#!/usr/bin/env python
__author__ = "Tom Kocmi"
import logging
import VectorModel
import Cons
import generateRules
import new_fixes
import time
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
start = time.time() # for counting the time
model = VectorModel.getVectorModel() # this will load existing model
# in case if you want to generate new model, put True in the bracket
prefixes, suffixes = new_fixes.generateFixes(model.vocab.keys())
rules = GenerateRules.generate(prefixes, suffixes, model)
print rules
# generate prefixes and suffixes from the vocabulary of the model
# if the _fixes already exist it will load them from file instead of generating new ones.
# In case that you want to forse it to generate, put True as a second parameter
# experiments with the model, the words must be in the dictionary
# print model.most_similar(positive=['winston', 'love'], negative=['war'])
# print model.doesnt_match("winston julia brother goldstein".split())
# print model.similarity("winston", "julia")
print "Time: " + str(time.time() - start)
#!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ = "Tom Kocmi"
import logging
import VectorModel
import Cons, Fixes, GenerateRules
import time
import pickle
import Queue
start = time.time() # for counting the time
logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)
model = VectorModel.getVectorModel()
# vocabulary = Fixes.downsampleVocabulary(model, Cons.MAXWORDS4AFFIXES)
# with open("models/vocabulary2.data", 'w') as f:
# pickle.dump(vocabulary, f)
# with open("models/vocabulary.data", 'r') as f:
# vocabulary = pickle.load(f)
prefixes, suffixes = Fixes.generateFixes(vocabulary)
# rules = GenerateRules.generate(prefixes, suffixes, model, vocabulary)
# with open("models/rules6.data", 'w') as f:
# pickle.dump(rules, f)