def test(path, mode, **kwargs):
if mode == "gan":
samplefiles = utils.parse_file([kwargs["samples"]], ext="h5")
trainfiles = utils.parse_file(path, ext="pth")
agent = gan.GAN(
lr=PARAMS["GAN"]["lr"],
x_size=PARAMS["GAN"]["x_size"],
u_size=PARAMS["GAN"]["u_size"],
z_size=PARAMS["GAN"]["z_size"],
)
for trainfile in trainfiles:
agent.load(trainfile)
agent.eval()
logger = logging.Logger(path="data/tmp.h5", max_len=500)
dataloader = gan.get_dataloader(samplefiles, shuffle=False)
for i, (state, action) in enumerate(tqdm.tqdm(dataloader)):
fake_action = agent.get_action(state)
state, action = map(torch.squeeze, (state, action))
fake_action = fake_action.ravel()
logger.record(state=state,
action=action,
fake_action=fake_action)
logger.close()
print(f"Test data is saved in {logger.path}")
def cpu_temp_get():
temp_vals = parse_file(path_temp, re_temp)
temp = '--'
try:
temp = '%02d%s' % (int(temp_vals['temp'][0]), temp_vals['unit'][0])
except Exception, e:
temp = 'error: %s,' % e
def pepperoni(source, d, backup, write, rules):
"""
Pepperoni will read the python file SOURCE and modify it to match PEP008 standards
"""
if source:
with open(source, "rb") as fh:
data = fh.readlines()
if data:
corrected = utils.parse_file(data, rwd=rules)
if d:
dest = d
else:
if backup:
dest = utils.bak_file(source)
else:
dest = source
if write:
with open(source, "wb") as fh:
fh.writelines(data)
with open(dest, "wb") as fh:
fh.writelines(corrected)
else:
sys.stderr(corrected)
else:
print "Warning: No python file passed. Nothing was changed."
def test():
input_path = 'mp1_ec/inputs/' + 'bigDots' + '.txt'
maze, states, pacman, dots = parse_file(input_path)
print("Now searching: {0}.txt".format('bigDots'))
results = get_distances(states, dots)
for a in results:
print(a, results[a])
def cpu_freq_get():
cpu_vals = parse_file(path_cpuinfo, re_cpu)
cpu = '--'
try:
cpu = '/'.join(map(mhz_to_ghz, cpu_vals['mhz']))
except Exception, e:
cpu = 'error: %s,' % e
def part1_2():
for file_name in FILE_NAMES_2:
input_path = 'mp1.2/inputs/' + file_name + '.txt'
maze, states, pacman, dots = parse_file(input_path)
print("Now searching: {0}.txt".format(file_name))
num_nodes_expanded, sol, order = astar_ec(states, pacman, dots)
output_path = 'mp1.2/outputs/' + file_name + '_sol_astar_multiple.txt'
print_sol_multiple(output_path, maze, sol, order, num_nodes_expanded)
def solve():
(days, books_num, libraries_num, books_scores, num_of_books_in_library,
signup_time_for_library, books_per_day_from_lib,
book_ids_for_library) = parse_file("inputs/d_tough_choices.txt")
sorted_libs = np.flip(np.argsort([len(x) for x in book_ids_for_library]))
sorted_books = book_ids_for_library[sorted_libs]
return (sorted_libs, sorted_books, num_of_books_in_library,
books_per_day_from_lib, days, signup_time_for_library)
def test_parse_file():
content = parse_file('test1.txt')
control = get_file_content('test3.txt')
actual_content = "\n".join(content)
error_msg = error_msg_tpl % (actual_content, control)
assert actual_content == control, error_msg
def test_circular_ref():
content = parse_file('test4.txt')
actual_content = "\n".join(content)
control = get_file_content('test5.txt')
error_msg = error_msg_tpl % (actual_content, control)
assert actual_content == control, error_msg
def part1_ec():
input_path = 'mp1_ec/inputs/bigDots.txt'
output_path = 'mp1_ec/outputs/bigDots_sol.txt'
#input_path = 'mp1.2/inputs/' + 'mediumSearch' + '.txt'
maze, states, pacman, dots = parse_file(input_path)
num_expanded, path, order, win = astar_ec_anim(states, pacman, dots, maze)
draw_sol(win, path, order)
print(num_expanded, len(path))
async def create_group(request):
data = await request.json()
employees = parse_file(data['file'])
async with request.app['db_pool'].acquire() as conn:
