论文方面更新
This commit is contained in:
markilue
parent
945a4c120c
commit
4fb91924f6
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@ -185,6 +185,7 @@ public class DataServiceImplTest {
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assertEquals(tagName1, result.get(0).getTagName());
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assertEquals(tagName2, result.get(1).getTagName());
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// if (result.get(0).getTagName().equals(tagName1) && result.get(1).getTagName().equals(tagName2)) {
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// DataSample[] datasamples1 = result.get(0).getSamples();
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// DataSample[] datasamples2 = result.get(1).getSamples();
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@ -15,6 +15,7 @@ class BaseGenerator(object):
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self.account_operator = RedisClient(type='account', website=self.website)
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self.credential_operator = RedisClient(type='credential', website=self.website)
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# 所有子类重写generate方法,写自己的生成逻辑
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def generate(self, username, password):
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"""
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generate method
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@ -22,6 +23,7 @@ class BaseGenerator(object):
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:param password: password
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:return:
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"""
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# 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
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raise NotImplementedError
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def init(self):
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@ -30,6 +32,7 @@ class BaseGenerator(object):
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"""
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pass
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# 模板代码设计模式，写标准的run流程
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def run(self):
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"""
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run main process
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@ -66,6 +69,7 @@ class Antispider6Generator(BaseGenerator):
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return
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login_url = 'https://antispider6.scrape.center/login'
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s = requests.Session()
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# 这里省去了注册账号的过程
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s.post(login_url, data={
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'username': username,
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'password': password
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@ -80,7 +84,6 @@ class Antispider6Generator(BaseGenerator):
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class Antispider7Generator(BaseGenerator):
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MAX_COUNT = 100
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def init(self):
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@ -24,6 +24,7 @@ def get_conn():
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"""
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for website in GENERATOR_MAP:
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if not hasattr(g, website):
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# 这里的get和set本质上可以看成建立一个map
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setattr(g, f'{website}_{credential}', RedisClient(credential, website))
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setattr(g, f'{website}_{account}', RedisClient(account, website))
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return g
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@ -10,6 +10,8 @@ class RedisClient(object):
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def __init__(self, type, website, host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD):
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"""
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type: 是存储的account信息；还是Cookie相关信息
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website: 爬取的对应网站
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init redis client
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:param host: redis host
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:param port: redis port
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@ -19,8 +21,10 @@ class RedisClient(object):
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self.type = type
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self.website = website
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# 这里的name相当于redis-key
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def name(self):
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"""
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这里是做二级分类，二级hash，方便存取
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get hash name
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:return: name of hash
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"""
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@ -1,6 +1,7 @@
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from accountpool.storages.redis import RedisClient
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import argparse
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# 将生成的账号导入账号池
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parser = argparse.ArgumentParser(description='AccountPool')
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parser.add_argument('website', type=str, help='website')
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args = parser.parse_args()
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@ -3,6 +3,7 @@ from acinonyx import run
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import requests
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from loguru import logger
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# 具体用法参考：https://pypi.org/project/acinonyx/
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# This is a script for registering account for antispider7, using acinonyx to accelerate.
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parser = argparse.ArgumentParser(description='AccountPool')
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@ -23,6 +24,7 @@ def register(username, password):
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if __name__ == '__main__':
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accounts = []
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# 注册
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for index in range(1, 1000):
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accounts.append((f'admin{index}', f'admin{index}'))
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run(register, accounts)
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@ -55,28 +55,28 @@ class model():
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# HI指标训练和合成
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if __name__ == '__main__':
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model = model(input_shape=6).getModel(model_Type='ae')
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model.compile(optimizer=tf.optimizers.Adam(0.001), loss=tf.losses.mse, metrics=['acc'])
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# model.summary()
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checkpoint = tf.keras.callbacks.ModelCheckpoint(
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filepath="AE_model.h5",
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monitor='val_loss',
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verbose=2,
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save_best_only=True,
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mode='min')
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lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=0.0001)
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model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
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# model.compile(optimizer=tf.optimizers.SGD(learning_rate=0.001), loss=FTMSE())
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model.summary()
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early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=20, mode='min', verbose=1)
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history = model.fit(train_data, train_data, epochs=1000, batch_size=100)
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HI_merge_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('mid').output).predict(train_data)
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print(HI_merge_data)
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acc = np.array(history.history.get('acc'))
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# if acc[299] > 0.9:
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np.save("HI_merge_data1.npy", HI_merge_data)
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model.save("AE_model.h5")
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# if __name__ == '__main__':
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# model = model(input_shape=6).getModel(model_Type='ae')
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# model.compile(optimizer=tf.optimizers.Adam(0.001), loss=tf.losses.mse, metrics=['acc'])
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# # model.summary()
