From 4fb91924f6d32cd82846b9cd4001fb851b89c83e Mon Sep 17 00:00:00 2001 From: markilue <745518019@qq.com> Date: Thu, 11 Jan 2024 15:59:36 +0800 Subject: [PATCH] =?UTF-8?q?=E8=AE=BA=E6=96=87=E6=96=B9=E9=9D=A2=E6=9B=B4?= =?UTF-8?q?=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../java/com/cqu/ge/DataServiceImplTest.java | 1 + .../AccountPool/accountpool/processors/generator.py | 15 +- .../AccountPool/accountpool/processors/server.py | 1 + .../AccountPool/accountpool/storages/redis.py | 6 +- .../大规模账号池的搭建/AccountPool/importer.py | 1 + .../大规模账号池的搭建/AccountPool/register.py | 2 + .../HI_create/HI_merge.py | 50 +++---- .../Model_train_test/RUL/FFTUtils.py | 6 +- .../Model_train_test/RUL/test/FFTTest.py | 20 +-- .../Model_train_test/RUL/test/test1.py | 33 +++++ .../condition_monitoring/healthyScore.py | 85 ++++++++++++ .../others_idea/CNN_GRU.py | 83 +++++------ .../condition_monitoring/plot/test_plot.py | 7 +- .../HI_create/step1_Original_data.py | 1 - .../HealthyScorePredict/__init__.py | 8 ++ .../dataset/HI_merge_data.npy | Bin 0 -> 10544 bytes .../HealthyScorePredict/test/__init__.py | 8 ++ .../HealthyScorePredict/test/healthyScore.py | 58 ++++++++ .../test/plotHealthyScore.py | 102 ++++++++++++++ pytorch_example/RUL/baseModel/dctAttention.py | 46 +++++++ .../RUL/baseModel/dctChannelAttention.py | 30 ++-- .../RUL/otherIdea/LSTM/loadData.py | 10 +- pytorch_example/RUL/otherIdea/LSTM/train.py | 4 +- .../adaNHDctLSTM/dataset_vibrate/loadData.py | 9 +- .../RUL/otherIdea/adaNHDctLSTM/model.py | 33 +++-- .../RUL/otherIdea/adaNHDctLSTM/train.py | 64 ++++++++- pytorch_example/temp/MSETest.py | 130 ++++++++++++++++++ 27 files changed, 684 insertions(+), 129 deletions(-) create mode 100644 TensorFlow_eaxmple/Model_train_test/RUL/test/test1.py create mode 100644 TensorFlow_eaxmple/Model_train_test/condition_monitoring/healthyScore.py create mode 100644 pytorch_example/HealthyScorePredict/__init__.py create mode 100644 pytorch_example/HealthyScorePredict/dataset/HI_merge_data.npy create mode 100644 pytorch_example/HealthyScorePredict/test/__init__.py create mode 100644 pytorch_example/HealthyScorePredict/test/healthyScore.py create mode 100644 pytorch_example/HealthyScorePredict/test/plotHealthyScore.py create mode 100644 pytorch_example/temp/MSETest.py diff --git a/GE_Migrating_data/src/main/java/com/cqu/ge/DataServiceImplTest.java b/GE_Migrating_data/src/main/java/com/cqu/ge/DataServiceImplTest.java index 54cef0b..db482e0 100644 --- a/GE_Migrating_data/src/main/java/com/cqu/ge/DataServiceImplTest.java +++ b/GE_Migrating_data/src/main/java/com/cqu/ge/DataServiceImplTest.java @@ -185,6 +185,7 @@ public class DataServiceImplTest { assertEquals(tagName1, result.get(0).getTagName()); assertEquals(tagName2, result.get(1).getTagName()); + // if (result.get(0).getTagName().equals(tagName1) && result.get(1).getTagName().equals(tagName2)) { // DataSample[] datasamples1 = result.get(0).getSamples(); // DataSample[] datasamples2 = result.get(1).getSamples(); diff --git a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/generator.py b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/generator.py index 7852131..ec656e3 100644 --- a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/generator.py +++ b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/generator.py @@ -14,7 +14,8 @@ class BaseGenerator(object): raise InitException self.account_operator = RedisClient(type='account', website=self.website) self.credential_operator = RedisClient(type='credential', website=self.website) - + + # 所有子类重写generate方法,写自己的生成逻辑 def generate(self, username, password): """ generate method @@ -22,14 +23,16 @@ class BaseGenerator(object): :param password: password :return: """ + # 这里直接抛出了NotImplementedError异常，因此子类必须实现该方法，否则运行时会报错 raise NotImplementedError - + def init(self): """ do init """ pass - + + # 模板代码设计模式，写标准的run流程 def run(self): """ run main process @@ -48,7 +51,7 @@ import requests class Antispider6Generator(BaseGenerator): - + def init(self): """ do init @@ -56,7 +59,7 @@ class Antispider6Generator(BaseGenerator): if self.account_operator.count() == 0: self.account_operator.set('admin', 'admin') self.account_operator.set('admin2', 'admin2') - + def generate(self, username, password): """ generate main process @@ -66,6 +69,7 @@ class Antispider6Generator(BaseGenerator): return login_url = 'https://antispider6.scrape.center/login' s = requests.Session() + # 这里省去了注册账号的过程 s.post(login_url, data={ 'username': username, 'password': password @@ -80,7 +84,6 @@ class Antispider6Generator(BaseGenerator): class Antispider7Generator(BaseGenerator): - MAX_COUNT = 100 def init(self): diff --git a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/server.py b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/server.py index 74b7208..f334a53 100644 --- a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/server.py +++ b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/processors/server.py @@ -24,6 +24,7 @@ def get_conn(): """ for website in GENERATOR_MAP: if not hasattr(g, website): + # 