db_group = deserialize_json(await db.get_last_group(conn))
# print('CALLED create_group()')
new_group = create_new_group(*db_group, employees)
await db.create_data(conn, new_group)
return web.json_response(employees)
def main():
try:
in_file_name = sys.argv[1]
except Exception:
sys.stderr.write('Usage: python scc.py ${in_file_name}')
graph = parse_file(in_file_name)
result = scc(graph)
def get_image_with_bbox(attrs):
images = parse_file(config.get('deepfashion', 'attributes_file'),
val_type=int, key_item_id=None, validate_fields=False)
attrs = parse_attr(attrs, _PREDEFINED_ATTR)
filtered = filter_items(images, attrs)
image_files = append_path(config.get('deepfashion', 'image_dir'), filtered, key='image_name')
boxes = bbox(filtered)
return image_files, boxes
def part1_1():
for file_name in FILE_NAMES_1:
input_path = 'mp1.1/inputs/' + file_name + '.txt'
print("Now searching: {0}.txt".format(file_name))
maze, states, pacman, dots = parse_file(input_path)
for search in SEARCHES_1:
visited, num_nodes_expanded = search(states, pacman, dots[0])
sol = visited_to_path(visited, dots[0])
output_path = 'mp1.1/outputs/' + file_name + '_sol_' + search.__name__ + '.txt'
print_sol(output_path, maze, sol, num_nodes_expanded)
def matching_helper(f1,
f2,
lang,
ins=None,
args=None,
entryfnc="main",
datadir="data"):
f1 = get_full_data_filename(f1, reldir=datadir)
f2 = get_full_data_filename(f2, reldir=datadir)
parser = getlangparser(lang)
inter = getlanginter(lang)
m1 = parse_file(f1, parser)
m2 = parse_file(f2, parser)
M = Matching(ignoreio=not ins, ignoreret=not args)
return not not M.match_programs(
m1, m2, inter, ins=ins, args=args, entryfnc=entryfnc)
def minimum_spanning_tree(input='', starting_vert=0, verbose=True):
"""Parse the input file (utils.parse_file) and runs the algorithm using
the edges and the vertices obtained from parsing. Returns the edges list
of a minimum spanning tree (there may be multiples MST).
Args:
input (str): Input file path (.txt format)
starting_vert (int, optional): Source vertex key
verbose (bool, optional): Whether, or not, it should print the result
Returns:
list: Chosen edges with new values for 'value' and 'parent_key'
Raises:
FileNotFoundError: Raise when input file is not found
"""
try:
vertices, edges = parse_file(input, verbose)
except FileNotFoundError as e:
raise e
# Forest array used for avoiding cyclic trees
forests = [i for i in range(len(vertices))]
chosen_edges = []
sorted_edges = sorted(edges, key=lambda x: x.weight)
if verbose:
print('\nForest array: ', forests, end='\n\n')
print('#### SORTED EDGES ####', *sorted_edges, sep='\n')
for edge in sorted_edges:
# If both vertices are not in the same forest
if forests[edge.vert_pair[0].key] != forests[edge.vert_pair[1].key]:
chosen_edges.append(edge)
# Unite both forests, updating each one of its vertex's forest index to the first vertex's forest index
first_forest_val = forests[edge.vert_pair[0].key]
second_forest_val = forests[edge.vert_pair[1].key]
for i, forest in enumerate(forests):
if forest in [first_forest_val, second_forest_val]:
forests[i] = first_forest_val
if verbose:
print('\nForest array: ', forests, end='\n\n')
print('#### CHOSEN EDGES ####')
for e in chosen_edges:
print(
f'Weight: {e.weight} \tVertices: {e.vert_pair[0].key}-{e.vert_pair[1].key}'
)
return chosen_edges
def process_inifile(inifile):
width_size = float(inifile.split("geometry_")[1].split("_")[0])
trajfile = "trajectories/traj" + inifile.split("ini")[2]
if not path.exists(trajfile):
logging.critical("trajfile <%s> does not exist"%trajfile)
exit(FAILURE)
fps, N, traj = parse_file(trajfile)
name = "times" + inifile.split("ini")[2]
J = rolling_flow(fps, N, traj, 61, name)
return [N, width_size, J]
def solve_c():
