver.wall_time())
print("Problem solved in %f milliseconds" % solver.wall_time())
# The objective value of the solution.
print('Optimal objective value = %f' % solver.Objective().Value())
print("Optimal objective value = %f" % solver.Objective().Value())
# The value of each variable in the solution.
for variable in variable_list:
print('%s = %f' % (variable.name(), variable.solution_value()))
print("%s = %f" % (variable.name(), variable.solution_value()))
print('Advanced usage:')
print('Problem solved in %d iterations' % solver.iterations())
print("Advanced usage:")
print("Problem solved in %d iterations" % solver.iterations())
for variable in variable_list:
print('%s: reduced cost = %f' %
(variable.name(), variable.reduced_cost()))
print("%s: reduced cost = %f" % (variable.name(), variable.reduced_cost()))
activities = solver.ComputeConstraintActivities()
for i, constraint in enumerate(constraint_list):
print(('constraint %d: dual value = %f\n'
' activity = %f' %
(i, constraint.dual_value(), activities[constraint.index()])))
print(
(
"constraint %d: dual value = %f\n activity = %f"
% (i, constraint.dual_value(), activities[constraint.index()])
)
)
def main():
RunLinearExampleNaturalLanguageAPI('GLOP')
RunLinearExampleNaturalLanguageAPI('GLPK_LP')
RunLinearExampleNaturalLanguageAPI('CLP')
RunLinearExampleNaturalLanguageAPI('PDLP')
RunLinearExampleNaturalLanguageAPI("GLOP")
RunLinearExampleNaturalLanguageAPI("GLPK_LP")
RunLinearExampleNaturalLanguageAPI("CLP")
RunLinearExampleNaturalLanguageAPI("PDLP")
RunLinearExampleCppStyleAPI('GLOP')
RunLinearExampleCppStyleAPI('GLPK_LP')
RunLinearExampleCppStyleAPI('CLP')
RunLinearExampleCppStyleAPI('PDLP')
RunLinearExampleCppStyleAPI("GLOP")
RunLinearExampleCppStyleAPI("GLPK_LP")
RunLinearExampleCppStyleAPI("CLP")
RunLinearExampleCppStyleAPI("PDLP")
if __name__ == '__main__':
if __name__ == "__main__":
main()