177 lines
7.5 KiB
Python
177 lines
7.5 KiB
Python
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# coding=UTF-8
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import numpy as np
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from tensorflow.keras.layers import LSTM, GRU, SimpleRNN, Dense, Input, LeakyReLU
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from tensorflow.keras.models import Sequential
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from tcn.tcn import TCN
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class modelss:
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def __init__(self,X_train, X_test, Y_train, Y_test, scaled_tool):
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self.X_train = X_train
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self.Y_train = Y_train
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self.X_test = X_test
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self.Y_tedst = Y_test
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self.scaled = scaled_tool
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self.epochs = 60
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self.batch = 32
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self.units = 20
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def run_tcn_gru(self):
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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####搭建预测模型
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model = Sequential()
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model.add(Input(batch_shape=(None, X_train.shape[1], X_train.shape[2])))
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model.add(TCN(nb_filters=10, kernel_size=2, dilations=[1, 2, 4], return_sequences=True))
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model.add(GRU(units=self.units, return_sequences= False))
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model.add(Dense(10))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(1))
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###配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.summary()
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_tcn_lstm(self):
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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####搭建预测模型
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model = Sequential()
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model.add(Input(batch_shape=(None, X_train.shape[1], X_train.shape[2])))
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model.add(TCN(nb_filters=10, kernel_size=2, dilations=[1, 2, 4], return_sequences=True))
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model.add(LSTM(units=self.units, return_sequences= False))
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model.add(Dense(10))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(1))
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###配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.summary()
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_tcn_rnn(self):
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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####搭建预测模型
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model = Sequential()
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model.add(Input(batch_shape=(None, X_train.shape[1], X_train.shape[2])))
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model.add(TCN(nb_filters=10, kernel_size=2, dilations=[1,2,4],return_sequences=True))
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model.add(SimpleRNN(units=self.units, return_sequences= False))
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model.add(Dense(10))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(1))
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###配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.summary()
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_tcn_bpnn(self):
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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####搭建预测模型
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model = Sequential()
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model.add(Input(batch_shape=(None, X_train.shape[1], X_train.shape[2])))
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model.add(TCN(nb_filters=10, kernel_size=2, dilations=[1, 2, 4], return_sequences=False))
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model.add(Dense(self.units))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(self.units))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(self.units))
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model.add(LeakyReLU(alpha=0.3))
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model.add(Dense(1))
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###配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.summary()
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_GRU(self):
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# 张量转化
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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# 搭建预测模型
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model = Sequential()
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model.add(GRU(units=self.units, input_shape=(X_train.shape[1],1)))
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model.add(Dense(1))
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# 配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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# model.summary()
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# model.predict()对模型进行预测
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_LSTM(self):
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# 张量转化
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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# 搭建预测模型
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model = Sequential()
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model.add(LSTM(units=self.units, input_shape=(X_train.shape[1],1)))
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model.add(Dense(1))
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# 配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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# model.summary()
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# model.predict()对模型进行预测
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_RNN(self):
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# 张量转化
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X_train = np.reshape(self.X_train, (self.X_train.shape[0], self.X_train.shape[1], 1))
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X_test = np.reshape(self.X_test, (self.X_test.shape[0], self.X_test.shape[1], 1))
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# 搭建预测模型
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model = Sequential()
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model.add(SimpleRNN(units=self.units, input_shape=(X_train.shape[1],1)))
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model.add(Dense(1))
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# 配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.fit(X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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# model.summary()
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# model.predict()对模型进行预测
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Y_pre = model.predict(X_test)
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Y_pre = Y_pre.reshape(X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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def run_BPNN(self):
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model = Sequential()
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model.add(Dense(self.units, activation='relu', input_shape=(self.X_train.shape[1],)))
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model.add(Dense(self.units, activation='relu'))
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model.add(Dense(self.units, activation='relu'))
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model.add(Dense(1))
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# 配置和训练
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model.compile(optimizer='Adam', loss='mse', metrics='mae')
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model.fit(self.X_train, self.Y_train, epochs=self.epochs, batch_size=self.batch)
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# model.summary()
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# model.predict()对模型进行预测
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Y_pre = model.predict(self.X_test)
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Y_pre = Y_pre.reshape(self.X_test.shape[0], 1)
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Y_pre = self.scaled.inverse_transform(Y_pre)
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return Y_pre
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