acc_list = [] training_time = [] testing_time = [] dacc_list = [] dtraining_time = [] dtesting_time = [] bacc_list = [] btraining_time = [] btesting_time = [] entry_size = len(train_bin[0]) #Wisard for r in range(num_runs): wisard = wnn.Wisard(entry_size, tuple_bit, num_classes) #Training start = timer() wisard.train(train_bin, train_label) training_time.append(timer() - start) #Testing start = timer() rank_result = wisard.rank(test_bin) testing_time.append(timer() - start) #Accuracy num_hits = 0 for i in range(test_length):
p += 1 test_bin[i] = np.append(test_bin[i], binarr) i += 1 #print test_label #Wisard num_classes = 2 tuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30] acc_list = [] test_length = len(test_label) entry_size = len(train_bin[0]) #print entry_size for t in tuple_list: wisard = wnn.Wisard(entry_size, t, num_classes) wisard.train(train_bin, train_label) rank_result = wisard.rank(test_bin) num_hits = 0 for i in range(test_length): #if rank_result[i] == test_label[i]: if not (rank_result[i] ^ test_label[i]): num_hits += 1 acc_list.append(float(num_hits) / float(test_length)) #Bloom Wisard btuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 40, 56] bacc_list = []
#Discriminator disc = wnn.Discriminator(20, 4) disc.train(a) print disc.rank(a), disc.rank(b) #BloomDiscriminator bloom_disc = wnn.BloomDiscriminator(20, 4, 1000) bloom_disc.train(a) print bloom_disc.rank(a), bloom_disc.rank(b) bloom_disc.info() #Wisard print "Wisard" wisard2 = wnn.Wisard(20, 4, 2) c = [a, b] #t = np.ndarray(shape=(2, 20), buffer=np.array(c, dtype=bool), dtype=bool) wisard2.train(c, [0, 1]) #print t print wisard2.rank(c) wisard2.info() #BloomWisard print "Bloom Wisard" bwisard = wnn.BloomWisard(20, 4, 2, 1000) bwisard.train(c, [0, 1]) print bwisard.rank(c)