def learning(weight):
for line in iter(sys.stdin.readline, ""):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
pre_label = po.predict_one(weight, phi)
if int(pre_label) != int(label):
update_weights(weight, phi, int(label))
def predict_all(network):
# predict all
for line in iter(sys.stdin.readline, ""):
phi = cf.create_features(line)
result, y = predict_nn(network, phi)
# 1.0 * sign(result)
print int(copysign(1, result))
def predict_all(network):
# predict all
for line in iter(sys.stdin.readline, ""):
phi = cf.create_features(line)
result, y = predict_nn(network, phi)
# 1.0 * sign(result)
print int(copysign(1, result))
def learning(network):
lines = sys.stdin.readlines()
for i in range(FLAGS.iteration):
sys.stdout.write("\rIteration:%d" % (i+1))
sys.stdout.flush()
for line in lines:
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
update_nn(network, phi, label)
def margin_learning(weight, margin):
last = defaultdict(lambda: 0)
for index, line in enumerate(iter(sys.stdin.readline, "")):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
val = po.get_score(weight, phi, FLAGS.l1_value, index,
last) * float(label)
if val <= margin:
update_weights(weight, phi, int(label))
def learning(network):
lines = sys.stdin.readlines()
for i in range(FLAGS.iteration):
sys.stdout.write("\rIteration:%d" % (i + 1))
sys.stdout.flush()
for line in lines:
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
update_nn(network, phi, label)
def margin_learning(weight, margin):
last = defaultdict(lambda : 0)
for index, line in enumerate(iter(sys.stdin.readline, "")):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
val = po.get_score(weight, phi,
FLAGS.l1_value,
index, last) * float(label)
if val <= margin:
update_weights(weight, phi, int(label))
def make_df(evals, facs, dropcols=to_drop):
dfbeta = cf.create_features(facs, evals)
evals.drop([
'ACTIVITY_LOCATION', 'EVALUATION_IDENTIFIER', 'EVALUATION_TYPE',
'EVALUATION_DESC', 'EVALUATION_AGENCY', 'FOUND_VIOLATION'
],
inplace=True,
axis=1)
evals[['month', 'day',
'year']] = evals['EVALUATION_START_DATE'].str.split('/',
expand=True)
dfbeta.drop(dropcols, inplace=True, axis=1)
return pd.merge(evals, dfbeta, on='ID_NUMBER', how='left')
def average_learning(weight):
updates = 0.0
average = defaultdict(lambda: 0.0)
for line in iter(sys.stdin.readline, ""):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
pre_label = po.predict_one(weight, phi)
if int(pre_label) != int(label):
update_weights(weight, phi, int(label))
updates += 1.0
for key, value in weight.items():
average[key] = (average[key] * (updates - 1.0) + value) / updates
# copy to weight from average
for key in weight.keys():
weight[key] = average[key]
def average_learning(weight):
updates = 0.0
average = defaultdict(lambda : 0.0)
for line in iter(sys.stdin.readline, ""):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
pre_label = po.predict_one(weight, phi)
if int(pre_label) != int(label):
update_weights(weight, phi, int(label))
updates += 1.0
for key, value in weight.items():
average[key] = (average[key] * (updates - 1.0) + value) / updates
# copy to weight from average
for key in weight.keys():
weight[key] = average[key]
def predict_all(model_file):
weight = defaultdict(lambda : 0.0)
# load model_file
fin = open(model_file)
for line in iter(fin.readline, ""):
parts = line.rstrip("\n").split()
value = parts.pop()
name = " ".join(parts)
weight[name] = float(value)
fin.close()
# predict all
for line in iter(sys.stdin.readline, ""):
phi = cf.create_features(line)
y = po.predict_one(weight, phi)
print y
def predict_all(model_file):
weight = defaultdict(lambda : 0.0)
# load model_file
fin = open(model_file)
for line in iter(fin.readline, ""):
parts = line.rstrip("\n").split()
value = parts.pop()
name = " ".join(parts)
weight[name] = float(value)
fin.close()
# predict all
for line in iter(sys.stdin.readline, ""):
phi = cf.create_features(line)
y = po.predict_one(weight, phi)
print int(y)
return data
def scan_for_images(source_directory: str, project_name: str) -> None:
image_path_list = create_image_path_list(source_directory)
with open(f"{os.path.join(GloabalDir.projects,project_name)}.info",
"w") as info:
data = {"initial_absolute_path": os.path.abspath(source_directory)}
info.write(json.dumps(data))
f = h5py.File(f"{os.path.join(GloabalDir.projects,project_name)}.hdf5",
"w")
for image_path in image_path_list:
file, relative_path = image_path
g = f.create_group(file)
g.attrs['path'] = relative_path
f.close()
if __name__ == "__main__":
options = vars(parser.parse_args())
scan_for_images(options['source'], options['project'])
create_features.create_features(options['project'])
create_neighbours.create_neighbours(options['project'],
options['k_nearest'])
