def get_model(args, filename, allow_mask=True):
loader = HMMLoader(args.mathType) # TODO: rename HMMLoader to ModelLoader
register_classifier_states(loader)
register_annotation_states(loader)
register_cannotation_states(loader)
register_annotations(loader)
for i in range(0, len(args.bind_file), 2):
loader.addFile(args.bind_file[i], args.bind_file[i + 1])
for i in range(0, len(args.bind_constant_file), 2):
loader.addConstant(
args.bind_constant_file[i],
loader.load(args.bind_constant_file[i + 1])
)
for i in range(0, len(args.bind_constant_file), 2):
loader.addConstant(
args.bind_constant_file[i],
loader.loads(args.bind_constant_file[i + 1]),
)
model = loader.load(filename)
if type(model) is dict and 'model' in model:
model = model["model"]
if args.add_masked_to_distribution and allow_mask:
model.add_soft_masking_to_distribution()
return model
def load_model(self, fname):
loader = HMMLoader(float)
register_classifier_states(loader)
register_annotation_states(loader)
register_cannotation_states(loader)
self.fname = fname
self.model = loader.load(fname)
self.states_dict = dict()
for i, state in enumerate(self.model['model'].states):
self.states_dict[state.onechar] = i
def get_model(args, filename, allow_mask=True):
loader = HMMLoader(args.mathType) # TODO: rename HMMLoader to ModelLoader
register_classifier_states(loader)
register_annotation_states(loader)
register_cannotation_states(loader)
register_annotations(loader)
for i in range(0, len(args.bind_file), 2):
loader.addFile(args.bind_file[i], args.bind_file[i + 1])
for i in range(0, len(args.bind_constant_file), 2):
loader.addConstant(args.bind_constant_file[i],
loader.load(args.bind_constant_file[i + 1]))
for i in range(0, len(args.bind_constant_file), 2):
loader.addConstant(
args.bind_constant_file[i],
loader.loads(args.bind_constant_file[i + 1]),
)
model = loader.load(filename)
if type(model) is dict and 'model' in model:
model = model["model"]
if args.add_masked_to_distribution and allow_mask:
model.add_soft_masking_to_distribution()
return model