(days, books_num, libraries_num, books_scores, num_of_books_in_library, signup_time_for_library,
books_per_day_from_lib, book_ids_for_library) = parse_file("inputs/c_incunabula.txt")
res = parse("inputs/c_incunabula.txt")
# Sort by (total_book_score/signup_time), then do greedy
# print (initial_library_scores(res))
scores = initial_library_scores(res)
sorted_libs = np.flip(np.argsort([score[0] for score in scores]))
sorted_books = book_ids_for_library[sorted_libs]
return (sorted_libs, sorted_books , num_of_books_in_library, books_per_day_from_lib,
days, signup_time_for_library)
def process_inifile(inifile):
width_size = float(inifile.split("geometry_")[1].split("_")[0])
trajfile = "trajectories/traj" + inifile.split("ini")[2]
if not path.exists(trajfile):
logging.critical("trajfile <%s> does not exist" % trajfile)
exit(FAILURE)
fps, N, traj = parse_file(trajfile)
name = "times" + inifile.split("ini")[2]
J = rolling_flow(fps, N, traj, 61, name)
return [N, width_size, J]
def test_ignore_lines():
ignore_lines = [
"^\#.*",
]
content = parse_file('test6.txt', ignore_lines=ignore_lines)
actual_content = "\n".join(content)
control = get_file_content('test7.txt')
error_msg = error_msg_tpl % (actual_content, control)
assert actual_content == control, error_msg
def main():
try:
sm = sys.argv[1].lower()
file_to_read = sys.argv[2]
file_to_write = sys.argv[3]
except IndexError:
print(
"Enter valid command arguments !Usage : python bw.py <method> <problem file> <solution file>"
)
exit(0)
data_folder = os.path.join("input_files")
file_to_open = os.path.join(data_folder, file_to_read)
try:
with open(file_to_open, 'r') as f:
objects, begin_config, goal_config = parse_file(f)
initial_state = BlockState(begin_config, len(begin_config),
objects)
if sm == "breadth":
state, nodes, max_depth, running_time = s.bfs_search(
initial_state, goal_config)
elif sm == "depth":
state, nodes, max_depth, running_time = s.dfs_search(
initial_state, goal_config)
elif sm == "best":
state, nodes, max_depth, running_time = s.best_first_search(
initial_state, goal_config)
elif sm == "astar":
state, nodes, max_depth, running_time = s.a_star_search(
initial_state, goal_config)
else:
print(
"Enter valid command arguments !Usage : python bw.py <method> <problem file> <solution file>"
)
exit(0)
moves = s.calculate_path_to_goal(state)
write_in_file(moves, file_to_write)
print("cost_of_path:", state.cost)
print("nodes_expanded:", nodes)
print("max_search_depth:", max_depth)
print("running_time:", running_time)
valid = s.is_valid(initial_state, moves, goal_config)
if valid:
print('valid_solution: true')
else:
print('valid_solution: false')
except EnvironmentError:
print("File not found!")
def solve_b():
(days, books_num, libraries_num, books_scores, num_of_books_in_library,
signup_time_for_library, books_per_day_from_lib,
book_ids_for_library) = utils.parse_file("inputs/b_read_on.txt")
sorted_libs = np.argsort(signup_time_for_library)
sorted_books = book_ids_for_library[sorted_libs]
# print(sorted_libs)
# print(signup_time_for_library[sorted_libs])
return (sorted_libs, sorted_books, num_of_books_in_library,
books_per_day_from_lib, days, signup_time_for_library)
def main():
events.extend(parse_file("Files/Trading/"))
events.extend(parse_file("Files/Deposits/"))
events.extend(parse_file("Files/Buy/"))
events.extend(parse_file("Files/Staking/"))
events.extend(parse_file("Files/Conversions/"))
events.extend(parse_file("Files/Airdrops/"))
generate_file(events)
def minimum_spanning_tree(input='', starting_vert=0, verbose=True):
"""Parse the input file (utils.parse_file) and runs the algorithm using
the edges and the vertices obtained from parsing. Returns the modified
vertices list ('value' and 'parent_key' have new values).
