def test_load_data(self):
data = load_data(self.organisation)
assert_equals(sorted(data.keys()), sorted(['pull_requests',
'pull_requests_per_project',
'pull_request_comments',
'pull_request_comments_per_project',
'projects',
'projects_with_pulls']))
def test_load_data(self):
data = load_data(self.organisation)
assert_equals(
sorted(data.keys()),
sorted([
'pull_requests', 'pull_requests_per_project',
'pull_request_comments', 'pull_request_comments_per_project',
'projects', 'projects_with_pulls'
]))
def add_deal(agent_obj):
username = agent_obj.username
profiles_data = load_data(PROFILES_FILE_PATH)
profiles_founded = None
for profile in profiles_data:
profiles_founded = profile[username]
print(profiles_founded)
id_selected = str(input('Please your profile for deal(enter id):'))
for profile_founded in profiles_founded:
if profile_founded['id'] == id_selected:
deal = Agent.add_deal(agent_obj)
data = [{
'agent_name': '{}'.format(agent_obj.username),
'profile_id': '{}'.format(id_selected),
'deal': deal
}]
save_to_file(DEALS_FILE_PATH, data)
print('Added deal successfully')
agent()
from theano.tensor.shared_randomstreams import RandomStreams
from dA import dA
try:
import PIL.Image as Image
except ImportError:
import Image
learning_rate = 0.1
training_epochs = 100
batch_size = 20
output_folder = 'dA_plots'
print('... loading data')
datasets = store.load_data()
train_set_x, train_set_y = datasets[0]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
# start-snippet-2
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
# end-snippet-2
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
def test_update_data(self):
data = load_data('github')
assert data
def seed_data(self):
self.organisation = 'yola'
load_data(self.organisation)
def search():
return load_data(PROFILES_FILE_PATH)
def seed_data(self):
self.organisation = 'yola'
load_data(self.organisation)
def test_update_data(self):
data = load_data('github')
assert data
def check_agent_username(username):
agents_data = load_data(AGENTS_FILE_PATH)
_ = [Agent(**d) for d in agents_data]
agent = Supervisor.search_username(username)
return agent
from theano.tensor.shared_randomstreams import RandomStreams
from dA import dA
try:
import PIL.Image as Image
except ImportError:
import Image
learning_rate = 0.1
training_epochs = 100
batch_size = 20
output_folder = 'dA_plots'
print('... loading data')
datasets = store.load_data()
train_set_x, train_set_y = datasets[0]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
# start-snippet-2
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix('x') # the data is presented as rasterized images
# end-snippet-2
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
