import keras
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.layers import Dense, LSTM, Activation, Dropout
from keras.optimizers import SGD

colum = 13
step = 50
df = pd.read_csv('文件名')
df_for_training=df[:24000]
df_for_testing=df[24000:]
# print(df_for_testing.iloc[:, 2:])

scaler = MinMaxScaler(feature_range=(-1, 1))
df_for_training_scaled = scaler.fit_transform(df_for_training.iloc[:, 2:])
print("df_for_training_scaled shape:",df_for_training_scaled.shape)
#print(df_for_training_scaled.shape)
#print(df_for_testing_scaled.shape)

#抽取特征与标签列
def createXY(dataset, n_past):
    dataX = []
    dataY = []
    for i in range(n_past, len(dataset)):
        dataX.append(dataset[i - n_past:i, 1:dataset.shape[1]])
        dataY.append(dataset[i, 0])
    return np.array(dataX), np.array(dataY)
#trainX,testX数据的shape为[samples, steps, features]
trainX, trainY = createXY(df_for_training_scaled, step)
testX, testY = createXY(df_for_testing_scaled, step)

#构建模型
def build_model():
    model = tf.keras.models.Sequential()
    #经试验，如果下一层还是LSTM的话必须将return_sequences设置为True
    model.add(LSTM(20, input_shape=(step, column), return_sequences=True))
    model.add(LSTM(20))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))
    model.add(Dense(1))
    model.compile(loss='mae', optimizer=SGD(lr=0.1,momentum=0.3))
    return model

lstm_net = build_model()
lstm_net.fit(trainX, trainY, batch_size=8, epochs=100)
predictions = lstm_net.predict(testX)

#进行逆变换
test_Predict_copy = np.repeat(predictions,column+1,axis=-1)
test_Predict_inverse = scaler.inverse_transform(test_Predict_copy)[:,0]
#test_Predict_inverse = scaler.inverse_transform(test_Predict)
test_label = np.array(df_for_testing['omega_vsg_1'])[50:]
testY_copy = np.repeat(np.reshape(testY,(len(testY), 1)),column+1,axis=-1)

testY_inverse = scaler = scaler.inverse_transform(testY_copy)[:,0]

plt.plot(test_Predict_inverse,color='red')
plt.plot(test_label,color='green')
plt.plot(testY_inverse, color='blue')
plt.savefig('result_test.png')