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LSTM_NASA_B0005_predict_MCsimulation.py
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LSTM_NASA_B0005_predict_MCsimulation.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Sep 22 13:23:14 2019
@author: Yang
"""
import numpy
#import keras
import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#import seaborn as sns
#from sklearn import metrics # for the check the error and accuracy of the model
#from sklearn.metrics import mean_squared_error
## for Deep-learing:
#import keras
#from keras.layers import Dense
#from keras.models import Sequential
#from keras.utils import to_categorical
#from keras.optimizers import SGD
#from keras.callbacks import EarlyStopping
#from keras.utils import np_utils
#import itertools
#from keras.layers import LSTM
#from keras.layers import Bidirectional
#from keras.layers.convolutional import Conv1D
#from keras.layers.convolutional import MaxPooling1D
#from keras.layers import Dropout
io = r'C:\Users\Yang\Desktop\毕业设计\电池容量数据\NASA实验数据\Battery Dataset One\B0005&6&7&18_discharge&capacity.xlsx'
B0005_data=pd.read_excel(io,sheet_name='B0005',header=0)
#features=['Batt_name','cycle','amb_temp','voltage_battery','current_battery','temp_battery','current_load','voltage_load','time','Capacity','H']
f1=['cycle','Capacity']
dataset=B0005_data[f1]
data_train=dataset[(dataset['cycle']<80)]
data_set_train=data_train.iloc[:,1:2].values
data_test=dataset[(dataset['cycle']>=80)]
data_set_test=data_test.iloc[:,1:2].values
from sklearn.preprocessing import MinMaxScaler #标准化
sc=MinMaxScaler(feature_range=(0,1))
data_set_train=sc.fit_transform(data_set_train)
data_set_test=sc.transform(data_set_test) # ????
#X_train=[]
#y_train=[]
#take the last 10t to predict 10t+1
#for i in range(5,79):
#X_train.append(data_set_train[i-5:i,0])
#y_train.append(data_set_train[i,0])
#X_train,y_train=np.array(X_train),np.array(y_train)
#X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))#X_train.shape[0]=69,X_train.shape[1]=10,将X_train变成（69,10,1）矩阵；
#Apply LSTM
#rate=0.5
#regress=Sequential()
#regress.add(Bidirectional(LSTM(units=20, return_sequences=True), input_shape=(X_train.shape[1],1)))
#regress.add(Dropout(rate))
#regress.add(LSTM(units=20, return_sequences=True))
#regress.add(Dropout(rate))
#regress.add(LSTM(units=20, return_sequences=True))
#regress.add(Dropout(rate))
#regress.add(LSTM(units=20))
#regress.add(Dropout(rate))
#regress.add(Dense(units=1))
#regress.compile(optimizer='Adam',loss='mean_squared_error')
#regress.fit(X_train,y_train,epochs=200,batch_size=50)
from keras.models import load_model
regress=load_model('B0005_cycle60_training model_1')
#MC Dropout
import keras.backend as K
class KerasDropoutPrediction(object):
def __init__(self,model):
self.f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[-1].output])
def predict(self, x, n_iter = 100):
result = []
for i in range(n_iter):
result.append(self.f((x, 1)))
result=np.array(result)
prediction = result.mean(axis=0)
uncertainty = result.std(axis=0)
return prediction, uncertainty
kdp = KerasDropoutPrediction(regress)
#Test the trained model
#Predictions
data_total=pd.concat((data_train['Capacity'],data_test['Capacity']),axis=0)
inputs=numpy.zeros(shape=(94,))
inputs[:5]=data_total[len(data_total)-len(data_test)-5:len(data_total)-len(data_test)].values
inputs=inputs.reshape(-1,1)
inputs=sc.transform(inputs)
#每一次的迭代输入
X_test=[]
X_test=np.array(X_test)
sigma=numpy.zeros(shape=(89,))
for i in range(5,94):
X_test=inputs[i-5:i,0]
X_test=np.reshape(X_test,(1,X_test.shape[0],1))
pred = kdp.predict(X_test, n_iter = 100)
inputs[i]=pred[0].reshape(1,-1)
sigma[i-5]=pred[1].reshape(1,-1)
inputs=sc.inverse_transform(inputs)
#variance=sc.inverse_transform(sigma)
inputs=inputs[:,0]
prediction_1=inputs[5:]
##画图
import matplotlib.pyplot as plt
plt.plot(dataset['cycle'],dataset['Capacity'],'.k')
X=dataset['cycle']
X=np.array(X[79:]).astype(float)
plt.plot(X,prediction_1[:],'g')