def normalizer(plot=False): data = read_data() ts = data[:, 0] num_req = data[:, 1] print('-----------------------') from sklearn import preprocessing # max_abs_scaler = preprocessing.StandardScaler() # num_req_normalize = max_abs_scaler.fit_transform(num_req.reshape(-1, 1)) # print('-----------------------') minMaxScaler = preprocessing.MinMaxScaler() num_req_normalize = minMaxScaler.fit_transform(num_req.reshape(-1, 1)) print('-----------------------', len(ts), len(num_req_normalize)) print('min = ', min(num_req_normalize), ' max = ', max(num_req_normalize)) print('-----------------------') # min1=min(num_req) # max1=max(num_req) # #num_req_normalize = num_req / (max1 - min1) # min2 = min(num_req_normalize) # max2 = max(num_req_normalize) # print('min = ', min2, ' max = ', max2) num_req_normalize = perform_knn(num_req_normalize) print('min = ', min(num_req_normalize), ' max = ', max(num_req_normalize)) if plot: fig = plt.figure(figsize=(8, 6)) plt.subplot(211) plt.plot(ts, num_req, color='red', label='REQ-data original') plt.ylabel('Num of REQ original') plt.legend() plt.xlabel('Time symbol') plt.subplot(212) plt.plot(ts, num_req_normalize, color='green', label='REQ-data normalized') plt.ylabel('Num of REQ normalized') plt.legend() plt.xlabel('Time symbol') plt.pause(3) plt.close() return ts, num_req_normalize, minMaxScaler
def normalizer(plot=False): ts, ts_train, ts_valid, ts_test, cpu_values, cpu_train, cpu_valid, cpu_test, \ ram_values, ram_train, ram_valid, ram_test=split_data(plot=False) std_cpu = np.std(cpu_values) std_ram = np.std(ram_values) mean_cpu = np.mean(cpu_values) mean_ram = np.mean(ram_values) #cpu_values_normalize = (np.array(cpu_values) - mean_cpu) / std_cpu #ram_values_normalize = (np.array(ram_values) - mean_ram) / std_ram cpu_max = np.max(cpu_values) cpu_min = np.min(cpu_values) ram_max = np.max(ram_values) ram_min = np.min(ram_values) print(cpu_max, cpu_min) print(ram_max, ram_min) print('----------------------') from sklearn import preprocessing # min_max_scaler = preprocessing.MinMaxScaler() # cpu_values_normalize = min_max_scaler.fit_transform(cpu_values.reshape(-1,1)) # ram_values_normalize = min_max_scaler.fit_transform(ram_values.reshape(-1, 1)) cpu_values_normalize = cpu_values / (cpu_max - cpu_min) ram_values_normalize = ram_values / (ram_max - ram_min) from knn import perform_knn cpu_values_normalize = perform_knn(cpu_values_normalize, 2) ram_values_normalize = perform_knn(ram_values_normalize, 2) cpu_max = np.max(cpu_values_normalize) cpu_min = np.min(cpu_values_normalize) ram_max = np.max(ram_values_normalize) ram_min = np.min(ram_values_normalize) print(cpu_max, cpu_min) print(ram_max, ram_min) print('----------------------') # mean_cpu_n=np.mean(cpu_values_normalize) # mean_ram_n = np.mean(ram_values_normalize) # std_cpu_n = np.std(cpu_values_normalize) # std_ram_n = np.std(ram_values_normalize) # cpu_values_normalize = (np.array(cpu_values_normalize) - mean_cpu_n) / std_cpu_n # ram_values_normalize = (np.array(ram_values_normalize) - mean_ram_n) /std_ram_n if plot: plt.subplot(2, 1, 1) plt.plot(ts, cpu_values_normalize, color='cyan', label='CPU') plt.ylabel('CPU Req normalized') plt.xlabel('Time symbol') plt.legend() plt.subplot(2, 1, 2) plt.plot(ts, ram_values_normalize, color='green', label='RAM') plt.ylabel('RAM Req normalized') plt.legend() plt.xlabel('Time symbol') plt.pause(3) plt.close() return std_cpu,std_ram,mean_cpu,mean_ram,ts,ts_train,ts_valid,ts_test, \ cpu_values_normalize,ram_values_normalize