def __init__(self, epochs=10, eta=.0001): self.decoder = ViterbiDecompounder() self.parameters_for_epoch = [] self.n_epochs = epochs self.eta = eta self.n_features = ViterbiDecompounder.n_features
modelSetup, nAccuracy=args.nAccuracy, globalNN=args.globalNN, similarityThreshold=args.similarityThreshold, prototype_file=args.prototypeFile) if args.mode == "lattices": for line in sys.stdin: print( base_decompounder.get_decompound_lattice( line.decode('utf8').rstrip('\n').title(), )) elif args.mode == "w2v_dict": for word in base_decompounder.model.vocab.keys(): print word.encode('utf-8') elif args.mode in ["1-best", "dict_w2v"]: vit = ViterbiDecompounder() vit.load_weights(modelSetup["WEIGHTS"]) words = [] if args.mode == "1-best": words = map(lambda line: line.decode('utf8').strip(), sys.stdin) else: words = base_decompounder.model.vocab.keys() print >> sys.stderr, "# words: %d" % len(words) def process_word(word): lattice = Lattice(base_decompounder.get_decompound_lattice(word)) viterbi_path = vit.viterbi_decode(Compound(word, None, lattice)) return [ word.encode('utf-8'),