def todo(files):
coords = []
with open(files, 'r') as f:
i = 0
for line in f.readlines():
line = [float(x.replace('\n', '')) for x in line.split(' ')]
coords.append([])
for j in range(1, 3):
coords[i].append(line[j])
i += 1
if __name__ == '__main__':
#coords = [[round(random.uniform(-1000,1000),4),round(random.uniform(-1000,1000),4)] for i in range(100)]
sa = SimAnneal(coords, stopping_iter=5000)
sa.anneal()
sa.visualize_routes()
sa.plot_learning()
from anneal import SimAnneal
from visualize_tsp import plotTSP
import matplotlib.pyplot as plt
import random, os, sys, time
def dotrial(t):
print("doing %d" % t.id)
t.anneal()
return t
if __name__ == '__main__':
ncities=100
ntrials=int(sys.argv[1])
random.seed(0)
coords = [[round(random.uniform(-1000,1000),4),round(random.uniform(-1000,1000),4)] for i in range(ncities)]
plotTSP([range(ncities),], coords)
t0=time.time()
trials = [SimAnneal(coords, stopping_iter = 500000, alpha=0.9995, id=i) for i in range(ntrials)]
got=map(dotrial, trials)
best=sorted([(t.best_fitness, t) for t in got])[0][1]
print("Time %f, Fitness %f" % (time.time()-t0, best.best_fitness))
best.visualize_routes()
best.plot_learning()
test_loader = InstanceLoader("test")
with open('TSP-closest-anneal-log.dat', 'w') as logfile:
print("inst_i\tinst_size\tclosest_fitness\tsa_fitness\tsa_iter",
file=logfile)
# Run for a number of epochs
for epoch_i in range(1):
sa_acc = 0
cn_acc = 0
print("Testing model...", flush=True)
for (inst_i, inst) in enumerate(
test_loader.get_instances(len(test_loader.filenames))):
_, Mw, _ = inst
Mw = np.round(Mw * bins)
sa = SimAnneal(Mw)
cn_acc += sa.best_fitness
print("{inst_i}\t{inst_size}\t{closest_fitness}\t".format(
inst_i=inst_i,
inst_size=Mw.shape[0],
closest_fitness=sa.best_fitness),
end="",
file=logfile)
sa.anneal()
sa_acc += sa.best_fitness
print("{sa_fitness}\t{sa_iter}".format(
sa_fitness=sa.best_fitness, sa_iter=sa.iteration),
file=logfile)
#end
cn_acc /= inst_i
from anneal import SimAnneal
import matplotlib.pyplot as plt
import random
coords = []
with open('coord.txt', 'r') as f:
i = 0
for line in f.readlines():
line = [float(x.replace('\n', '')) for x in line.split(' ')]
coords.append([])
for j in range(1, 3):
coords[i].append(line[j])
i += 1
if __name__ == '__main__':
#coords = [[round(random.uniform(-1000,1000),4),round(random.uniform(-1000,1000),4)] for i in range(100)]
sa = SimAnneal(coords, stopping_iter=5000)
sa.anneal()
sa.visualize_routes()
sa.plot_learning()
def dotrial(t):
print("doing %d" % t.id)
random.seed(t)
t.anneal()
return t
if __name__ == '__main__':
t0 = time.time()
ncities = 100
ntrials = int(sys.argv[1])
random.seed(0)
coords = [[
round(random.uniform(-1000, 1000), 4),
round(random.uniform(-1000, 1000), 4)
] for i in range(ncities)]
p = Pool(int(os.getenv("SLURM_CPUS_PER_TASK",
"3"))) # get cpus from slurm, default to 3
trials = [
SimAnneal(coords, stopping_iter=500000, alpha=0.9995, id=i)
for i in range(ntrials)
]
got = p.map(dotrial, trials)
best = sorted([(t.best_fitness, t) for t in got])[0][1]
print("Time %f, Fitness %f" % (time.time() - t0, best.best_fitness))
best.visualize_routes()
best.plot_learning()
data = data + adjust
"""
# specify document name
file_name = "multivar_data_nb.txt"
all_data = np.loadtxt(file_name, delimiter=',')
# last column of data contains the classes for each input vector
data = all_data[:, :-1]
"""
# get the parameters
training_length, num_properties = data.shape
properties = {
"reservoir_size": 20,
