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import keras
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import matplotlib.pyplot as plt
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import pandas as pd
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import tensorflow as tf
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from keras.layers import Dense, LSTM, Activation, Dropout
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from keras.optimizers import SGD
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colum = 13
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step = 50
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df = pd.read_csv('文件名')
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df_for_training=df[:24000]
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df_for_testing=df[24000:]
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# print(df_for_testing.iloc[:, 2:])
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scaler = MinMaxScaler(feature_range=(-1, 1))
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df_for_training_scaled = scaler.fit_transform(df_for_training.iloc[:, 2:])
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print("df_for_training_scaled shape:",df_for_training_scaled.shape)
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#print(df_for_training_scaled.shape)
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#print(df_for_testing_scaled.shape)
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#抽取特征与标签列
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def createXY(dataset, n_past):
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dataX = []
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dataY = []
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for i in range(n_past, len(dataset)):
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dataX.append(dataset[i - n_past:i, 1:dataset.shape[1]])
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dataY.append(dataset[i, 0])
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return np.array(dataX), np.array(dataY)
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#trainX,testX数据的shape为[samples, steps, features]
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trainX, trainY = createXY(df_for_training_scaled, step)
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testX, testY = createXY(df_for_testing_scaled, step)
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#构建模型
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def build_model():
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model = tf.keras.models.Sequential()
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#经试验，如果下一层还是LSTM的话必须将return_sequences设置为True
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model.add(LSTM(20, input_shape=(step, column), return_sequences=True))
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model.add(LSTM(20))
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model.add(Activation('relu'))
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model.add(Dropout(0.2))
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model.add(Dense(1))
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model.compile(loss='mae', optimizer=SGD(lr=0.1,momentum=0.3))
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return model
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lstm_net = build_model()
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lstm_net.fit(trainX, trainY, batch_size=8, epochs=100)
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predictions = lstm_net.predict(testX)
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#进行逆变换
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test_Predict_copy = np.repeat(predictions,column+1,axis=-1)
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test_Predict_inverse = scaler.inverse_transform(test_Predict_copy)[:,0]
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#test_Predict_inverse = scaler.inverse_transform(test_Predict)
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test_label = np.array(df_for_testing['omega_vsg_1'])[50:]
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testY_copy = np.repeat(np.reshape(testY,(len(testY), 1)),column+1,axis=-1)
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testY_inverse = scaler = scaler.inverse_transform(testY_copy)[:,0]
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plt.plot(test_Predict_inverse,color='red')
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plt.plot(test_label,color='green')
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plt.plot(testY_inverse, color='blue')
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plt.savefig('result_test.png') |
