def __init__(self, featureSet, options):
self.featureSet = featureSet
self.params = '-b 1'
self.lmw = options['lmw']
modelName = options['modelName']
sys.stderr.write('loading transition model...')
self.transProbs = Bigram.getModelFromFile(options['bigramModelFile'])
sys.stderr.write('done\nloading observation model...')
self.model = load_model('{0}.model'.format(modelName))
self.labelCounter = options['labelCounter']
self.featCounter = options['featCounter']
sys.stderr.write('done\n')
def load(ifn):
ed = LiblinearWrapper()
ed.class_cache = dict([(l.strip().split('\t')[0],
int(l.strip().split('\t')[1]))
for l in open('{0}.labelNumbers'.format(ifn))])
ed.feat_cache = dict([(l.strip().split('\t')[0],
int(l.strip().split('\t')[1]))
for l in open('{0}.featureNumbers'.format(ifn))])
ed.model = load_model('{0}.model'.format(ifn))
return ed
def load(ifn):
ed = LiblinearWrapper()
ed.class_cache = dict([(l.strip().split('\t')[0],
int(l.strip().split('\t')[1]))
for l in open('{0}.labelNumbers'.format(ifn))])
ed.feat_cache = dict([(l.strip().split('\t')[0],
int(l.strip().split('\t')[1]))
for l in open('{0}.featureNumbers'.format(ifn))])
ed.model = load_model('{0}.model'.format(ifn))
return ed