Args:
input (str): Input file path (.txt format)
starting_vert (int, optional): Source vertex key
verbose (bool, optional): Whether, or not, it should print the result
Returns:
list: Chosen edges with new values for 'value' and 'parent_key'
Raises:
FileNotFoundError: Raise when input file is not found
"""
try:
vertices, edges = parse_file(input, verbose)
except FileNotFoundError as e:
raise e
# settings
vertices[starting_vert].value = 0
queue = vertices[:] # temporary vertex queue list
chosen_vertices = []
while len(queue):
# queue = sorted(queue, key=lambda v: v.value)
min_heapify(queue)
vertex = queue.pop(0)
chosen_vertices.append(vertex)
next_edges = search_vertex_edges(edges=edges,
vertex_key=vertex.key,
excluded_verts=chosen_vertices)
# checking the adjacent new edges
for edge in next_edges:
other_vertex = edge.get_other_vertex(vertex.key)
if edge.weight < other_vertex.value:
other_vertex.parent_key = vertex.key
other_vertex.value = edge.weight
if verbose:
print_vertex_tree(chosen_vertices)
return chosen_vertices
def test_list_comp():
f = get_full_data_filename("comp.py")
parser = getlangparser("py")
inter = getlanginter("py")
m = parse_file(f, parser)
inter = inter(entryfnc="main")
ios = [([], []), ([1], [2]), ([1, 2, 3], [2, 3, 4])]
retvar = prime(VAR_RET)
for i, o in ios:
trace = inter.run(m, args=[i])
print(trace)
value = trace[-1][2][retvar]
assert value == o
def get_mem_stat():
stat = parse_file(path_meminfo, re_all)
mem = dict([(k, get_int(v)) for k, v in stat.items()])
mem_free = mem['free'] + mem['buffers'] + mem['cached']
mem_used = mem['total'] - mem_free
mem_usage = mem_used / mem['total'] * 100.0
if mem['swap_total'] == 0:
swap_usage = 0
else:
swap_usage = 100 - (mem['swap_free'] / mem['swap_total'] * 100.0)
swap_used = (mem['swap_total'] - mem['swap_free']) / 2 ** 20
s = 'ram: %.2fG (%02d%%)' % (mem_used / 2 ** 20, mem_usage)
if swap_usage > 0:
s += ' swap: %.1fG (%02d%%)' % (swap_used, swap_usage)
return s
def update_output(list_of_contents, model_type, n_init, max_iter, n_clusters,
eps, min_samples, column1, column2, list_of_names,
list_of_dates):
if list_of_contents:
if column1 == column2:
return html.P("Select different columns!")
contents = list_of_contents[0]
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
df = parse_file(decoded, content_type, list_of_names)
except Exception as e:
print(e)
return html.Div(['There was an error processing this file.'])
if len(df.columns) > 8:
return html.P("Dataframe has too many columns!")
model = Modeller(
df, model_type,
**dict(n_init=n_init,
max_iter=max_iter,
n_clusters=n_clusters,
eps=eps,
min_samples=min_samples))
try:
labels = model.set_up_model([column1, column2])
except Exception as e:
return html.P(str(e))
if not labels:
return html.P("Please, fill in all the parameters in green.")
return dcc.Graph(id="chart",
figure=labels,
style={
'display': 'block',
'height': 600,
'width': 900,
'margin-left': 'auto',
'margin-right': 'auto'
})
return html.P("")
def cpu_stat_next():
try:
stat_vals = parse_file(path_stats, re_stats)
stat_vals = dict([(k, [int(w) for w in v]) for k, v in stat_vals.items()])
total = list()
for i in stat_vals['cpu']:
dtotal = \
stat_vals['user'][i] - old_stats['user'][i] + \
stat_vals['system'][i] - old_stats['system'][i] + \
stat_vals['nice'][i] - old_stats['nice'][i] + \
stat_vals['idle'][i] - old_stats['idle'][i] + \
stat_vals['iowait'][i] - old_stats['iowait'][i]
if dtotal == 0:
total.append("00%")
else:
idle = stat_vals['idle'][i] + stat_vals['iowait'][i] - old_stats['idle'][i] - old_stats['iowait'][i]
total.append('%02d%%' % (99 - (idle * 99 / dtotal)))
old_stats.update(stat_vals)
return ','.join(total)
except Exception, e:
return 'error: %s,' % e
def plot(path, mode, **kwargs):
import figures
if mode == "sample":
files = utils.parse_file(path)
canvas = []
for file in tqdm.tqdm(files):
canvas = figures.plot_single(file, canvas=canvas)
if mode == "hist":
figures.plot_hist(path)
if mode == "gan":
figures.plot_gan(path)
if mode == "copdac":
figures.train_plot(path)
# if kwargs["train"]:
# figures.train_plot(path)
# else:
# dataset = logging.load(path)
# figures.plot_mult(dataset)
figures.show()
def run(path, **kwargs):
logger = logging.Logger(log_dir=".", file_name=kwargs["out"], max_len=100)
data = logging.load(path)
expname = os.path.basename(path)
envname, agentname, *_ = expname.split("-")
env = getattr(envs, envname)(initial_perturb=[1, 0.0, 0,
np.deg2rad(10)],
dt=0.01,
max_t=40,
solver="rk4",
ode_step_len=1)
agent = getattr(agents, agentname)(env,
lrw=1e-2,
lrv=1e-2,
lrtheta=1e-2,
w_init=0.03,
v_init=0.03,
theta_init=0,
maxlen=100,
batch_size=16)
agent.load_weights(data)
print(f"Runnning {expname} ...")
