def initialize():
print("initializing")
print(".......")
print(".......")
print("number of intersections:")
n = int(input())
data = []
importance = []
for i in range(n):
global adj_list
print(
"input the nodes associated with current node and the congestion and importance of the road conncting them in the format \"road cogestion imortance\""
+ str(i + 1))
inp = input()
inp = inp.split()
lst = [int(j) for j in inp]
final_lst = []
index = 0
while index < len(lst):
final_lst.append([lst[index], lst[index + 1], lst[index + 2]])
index = index + 3
# print(final_lst)
adj_list.append(final_lst)
lst2 = [0] * (n)
lst_imp = [0] * (n)
for j in final_lst:
# print(j)
lst2[j[0] - 1] = j[1]
lst_imp[j[0] - 1] = j[2]
data.append(lst2)
importance.append(lst_imp)
data_max = {'n': n, 'congestion_graph': data, 'importance': importance}
dzn = pm.dict2dzn(data_max, fout='max_find.dzn')
sol_max = pm.minizinc('maximum_calculator.mzn', 'max_find.dzn')
max_cost = sol_max[0]['max_cost']
max_imp = sol_max[0]['max_imp']
print("max cost = ", max_cost)
print("max imp = ", max_imp)
max_turn = 25
print("how fast do you want the system to update signals?")
signal_period = int(input())
prev = [[0] * (n)] * (n)
data = {
'n': n,
'congestion_graph': data,
'max_cost': max_cost,
'max_turn': max_turn,
'prev': prev,
'importance': importance,
'max_imp': max_imp
}
# print(data)
dzn = pm.dict2dzn(data, fout='datafrompython.dzn')
dzn = pm.dict2dzn(data, fout='test_data0.dzn')
return [dzn, signal_period]
def update_cost(solns, dzn, period):
data = pm.dzn2dict('datafrompython.dzn')
decision = solns[0]['decision']
gr = data['congestion_graph']
rate_dec = 2
for i in range(len(adj_list)):
reduction = rate_dec * period
for j in range(len(adj_list)):
if decision[i][j] == 1:
if reduction > gr[i][j]:
reduction = gr[i][j]
gr[i][j] -= reduction
prev_queue.append([i, j])
if len(prev_queue) > 100:
prev_queue.popleft()
elif j in adj_list[i]:
gr[j][i] += reduction // 3
# print("congestion_graph")
# print(gr)
data['congestion_graph'] = gr
n = len(gr)
prev = [[0] * n for i in range(n)]
for pair in prev_queue:
prev[pair[0]][pair[1]] += 1
data['prev'] = prev
global iteration
iteration += 1
file_name = 'test_data' + str(iteration) + '.dzn'
dzn = pm.dict2dzn(data)
return dzn
def main() -> int:
'''
Sequential operation:
- load .dzn file
- process it and save it in a new file
- try to find solutions with MiniZinc and Gecode solver (optional)
- plot in a graph all path obtained for visual feedback (optional)
'''
data = load_dataset(args.dzn_path)
if not data:
raise Exception(f"an error occurred while loading the dataset")
data = process(data, args.dzn_path, args.order)
# Save processed data to {args.output}.dzn file
pymzn.dict2dzn(data, fout=args.output)
print("Preprocessing completed.")
if not args.solve:
return -1
if not file_exists(args.model, MZN_EXT):
return -1
print(f"Running {args.model} with {args.output}({args.dzn_path})...")