这里的get和set本质上可以看成建立一个map setattr(g, f'{website}_{credential}', RedisClient(credential, website)) setattr(g, f'{website}_{account}', RedisClient(account, website)) return g diff --git a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/storages/redis.py b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/storages/redis.py index cd42903..10f7441 100644 --- a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/storages/redis.py +++ b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/accountpool/storages/redis.py @@ -10,6 +10,8 @@ class RedisClient(object): def __init__(self, type, website, host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD): """ + type: 是存储的account信息；还是Cookie相关信息 + website: 爬取的对应网站 init redis client :param host: redis host :param port: redis port @@ -18,9 +20,11 @@ class RedisClient(object): self.db = redis.StrictRedis(host=host, port=port, password=password, decode_responses=True) self.type = type self.website = website - + + # 这里的name相当于redis-key def name(self): """ + 这里是做二级分类，二级hash，方便存取 get hash name :return: name of hash """ diff --git a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/importer.py b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/importer.py index 917d328..926995b 100644 --- a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/importer.py +++ b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/importer.py @@ -1,6 +1,7 @@ from accountpool.storages.redis import RedisClient import argparse +# 将生成的账号导入账号池 parser = argparse.ArgumentParser(description='AccountPool') parser.add_argument('website', type=str, help='website') args = parser.parse_args() diff --git a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/register.py b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/register.py index 782398a..544f1a8 100644 --- a/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/register.py +++ b/Spider/Chapter10_模拟登录/大规模账号池的搭建/AccountPool/register.py @@ -3,6 +3,7 @@ from acinonyx import run import requests from loguru import logger +# 具体用法参考：https://pypi.org/project/acinonyx/ # This is a script for registering account for antispider7, using acinonyx to accelerate. parser = argparse.ArgumentParser(description='AccountPool') @@ -23,6 +24,7 @@ def register(username, password): if __name__ == '__main__': accounts = [] + # 注册 for index in range(1, 1000): accounts.append((f'admin{index}', f'admin{index}')) run(register, accounts) diff --git a/TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/HI_create/HI_merge.py b/TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/HI_create/HI_merge.py index d23c3fa..bdcce01 100644 --- a/TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/HI_create/HI_merge.py +++ b/TensorFlow_eaxmple/Model_train_test/2012轴承数据集预测挑战/HI_create/HI_merge.py @@ -55,28 +55,28 @@ class model(): # HI指标训练和合成 -if __name__ == '__main__': - model = model(input_shape=6).getModel(model_Type='ae') - model.compile(optimizer=tf.optimizers.Adam(0.001), loss=tf.losses.mse, metrics=['acc']) - # model.summary() - - checkpoint = tf.keras.callbacks.ModelCheckpoint( - filepath="AE_model.h5", - monitor='val_loss', - verbose=2, - save_best_only=True, - mode='min') - lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=0.0001) - - model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse) - # model.compile(optimizer=tf.optimizers.SGD(learning_rate=0.001), loss=FTMSE()) - model.summary() - early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=20, mode='min', verbose=1) - - history = model.fit(train_data, train_data, epochs=1000, batch_size=100) - HI_merge_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('mid').output).predict(train_data) - print(HI_merge_data) - acc = np.array(history.history.get('acc')) - # if acc[299] > 0.9: - np.save("HI_merge_data1.npy", HI_merge_data) - model.save("AE_model.h5") +# if __name__ == '__main__': +# model = model(input_shape=6).getModel(model_Type='ae') +# model.compile(optimizer=tf.optimizers.Adam(0.001), loss=tf.losses.mse, metrics=['acc']) +# # model.summary() +# +# checkpoint = tf.keras.callbacks.ModelCheckpoint( +# filepath="AE_model.h5", +# monitor='val_loss', +# verbose=2, +# save_best_only=True, +# mode='min') +# lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=0.0001) +# +# model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse) +# # model.compile(optimizer=tf.optimizers.SGD(learning_rate=0.001), loss=FTMSE()) +# model.summary() +# early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=20, mode='min', verbose=1) +# +# history = model.fit(train_data, train_data, epochs=1000, batch_size=100) +# HI_merge_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('mid').output).predict(train_data) +# print(HI_merge_data) +# acc = np.array(history.history.get('acc')) +# # if acc[299] > 0.9: +# np.save("HI_merge_data1.npy", HI_merge_data) +# model.save("AE_model.h5") diff --git a/TensorFlow_eaxmple/Model_train_test/RUL/FFTUtils.py b/TensorFlow_eaxmple/Model_train_test/RUL/FFTUtils.py index 0929169..d7f47f5 100644 --- a/TensorFlow_eaxmple/Model_train_test/RUL/FFTUtils.py +++ b/TensorFlow_eaxmple/Model_train_test/RUL/FFTUtils.py @@ -48,8 +48,8 @@ if __name__ == '__main__': 0.343256533146, 0.008932195604]) - print(tf.signal.fft(array)) + print(np.fft.fft(array)) array_fft = np.fft.fft(array) - # print(ifft(array)) - print(array_fft) + print(ifft(array)) + # print(array_fft) diff --git a/TensorFlow_eaxmple/Model_train_test/RUL/test/FFTTest.py b/TensorFlow_eaxmple/Model_train_test/RUL/test/FFTTest.py index 315c415..2b288ab 100644 --- a/TensorFlow_eaxmple/Model_train_test/RUL/test/FFTTest.py +++ b/TensorFlow_eaxmple/Model_train_test/RUL/test/FFTTest.py @@ -40,16 +40,16 @@ angle1 = np.arctan2(imag, real) print(angle) print(angle1) -# result = [] -# (num,) = amp.shape -# (total,) = array.shape -# for j in range(total): -# wk = 2 * np.pi * np.arange(num) / num -# cur = np.sum(amp * np.cos(wk * j + angle)) -# result.append(cur) -# -# print(result) -# print(array) +result = [] +(num,) = amp.shape +(total,) = array.shape +for j in range(total): + wk = 2 * np.pi * np.arange(num) / num + cur = np.sum(amp * np.cos(wk * j + angle)) + result.append(cur) + +print(result) +print(array) # # print(array_fft) # # plt.subplot(2, 1, 1) diff --git a/TensorFlow_eaxmple/Model_train_test/RUL/test/test1.py b/TensorFlow_eaxmple/Model_train_test/RUL/test/test1.py new file mode 100644 index 0000000..d6b9353 --- /dev/null +++ b/TensorFlow_eaxmple/Model_train_test/RUL/test/test1.py @@ -0,0 +1,33 @@ +#-*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2024/1/11 14:20 +@Usage : +@Desc : +''' + +import requests + +import numpy as np +import matplotlib.pyplot as plt + +def getData(): + url = 'http://172.28.9.61:8100/api/data/signalData?dataset_name=Shaft2_radial&thing_id=windturbine-rmq-01&time=20240110200508000' + + response = requests.get(url) + + y = response.json()['data']['timeSequence_y'] + + y = np.array(y) + amp = np.abs(np.fft.fft(y) / len(y)) + + plt.plot(amp[:len(amp)//2]) + plt.show() + + print(1) + + + +if __name__ == '__main__': + getData() \ No newline at end of file diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/healthyScore.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/healthyScore.py new file mode 100644 index 0000000..d16a0e5 --- /dev/null +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/healthyScore.py @@ -0,0 +1,85 @@ +# -*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2023/12/22 19:42 +@Usage : +@Desc : 测试计算健康度 +''' + +import matplotlib.pyplot as plt +import math +import numpy as np + +ucl = 4 + + +def func(x, ucl): + y = [] + z = [] + s = [] + g = [] + f = [] + for i in x: + each_g = -math.exp(math.log(2) / ucl * i) + 2 + each_f = 2 / (1 + math.exp((i - ucl) * 1.5)) - 1 + if i < ucl: + each_z = (2 / (1 + math.exp(-(ucl / i - 1))) - 1) + else: + each_z = (2 / (1 + math.exp(((i - ucl) / (2 * ucl - i)))) - 1) + each_s = math.cos(math.pi * i / (2 * ucl)) + each_y = 50 + 50 * each_z + y.append(each_y) + z.append(each_z) + s.append(each_s) + g.append(each_g) + f.append(each_f) + return y, z, s, g, f + pass + + +def healthy_score(x): + weight = 2 / (1 + math.exp((x - ucl) * 1.5)) - 1 + healthy_score = 60 + 40 * weight + return healthy_score + + +x = np.linspace(0.1, 2 * ucl - 0.1, 40) +y, z, s, g, f = func(x, 4) +print(x) + +print(z) +print(f) +print(s) +print(healthy_score(ucl)) +# plt.subplot(4, 1, 1) +# plt.plot(x, y) +# plt.subplot(4, 1, 2) +# plt.plot(x, z) +# plt.subplot(4, 1, 3) +# plt.plot(x, f) +# plt.subplot(4, 1, 4) +# plt.plot(x, s) +# # plt.subplot(4, 1, 4) +# # plt.plot(x, g) +# plt.show() +plt.figure(2) +plt.rc('font', family='Times New Roman') # 全局字体样式 + +plt.plot(x, f, label='a') +plt.plot(x, z, label='b') +plt.plot(x, s, label='c') +font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16} # 设置坐标标签的字体大小，字体 +font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 13} # 设置坐标标签的字体大小，字体 +indices = [i for i in range(8)] +y_indices = [-1, -0.5, 0, 0.5, 1] +x_classes = ['UCL/4', 'UCL/2', '3UCL/4', 'UCL', '5UCL/4', '3UCL/2', '7UCL/4', '2UCL'] +y_classes = ['0', '25', '50', '75', '100'] +plt.xticks([index + 0.5 for index in indices], x_classes, fontproperties=font1) # 设置横坐标方向，rotation=45为45度倾斜 +plt.yticks([index for index in y_indices], y_classes, fontproperties=font1) # 设置横坐标方向，rotation=45为45度倾斜 +# pad调整label与坐标轴之间的距离 +plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=5, width=1, pad=1) +plt.ylabel('Healthy Mapping', fontdict=font2) +plt.xlabel('R$_t$', fontdict=font2) +plt.legend(fontsize=16) +plt.show() diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/others_idea/CNN_GRU.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/others_idea/CNN_GRU.py index e2bb0f0..2ac7b5b 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/others_idea/CNN_GRU.