_run(env, agent, logger, expname, **kwargs)
logger.close()
if kwargs["with_plot"]:
import figures
files = utils.parse_file(kwargs["out"])
canvas = []
for file in tqdm.tqdm(files):
canvas = figures.plot_single(file, canvas=canvas)
figures.show()
def main():
try:
file_to_read = sys.argv[1]
except IndexError:
print("Enter valid command arguments !" + '\n' +
"Usage : python fa.py <automaton description>")
exit(0)
data_folder = os.path.join("automaton descriptions")
file_to_open = os.path.join(data_folder, file_to_read)
try:
with open(file_to_open, 'r', encoding="utf8") as f:
# parsing of file
num_of_states, initial, final_states, transitions = parse_file(f)
# initialization of automaton
automaton = FiniteAutomaton(num_of_states, initial, final_states,
transitions)
# initialize Insert GUI
root = tk.Tk()
insert_gui = InsertGUI(root, automaton)
root.mainloop()
except EnvironmentError:
print("File not found!")
if X_test[i][j] != 0:
_prob += math.log(
self.prob[int(c) - 1][j]) * X_test[i][j]
if _prob > _max:
_max = _prob
_c = c
y_pred.append(_c)
return y_pred
def accuracy(self, y_test, y_pred):
count = 0
for i in range(len(y_test)):
if str(y_test[i]) == str(y_pred[i]):
count += 1
print('Acc: %.2f' % (count * 100 / len(y_test)), end=' %')
if __name__ == '__main__':
X_, y = utils.parse_file('training_data.txt')
utils.build_dict(X_)
DICT = utils.load_dict()
X = np.zeros((len(X_), len(DICT)))
for i in range(len(X_)):
X[i] = utils.bag_of_word(X_[i], DICT)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)
model = MultinomialNB(0.1)
model.fit(X_train, y_train)
print(X_test.shape[1])
y_pred = model.predict(X_test)
model.accuracy(y_test, y_pred)
def cpu_stat_init(): stat_vals = parse_file(path_stats, re_stats) # convert values to int's stat_vals = dict([(k, [int(w) for w in v]) for k, v in stat_vals.items()]) old_stats.update(stat_vals)
import argparse
from models import Grid, Position, LawnMower
from utils import parse_file
parser = argparse.ArgumentParser(description='Lawn mowers processor.')
parser.add_argument('lm_data_file',
metavar='lm-data-file',
type=str,
help='Lawn mowers data file locations.')
args = parser.parse_args()
if __name__ == '__main__':
parse_data = parse_file(args.lm_data_file)
grid = Grid(*parse_data['grid'])
lm_actions = []
for lm in parse_data['lawn_mowers']:
position = Position(*lm['position'], grid=grid)
orientation = lm['orientation']
lm_actions.append([LawnMower(position, orientation), lm['actions']])
for lawn_mower, actions in lm_actions:
lawn_mower.perform_actions(actions)
print(lawn_mower.position.x, lawn_mower.position.y,
lawn_mower.orientation)
def plot_accuracies(accs, names):
""" Plot different accuracies using 'matplotlib'. """
for i in range(len(accs)):
plt.plot([0.0, 0.1, 0.2, 0.5], accs[i], label=names[i])
plt.grid()
plt.ylabel('Accuracy (%)')
plt.xlabel('Probability of a removed value (p)')
plt.title('Decision Tree Learning Accuracy - Various Datasets')
plt.legend()
plt.show()
if __name__ == '__main__':
# CAR DATASET
car = parse_file("datasets/car.txt")
car_attrnames = [
"buying", "maint", "doors", "persons", "lug_boot", "safety", "class"
]
car_dataset = DataSet(name="car",
examples=car,
attrnames=car_attrnames,
target="class")
car_accuracy = test(car_dataset)
# PHISHING DATASET
phishing = parse_file("datasets/phishing.txt")
phishing_attrnames = [
"SFH", "popUpWindow", "SSLfinal_State", "Request_URL", "URL_of_Anchor",
"web_traffic", "URL_Length", "age_of_domain", "having_IP_Address",
"Result"
def main():
args = get_argparser()
attributes = utils.parse_file(args.attribute)
attribute = args.model_weight.split("/")[-2]
attributes = attributes[attribute]
attribute_model =\
backbone.Resnet.get_model(args.version, len(attributes),
False,
args.model_weight, 7, train=False
)
preprocess = utils.preprocess
