def __init__(self, config_file):
"""Constructor"""
# Load options
util.load_config(self, config_file)
# Set up the collection of verses
self.verses = load_verses(self.general.output_file)
self.n_verses = len(self.verses)
# Store the verses by rhyme
self.rhymes = defaultdict(lambda: set())
for i, verse in enumerate(self.verses):
self.rhymes[poetry.verse_rhyme(verse)].add(i)
# Total number of rhymes
self.n_rhymes = len(self.rhymes)
for k, v in self.rhymes.items():
self.rhymes[k] = list(v)
# Probability of picking a rhyme
# This probability is proportional to the number of verses for each rhyme.
# In particular, for rhymes with only one verse, the probability is set to 0
self.p_rhymes = {r: (len(v) - 1) for r, v in self.rhymes.items()}
self.names_rhymes, self.p_rhymes = zip(*self.p_rhymes.items())
self.p_rhymes = np.asarray(self.p_rhymes, dtype=float)
self.p_rhymes /= self.p_rhymes.sum()
# Title generator
self.tg = title.get_title_generator(self.title)
python类load_config()的实例源码
def main(models, source_file, nbest_file, saveto, b=80,
normalize=False, verbose=False, alignweights=False):
# load model model_options
options = []
for model in models:
options.append(load_config(model))
fill_options(options[-1])
rescore_model(source_file, nbest_file, saveto, models, options, b, normalize, verbose, alignweights)
def main(models, source_file, nbest_file, saveto, b=80,
normalize=False, verbose=False, alignweights=False):
# load model model_options
options = []
for model in models:
options.append(load_config(model))
fill_options(options[-1])
rescore_model(source_file, nbest_file, saveto, models, options, b, normalize, verbose, alignweights)
def __init__(self, config_file):
"""Init from yaml"""
self.config_file = config_file
# Load from yaml config file
util.load_config(self, config_file)
# Initialize other relevant variables
self.n_pentameters = 0
self.n_length_removed = 0
self.n_pentameters_epoch = 0
self.last_tweet = 0
self.last_quatrain_tweet = 0
self.start = time.time()
# Initialize reddit
self.init_reddit()
def main(source_file, target_file, output_file, scorer_settings):
# load model model_options
options = []
for model in scorer_settings.models:
options.append(load_config(model))
fill_options(options[-1])
rescore_model(source_file, target_file, output_file, scorer_settings, options)
def _load_model_options(self):
"""
Loads config options for each model.
"""
options = []
for model in self._models:
m = load_config(model)
if not 'concatenate_lm_decoder' in m:
m['concatenate_lm_decoder'] = False
options.append(m)
# backward compatibility
fill_options(options[-1])
self._options = options
def main(source_file, nbest_file, output_file, rescorer_settings):
# load model model_options
options = []
for model in rescorer_settings.models:
options.append(load_config(model))
fill_options(options[-1])
rescore_model(source_file, nbest_file, output_file, rescorer_settings, options)
def main(args):
paser = argparse.ArgumentParser()
paser.add_argument('-c', '--conf', help='path to the config file')
args = paser.parse_args()
Config = load_config(args.conf)
solve(Config)
