def take_samples(G, C, M, k, p, num_samples):
low_g, up_g = 0, 2 * pi
low_b, up_b = 0, pi / 2
best_sol = common.brute_force(G, k)
samples = []
for i in range(num_samples):
angles = [
random.uniform(low_g, up_g) if j < p else random.uniform(
low_b, up_b) for j in range(2 * p)
]
value = -qaoa(angles, G, C, M, k, p)
samples.append(value / best_sol)
if (i + 1) % 100 == 0: print('\ti: ' + str(i+1) + '\tavg: ' + str(np.average(samples)) \
+ '\tstd: ' + str(np.std(samples)) + '\terr: ' + str(1.96*np.std(samples)/np.sqrt(i+1)) \
+ '\tstd_err: ' + str(np.sqrt(1.96*np.sqrt(2/num_samples)*np.std(samples)**2)))
return samples
return best
if __name__ == '__main__':
#indices = np.linspace(rank*s_per_rank, (rank+1)*s_per_rank-1, s_per_rank)
gi = 91
s_per_rank = 100
z = 2.576 # z* for 99% confidence interval
max_p = 6
max_exp, max_std, error = [], [], []
for p in range(1, max_p + 1):
# do work over indices
rank_exp = work(gi, p, s_per_rank)
# gather best exp
data = None
if rank == 0: data = np.empty(size, dtype='d')
comm.Gather(rank_exp, data, root=0)
if rank == 0:
best = common.brute_force(gi)
data = [x / best for x in data]
max_exp.append(np.average(data))
max_std.append(np.std(data))
error.append(z * np.std(data) / np.sqrt(size))
pickle.dump([
gi, [i + 1 for i in range(p)], max_exp, max_std, error,
s_per_rank, size
], open('data/' + str(gi) + '.complete', 'wb'))
disp=False,
niter_success=10)
if -optimal.fun > best_exp:
best_exp = -optimal.fun
best_angles = optimal.x
return best_exp, best_angles
if __name__ == '__main__':
random.seed(1)
seed = random.randint(1, 1000) + rank
random.seed(seed)
num_nodes = 6
k = int(num_nodes / 2)
G = nx.fast_gnp_random_graph(num_nodes, 0.5)
while not nx.is_connected(G):
G = nx.fast_gnp_random_graph(num_nodes, 0.5)
C = common.create_C(G, k)
M = common.create_complete_M(num_nodes, k)
best_sol = common.brute_force(G, k)
all_exps, all_angles = [], []
for p in range(3, 9):
rank_exp, rank_angles = get_angles(G, C, M, k, p)
all_exps.append(rank_exp / best_sol)
all_angles.append(rank_angles)
pickle.dump([all_exps, all_angles],
open('interpolation/complete-6/' + str(seed) + '.seed',
'wb'))
disp=False)
data.append(-optimal.fun)
return data
if __name__ == '__main__':
random.seed(10 + rank)
gi = random.randint(163, 955)
print('aquired gi: ' + str(gi) + ' from: ' + str(rank))
s_per_rank = 2
max_p = 2
z = 2.576 # z* for 99% confidence interval
max_exp, max_std, error = [], [], []
for p in range(1, max_p + 1):
# do work over indices
best_exps = work(gi, p, s_per_rank)
print(best_exps)
brute = common.brute_force(gi)
best = np.average(best_exps)
std = np.std(best_exps)
approx = best / brute
max_exp.append(approx)
error.append(z * np.std(best_exps) / np.sqrt(s_per_rank))
print('gi: ' + str(gi) + ', max_exp: ' + str(max_exp) + ', error: ' +
str(error))
pickle.dump([max_exp, error],
open('init-complete/' + str(gi) + '.mpi', 'wb'))
#pickle.dump([gi, [i+1 for i in range(p)], max_exp, max_std, error, s_per_rank, size], open('data/' + str(gi) + '.mpi-k', 'wb'))