total = 0
correct = 0
total_per_attribute = {}
correct_per_attribute = {}
preds, labels = [], []
if args.cuda:
attribute_model.cuda()
test_set = utils.parse_val_file(args.val_file)
with torch.no_grad():
for image in test_set:
total += 1
label = image.strip()[-1]
rgb = Image.open(image.strip()[:-2]).convert("RGB")
preprocessor = preprocess()
tensor = preprocessor(rgb).view(1, 3, 224, 224)
if args.cuda:
tensor = tensor.cuda()
output = attribute_model(tensor)
pred = torch.max(output, 1)[1]
print (attributes[pred], attributes[int(label)],
attributes[pred] == attributes[int(label)])
if attributes[int(label)] not in total_per_attribute.keys():
total_per_attribute[attributes[int(label)]] = 1
else:
total_per_attribute[attributes[int(label)]] += 1
if attributes[pred] == attributes[int(label)]:
if attributes[pred] not in correct_per_attribute.keys():
correct_per_attribute[attributes[int(label)]] = 1
else:
correct_per_attribute[attributes[int(label)]] += 1
correct += 1
print (float(correct)/total, total, correct)
preds.append(pred.cpu().numpy()[0])
labels.append(int(label))
save_path = os.path.join(
args.save_dir, attribute)
# +"/"+image.split("/")[-1][:-2]
os.makedirs(save_path, exist_ok=True)
save_img = save_path + \
"/"+image.split("/")[-1][:-3]
if args.save_img and not os.path.exists(save_img):
utils.save_img(save_img, rgb, attribute, {attributes[
int(label)]: attributes[int(pred)]})
save_plt = save_path+"/"+"cmatrix.jpg"
utils.plot_confusion_matrix(
labels, preds, np.array(attributes), True, "Confusion Matrix")
plt.savefig(save_plt)
print (total_per_attribute.keys(), np.array(
list(correct_per_attribute.values()))/np.array(list(total_per_attribute.values())))
def test_sample_5(self):
graph = parse_file("sample-5.txt")
result = scc(graph)
answer = [6, 3, 2, 1]
self.assertEqual(result, answer)
from collections import Counter # In[2]: data_dir = "./data/" train_filename = "topicclass/topicclass_train.txt" valid_filename = "topicclass/topicclass_valid.txt" test_filename = "topicclass/topicclass_test.txt" # In[3]: train_X, train_y = utils.parse_file(data_dir + train_filename) valid_X, valid_y = utils.parse_file(data_dir + valid_filename) test_X = utils.parse_file(data_dir + test_filename, has_labels=False) # In[4]: vocab_size = len(utils.word2index) - 1 # In[5]: sparse_train_X = sp.dok_matrix((len(train_X), vocab_size), dtype=np.int8) sparse_valid_X = sp.dok_matrix((len(valid_X), vocab_size), dtype=np.int8)
def train(sample, mode, **kwargs):
samplefiles = utils.parse_file(sample, ext="h5")
if mode == "gan" or mode == "all":
torch.manual_seed(0)
np.random.seed(0)
gandir = kwargs["gan_dir"]
histpath = os.path.join(gandir, "train-history.h5")
print("Train GAN ...")
agent = gan.GAN(
lr=kwargs["gan_lr"],
x_size=PARAMS["GAN"]["x_size"],
u_size=PARAMS["GAN"]["u_size"],
z_size=PARAMS["GAN"]["z_size"],
use_cuda=kwargs["use_cuda"],
)
if kwargs["continue"] is not None:
epoch_start = agent.load(kwargs["continue"])
logger = logging.Logger(path=histpath,
max_len=kwargs["save_interval"],
mode="r+")
else:
epoch_start = 0
logger = logging.Logger(path=histpath,
max_len=kwargs["save_interval"])
t0 = time.time()
for epoch in tqdm.trange(epoch_start,
epoch_start + 1 + kwargs["max_epoch"]):
dataloader = gan.get_dataloader(samplefiles,
shuffle=True,
batch_size=kwargs["batch_size"])
loss_d = loss_g = 0
for i, data in enumerate(tqdm.tqdm(dataloader)):
agent.set_input(data)
agent.train()
loss_d += agent.loss_d.mean().detach().numpy()
loss_g += agent.loss_g.mean().detach().numpy()
logger.record(epoch=epoch, loss_d=loss_d, loss_g=loss_g)
if (epoch % kwargs["save_interval"] == 0
or epoch == epoch_start + 1 + kwargs["max_epoch"]):
savepath = os.path.join(gandir, f"trained-{epoch:05d}.pth")
agent.save(epoch, savepath)
tqdm.tqdm.write(f"Weights are saved in {savepath}.")