"training_length": training_length,
"num_properties": num_properties,
"num_classes": 2
}
if __name__ == "__main__":
sa = SimAnneal(data, properties, stopping_iter=5000)
sa.anneal()
plt.figure()
sa.plot_best()
plt.show()
plt.figure()
from anneal import SimAnneal
import matplotlib.pyplot as plt
import random
coords = []
with open('coord.txt', 'r') as f:
i = 0
for line in f.readlines():
line = [float(x.replace('\n', '')) for x in line.split(' ')]
coords.append([])
for j in range(1, 3):
coords[i].append(line[j])
i += 1
if __name__ == '__main__':
#coords = [[round(random.uniform(-1000,1000),4),round(random.uniform(-1000,1000),4)] for i in range(100)]
sa = SimAnneal(coords, stopping_iter=5000)
sa.Anneal()
sa.visualizeRotes()
sa.plotLearning()
from anneal import SimAnneal
import matplotlib.pyplot as plt
import random
import drawing
cards = []
vehicles = []
with open("data2.txt", "r") as f:
ncard = int(f.readline().replace("\n", ""))
for i in range(ncard):
line = f.readline()
line = [float(x.replace("\n", "")) for x in line.split(" ")]
cards.append(line)
print("cards = ", cards)
nvehicle = int(f.readline().replace("\n", ""))
for i in range(nvehicle):
line = f.readline()
line = [float(x.replace("\n", "")) for x in line.split(" ")]
vehicles.append(line)
print("vehicles = ", vehicles)
if __name__ == "__main__":
# coords = [[random.uniform(-1000, 1000), random.uniform(-1000, 1000)] for i in range(100)]
sa = SimAnneal(cards, vehicles, stopping_iter=5000)
if sa.anneal() == True:
# sa.visualize_routes()
drawing.draw_simulate(sa.cards, sa.vehicles, sa.best_solution)
sa.plot_learning()
# print(partition[1])
partial_dm = gmaps.distance_matrix(
[vertex[v]['geometry']['location'] for v in partition[0]],
[vertex[v]['geometry']['location'] for v in partition[1]])
# pprint.pprint(partial_dm)
storeDataFrame(partial_dm, partition[0], partition[1], dfDist, dfTime)
time.sleep(3)
dfDist.to_csv('dfDist.csv')
dfTime.to_csv('dfTime.csv')
city_list = []
for idx, v in enumerate(vertex.keys()):
loc = vertex[v]['geometry']['location']
city_list.append([loc['lat'], loc['lng']])
pprint.pprint(city_list)
s****t = SimAnneal(coords=city_list,
dist=dfDist.to_numpy(),
alpha=0.999,
stopping_iter=1000000)
s****t.batch_anneal(times=30)
solidxs = s****t.best_solution
startidx = list(vertex.keys()).index(starting_vertex['place_id'])
solidxs = solidxs[solidxs.index(startidx):] + solidxs[0:solidxs.
index(startidx)]
solidxs.append(solidxs[0])
print(f'https://www.google.com/maps/dir/' +
('/').join([(',').join([str(s) for s in city_list[idx]])
for idx in solidxs]))
print(f'https://maps.openrouteservice.org/directions?' + 'n1=' +
str(city_list[solidxs[0]][0]) + '&n2=' +
str(city_list[solidxs[0]][1]) + '&n3=13' + '&a=' +
','.join(','.join([str(s) for s in city_list[idx]])
for idx in solidxs) + '&b=0&c=0&k1=en-US&k2=km')
k=1
while k <= n:
rand_pt=round(random.uniform(1, 100),4)
ptime=np.append(ptime,rand_pt)
k+=1
'''
fitness_list = ()
matrix = np.vstack((job_number, ptime)) #將兩個矩陣合併
i = 1
while i <= 5:
if __name__ == '__main__':
sa = SimAnneal(matrix,
alpha=0.95,
T=100,
stopping_iter=100,
stopping_T=0.0001) #
sa.anneal()
sa.spt()
sa.plot_learning()
each_gap_fitness = round(
(sa.best_fitness - sa.spt()) / sa.best_fitness, 8)
print('Each Time Gap', each_gap_fitness * 100, '%')
print('')
fitness_list = np.append(fitness_list, sa.best_fitness)
i += 1
# print(fitness_list)
print(sa.T)
print(sa.iteration)
mean_fitness = sum(fitness_list) / (i - 1)