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/others_idea/CNN_GRU.py @@ -210,28 +210,29 @@ if __name__ == '__main__': print("===============================") - test_data, test_label = get_training_data(total_data[20000:, :]) - predicted_data = newModel.predict(test_data) - rows, cols = predicted_data.shape - print("=====================================") - print(predicted_data) - print(predicted_data.shape) - print("=====================================") + # test_data, test_label = get_training_data(total_data[20000:, :]) + # predicted_data = newModel.predict(test_data) + # rows, cols = predicted_data.shape + # print("=====================================") + # print(predicted_data) + # print(predicted_data.shape) + # print("=====================================") - temp = np.abs(predicted_data - test_label) - temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape)) - temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape) - temp3 = temp1 / temp2 - mse = np.sum((temp1 / temp2) ** 2, axis=1) - print("====================") - print("new_mse:",mse) - print(mse.shape) - np.savetxt("mse", mse, delimiter=',') - print("===================") + # temp = np.abs(predicted_data - test_label) + # temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape)) + # temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape) + # temp3 = temp1 / temp2 + # mse = np.sum((temp1 / temp2) ** 2, axis=1) + # print("====================") + # print("new_mse:",mse) + # print(mse.shape) + # np.savetxt("mse", mse, delimiter=',') + # print("===================") - plt.plot(mse[2000:]) - plt.plot(mean) - plt.plot(max) + # plt.plot(mse[2000:]) + plt.plot(mse) + # plt.plot(mean) + # plt.plot(max) plt.show() @@ -239,24 +240,24 @@ if __name__ == '__main__': - data = pd.DataFrame(mse).ewm(span=3).mean() - print(data) - data =np.array(data) - - index,_ = data.shape - - - - for i in range(2396): - if data[i,0] >5: - data[i,0] = data[i-1,:] - print(data) - mean = data[2000:2396,:].mean() - std = data[2000:2396,:].std() - mean=np.broadcast_to(mean,shape=[500,]) - std=np.broadcast_to(std,shape=[500,]) - plt.plot(data[2000:2396,:]) - plt.plot(mean) - plt.plot(mean+3*std) - plt.plot(mean-3*std) - plt.show() + # data = pd.DataFrame(mse).ewm(span=3).mean() + # print(data) + # data =np.array(data) + # + # index,_ = data.shape + # + # + # + # for i in range(2396): + # if data[i,0] >5: + # data[i,0] = data[i-1,:] + # print(data) + # mean = data[2000:2396,:].mean() + # std = data[2000:2396,:].std() + # mean=np.broadcast_to(mean,shape=[500,]) + # std=np.broadcast_to(std,shape=[500,]) + # plt.plot(data[2000:2396,:]) + # plt.plot(mean) + # plt.plot(mean+3*std) + # plt.plot(mean-3*std) + # plt.show() diff --git a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py index fc652d1..84082b4 100644 --- a/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py +++ b/TensorFlow_eaxmple/Model_train_test/condition_monitoring/plot/test_plot.py @@ -917,6 +917,7 @@ def plot_loss(): if __name__ == '__main__': + # plot_mse_single() # model_list = ["DCConv", "GRU"] # # for model_name in model_list: @@ -1030,8 +1031,8 @@ if __name__ == '__main__': # ] # # acc(list) # - list=[98.56,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74,88.36,95.52] - plot_FNR1(list) + # list=[98.56,98.95,99.95,96.1,95,99.65,76.25,72.64,75.87,68.74,88.36,95.52] + # plot_FNR1(list) # # # # # # list=[3.43,1.99,1.92,2.17,1.63,1.81,1.78,1.8,0.6] # list=[3.43,0,0,0,0,0,1.8,0,0,0,0,0,0.6,0] @@ -1048,7 +1049,7 @@ if __name__ == '__main__': # 单独预测图 # plot_mse('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/banda_joint_result_predict3.csv',data='E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\JM_banda/raw_data.csv') # plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_D/banda\RNet_D_banda_mse_predict1.csv') - # plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_3\RNet_3_timestamp120_feature10_result.csv') + plot_mse_single('E:\self_example\TensorFlow_eaxmple\Model_train_test\condition_monitoring\self_try\compare\mse\RNet_3\RNet_3_timestamp120_feature10_result.csv') # 画3d图 # plot_3d() diff --git a/TensorFlow_eaxmple/Model_train_test/wind_turbine_predict/HI_create/step1_Original_data.py b/TensorFlow_eaxmple/Model_train_test/wind_turbine_predict/HI_create/step1_Original_data.py index dad46ea..6b3b285 100644 --- a/TensorFlow_eaxmple/Model_train_test/wind_turbine_predict/HI_create/step1_Original_data.py +++ b/TensorFlow_eaxmple/Model_train_test/wind_turbine_predict/HI_create/step1_Original_data.py @@ -25,7 +25,6 @@ z = 0 L.sort(key=lambda x:x.split(".")[1].split("_")[1]) for filename in L: print("读取了{0}个文件".format(z+1)) - # data_single = pd.read_csv(filename, dtype=np.float32, header=None) data_single = np.loadtxt(filename, delimiter=',',dtype=np.str) # data_single = data_single.iloc[0, :].values if z == 0: diff --git a/pytorch_example/HealthyScorePredict/__init__.py b/pytorch_example/HealthyScorePredict/__init__.py new file mode 100644 index 0000000..5b535ec --- /dev/null +++ b/pytorch_example/HealthyScorePredict/__init__.py @@ -0,0 +1,8 @@ +#-*- encoding:utf-8 -*- + +''' 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z_Ak4T`+2`_VZ?jiQdm>mEi8Q2tn^DUV=&H;LRSvBA@EBRq&VSC_#lj zTLg=KUn!