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#
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# checkpoint = tf.keras.callbacks.ModelCheckpoint(
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# filepath="AE_model.h5",
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# monitor='val_loss',
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# verbose=2,
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# save_best_only=True,
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# mode='min')
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# lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=10, min_lr=0.0001)
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#
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# model.compile(optimizer=tf.optimizers.SGD(), loss=tf.losses.mse)
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# # model.compile(optimizer=tf.optimizers.SGD(learning_rate=0.001), loss=FTMSE())
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# model.summary()
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# early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=20, mode='min', verbose=1)
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#
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# history = model.fit(train_data, train_data, epochs=1000, batch_size=100)
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# HI_merge_data = tf.keras.models.Model(inputs=model.input, outputs=model.get_layer('mid').output).predict(train_data)
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# print(HI_merge_data)
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# acc = np.array(history.history.get('acc'))
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# # if acc[299] > 0.9:
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# np.save("HI_merge_data1.npy", HI_merge_data)
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# model.save("AE_model.h5")
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@ -48,8 +48,8 @@ if __name__ == '__main__':
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0.343256533146,
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0.008932195604])
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print(tf.signal.fft(array))
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print(np.fft.fft(array))
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array_fft = np.fft.fft(array)
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# print(ifft(array))
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print(array_fft)
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print(ifft(array))
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# print(array_fft)
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@ -40,16 +40,16 @@ angle1 = np.arctan2(imag, real)
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print(angle)
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print(angle1)
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# result = []
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# (num,) = amp.shape
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# (total,) = array.shape
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# for j in range(total):
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# wk = 2 * np.pi * np.arange(num) / num
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# cur = np.sum(amp * np.cos(wk * j + angle))
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# result.append(cur)
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#
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# print(result)
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# print(array)
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result = []
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(num,) = amp.shape
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(total,) = array.shape
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for j in range(total):
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wk = 2 * np.pi * np.arange(num) / num
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cur = np.sum(amp * np.cos(wk * j + angle))
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result.append(cur)
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print(result)
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print(array)
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# # print(array_fft)
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#
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# plt.subplot(2, 1, 1)
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@ -0,0 +1,33 @@
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#-*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2024/1/11 14:20
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@Usage :
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@Desc :
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'''
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import requests
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import numpy as np
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import matplotlib.pyplot as plt
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def getData():
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url = 'http://172.28.9.61:8100/api/data/signalData?dataset_name=Shaft2_radial&thing_id=windturbine-rmq-01&time=20240110200508000'
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response = requests.get(url)
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y = response.json()['data']['timeSequence_y']
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y = np.array(y)
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amp = np.abs(np.fft.fft(y) / len(y))
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plt.plot(amp[:len(amp)//2])
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plt.show()
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print(1)
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if __name__ == '__main__':
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getData()
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@ -0,0 +1,85 @@
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# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/12/22 19:42
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@Usage :
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@Desc : 测试计算健康度
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'''
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import matplotlib.pyplot as plt
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import math
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import numpy as np
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ucl = 4
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def func(x, ucl):
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y = []
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z = []
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s = []
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g = []
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f = []
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for i in x:
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each_g = -math.exp(math.log(2) / ucl * i) + 2
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each_f = 2 / (1 + math.exp((i - ucl) * 1.5)) - 1
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if i < ucl:
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each_z = (2 / (1 + math.exp(-(ucl / i - 1))) - 1)
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else:
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each_z = (2 / (1 + math.exp(((i - ucl) / (2 * ucl - i)))) - 1)
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each_s = math.cos(math.pi * i / (2 * ucl))
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each_y = 50 + 50 * each_z
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y.append(each_y)
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z.append(each_z)
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s.append(each_s)
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g.append(each_g)
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f.append(each_f)
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return y, z, s, g, f
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pass
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def healthy_score(x):
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weight = 2 / (1 + math.exp((x - ucl) * 1.5)) - 1
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healthy_score = 60 + 40 * weight
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return healthy_score
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x = np.linspace(0.1, 2 * ucl - 0.1, 40)
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y, z, s, g, f = func(x, 4)
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print(x)
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print(z)
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print(f)
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print(s)
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print(healthy_score(ucl))
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# plt.subplot(4, 1, 1)
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# plt.plot(x, y)
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# plt.subplot(4, 1, 2)
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# plt.plot(x, z)
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# plt.subplot(4, 1, 3)
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# plt.plot(x, f)
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# plt.subplot(4, 1, 4)
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# plt.plot(x, s)
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# # plt.subplot(4, 1, 4)
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# # plt.plot(x, g)