print(f"Elapsed time: {time.time() - t0:5.2f} sec")
if mode == "copdac" or mode == "all":
np.random.seed(1)
env = envs.BaseEnv(initial_perturb=[0, 0, 0, 0.2])
copdacdir = kwargs["copdac_dir"]
agentname = "COPDAC"
Agent = getattr(agents, agentname)
agent = Agent(
env,
lrw=PARAMS["COPDAC"]["lrw"],
lrv=PARAMS["COPDAC"]["lrv"],
lrtheta=PARAMS["COPDAC"]["lrtheta"],
w_init=PARAMS["COPDAC"]["w_init"],
v_init=PARAMS["COPDAC"]["v_init"],
theta_init=PARAMS["COPDAC"]["lrv"],
maxlen=PARAMS["COPDAC"]["maxlen"],
batch_size=PARAMS["COPDAC"]["batch_size"],
)
expname = "-".join([type(n).__name__ for n in (env, agent)])
if kwargs["with_gan"]:
expname += "-gan"
agent.set_gan(kwargs["with_gan"], PARAMS["COPDAC"]["lrg"])
if kwargs["with_reg"]:
expname += "-reg"
agent.set_reg(PARAMS["COPDAC"]["lrc"])
histpath = os.path.join(copdacdir, expname + ".h5")
if kwargs["continue"] is not None:
epoch_start, i = agent.load(kwargs["continue"])
logger = logging.Logger(path=histpath, max_len=100, mode="r+")
else:
epoch_start, i = 0, 0
logger = logging.Logger(path=histpath, max_len=100)
print(f"Training {agentname}...")
epoch_end = epoch_start + kwargs["max_epoch"]
for epoch in tqdm.trange(epoch_start, epoch_end):
dataloader = gan.get_dataloader(samplefiles,
keys=("state", "action", "reward",
"next_state"),
shuffle=True,
batch_size=64)
for data in tqdm.tqdm(dataloader, desc=f"Epoch {epoch}"):
agent.set_input(data)
agent.train()
if i % kwargs["save_interval"] == 0 or i == len(dataloader):
logger.record(epoch=epoch,
i=i,
w=agent.w,
v=agent.v,
theta=agent.theta,
loss=agent.get_losses())
i += 1
logger.close()
def test_sample_1(self):
graph = parse_file("sample-1.txt")
result = scc(graph)
answer = [3, 3, 3]
self.assertEqual(result, answer)
def main():
execution_times = []
nodes_problem = []
list_files = []
movements = []
all_files = []
data_for_excel = []
while True:
try:
print(
"\nEscoja que tipo de algoritmos desea realizar: "
"\n[D]esinformados: Breadth-First Search & Depth-First Search"
"\n[I]nformados: A-star (3 heuristicas)")
enter = input().lower()
if enter == "d":
busqueda = ["breadth", "depth"]
path = "bd-input_files/"
hoja = "Desinformed"
break
elif enter == "i":
busqueda = ["heuristica_1", "heuristica_2", "ambas"]
path = "astar-input_files/"
hoja = "A-star"
break
except Exception:
print("Solo ingrese una letra, porfavor")
exit(0)
for sm in busqueda:
for file in listdir(path):
# obtener datos iniciales del problema
objects, begin_config, goal_config = parse_file(path + file)
# obtener el estado inicial
initial_state = BlockState(begin_config, len(begin_config),
objects)
# realizar el tipo de busqueda especificado
if sm == "breadth":
state, nodes, running_time = s.bfs_search(
initial_state, goal_config)
elif sm == "depth":
state, nodes, running_time = s.dfs_search(
initial_state, goal_config)
elif enter == "i":
state, nodes, running_time = s.a_star_search(
initial_state, goal_config, sm)
# es valida nuestra solución
moves = s.calculate_path_to_goal(state)
valid = s.is_valid(initial_state, moves, goal_config)
# imprimir resultados en consola del problema
print("\nArchivo: ", file.replace(".pddl", ""))
print("Método de Busqueda: ", sm)
print("Se alcanzó el estado final?: ", "Si" if valid else "No")
print("Cantidad de movimientos (Profundidad maxima recorrida): ",
len(moves))
print("Nodos expandidos: ", nodes)
print("Tiempo de Ejecución: {0:.06f} segundos\n".format(
running_time))
# escribir que movimientos hacer para resolver el problema
write_in_file("solution_files/" + file.replace(".pddl", ".txt"),
moves)
# datos para los plot
list_files.append(file.replace(".pddl", "")[11:])
movements.append(len(moves))
nodes_problem.append(nodes)
execution_times.append(running_time)
# datos para el excel
all_files.append(file.replace(".pddl", "")[11:])
fila = [running_time, nodes, len(moves), sm]
data_for_excel.append(fila)
# imprimimos los tres tipos de plot para cada tipo de busqueda
print_plot(list_files, execution_times, 'Archivo vs Tiempo',
'Tiempo (segundos)')
