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use black on examples/python

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This commit is contained in:
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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40
examples/python/tasks_and_workers_assignment_sat.py
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@@ -11,6 +11,7 @@
# 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.
"""Tasks and workers to group assignment to average sum(cost) / #workers."""
from typing import Sequence
@@ -26,8 +27,10 @@ class ObjectivePrinter(cp_model.CpSolverSolutionCallback):
self.__solution_count = 0
def on_solution_callback(self):
print('Solution %i, time = %f s, objective = %i' %
(self.__solution_count, self.WallTime(), self.ObjectiveValue()))
print(
"Solution %i, time = %f s, objective = %i"
% (self.__solution_count, self.WallTime(), self.ObjectiveValue())
)
self.__solution_count += 1
@@ -50,13 +53,13 @@ def tasks_and_workers_assignment_sat():
x = {}
for i in all_workers:
for j in all_groups:
x[i, j] = model.NewBoolVar('x[%i,%i]' % (i, j))
x[i, j] = model.NewBoolVar("x[%i,%i]" % (i, j))
## y_kj is 1 if task k is assigned to group j
y = {}
for k in all_tasks:
for j in all_groups:
y[k, j] = model.NewBoolVar('x[%i,%i]' % (k, j))
y[k, j] = model.NewBoolVar("x[%i,%i]" % (k, j))
# Constraints
@@ -75,13 +78,11 @@ def tasks_and_workers_assignment_sat():
scaled_sum_of_costs_in_group = []
scaling = 1000 # We introduce scaling to deal with floating point average.
for j in all_groups:
n = model.NewIntVar(1, num_workers, 'num_workers_in_group_%i' % j)
n = model.NewIntVar(1, num_workers, "num_workers_in_group_%i" % j)
model.Add(n == sum(x[i, j] for i in all_workers))
c = model.NewIntVar(0, sum_of_costs * scaling,
'sum_of_costs_of_group_%i' % j)
c = model.NewIntVar(0, sum_of_costs * scaling, "sum_of_costs_of_group_%i" % j)
model.Add(c == sum(y[k, j] * task_cost[k] * scaling for k in all_tasks))
a = model.NewIntVar(0, sum_of_costs * scaling,
'average_cost_of_group_%i' % j)
a = model.NewIntVar(0, sum_of_costs * scaling, "average_cost_of_group_%i" % j)
model.AddDivisionEquality(a, c, n)
averages.append(a)
@@ -92,7 +93,7 @@ def tasks_and_workers_assignment_sat():
model.Add(sum(num_workers_in_group) == num_workers)
# Objective.
obj = model.NewIntVar(0, sum_of_costs * scaling, 'obj')
obj = model.NewIntVar(0, sum_of_costs * scaling, "obj")
model.AddMaxEquality(obj, averages)
model.Minimize(obj)
@@ -105,17 +106,18 @@ def tasks_and_workers_assignment_sat():
if status == cp_model.OPTIMAL:
for j in all_groups:
print('Group %i' % j)
print("Group %i" % j)
for i in all_workers:
if solver.BooleanValue(x[i, j]):
print(' - worker %i' % i)
print(" - worker %i" % i)
for k in all_tasks:
if solver.BooleanValue(y[k, j]):
print(' - task %i with cost %i' % (k, task_cost[k]))
print(' - sum_of_costs = %i' %
(solver.Value(scaled_sum_of_costs_in_group[j]) // scaling))
print(' - average cost = %f' %
(solver.Value(averages[j]) * 1.0 / scaling))
print(" - task %i with cost %i" % (k, task_cost[k]))
print(
" - sum_of_costs = %i"
% (solver.Value(scaled_sum_of_costs_in_group[j]) // scaling)
)
print(" - average cost = %f" % (solver.Value(averages[j]) * 1.0 / scaling))
tasks_and_workers_assignment_sat()
@@ -123,9 +125,9 @@ tasks_and_workers_assignment_sat()
def main(argv: Sequence[str]) -> None:
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
raise app.UsageError("Too many command-line arguments.")
tasks_and_workers_assignment_sat()
if __name__ == '__main__':
if __name__ == "__main__":
app.run(main)