#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