def list_file(path, query='', max_items=0, subtitle=tasks_msg):
"""
Creates object that represents XML items list of tasks in file given by `path`
that contains `query` as substring. Max length of list is given by `max_items`,
0 means all tasks.
"""
alist = AlfredList()
if len(sys.argv) > 1:
query = sys.argv[1].lower()
items_added = 0
with open(path, 'r') as f:
for idx, line in enumerate(f):
if is_not_done_task(line) and re.search(query, line.lower()):
should_add = False
if not only_tagged_today:
should_add = True
else:
if today_tag in line:
should_add = True
line = line.replace(today_tag, '')
# when user displays only tasks with @today tag displaying it is reduntant
if should_add:
items_added += 1
alist.append(create_arg(path, idx), format_line(line),
subtitle)
if max_items and max_items <= items_added:
break
return alist
def list_file(path, query='', max_items=0, subtitle=tasks_msg):
"""
Creates object that represents XML items list of tasks in file given by `path`
that contains `query` as substring. Max length of list is given by `max_items`,
0 means all tasks.
"""
alist = AlfredList()
if len(sys.argv) > 1:
query = sys.argv[1].lower()
items_added = 0
with open(path, 'r') as f:
for idx, line in enumerate(f):
if is_not_done_task(line) and re.search(query, line.lower()):
should_add = False
if not only_tagged_today:
should_add = True
else:
if today_tag in line:
should_add = True
line = line.replace(today_tag, '')
# when user displays only tasks with @today tag displaying it is reduntant
if should_add:
items_added += 1
alist.append(create_arg(path, idx), format_line(line), subtitle)
if max_items and max_items <= items_added:
break
return alist
def wait_results(results, f, log=False, datasets=None):
clean_results = []
for dataset_name, portfolios in results.items():
# wait on everything
for clf_name in portfolios.keys():
for i in range(0, len(portfolios[clf_name])):
model_params, trading_params, res = portfolios[clf_name][i]
res = compss_wait_on(res)
pfs, total_time = res
portfolios[clf_name][i] = (model_params, trading_params, res)
if log:
f.write(format_line(dataset_name, clf_name,
datasets[dataset_name.split(':')[0]][1],
trading_params,
model_params, pfs, total_time) + '\n')
params = (dataset_name, clf_name, model_params, trading_params,
total_time)
clean_results.append((params, pfs))
return clean_results
def read_data(file_name):
x_file = open(file_name, "r")
x_line = x_file.read().splitlines()
global sentences_index
for line in x_line:
line_ = format_line(line)
sub_words = all_sub_words(line_)
sentences[sentences_index] = sentence_path(line, file_name)
for word in sub_words:
# prevent duplication of sentences
if line not in [
sentences[sentence_.id].sentence
for sentence_ in data_dict[word]
]:
if len(data_dict[word]) < 5:
data_dict[word].append(
subString(sentences_index, 0, line_.index(word)))
else:
is_best_score(word, data_dict[word])
sentences_index += 1
def read_data(file_name):
x_file = open(file_name, "r")
x_line = x_file.read().splitlines()
global sentences_id
line_number = 1
for line in x_line:
line_ = format_line(line)
sub_words = all_sub_words(line_)
sentences[sentences_id] = sentence_path(line, file_name)
for word in sub_words:
# prevent duplication of sentences
if line not in [
sentences[sentence_.id].sentence
for sentence_ in data_dict[word]
]:
if len(data_dict[word]) < RESULT_LEN:
data_dict[word].append(
subString(sentences_id, 0, line_number))
sentences_id += 1
line_number += 1
integrator = FirstOrderHold(m, K)
problem = SCProblem(m, K)
last_nonlinear_cost = None
converged = False
for it in range(iterations):
t0_it = time()
print('-' * 50)
print('-' * 18 + f' Iteration {str(it + 1).zfill(2)} ' + '-' * 18)
print('-' * 50)
t0_tm = time()
A_bar, B_bar, C_bar, S_bar, z_bar = integrator.calculate_discretization(
X, U, sigma)
print(format_line('Time for transition matrices', time() - t0_tm, 's'))
problem.set_parameters(A_bar=A_bar,
B_bar=B_bar,
C_bar=C_bar,
S_bar=S_bar,
z_bar=z_bar,
X_last=X,
U_last=U,
sigma_last=sigma,
weight_nu=w_nu,
weight_sigma=w_sigma,
tr_radius=tr_radius)
while True:
error = problem.solve(verbose=verbose_solver,
The next feature will save the data in an file(Probably of the json type)
"""
from offline import init
from utils import format_line
from online import get_best_k_completions
if __name__ == '__main__':
STOP_INPUT = '#'
print("Loading the file and preparing the system....")
init()
string_to_complete = input("The system is ready. Enter your text:")
while string_to_complete:
if string_to_complete[-1] != STOP_INPUT:
string_to_complete = format_line(string_to_complete)
suggestions = get_best_k_completions(string_to_complete)
if suggestions:
print(f"There are {len(suggestions)} suggestions")
for i in range(len(suggestions)):
print(
f'{i + 1}. {suggestions[i].get_complete_sentence()} , path = {suggestions[i].get_source_text()}'
)
else:
print("There are'nt suggestions")
print(string_to_complete, end='')
string_to_complete += input()
integrator = Integrator(m, K)
problem = SCProblem(m, K)
last_linear_cost = None
converged = False
for it in range(iterations):
t0_it = time()
print('-' * 50)
print('-' * 18 + f' Iteration {str(it + 1).zfill(2)} ' + '-' * 18)
print('-' * 50)
t0_tm = time()
A_bar, B_bar, C_bar, S_bar, z_bar = integrator.calculate_discretization(
X, U, sigma)
print(format_line('Time for transition matrices', time() - t0_tm, 's'))
problem.set_parameters(A_bar=A_bar,
B_bar=B_bar,
C_bar=C_bar,
S_bar=S_bar,
z_bar=z_bar,
X_last=X,
U_last=U,
sigma_last=sigma,
weight_sigma=w_sigma,
weight_nu=w_nu,
weight_delta=w_delta,
weight_delta_sigma=w_delta_sigma)
while True: