def extract_feature_by_kmeans(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number) :
file_name = gen_feature_fname(class_num, subsample_size, window_size, cluster_num)
start = time.time()
train_X, test_X, train_y, test_y = FetchFile.gen_data(class_num, subsample_size, window_size, rnd_number)
print("Generate Data Time: ",time.time()-start)
features = learnvocabulary(train_X, cluster_num, max_iter)
features = mynormalize_multi(features)
write_features(file_name, features)
print("=== Feature Extraction Finish ===")
def grid_search_for_neighbor_multiprocess(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number, neighbor_num_seq): #Classification.classifiy(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number, neighbor_num) train_X, test_X, train_y, test_y = FetchFile.gen_data(class_num, subsample_size, window_size, rnd_number) jobs = [] #for neighbor_num in [2**i for i in range(neighbor_log2_num)]: for neighbor_num in neighbor_num_seq: p = multiprocessing.Process(target=Classification.classifiy, args=(class_num, subsample_size, window_size, cluster_num, \ max_iter, rnd_number, neighbor_num, train_X, train_y, test_X, test_y)) jobs.append(p) p.start() # end for print(jobs)
def grid_search_for_neighbor(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number, neighbor_num_seq): train_X, test_X, train_y, test_y = FetchFile.gen_data(class_num, subsample_size, window_size, rnd_number) #for neighbor_num in [2**i for i in range(neighbor_log2_num)]: for neighbor_num in neighbor_num_seq: Classification.classifiy(class_num, subsample_size, window_size, cluster_num, max_iter, rnd_number, neighbor_num, train_X, train_y, test_X, test_y)