`zJ5uoUX5v*}StoM#))>K(1opTvV4KLDN39q1>-?ACr-T!NzBD2|nzbf7cPWd}mP<3{);K3~=g3i;j1x<903x26PA~>m(yyc2N zM2f{!nD67-B2HsSvOreNvq literal 0 HcmV?d00001 diff --git a/pytorch_example/HealthyScorePredict/test/__init__.py b/pytorch_example/HealthyScorePredict/test/__init__.py new file mode 100644 index 0000000..cedba65 --- /dev/null +++ b/pytorch_example/HealthyScorePredict/test/__init__.py @@ -0,0 +1,8 @@ +#-*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2024/1/10 14:56 +@Usage : +@Desc : +''' \ No newline at end of file diff --git a/pytorch_example/HealthyScorePredict/test/healthyScore.py b/pytorch_example/HealthyScorePredict/test/healthyScore.py new file mode 100644 index 0000000..bfc0137 --- /dev/null +++ b/pytorch_example/HealthyScorePredict/test/healthyScore.py @@ -0,0 +1,58 @@ +# -*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2023/12/22 19:42 +@Usage : +@Desc : 测试计算健康度 +''' + +import matplotlib.pyplot as plt +import math +import numpy as np + + + + +def func(x, ucl): + f = [] + for i in x: + each_f = 2 / (1 + math.exp((i - ucl) * 1.5)) - 1 + f.append(each_f) + return f + pass + + +def cal_healthy_score(x,ucl): + healthy_score = [] + for i in x: + weight = 2 / (1 + math.exp((i - ucl) * 1.5)) - 1 + healthy_score.append(50 + 50 * weight) + return healthy_score + + +if __name__ == '__main__': + ucl = 4 + x = np.linspace(0.1, 2 * ucl - 0.1, 40) + y, z, s, g, f = func(x, 4) + + plt.figure(2) + plt.rc('font', family='Times New Roman') # 全局字体样式 + + plt.plot(x, f, label='a') + plt.plot(x, z, label='b') + plt.plot(x, s, label='c') + font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16} # 设置坐标标签的字体大小，字体 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 13} # 设置坐标标签的字体大小，字体 + indices = [i for i in range(8)] + y_indices = [-1, -0.5, 0, 0.5, 1] + x_classes = ['UCL/4', 'UCL/2', '3UCL/4', 'UCL', '5UCL/4', '3UCL/2', '7UCL/4', '2UCL'] + y_classes = ['0', '25', '50', '75', '100'] + plt.xticks([index + 0.5 for index in indices], x_classes, fontproperties=font1) # 设置横坐标方向，rotation=45为45度倾斜 + plt.yticks([index for index in y_indices], y_classes, fontproperties=font1) # 设置横坐标方向，rotation=45为45度倾斜 + # pad调整label与坐标轴之间的距离 + plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=5, width=1, pad=1) + plt.ylabel('Healthy Mapping', fontdict=font2) + plt.xlabel('R$_t$', fontdict=font2) + plt.legend(fontsize=16) + plt.show() diff --git a/pytorch_example/HealthyScorePredict/test/plotHealthyScore.py b/pytorch_example/HealthyScorePredict/test/plotHealthyScore.py new file mode 100644 index 0000000..8eb31b4 --- /dev/null +++ b/pytorch_example/HealthyScorePredict/test/plotHealthyScore.py @@ -0,0 +1,102 @@ +# -*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2024/1/10 14:56 +@Usage : +@Desc : +''' +import numpy as np +import matplotlib.pyplot as plt +from healthyScore import cal_healthy_score + + +def calHealthyScore(data, scope): + mu = np.mean(data[:scope], axis=0) + sigma = np.std(data[:scope], axis=0) + + healthy_score = cal_healthy_score(data, mu + 3 * sigma) + + print(healthy_score) + # 画健康度图 + font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16} # 设置坐标标签的字体大小，字体 + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 13} # 设置坐标标签的字体大小，字体 + plt.rc('font', family='Times New Roman') # 全局字体样式 + plt.ylabel('Healthy Score', fontdict=font2) + plt.xlabel('time', fontdict=font2) + # plt.legend(fontsize=16) + plt.plot(healthy_score) + plt.show() + + return np.array(healthy_score) + + +# LSTM_cell的数目,维度,是否正则化 +def getData(filter_num, dims, if_norm: bool = False): + # 数据读入 + HI_merge_data_origin = np.load("../dataset/HI_merge_data.npy") + HI_merge_data = HI_merge_data_origin[0:1250, 1] + HI_merge_data = calHealthyScore(HI_merge_data, 800) + np.savetxt("../dataset/Healthy_score.csv",HI_merge_data,delimiter=',') + # plt.plot(HI_merge_data) + # plt.show() + + # 去除掉退化特征不明显前面的点 + # HI_merge_data = np.loadtxt("E:\self_example\pytorch_example\RUL\dataset\smallVHI.csv", delimiter=",") + + # # 是否正则化 + # if if_norm: + # HI_merge_data = normalization(HI_merge_data) + # + # # plt.plot(HI_merge_data) + # # plt.show() + # (total_dims,) = HI_merge_data.shape + # + # # # 将其分成重叠采样状态-滑动窗口函数 + # predict_data = np.empty(shape=[total_dims - filter_num, filter_num]) + # + # # 重叠采样获取时间部和训练次数 + # for dim in range(total_dims - filter_num): + # predict_data[dim] = HI_merge_data[dim:dim + filter_num] + # + # train_label = predict_data[dims:, :] + # train_label_single = HI_merge_data[dims + filter_num - 1:-1] + # + # # 再重叠采样获取一个点的维度 + # '''train_data.shape:(sample,filter_num) -> (sample,filter_num,dims)''' + # + # # # 将其分成重叠采样状态-滑动窗口函数 + # train_data = np.empty(shape=[dims, total_dims - filter_num - dims, filter_num]) + # + # for dim in range(dims): + # train_data[dim] = predict_data[dim:total_dims - filter_num - dims + dim, :] + # + # # 转置变成想要的数据 (dims,sample,filter_num) -> (sample,filter_num,dims) + # + # train_data = np.transpose(train_data, [1, 2, 0]) + # + # # todo 解决模型保存时,query无法序列化的问题 + # total_data = HI_merge_data + # + # print("total_data.shape:", total_data.shape) + # print("train_data.shape:", train_data.shape) # (20, 1200, 30) + # print("train_label.shape:", train_label.shape) # (20, 1200) + # print("train_label_single.shape:", train_label_single.shape) + # + # # 