def __init__(self, config, data_seq_list, opt_method):
self.config = config
self.data_seq_list = data_seq_list
self.opt_method = opt_method
if opt_method.lower() == 'adam':
self.config.pre_compute_data_var = False
# if wb.is_linux():
# self.feats = feat.FastFeats(self.config.feat_dict)
# else:
self.feats = feat.Feats(self.config.feat_dict)
self.update_op = None
self.data_exp = None
self.data_var = None
def __init__(self, config, data, opt_method):
self.config = config
self.data = data
self.opt_method = opt_method
if opt_method.lower() == 'adam':
self.config.pre_compute_data_var = False
# self.len_factor = self.config.pi_true / self.config.pi_0
wftype, cftype = feat.separate_type(feat.read_feattype_file(self.config.feat_type_file))
if self.data.word_to_class is not None:
wftype.update(cftype)
# if wb.is_linux():
# self.feat = feat.FastFeats(wftype)
# else:
self.feat = feat.Feats(wftype)
self.update_op = None
self.data_exp = None
self.data_var = None
def __init__(self, config, data_seq_list, opt_method):
self.config = config
self.data_seq_list = data_seq_list
self.opt_method = opt_method
# tag features
# if wb.is_linux():
# self.feats = feat.FastFeats(self.config.feat_dict)
# else:
self.feats = feat.Feats(self.config.feat_dict)
# update
self.update_op = None
# save the trans_matrix
self.trans_matrix = np.zeros(
[self.config.tag_size**(self.get_order() - 1)] * 2)
self.trans_matrix_tail = np.zeros_like(
self.trans_matrix) # the trans_matrix at the last position
self.need_update_trans_matrix = True # if true, then recompute the trans_matrix
# used to compute the get_exp()
self.tag_map_ids = None
self.tag_map_ids_extra = None
def __str__(self):
max_order = feat.Feats(self.feat_dict).get_order()
return 'mix{}g'.format(max_order)