print_plot(list_files, nodes_problem, 'Archivo vs Espacio',
'Cantidad de nodos')
print_plot(list_files, movements, 'Archivo vs Movimientos',
'Cantidad de Movimientos')
# re-inicializando arreglos
list_files = []
execution_times = []
nodes_problem = []
movements = []
#subir datos a un excel
columna = ["Tiempo", "Nodos", "Movimientos", "Busqueda"]
create_Excel(data_for_excel, all_files, columna, hoja)
# imprimir datos para comparar tipos de busqueda
print_box("A-star", "Tiempo")
print_box("A-star", "Nodos")
print_box("A-star", "Movimientos")
def get_image_files(attrs):
images = parse_file(config.get('celeba', 'attributes_file'))
attrs = parse_attr(attrs, _PREDEFINED_ATTR)
filtered = filter_items(images, attrs)
return append_path(config.get('celeba', 'image_dir'), filtered)
def test():
def loadCar():
for i in car.index:
CARDICT[i] = Car(i, *(car.loc[i, :]))
def loadRoad():
for i in road.index:
ROADDICT[i] = Road(i, *(road.loc[i, :]))
if road.loc[i].isDuplex == 1:
ROADDICT[-i] = Road(-i, *(road.loc[i, :]))
ROADDICT[-i].FROM, ROADDICT[-i].TO = ROADDICT[
-i].TO, ROADDICT[-i].FROM
# exchange from to for -road
def loadCross():
for i in cross.index:
CROSSDICT[i] = Cross(i, *(cross.loc[i, :]))
PATH_CAR = car_path
PATH_ROAD = road_path
PATH_CROSS = cross_path
#PATH_ANSWER = './toyconfig/answer.txt'
road = parse_file(PATH_ROAD)
cross = parse_file(PATH_CROSS)
car = parse_file(PATH_CAR)
# ans = parse_answer(PATH_ANSWER)
# create road object
loadRoad()
# create cross object
loadCross()
# create car object
loadCar()
# load route to car
# =============================================================================
# for i in ans.index:
# CARDICT[i].initRoute(ans.loc[i].StartTime, ans.loc[i].values[1:][ans.loc[i].values[1:]>0]) # del 0
# =============================================================================
def caculateOneCAR(CarID):
car = CARDICT[CarID]
testDijkstra = Dijkstra(car.FROM, car.TO, CROSSDICT, ROADDICT)
route = testDijkstra.getdis()
routeRoad = testDijkstra.crossToRoad(route)
disDic = testDijkstra.disDic
length = disDic[car.TO]
roadSpeed = []
for i in routeRoad:
roadSpeed.append(ROADDICT[i].SPEED)
speed = min(min(roadSpeed), car.SPEED)
time = length // speed + 1 # ceil is better
return np.abs(np.array(routeRoad)).tolist(), time
answer = [[], [], []]
latestPlanTime = 0
for carID in CARDICT.keys():
if CARDICT[carID].PLANTIME > latestPlanTime:
latestPlanTime = CARDICT[carID].PLANTIME
thisTime = latestPlanTime
carlist = []
for i in CARDICT.keys():
carlist.append(i)
batchsize = 200
for i in range(len(carlist) // batchsize):
batch = carlist[i * batchsize:(i + 1) * batchsize]
batchAnswer = [[], [], []]
for carID in batch:
routeRoad, time = caculateOneCAR(carID)
batchAnswer[0].append(CARDICT[carID].ID)
batchAnswer[1].append(time)
batchAnswer[2].append(routeRoad)
#print(i)
#print(batchAnswer[2])
thisTime += max(batchAnswer[1])
answer[0].extend(batchAnswer[0])
answer[1].extend(np.array([thisTime] * batchsize) + 1)
answer[2].extend(batchAnswer[2])
if len(carlist) % batchsize != 0:
lastBatch = carlist[-(len(carlist) % batchsize):]
batchAnswer = [[], [], []]
for carID in lastBatch:
routeRoad, time = caculateOneCAR(carID)
batchAnswer[0].append(CARDICT[carID].ID)
batchAnswer[1].append(time)
batchAnswer[2].append(routeRoad)
thisTime += max(batchAnswer[1])
answer[0].extend(batchAnswer[0])
answer[1].extend(np.array([thisTime] * len(lastBatch)) + 1)
answer[2].extend(batchAnswer[2])
# =============================================================================
#
# for i in CARDICT.keys():
#
# routeRoad, time = caculateOneCAR(i)
# answer[0].append(CARDICT[i].ID)
# answer[1].append(np.array(thisTime) + 1)
# answer[2].append(routeRoad)
#
# thisTime += time
# =============================================================================
def writeAnswer(answer):
fp = open(answer_path, 'w')
seq = []
for i in range(len(answer[0])):
route = ', '.join(list(map(str, answer[2][i])))
seq.append('(%d, %d, %s)\n' %
(answer[0][i], answer[1][i], route))
fp.writelines(seq)
writeAnswer(answer)