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# plt.show()
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plt.figure(2)
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plt.rc('font', family='Times New Roman') # 全局字体样式
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plt.plot(x, f, label='a')
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plt.plot(x, z, label='b')
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plt.plot(x, s, label='c')
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font2 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 16} # 设置坐标标签的字体大小，字体
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font1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 13} # 设置坐标标签的字体大小，字体
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indices = [i for i in range(8)]
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y_indices = [-1, -0.5, 0, 0.5, 1]
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x_classes = ['UCL/4', 'UCL/2', '3UCL/4', 'UCL', '5UCL/4', '3UCL/2', '7UCL/4', '2UCL']
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y_classes = ['0', '25', '50', '75', '100']
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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
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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
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# pad调整label与坐标轴之间的距离
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plt.tick_params(bottom=True, top=False, left=True, right=False, direction='inout', length=5, width=1, pad=1)
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plt.ylabel('Healthy Mapping', fontdict=font2)
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plt.xlabel('R$_t$', fontdict=font2)
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plt.legend(fontsize=16)
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plt.show()
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@ -210,28 +210,29 @@ if __name__ == '__main__':
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print("===============================")
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test_data, test_label = get_training_data(total_data[20000:, :])
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predicted_data = newModel.predict(test_data)
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rows, cols = predicted_data.shape
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print("=====================================")
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print(predicted_data)
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print(predicted_data.shape)
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print("=====================================")
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# test_data, test_label = get_training_data(total_data[20000:, :])
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# predicted_data = newModel.predict(test_data)
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# rows, cols = predicted_data.shape
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# print("=====================================")
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# print(predicted_data)
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# print(predicted_data.shape)
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# print("=====================================")
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temp = np.abs(predicted_data - test_label)
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temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
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temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
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temp3 = temp1 / temp2
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mse = np.sum((temp1 / temp2) ** 2, axis=1)
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print("====================")
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print("new_mse:",mse)
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print(mse.shape)
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np.savetxt("mse", mse, delimiter=',')
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print("===================")
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# temp = np.abs(predicted_data - test_label)
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# temp1 = (temp - np.broadcast_to(np.mean(temp, axis=0), shape=predicted_data.shape))
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# temp2 = np.broadcast_to(np.sqrt(np.var(temp, axis=0)), shape=predicted_data.shape)
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# temp3 = temp1 / temp2
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# mse = np.sum((temp1 / temp2) ** 2, axis=1)
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# print("====================")
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# print("new_mse:",mse)
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# print(mse.shape)
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# np.savetxt("mse", mse, delimiter=',')
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# print("===================")
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plt.plot(mse[2000:])
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plt.plot(mean)
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plt.plot(max)
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# plt.plot(mse[2000:])
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plt.plot(mse)
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# plt.plot(mean)
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# plt.plot(max)
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plt.show()
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@ -239,24 +240,24 @@ if __name__ == '__main__':
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data = pd.DataFrame(mse).ewm(span=3).mean()
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print(data)
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data =np.array(data)
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index,_ = data.shape
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for i in range(2396):
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if data[i,0] >5:
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data[i,0] = data[i-1,:]
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print(data)
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mean = data[2000:2396,:].mean()
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std = data[2000:2396,:].std()
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mean=np.broadcast_to(mean,shape=[500,])
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std=np.broadcast_to(std,shape=[500,])
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plt.plot(data[2000:2396,:])
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plt.plot(mean)
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plt.plot(mean+3*std)
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plt.plot(mean-3*std)
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plt.show()
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# data = pd.DataFrame(mse).ewm(span=3).mean()
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# print(data)
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# data =np.array(data)
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#
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# index,_ = data.shape
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#
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#
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#
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# for i in range(2396):
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# if data[i,0] >5:
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# data[i,0] = data[i-1,:]
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# print(data)
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# mean = data[2000:2396,:].mean()
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# std = data[2000:2396,:].std()
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# mean=np.broadcast_to(mean,shape=[500,])
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# std=np.broadcast_to(std,shape=[500,])
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# plt.plot(data[2000:2396,:])
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# plt.plot(mean)
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# plt.plot(mean+3*std)
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# plt.plot(mean-3*std)
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# plt.show()
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@ -917,6 +917,7 @@ def plot_loss():
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if __name__ == '__main__':
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# plot_mse_single()
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# model_list = ["DCConv", "GRU"]
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#
|
||||
# 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()
|
||||
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,8 @@
|
|||
#-*- encoding:utf-8 -*-
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2024/1/10 14:55
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
Binary file not shown.
|
|
@ -0,0 +1,8 @@
|
|||
#-*- encoding:utf-8 -*-
|
||||
|
||||
'''
|
||||
@Author : dingjiawen
|
||||
@Date : 2024/1/10 14:56
|
||||
@Usage :
|
||||
@Desc :
|
||||
'''
|
||||
|
|
@ -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()
|
||||
|
|
@ -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)
|
||||
|
|
@ -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))
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
Loading…
Reference in New Issue