#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) bwisard.info() print "Dict Wisard" wisard = wnn.DictWisard(20, 4, 2) wisard.train(c, [0, 1]) #print t print wisard.rank(c) wisard.info() print "Done"
#Accuracy
num_hits = 0
for i in range(test_length):
if rank_result[i] == test_label[i]:
num_hits += 1
acc_list.append(float(num_hits) / float(test_length))
wisard_stats = wisard.stats()
del wisard
#DictWisard
for r in range(num_runs):
dwisard = wnn.DictWisard(entry_size, tuple_bit, num_classes)
#Training
start = timer()
dwisard.train(train_bin, train_label)
dtraining_time.append(timer() - start)
#Testing
start = timer()
rank_result = dwisard.rank(test_bin)
dtesting_time.append(timer() - start)
#Accuracy
num_hits = 0
for i in range(test_length):
rank_result = wisard.rank(folds_test_bin[f])
num_hits = 0
for i in range(test_length):
#if rank_result[i] == folds_test_label[f][i]:
if not (rank_result[i] ^ test_label[i]):
num_hits += 1
acc_list[j] += (float(num_hits) / float(test_length))
j += 1
#Dic Wisard
j = 0
for t in tuple_list:
dwisard = wnn.DictWisard(entry_size, t, num_classes)
dwisard.train(folds_train_bin[f], folds_train_label[f])
rank_result = dwisard.rank(folds_test_bin[f])
num_hits = 0
for i in range(test_length):
#if rank_result[i] == folds_test_label[f][i]:
if not (rank_result[i] ^ test_label[i]):
num_hits += 1
dacc_list[j] += (float(num_hits) / float(test_length))
j += 1
#Bloom Wisard
j = 0
