def test_some_sequences(self):
data = np.load('dp_solver.npy', allow_pickle=True)[:100]
problem = load_problem(T=6)
for i in data:
d = i[0]
seq = i[1:].astype(int)
solver = solve_sequence(problem=problem, seq=seq)
self.assertTrue(solver.result.feasible)
self.assertAlmostEqual(d, solver.result.distance)
def repopulate_queue(Q, solutions):
rand = np.random.permutation(len(solutions))
old_pop = [solutions[rand[i]] for i in range(10)]
problem = load_problem()
for s in old_pop:
if s:
solver = solve_sequence(problem, s[-1])
if solver.feasible:
res = sliding_window(2,
1,
solver,
return_first=False,
restrict_available=False)
if res.feasible:
Q.append(res)
def test_distance(self):
# np random seed = 10
data = np.load('level_order_solver.npy', allow_pickle=True)
problem = load_problem(T=6)
truncation_args = {'limit': 1000, 'method': "distance"}
optimizer = HeuristicTreeSolver(problem=problem,
max_height=10,
truncation_args=truncation_args)
results = optimizer.solve()
for i, j in enumerate(data):
d = j[0]
seq = j[1:][0]
solver = results['solvers'][i]
self.assertTrue(solver.result.feasible)
self.assertAlmostEqual(d, solver.result.distance)
self.assertEqual(seq, solver.result.seq)
res = solve(1, problem)
else:
S = transition(pop)
if S is not None:
res = solve(n_ships, problem, sampling=S)
else:
break
results.append(res.opt.data["solver"][0])
_X, _F, pop = res.X, res.F[0], res.pop
# prepare for the next iteration
I, X, F = pop.get("feasible", "X", "F")
if verbose:
print(n_ships, _F, _X.tolist(), res.algorithm.n_gen, len(I))
if len(I) == 0:
break
pop = pop[I[:, 0]]
n_ships += 1
return {"solvers": results}
if __name__ == "__main__":
np.random.seed(1)
res = solve_level_ga(load_problem(6), verbose=True)
print(res['solvers'])
Q, bd, bs = self._next(current=current, Q=Q, depth=depth)
if bd <= best_distance:
best_distance = bd
best_solver = bs
self.current_best = bs
# ------------------------------------------------------------------------------------
result = {}
result["results"] = self.best_distances
result["solvers"] = self.best_solvers
result["selected"] = selected
return result
if __name__ == "__main__":
# Create sequence solver object
P = load_problem(T=6)
truncation_args = {'limit': 1000, 'method': "distance"}
SeqSolver = HeuristicTreeSolver(problem=P,
max_depth=10,
truncation_args=truncation_args)
# truncation_args = {'limit': 1000, 'method': "decomposition", 'w': 0.306}
start = time.time()
result = SeqSolver.solve()
end = time.time()
print(f"{end - start}")
def sliding_window(insert, remove, S, return_first=False, restrict_available=False):
"""
:param insert: Number of ships to expand by
:param remove: Number of ships to remove
:param S: DPSolver instance
:return:
"""
success = 0
fail = 0
best_dist = 1e10
best_solver = None
# Algorithm works on gaps between ships not ships themselves, hence remove += 1
remove += 1
problem = load_problem()
if isinstance(S, list):
_solver = solve_sequence(problem=problem, seq=S)
elif isinstance(S, DPSolver):
_solver = copy.copy(S)
_solver.get_result()
else:
print("FAIL SLIDING WINDOW")
return
sequence, schedule, distances = _solver.seq[:], _solver.result.schedule[:], [min(s.distances) for s in _solver.states]
for i in range(len(sequence) - remove):
window = sequence[i+1:i + remove]
start = min(S.states[i].schedule) # Minimum feasible available time from the ship states before the window
end = problem.get_ship(window[-1]).times[1] # Maximum available time for the ship in WA following the window
avail = problem.ships_in_working_area(start=start, end=end)
avail = set(avail) - set(sequence[:i+1] + sequence[i + remove:])
if 0 in avail:
avail.remove(0)
sub_seq = list(itertools.permutations(avail, insert))
if restrict_available:
sub_seq = [s for s in sub_seq if len(set(sequence[i + 1:i + remove]) - set(s)) == 0]
candidates = [] # Candidate solutions for full evaluation
for ship in sub_seq:
solver = copy.copy(_solver)
solver.get_result()
# Update the solver to the correct transition point
while len(solver.seq) != len(sequence[:i+1]):
solver.pop_state()
# Evaluate the transition with the new permutation
skip = False
for l, j in enumerate(ship):
b = solver.next(j)
new_sched = solver.states[-1].schedule
new_dist = min(solver.states[-1].distances) if len(solver.states[-1].distances) else 1e6
# If the new schedule has less possible transitions or the new minimum distance is greate
if len(new_sched) < len(schedule) or new_dist > distances[i+1+l]:
skip = True
break
if skip: # Move to next permutation
fail += 1
continue
else: # Update candidate permutations based on distance
success += 1
heapq.heappush(candidates, (min(solver.states[-1].distances), copy.copy(solver)))
# Evaluate all candidate solutions
for c in range(len(candidates)):
d, sver = heapq.heappop(candidates)
for s in sequence[i + remove:]:
sver.next(s)
result = sver.get_result()
if result.feasible:
if return_first:
return sver
if result.distance < best_dist:
best_dist = result.distance
best_solver = sver
return best_solver
intermediate_results = sorted(intermediate_results, key=lambda x: x.dist)
solvers[length] = intermediate_results[0]
solver = solvers[length]
if __name__ == "__main__":
from dsp.Problem import Problem, load_problem
from dsp.Solver import DPSolver
from dsp.HTSolver import HeuristicTreeSolver
import time
import pprint as pp
# Create sequence solver object
P = load_problem()
truncation_args = {'limit': 100, 'method': "distance"}
# ALPHA = set(P.ships_in_working_area())
# SeqSolver = SequenceSolver(problem=P, height_limit=5)
# result = SeqSolver.sequence_search(available=ALPHA, truncation_args=truncation_args)
sol = solve_sequence(problem=P, seq=[0, 32, 30, 63, 12, 0])
# sol2 = solve_sequence(problem=P, seq=[0, 32, 30, 63, 12, 0])
sol2 = solve_sequence(problem=P, seq=[0, 8, 5, 30, 63, 12, 0])
# start = time.time()
# result = old_sliding_window(1, 1, sol)
# end = time.time()
# print(f"Old Result 2 Time: {end-start}")
# print(f"Old Result: {result}")
#
# start = time.time()