# Default solver is Gecode (pymzn.Solver)
sols = pymzn.minizinc(args.model,
args.output,
timeout=args.limit,
all_solutions=True)
if not sols:
print("No solutions found.")
return -1
path, vehicle = print_solution(sols)
if args.plot:
plot_path(data, path, vehicle)
return 0
def initialize():
#global adj_list
#adj_list=[]
print("initializing")
print(".......")
print(".......")
print("number of intersections:")
n = int(input())
data = []
for i in range(n):
# print("how many nodes associated with node "+str(i))
#m=input(i)
global adj_list
print(
"input the nodes associated with node and the density of the road conncting them "
+ str(i + 1))
inp = input()
inp = inp.split()
lst = [int(j) for j in inp]
final_lst = []
index = 0
while index < len(lst):
final_lst.append([lst[index], lst[index + 1]])
index = index + 2
print(final_lst)
adj_list.append(final_lst)
lst2 = [0] * (n)
for j in final_lst:
print(j)
lst2[j[0] - 1] = j[1]
# for j in range len(lst):
data.append(lst2)
max_cost = 40
max_turn = 25
print("how fast do you want the system to update signals?")
signal_period = int(input())
prev = [[0] * (n)] * (n)
data = {
'n': n,
'congestion_graph': data,
'max_cost': max_cost,
'max_turn': max_turn,
'prev': prev
}
print(data)
dzn = pm.dict2dzn(data, fout='datafrompython.dzn')
#pm.dict2dzn(dzn, fout='capacity.dzn')
return [dzn, signal_period]
def update_cost(solns, dzn, period):
data = pm.dzn2dict('datafrompython.dzn')
decision = solns[0]['decision']
gr = data['congestion_graph']
#print("decision")
#print(decision)
rate_dec = 2
for i in range(len(adj_list)):
reduction = 3 * period
for j in range(len(adj_list)):
if decision[i][j] == 1:
if reduction > gr[i][j]:
reduction = gr[i][j]
gr[i][j] -= reduction
prev_queue.append([i, j])
if len(prev_queue) > 100:
prev_queue.popleft()
elif j in adj_list[i]:
gr[j][i] += reduction // 3
print("congestion_graph")
print(gr)
data['congestion_graph'] = gr
n = len(gr)
prev = [[0] * n for i in range(n)]
#print("prev queue")
#print(prev_queue)
#(prev[0])[1]=-2
# print(prev)
#exit()
for pair in prev_queue:
# print(pair)
#print(pair[0])
#print(pair[1])
#prev[0][0]=1
prev[pair[0]][pair[1]] += 1
# print(prev)
data['prev'] = prev
# print("prev")
#print(prev)
dzn = pm.dict2dzn(data)
return dzn
def test_dzn(self):
dzn = pymzn.dict2dzn({'x': 1, 'y': 2})
self.assertIn('x = 1;', dzn)
self.assertIn('y = 2;', dzn)
def test_dzn(self):
dzn = pymzn.dict2dzn({'x': 1, 'y': 2})
self.assertIn('x = 1;', dzn)
self.assertIn('y = 2;', dzn)
# indicate non-edges for non-wrapping output images and similar
adj_grid[d_idx, y, x] = 1 + (offset_cell[0] +
(offset_cell[1] * shape[0]))
return adj_grid
solns = pymzn.minizinc('knapsack.mzn', 'knapsack.dzn', data={'capacity': 20})
print(solns)
pymzn.dict2dzn(
{
'w': 8,
'h': 8,
'pattern_count': 95,
'direction_count': 4,
'adjacency_count': 992,
'pattern_names': [],
"relation_matrix": [],
"adjaceny_table": [],
"adjacency_matrix": []
},
fout='test.dzn')
def mz_init(prestate):
prestate.adjacency_directions_rc = {
i: CoordRC(a.y, a.x)
for i, a in prestate.adjacency_directions.items()
}
prestate = wfc.wfc_utilities.find_pattern_center(prestate)
wfc_state = types.SimpleNamespace(wfc_ns=prestate)
import pymzn
pymzn.dict2dzn({
'a': 2,
'b': {4, 6},
'c': {1, 2, 3},
'd': {
3: 4.5,
