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)
