#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with apriori ''' import apriori import random import loadText support = 0.4 loadText.importFromFile('spanish_db.txt') dataset = loadText.rawPriori #print dataset C1 = apriori.createC1(dataset) #print 'C1', C1 D = map(set,dataset) #print 'D', D L1, support_data = apriori.scanD(D,C1,support) #print 'L1', L1 #print 'support_data', support_data k_length = 2 transactions = apriori.aprioriGen(L1, k_length) #print 'transactions', transactions #print '\n*** *** ***' L,support_data = apriori.apriori(dataset, support) #print 'L', L #print 'support_data', support_data rules = apriori.generateRules(L, support_data, min_confidence=0.7) #print 'rules', rules
#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with markov models ''' import random import loadText loadText.importFromFile('snowflakes_db.txt') words = loadText.words neighbors = loadText.neighbors ## testing if __name__ == '__main__': #print '\n\n***\n' predicate = random.choice(words.keys()) sentence = [predicate] for i in range(8): c1 = set(words[sentence[-1]]) nextTo = neighbors[sentence[-1]] i_c_n = c1.intersection(nextTo) # if i_c_n == set([]): print 'no intersection of candidates and neighbors' association = random.choice(nextTo) else: association = random.choice(tuple(i_c_n)) print '\t association:', association sentence.append(association) print 'the current sentence is:', sentence
#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with apriori ''' import apriori import random import loadText support = 0.1 loadText.importFromFile('snowflakes_db.txt') dataset = loadText.rawPriori #print dataset C1 = apriori.createC1(dataset) #print 'C1', C1 D = map(set,dataset) #print 'D', D L1, support_data = apriori.scanD(D,C1,support) #print 'L1', L1 #print 'support_data', support_data print 'support_data' for k,v in support_data.iteritems(): print k,v k_length = 2 transactions = apriori.aprioriGen(L1, k_length) #print 'transactions', transactions #print '\n*** *** ***' L,support_data = apriori.apriori(dataset, support) #print 'L', L
#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with markov models ''' import random import loadText loadText.importFromFile('association_test_db_full.txt') words = loadText.words neighbors = loadText.neighbors ## testing if __name__ == '__main__': #print '\n\n***\n' predicate = random.choice(words.keys()) sentence = [predicate] for i in range(8): c1 = set(words[sentence[-1]]) nextTo = neighbors[sentence[-1]] i_c_n = c1.intersection(nextTo) # if i_c_n == set([]): print 'no intersection of candidates and neighbors' association = random.choice(nextTo) else: association = random.choice(tuple(i_c_n)) print '\t association:', association sentence.append(association) print 'the current sentence is:', sentence
#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with markov models ''' import random import loadText loadText.importFromFile('association_test_db_full.txt') words = loadText.words neighbors = loadText.neighbors ## testing if __name__ == '__main__': #print '\n\n***\n' predicate = random.choice(words.keys()) sentence = [predicate] for i in range(8): c1 = set(words[sentence[-1]]) nextTo = neighbors[sentence[-1]] i_c_n = c1.intersection(nextTo) # if i_c_n == set([]): print 'no intersection of candidates and neighbors' association = random.choice(nextTo) else: association = random.choice(tuple(i_c_n)) print '\t association:', association sentence.append(association)
#!/usr/bin/python # -*- coding: latin-1 -*- ''' Experiments with apriori ''' import apriori import random import loadText support = 0.4 loadText.importFromFile('spanish_db.txt') dataset = loadText.rawPriori #print dataset C1 = apriori.createC1(dataset) #print 'C1', C1 D = map(set, dataset) #print 'D', D L1, support_data = apriori.scanD(D, C1, support) #print 'L1', L1 #print 'support_data', support_data k_length = 2 transactions = apriori.aprioriGen(L1, k_length) #print 'transactions', transactions #print '\n*** *** ***' L, support_data = apriori.apriori(dataset, support) #print 'L', L #print 'support_data', support_data rules = apriori.generateRules(L, support_data, min_confidence=0.7) #print 'rules', rules