def open_add_entry_window(self):
if AugmentedMaps.debug:
print('Opening an image map')
self.__entryWindow = Preparation(self.database)
pos = self.frameGeometry().topLeft()
self.__entryWindow.move(pos.x() + 20, pos.y() + 20)
self.__entryWindow.show()
if __name__ == '__main__':
if len(sys.argv) < 4:
print 'please input params: basedir need_preprocess (1 need/ 0 no need. default 0) save_space (1 need/ 0 no need. default 1)'
exit(1)
basedir = sys.argv[1]
need_preprocess = sys.argv[2]
save_space = sys.argv[3]
# basedir = '../../../data/twitter/ModelInput-seq2seq-facts-mix-ret/'
# transform context/response pairs into corpus file and relation file
# the input files are train.txt/valid.txt/test.txt
# the format of each line is 'label \t context \t response'
prepare = Preparation()
# run with three files (train.txt.mz, valid.txt.mz, test.txt.mz) to generate unique ids
# for q/d in train/valid/test data. Since we will merge these three corpus files into a single file later
corpus, rels_train, rels_valid, rels_test = run_with_train_valid_test_corpus_given_qid_did_gen_unique_id_for_genres(
basedir + 'train.mz', basedir + 'valid.mz', basedir + 'test.mz')
for data_part in list(['train', 'valid', 'test']):
if data_part == 'train':
rels = rels_train
elif data_part == 'valid':
rels = rels_valid
else:
rels = rels_test
print 'total relations in ', data_part, len(rels)
prepare.save_relation(basedir + 'relation_' + data_part + '.txt', rels)
if save_space == '0':
N_IN = 5 # number of date for training
N_OUT = 1 # number of date for predict
PARTICLE = 150 # number of PSO particle
ITERATION = 7 # number of PSO iteration
C1 = 2.985
C2 = 1.066
W = 0.000482
# setup baseline model
ann = ANN(epochs=50, batch=13, n_in=N_IN, n_out=N_OUT)
# prepare train data
read_data = pd.read_csv(TRAIN_PATH)
preparation = Preparation(df=read_data)
data = preparation.calculate_per_change()
# create indicator
indicator = Indicator(data)
indicator_data = indicator.RSI()
indicator_data = indicator.EMA()
indicator_data = indicator.MACD()
indicator_data.dropna(inplace=True)
indicator_data['Change of EMA'] = (
(indicator_data['Close'] - indicator_data['ema_5_day']) /
indicator_data['ema_5_day']) * 100
data_set = indicator_data[['rsi', 'Histogram', 'Change of EMA', 'change']]
ann.split_data_scale_transform(data_set)
def __init__(self):
self.preparation = Preparation()
from customization import Customization
from preparation import Preparation
while True:
factory_cappuccino = CappuccinoFactory()
factory_black_coffee = BlackCoffeeFactory()
factory_lemon = LemonadeFactory()
factory_milk = HotMilkFactory()
factory_coca_cola = CocaColaFactory()
cust = Customization(float(input("Extra milk - ")),
float(input("Sugar - ")), float(input("Mug size - ")))
cust = (cust.extra_milk, cust.sugar, cust.mug_size)
prep = Preparation(float(input("Milk - ")), float(input("Water - ")),
float(input("Sugar - ")), float(input("Coke - ")),
float(input("Coffee - ")), float(input("Flavour - ")),
float(input("Tea - ")))
prep = (prep.milk, prep.water, prep.sugar, prep.coke, prep.liquid_coffee,
prep.added_flavour, prep.tea)
cappuccino = factory_cappuccino.get_product()
black_coffee = factory_black_coffee.get_product()
lemon = factory_lemon.get_product()
milk = factory_milk.get_product()
coca_cola = factory_coca_cola.get_product()
cappuccino.make(cappuccino, cust, prep)
cappuccino.set_milk()
cappuccino.set_sugar()
cappuccino.set_coffee()
print("\n")
# with warnings.catch_warnings():
# warnings.simplefilter("ignore")
# Instance.fxn()
parser = OptionParser()
for option in options:
param = option['name']
del option['name']
parser.add_option(*param, **option)
start = timer()
options, arguments = parser.parse_args()
main(options, arguments)
'''
if __name__ == "__main__":
prepared = Preparation()
servant = Servant()
opt = prepared.get_options()
options, args = prepared.read_options(opt)
start = timer()
main(options, args, prepared, servant)
#exit()
end_analysis = timer()
servant.extractCSV()
start_fv = timer()
data_set, feature_vector = servant.getInputData(prepared)
end_fv = timer()
conf = 'model_training.config'
def getting_variables_from_preparation(self):
self.max_number_of_players, self.distance_between_starts, self.players, self.rules, \
self.dice = Preparation().main()
def __init__(self):
self.preparation = Preparation()
self.tfidf = TFIDF()
def run(zipPath):
folderName = ZipExtract().get_zip_file(zipPath)
data = Preparation(folderName).getInstance()
data.train_test_move_to_dirs(data.train_test_separation
(data.train_test_make_dirs()))
r = line.strip().split()
word_dict[r[1]] = r[0]
return word_dict
if __name__ == '__main__':
if len(sys.argv) < 2:
print 'please input params: data_name search_field_label'
exit(1)
data_name = sys.argv[1] # udc or ms_v2
search_field_label = sys.argv[2] # body or title
basedir = '../../data/' + data_name + '/ModelInput/'
cur_data_dir = basedir + 'dmn_prf_model_input_' + search_field_label + '/'
if not os.path.exists(cur_data_dir):
os.makedirs(cur_data_dir)
prepare = Preparation() # train_rexpand_ctab_titleText.txt
train_file = 'train_rexpand_ctab_' + search_field_label + 'Text.txt'
valid_file = 'valid_rexpand_ctab_' + search_field_label + 'Text.txt'
test_file = 'test_rexpand_ctab_' + search_field_label + 'Text.txt'
corpus, rels_train, rels_valid, rels_test = prepare.run_with_train_valid_test_corpus_dmn(
basedir + train_file, basedir + valid_file,
basedir + test_file)
for data_part in list(['train', 'valid', 'test']):
if data_part == 'train':
rels = rels_train
elif data_part == 'valid':
rels = rels_valid
else:
rels = rels_test
print 'total relations in ', data_part, len(rels)