4: 1.3
},
'e': [[1, 2], [3, 4], [5, 6]]
})
data = {
'a': 2,
'b': {4, 6},
'c': {1, 2, 3},
'd': {
3: 4.5,
4: 1.3
},
'e': [[1, 2], [3, 4], [5, 6]]
}
data["set_a"] = {"f1", "f2"}
pymzn.dict2dzn(data, fout='../../minizinc_labor/pymzn_test_data.dzn')
print(type(data))
def save_as_dzn(n, matrix):
data = {'n': n, 'matrix': matrix}
pymzn.dict2dzn(data, fout='tsp_{}.dzn'.format(n))
def run_experiment():
def write_csv(data):
with open('intermediate_results.csv', 'a') as outfile:
writer = csv.writer(outfile)
writer.writerow(data)
#basic process, (10,5,5)
#max process (10000,200,50)
activities_set = [100, 200, 300]
ratios = [5, 10]
#M=45
skills_set = [50, 100]
M = 0.95
utils = [0.5, 0.3, 0.1]
#for density in densities:
densities = range(1, 10, 4)
densities = [d / 10 for d in densities]
#densities = [0.5]
scenarios = [(a, int(a / r), s, d, u) for a in activities_set
for r in ratios for s in skills_set for d in densities
for u in utils]
string_scenarios = []
for s in scenarios:
string_scenarios.append(
str(s[0]) + '_' + str(s[1]) + '_' + str(s[2]) + '_' + str(s[3]) +
'_' + str(s[4]))
runtimes_first = []
runtimes_last = []
runtimes_proof = []
attempts = []
#machines out of total
objs = []
for j, s in enumerate(scenarios):
attempts.append(0)
infeasible_flag = True
while infeasible_flag == True:
attempts[len(attempts) - 1] += 1
print('Current scenario running: ' + string_scenarios[j])
n_A, n_R, n_S, density, util = s
try:
instance_data = gen_SBPR_Completely_Random(
n_A, n_R, n_S, False, density, M,
util) #M/n_R)#(n_R-1)/n_R) #0.5)
#instance_data2 = gen_GAP_Completely_Random(n_A,n_R,verbose=True)
except ValueError:
print("Error")
continue
#instance_data = gen_SBPR(n_A,n_R,n_S)
print(os.path.join("data_files", "SBPR_" + str(s) + ".dzn"))
pymzn.dict2dzn(instance_data,
fout=os.path.join("data_files",
"SBPR_" + str(s) + ".dzn"))
#pymzn.dict2dzn(instance_data2, fout="arik_Gap.dzn")
mysolver = MySolver()
print('start solving...')
mzn_sol = pymzn.minizinc("SBPR_problem.mzn",
data=instance_data,
solver=mysolver,
all_solutions=True)
#mzn_sol = pymzn.minizinc("GAP_v1.mzn", data= instance_data2, solver=mysolver, all_solutions = True)
#mzn_sol = pymzn.minizinc("arik-problem-v2.mzn", data= instance_data,
#solver =pymzn.Gecode(solver_id = 'gecode'))
# solver='chuffed')# solver = pymzn.Chuffed(solver_id='chuffed'))
print(mzn_sol)
if mzn_sol.status == Status.COMPLETE:
objs.append(
(int(mzn_sol._solns[0]['Z']), int(mzn_sol._solns[0]['L'])))
#objs.append(mzn_sol._solns[0]['Z'])
split_obj = mzn_sol.log.split('objective=')[1:]
runtimes_first.append(
float(
mzn_sol.log.split('objective=')[1:][0].split(
'solveTime=')[1].split('\n')[0]))
runtimes_last.append(
float(
mzn_sol.log.split('objective=')[1:][-2].split(
'solveTime=')[1].split('\n')[0]))
runtimes_proof.append(
float(
mzn_sol.log.split('objective=')[1:][-1].split(
'solveTime=')[1].split('\n')[0]))
write_csv([
s,
float(
mzn_sol.log.split('objective=')[1:][-1].split(
'solveTime=')[1].split('\n')[0])
])
print(runtimes_first[len(runtimes_first) - 1])
print(runtimes_proof[len(runtimes_proof) - 1])
#print('Time to optimal:')
#print(tm.time() - start_time)
#runtimes.append(tm.time() - start_time)
infeasible_flag = False
else:
print('Infeasible for scenario ' + str(s))
print('Retrying...')