所有的原始数据;所有的训练数据;所有的训练标签(预测一个序列);所有的训练标签(预测一个点) + # return total_data, train_data, train_label, train_label_single + + +def standardization(data): + mu = np.mean(data, axis=0) + sigma = np.std(data, axis=0) + return (data - mu) / sigma + + +def normalization(data): + _range = np.max(data) - np.min(data) + return (data - np.min(data)) / _range + + +if __name__ == '__main__': + getData(10, 10, False) diff --git a/pytorch_example/RUL/baseModel/dctAttention.py b/pytorch_example/RUL/baseModel/dctAttention.py index 757116b..457bda9 100644 --- a/pytorch_example/RUL/baseModel/dctAttention.py +++ b/pytorch_example/RUL/baseModel/dctAttention.py @@ -105,6 +105,52 @@ class dct_channel_block(nn.Module): return x * lr_weight # result + +class dct_channel_block_withConv(nn.Module): + def __init__(self, channel): + super(dct_channel_block_withConv, self).__init__() + # self.avg_pool = nn.AdaptiveAvgPool1d(1) #innovation + self.fc = nn.Sequential( + nn.Linear(channel, channel * 2, bias=False), + nn.Dropout(p=0.1), + nn.ReLU(inplace=True), + nn.Linear(channel * 2, channel, bias=False), + nn.Sigmoid() + ) + + # self.dct_norm = nn.LayerNorm([512], eps=1e-6) + + self.dct_norm = nn.LayerNorm([channel], eps=1e-6) # for lstm on length-wise + # self.dct_norm = nn.LayerNorm([36], eps=1e-6)#for lstm on length-wise on ill with input =36 + + def forward(self, x): + b, c = x.size() # (B,C,L) (32,96,512) + + # list = [] + # for i in range(c): + # freq = dct.dct(x[:, :, i]) + # list.append(freq) + # + # stack_dct = torch.stack(list, dim=2) + + # change = x.transpose(2, 1) + stack_dct = dct.dct(x,norm='ortho') + + # stack_dct = stack_dct.transpose(2, 1) + + # stack_dct = torch.tensor(stack_dct) + ''' + for traffic mission:f_weight = self.dct_norm(f_weight.permute(0,2,1))#matters for traffic datasets + ''' + + lr_weight = self.dct_norm(stack_dct) + lr_weight = self.fc(stack_dct) + lr_weight = self.dct_norm(lr_weight) + + # print("lr_weight",lr_weight.shape) + return x * lr_weight # result + + if __name__ == '__main__': # input_data = torch.Tensor([[1, 2, 3], [4, 5, 6]]) # [2, 3] x = torch.rand((32, 10, 64)) diff --git a/pytorch_example/RUL/baseModel/dctChannelAttention.py b/pytorch_example/RUL/baseModel/dctChannelAttention.py index b6b7b92..38cc454 100644 --- a/pytorch_example/RUL/baseModel/dctChannelAttention.py +++ b/pytorch_example/RUL/baseModel/dctChannelAttention.py @@ -82,26 +82,25 @@ class dct_channel_block(nn.Module): def forward(self, x): b, t, c = x.size() # (B,C,L) (32,96,512) - # list = [] - # for i in range(c): - # freq = dct.dct(x[:, :, i]) - # list.append(freq) - # - # stack_dct = torch.stack(list, dim=2) + list = [] + for i in range(c): + freq = dct.dct(x[:, :, i],norm='ortho') + list.append(freq) - change = x.transpose(2, 1) - stack_dct = dct.dct(change,norm='ortho') - stack_dct = stack_dct.transpose(2, 1) + stack_dct = torch.stack(list, dim=2) + + # 经测试，上下两种不一样 + # change = x.transpose(2, 1) + # stack_dct_a = dct.dct(change,norm='ortho') + # stack_dct_b = stack_dct_a.transpose(2, 1) # stack_dct = torch.tensor(stack_dct) ''' for traffic mission:f_weight = self.dct_norm(f_weight.permute(0,2,1))#matters for traffic datasets ''' - lr_weight = self.dct_norm(stack_dct) lr_weight = self.fc(stack_dct) lr_weight = self.dct_norm(lr_weight) - # print("lr_weight",lr_weight.shape) return x * lr_weight # result @@ -147,12 +146,15 @@ class dct_channel_block1(nn.Module): # print("lr_weight",lr_weight.shape) return x * lr_weight # result + + if __name__ == '__main__': # input_data = torch.Tensor([[1, 2, 3], [4, 5, 6]]) # [2, 3] x = torch.rand((8, 7, 96)) - dct_model = dct_channel_block1(7) - result = dct_model.forward(x) - print(result) + dct_model = dct_channel_block(96) + result1,result2 = dct_model.forward(x) + print(result1) + print(result2) # print(x.shape) # m = nn.Linear(64, 2) # output = m(x) diff --git a/pytorch_example/RUL/otherIdea/LSTM/loadData.py b/pytorch_example/RUL/otherIdea/LSTM/loadData.py index 45ee895..f565413 100644 --- a/pytorch_example/RUL/otherIdea/LSTM/loadData.py +++ b/pytorch_example/RUL/otherIdea/LSTM/loadData.py @@ -47,12 +47,12 @@ def normalization(data): # LSTM_cell的数目,维度,是否正则化 def getData(filter_num, dims, if_norm: bool = False): # 数据读入 - HI_merge_data_origin = np.load("../../dataset/HI_merge_data.npy") - + # HI_merge_data_origin = np.load("../../dataset/HI_merge_data.npy") + # HI_merge_data = HI_merge_data_origin[0:1250, 1] # plt.plot(HI_merge_data[0:1250, 1]) - # 去除掉退化特征不明显前面的点 - HI_merge_data = HI_merge_data_origin[0:1250, 1] - # HI_merge_data = np.loadtxt("E:\self_example\pytorch_example\RUL\dataset\smallVHI.csv", delimiter=",") + + + HI_merge_data = np.loadtxt("E:\self_example\pytorch_example\HealthyScorePredict\dataset\Healthy_score.csv", delimiter=",") # 是否正则化 if if_norm: diff --git a/pytorch_example/RUL/otherIdea/LSTM/train.py b/pytorch_example/RUL/otherIdea/LSTM/train.py index 3f1838a..9a7284a 100644 --- a/pytorch_example/RUL/otherIdea/LSTM/train.py +++ b/pytorch_example/RUL/otherIdea/LSTM/train.py @@ -27,12 +27,12 @@ import RUL.baseModel.utils.utils as utils 超参数设置: ''' hidden_num = 10 # LSTM细胞个数 -feature = 2 # 一个点的维度 +feature = 10 # 一个点的维度 batch_size = 32 EPOCH = 1000 seed = 5 predict_num = 200 # 预测个数 -is_norm = False +is_norm = True is_single = True model_name = "LSTM" base_save = r"parameters/{0}_hidden{1}_feature{2}_predict{3}".format(model_name, hidden_num, feature, diff --git a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/dataset_vibrate/loadData.py b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/dataset_vibrate/loadData.py index dd3fa37..a578339 100644 --- a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/dataset_vibrate/loadData.py +++ b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/dataset_vibrate/loadData.py @@ -47,12 +47,11 @@ def normalization(data): # LSTM_cell的数目,维度,是否正则化 def getData(filter_num, dims, if_norm: bool = False): # 数据读入 - HI_merge_data_origin = np.load("E:\self_example\pytorch_example\RUL\dataset\HI_merge_data.npy") - + # HI_merge_data_origin = np.load("E:\self_example\pytorch_example\RUL\dataset\HI_merge_data.npy") + # HI_merge_data = HI_merge_data_origin[0:1250, 1] # plt.plot(HI_merge_data[0:1250, 1]) - # 去除掉退化特征不明显前面的点 - HI_merge_data = HI_merge_data_origin[0:1250, 1] - # HI_merge_data = np.loadtxt("E:\self_example\pytorch_example\RUL\dataset\smallVHI.csv", delimiter=",") + + HI_merge_data = np.loadtxt("E:\self_example\pytorch_example\HealthyScorePredict\dataset\Healthy_score.csv", delimiter=",") # 是否正则化 if if_norm: HI_merge_data = normalization(HI_merge_data) diff --git a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/model.py b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/model.py index df0db61..86d87a7 100644 --- a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/model.py +++ b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/model.py @@ -60,8 +60,8 @@ class AdaRNN(nn.Module): ) ### 不加初始权重会让Hard predict更不稳定，振幅更大 # 初始权重越大，振幅越大 - bottleneck[-1].weight.data.normal_(0, 0.03) - bottleneck[-1].bias.data.fill_(0.1) + # bottleneck[-1].weight.data.normal_(0, 0.03) + # bottleneck[-1].bias.data.fill_(0.1) if bottleneck_list[i][1]: bottleneck.append(nn.BatchNorm1d(bottleneck_list[i][0])) if bottleneck_list[i][2]: @@ -80,11 +80,11 @@ class AdaRNN(nn.Module): nn.Conv1d(in_channels=2, out_channels=64, kernel_size=5, padding='same'), nn.BatchNorm1d(64), nn.ReLU(), - nn.AdaptiveAvgPool1d(2), + nn.AdaptiveAvgPool1d(self.len_seq//2), nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, padding='same'), nn.BatchNorm1d(64), nn.ReLU(), - nn.AdaptiveAvgPool1d(2), + nn.AdaptiveAvgPool1d(self.len_seq//4), nn.Flatten(), nn.Dropout(0.2), nn.Linear(len_seq // 4 * 64, 64), @@ -261,9 +261,24 @@ class AdaRNN(nn.Module): if __name__ == '__main__': - x = torch.rand((32, 10, 64)) - y = x.mean(1) - z = x.std(1) - print(y.size()) - print(z.size()) + x = torch.rand((64, 2, 20)) + + x=nn.Conv1d(in_channels=2, out_channels=64, kernel_size=5, padding='same')(x) + x=nn.BatchNorm1d(64)(x) + x=nn.ReLU()(x) + x=nn.AdaptiveAvgPool1d(2)(x) + x=nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, padding='same')(x) + x=nn.BatchNorm1d(64)(x) + x=nn.ReLU()(x) + x=nn.AdaptiveAvgPool1d(2)(x) + x=nn.Flatten()(x) + x=nn.Dropout(0.2)(x) + x=nn.Linear(320, 64)(x) + x=nn.BatchNorm1d(64)(x) + x=nn.ReLU()(x) + x=nn.Linear(64, 1)(x) + # y = x.mean(1) + # z = x.std(1) + # print(y.size()) + # print(z.size()) pass diff --git a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/train.py b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/train.py index f3409e9..1d22726 100644 --- a/pytorch_example/RUL/otherIdea/adaNHDctLSTM/train.py +++ b/pytorch_example/RUL/otherIdea/adaNHDctLSTM/train.py @@ -30,14 +30,14 @@ from RUL.baseModel.CommonFunction import IsStopTraining 超参数设置: ''' # 数据准备 -is_norm = False +is_norm = True is_single = True tdc_loss_type = 'cos' num_domain = 2 # 划分为几个源域和目标域 # RNN相关 hidden_num = 10 # LSTM细胞个数 -feature = 2 # 一个点的维度 +feature = 20 # 一个点的维度 predict_num = 200 # 预测个数 batch_size = 32 model_name = "AdaRNN" @@ -55,10 +55,10 @@ epochs = 1000 transfer_loss_type = 'cos' # 目前测试cos最好 dw = 0.5 # 迁移loss权重 lr = 0.01 -fft_dw = 0.00 # fft_loss权重 整体效果，感觉是让Hard Predict更准了0.01 0.1较好 +fft_dw = 0.01 # fft_loss权重 整体效果，感觉是让Hard Predict更准了0.01 0.1较好 mdw = 0.0 # 手工特征权重 整体效果，0.3 0.5比较好 len_win = 0 # 窗口大小为0，暂时不知道有什么用 -seed = 250 +seed = 5 # 相关初始化工作 out_dir = './outputs' @@ -362,6 +362,60 @@ def train(model, train_loader_list, valid_loader, lr_patience, early_stop_patien pass +# 全局最优再保存 +def trainForTotal(model, train_loader_list, valid_loader, lr_patience, early_stop_patience, device): + optimizer = optim.SGD(model.parameters(), lr=lr) + + scheduler_model = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.5, + patience=lr_patience) + + best_score = np.inf + best_epoch, stop_round = 0, 0 + weight_mat, dist_mat = None, None + train_loss_list = [] + val_loss_list = [] + best_save_path = None + for epoch in range(epochs): + epoch_start_time = time.time() + train_loss, loss1, weight_mat, dist_mat = train_AdaRNN( + model, optimizer, train_loader_list, epoch, dist_mat, weight_mat) + + val_loss, val_loss_l1, val_loss_r = val_epoch( + model, valid_loader, device=device, scheduler=scheduler_model) + + pprint( + "[{:03d}/{:03d}] {:2.2f} sec(s) train_total_loss: {:3.9f} | train_mse_loss: {:3.9f} | train_fft_loss: {:3.9f} | train_transfer_loss: {:3.9f} " + " | val_loss: {:3.9f} | Learning rate : {:3.6f}".format( + epoch + 1, epochs, time.time() - epoch_start_time, + train_loss[0], train_loss[1], train_loss[3], train_loss[2], + val_loss, + optimizer.state_dict()['param_groups'][0]['lr'])) + totalLoss = train_loss[1]+val_loss + if len(val_loss_list) == 0 or totalLoss < min(val_loss_list): + pprint(f"保存模型最佳模型成功,Loss:{totalLoss}") + best_epoch = epoch + best_score = val_loss + # 保存模型参数 + if best_save_path != None: + utils.delete_file(os.path.join(output_path, best_save_path)) + best_save_path = save_model_name + "_epoch" + str(epoch) + \ + "_trainLoss" + str('%.5f' % train_loss[1]) + \ + "_valLoss" + str('%.5f' % val_loss) + ".pkl" + print(os.path.join(output_path, best_save_path)) + torch.save(model.state_dict(), + os.path.join(output_path, best_save_path)) + + train_loss_list.append(train_loss) + val_loss_list.append(totalLoss) + + if IsStopTraining(history_loss=val_loss_list, patience=early_stop_patience): + pprint("{0}次loss未下降,训练停止".format(early_stop_patience)) + break + + pprint('best val score:', best_score, '@', best_epoch) + return best_save_path + pass + def main_transfer(): if torch.cuda.is_available(): device = torch.device("cuda:0") @@ -382,6 +436,8 @@ def main_transfer(): pprint('train model...') best_save_path = train(model=model, train_loader_list=train_loader_list, valid_loader=valid_loader, lr_patience=20, early_stop_patience=50, device=device) + # best_save_path = trainForTotal(model=model, train_loader_list=train_loader_list, valid_loader=valid_loader, lr_patience=20, + # early_stop_patience=50, device=device) end = time.time() diff --git a/pytorch_example/temp/MSETest.py b/pytorch_example/temp/MSETest.py new file mode 100644 index 0000000..03ade45 --- /dev/null +++ b/pytorch_example/temp/MSETest.py @@ -0,0 +1,130 @@ +# -*- encoding:utf-8 -*- + +''' +@Author : dingjiawen +@Date : 2023/12/27 16:52 +@Usage : +@Desc : 测试MSE三点不足 +''' +import numpy as np +import matplotlib.pyplot as plt +import torch_dct as dct + + +def plot(x, y): + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + plt.figure(1) + y[-1] = 0.00832 + + '''保存的模型参数的路径''' + + from matplotlib import rcParams + + config = { + "font.family": 'Times New Roman', # 设置字体类型 + "axes.unicode_minus": False, # 解决负号无法显示的问题 + "axes.labelsize": 13 + } + rcParams.update(config) + predict1 = y[-1] + 0.2 + predict2 = y[-1] - 0.2 + + # 简单预测图 + plt.scatter(x, y, c='blue', s=16, label='Actual value') + plt.plot(x, y, linewidth=2, color='red', + label='Traning value') + plt.scatter([x[-1]], [predict1], c='black', + s=18, label='Predict data1') + plt.scatter([x[-1]], [predict2], c='black', + s=18, label='Predict data2') + plt.xlabel('Serial number of the fusion feature point', font=font1) + plt.ylabel('Virtual health indicator', font=font1) + plt.legend(loc='upper left', prop=font2) + + plt.show() + + a = np.hstack([y[:-1], predict1]) + + b = np.hstack([y[:-1], predict2]) + return y, a, b + + +def fft(x, a, b): + from matplotlib import rcParams + + # 幅值 + amp_y0 = np.abs(x / len(x)) + amp_y1 = np.abs(a / len(a)) + amp_y2 = np.abs(b / len(b)) + # 相角 + angle_y0 = np.angle(x) + angle_y1 = np.angle(a) + angle_y2 = np.angle(b) + + plt.figure(2) + config = { + "font.family": 'Times New Roman', # 设置字体类型 + "axes.unicode_minus": False, # 解决负号无法显示的问题 + "axes.labelsize": 13 + } + rcParams.update(config) + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + + plt.plot(amp_y1, linewidth=2, color='green', + label='Predict data1') + plt.plot(amp_y2, linewidth=2, color='red', + label='Predict data2') + plt.plot(amp_y0, linewidth=2, color='blue', + label='Original data') + plt.legend(loc='upper left', prop=font2) + plt.xlabel('Serial number of the fusion feature point', font=font1) + plt.ylabel('Amplitude', font=font1) + plt.show() + + plt.figure(3) + config = { + "font.family": 'Times New Roman', # 设置字体类型 + "axes.unicode_minus": False, # 解决负号无法显示的问题 + "axes.labelsize": 13 + } + rcParams.update(config) + font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 15} # 设置坐标标签的字体大小，字体 + + plt.plot(angle_y1, linewidth=2, color='green', + label='Predict data1') + plt.plot(angle_y2, linewidth=2, color='red', + label='Predict data2') + plt.plot(angle_y0, linewidth=2, color='blue', + label='Original data') + plt.legend(loc='upper left', prop=font2) + plt.xlabel('Serial number of the fusion feature point', font=font1) + plt.ylabel('Angle', font=font1) + plt.show() + pass + + +length = 30 + +# y = np.array([-0.029078494757, +# -0.33095228672, +# -0.12124221772, +# 0.553512275219, +# -0.158036053181, +# 0.268739402294, +# -0.638222515583, +# 0.233140587807, +# -0.173265621066, +# 0.467218101025, +# -0.372010827065, +# -0.136630430818, +# 0.343256533146, +# 0.008932195604]) + +y = np.random.random([length, ]) +x = list(range(0, len(y))) +print(y) +y, a, b = plot(x, y) +fft(y, a, b)