E.add_constraint(~p1_using_resource | ~p2_using_resource)
# Finally, we can add constraints to tell the system which processes use which resources:
# For example:
# process 0 uses resource:
E.add_constraint(r.get(0, 0))
# process 1 also uses resource
E.add_constraint(r.get(1, 0))
# process 2 also uses resource
E.add_constraint(r.get(2, 0))
# (So, processes 0, 1, and 2, cannot be run concurrently)
return E
if __name__ == "__main__":
T = example_theory()
print("\nSatisfiable: %s" % T.is_satisfiable())
#print("# Solutions: %d" % T.count_solutions())
print("solution:")
solution = T.solve()
for time in range(num_time_slots):
for processor in range(num_processors):
for process in range(num_processes):
var_name = 'schedule_' + str(time) + '_' + str(
processor) + '_' + str(process)
print(var_name, solution[var_name])
#print(" Solution: %s" % T.solve())
print('holding is exclusive', constraint)
E.add_constraint(constraint)
for i in range(num_processes):
for j in range(num_resources):
# holding implies max
constraint = ~h[i][j] | m[i][j]
print('holding implies max', constraint)
E.add_constraint(constraint)
# hard code 2 processes and 2 resources for now:
# These constraints ensure that the system is in a safe state
E.add_constraint(~m[0][0] | ~h[1][0] | ~m[1][1] | ~h[0][1])
E.add_constraint(~m[0][1] | ~h[1][1] | ~m[1][0] | ~h[0][0])
return E
if __name__ == "__main__":
T = example_theory()
print("\nSatisfiable: %s" % T.is_satisfiable())
print("# Solutions: %d" % T.count_solutions())
print(" Solution: %s" % T.solve())
print("\nVariable likelihoods:")
print(" %s: %.2f" % ("each m", T.likelihood(m[0][0]))) # by symmetry
print(" %s: %.2f" % ("each h", T.likelihood(h[0][0]))) # by symmetry
print()