dict_ = {
'scenarios': string_scenarios,
'objectives': objs,
'runtimes_first': runtimes_first,
'runtimes_last': runtimes_last,
'runrtimes_proof': runtimes_proof,
'attempts': attempts
}
res_df = pd.DataFrame(dict_)
print(res_df.head(10))
res_df.to_csv(os.path.join('results', 'results.csv'))
def export_dzn(self, file_name: str = None):
import pymzn
if file_name is None:
file_name = os.path.join(path_minizinc, "coloring_example_dzn.dzn")
pymzn.dict2dzn(self.dict_datas, fout=file_name)
print("Successfully dumped data file ", file_name)
import numpy as np
import pymzn as mz
np.random.seed(0)
################################################################################
# Define Sets
################################################################################
# Employee
num_employee = 80
Employee = np.char.add(np.full(num_employee, 'Employee'),
np.arange(num_employee).astype(str))
mz.dict2dzn({"emp": str(Employee)})
# Location
num_location = 4
Location = np.char.add(np.full(num_location, 'Location'),
np.arange(num_location).astype(str))
Location = Location.astype(set)
Location
# Shift
Shift = np.array(
['0816', '1018', '1220', '1420', '1620', '0812', '1014', '1216', '1418'])
num_shift = len(Shift)
# Skill
num_skill = 3
# indicate non-edges for non-wrapping output images and similar
adj_grid[d_idx, y, x] = 1 + (offset_cell[0] +
(offset_cell[1] * shape[0]))
return adj_grid
solns = pymzn.minizinc("knapsack.mzn", "knapsack.dzn", data={"capacity": 20})
print(solns)
pymzn.dict2dzn(
{
"w": 8,
"h": 8,
"pattern_count": 95,
"direction_count": 4,
"adjacency_count": 992,
"pattern_names": [],
"relation_matrix": [],
"adjaceny_table": [],
"adjacency_matrix": [],
},
fout="test.dzn",
)
def mz_init(prestate):
prestate.adjacency_directions_rc = {
i: CoordRC(a.y, a.x)
for i, a in prestate.adjacency_directions.items()
}
prestate = wfc.wfc_utilities.find_pattern_center(prestate)
wfc_state = types.SimpleNamespace(wfc_ns=prestate)
def exchange(self, agents, tasks, profits, initial_assignments, objective):
min_objective = int(objective * self.acceptance_ratio)
print('Objective: %d / Bound: %d' % (objective, min_objective))
candidates = set()
capacities = [0] + [a.capacity for a in agents]
profit_matrix, aff_mat, weight_matrix = matrizes(agents,
tasks,
pad_dummy_agent=True)
x = self.assignment_matrix(agents, tasks, initial_assignments)
initial_affinities = np.sum(aff_mat * x, axis=1, keepdims=True)
initial_profits = np.sum(profit_matrix * x, axis=1, keepdims=True)
aff_improv = (aff_mat - initial_affinities) * (aff_mat > 0)
aff_improv[:, [0]] -= initial_affinities
prof_diff = (profit_matrix - initial_profits) * (profit_matrix > 0)
prof_diff[:, [0]] -= initial_profits
delta_welfares = []
delta_profits = []
delta_weights = []
affected_agents = []
exchanged_tasks = []
# 1. Build a list of all potential, welfare-improving exchanges
for source_agent, affimp in enumerate(aff_improv.T):
# if source_agent == 0:
# continue # Don't initiate from non-assigned
pot_gains = affimp[affimp > 0]
task_ids = np.where(affimp > 0)[0] # row id
sorted_order = np.argsort(affimp[affimp > 0][::-1])
pot_gains = pot_gains[sorted_order]
task_ids = task_ids[sorted_order]
for dest_task, my_pot_gain in zip(task_ids, pot_gains):
dest_agent = np.where(x[dest_task, :])[0][0] # column id
source_offerings = x[:, source_agent]
dest_demand = aff_improv[:, dest_agent] > -my_pot_gain
dest_compatible = profit_matrix[:,
dest_agent] > 0 # Could also be aff_mat or weight_matrix
potential_exchanges = np.logical_and(source_offerings,
dest_demand)
potential_exchanges = np.logical_and(potential_exchanges,
dest_compatible)
for source_task in np.where(potential_exchanges)[0]:
welfare_improv = my_pot_gain + aff_improv[source_task,
dest_agent]
profit_change = prof_diff[
dest_task, source_agent] + prof_diff[source_task,
dest_agent]
assert (x[source_task, source_agent])
assert (x[dest_task, dest_agent])
assert (welfare_improv >= 0)
assert (
welfare_improv == aff_improv[source_task, dest_agent] +
aff_improv[dest_task, source_agent])
ex1 = (source_agent, source_task, dest_agent,
aff_improv[source_task,
dest_agent], prof_diff[source_task,
dest_agent],
(-weight_matrix[source_task, source_agent],
weight_matrix[source_task, dest_agent]))
ex2 = (dest_agent, dest_task, source_agent,
aff_improv[dest_task,
source_agent], prof_diff[dest_task,
source_agent],
(-weight_matrix[dest_task, dest_agent],
weight_matrix[dest_task, source_agent]))
candidates.add(
((-welfare_improv, -profit_change), ex1, ex2))
for _, ex1, ex2 in sorted(candidates, key=lambda trans: trans[0]):
delta_welfares.append(ex1[3])
delta_welfares.append(ex2[3])
delta_profits.append(ex1[4])
delta_profits.append(ex2[4])
delta_weights.append(list(ex1[5]))
delta_weights.append(list(ex2[5]))
affected_agents.append([source_agent + 1, dest_agent + 1])
affected_agents.append([dest_agent + 1, source_agent + 1])
exchanged_tasks.append(source_task)
exchanged_tasks.append(dest_task)
weight_budget = np.array(capacities) - np.sum(weight_matrix * x,
axis=0)
assert (len(delta_welfares) == len(candidates) * 2)
assert (len(weight_budget) == len(agents) + 1)
data = {
'n_agents': len(agents) + 1,
'n_tasks': len(tasks),
'n_exchanges': len(delta_welfares),
'profit_budget': objective - min_objective,
'weight_budget': weight_budget,
'delta_welfares': delta_welfares,
'delta_profits': delta_profits,
'delta_weights': delta_weights,
'agents': affected_agents,
'task_ids': exchanged_tasks
}
print('Exchanges: %d' % len(delta_welfares))
if len(delta_welfares) > 0:
pymzn.dict2dzn(data, fout='neg1.dzn')
start = time.time()
output = pymzn.minizinc('negotiation.mzn',
solver='gecode',
data=data,
timeout=30) # Not a MIP problem
duration = time.time() - start
sel_exchanges = output[0]['assignment']
affinity_improvement = output[0]['objective']
nb_exchanges = np.count_nonzero(sel_exchanges)
print('Applied Exchanges: %d / Improvement: %d / Time: %d' %
(nb_exchanges, affinity_improvement, duration))
if nb_exchanges == 0:
return initial_assignments, objective
for ex_id in np.where(sel_exchanges)[0]:
task_id = exchanged_tasks[ex_id]
source_agent, dest_agent = affected_agents[ex_id]
source_agent, dest_agent = source_agent - 1, dest_agent - 1
assert (x[task_id, source_agent])
assert (not x[task_id, dest_agent])
x[task_id, source_agent] = 0
x[task_id, dest_agent] = 1
new_assignments = self.assignment_mat_to_dict(agents, tasks, x)
new_objective = np.sum(profit_matrix * x)
assert (np.sum(aff_mat * x) == (np.sum(initial_affinities) +
affinity_improvement))
assert (np.all(np.count_nonzero(x, axis=1) == 1))
assert (np.all(np.sum(weight_matrix * x, axis=0) <= capacities))
assert (new_objective >= min_objective)
return new_assignments, new_objective
else:
return initial_assignments, objective
