gru和mlp组合预测代码

以下是一个使用GRU和MLP组合进行预测的代码示例：

首先，导入所需的库和模块：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, GRU
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler

接下来，读取数据集并进行预处理：

# 读取数据集
dataset = pd.read_csv('data.csv', header=None)

# 将数据集转换为 numpy 数组
data = dataset.values.astype('float32')

# 将数据集标准化
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# 分割数据集
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train, test = data[0:train_size,:], data[train_size:len(data),:]

# 创建训练集和测试集
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 将数据集转换为三维数组
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

然后，定义 GRU 和 MLP 模型：

# 定义 GRU 模型
model_gru = Sequential()
model_gru.add(GRU(units=50, input_shape=(look_back, 1)))
model_gru.add(Dense(units=1))
model_gru.compile(loss='mean_squared_error', optimizer='adam')

# 定义 MLP 模型
model_mlp = Sequential()
model_mlp.add(Dense(units=50, input_dim=look_back))
model_mlp.add(Dense(units=1))
model_mlp.compile(loss='mean_squared_error', optimizer='adam')

接下来，训练 GRU 模型并进行预测：

# 训练 GRU 模型
model_gru.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 进行预测
trainPredict_gru = model_gru.predict(trainX)
testPredict_gru = model_gru.predict(testX)

# 将预测结果转换为原始值
trainPredict_gru = scaler.inverse_transform(trainPredict_gru)
trainY = scaler.inverse_transform([trainY])
testPredict_gru = scaler.inverse_transform(testPredict_gru)
testY = scaler.inverse_transform([testY])

然后，训练 MLP 模型并进行预测：

# 训练 MLP 模型
model_mlp.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 进行预测
trainPredict_mlp = model_mlp.predict(trainX)
testPredict_mlp = model_mlp.predict(testX)

# 将预测结果转换为原始值
trainPredict_mlp = scaler.inverse_transform(trainPredict_mlp)
trainY = scaler.inverse_transform([trainY])
testPredict_mlp = scaler.inverse_transform(testPredict_mlp)
testY = scaler.inverse_transform([testY])

最后，将 GRU 和 MLP 的预测结果进行加权平均并计算均方根误差：

# 将 GRU 和 MLP 的预测结果进行加权平均
trainPredict = (0.5 * trainPredict_gru) + (0.5 * trainPredict_mlp)
testPredict = (0.5 * testPredict_gru) + (0.5 * testPredict_mlp)

# 计算训练集和测试集的均方根误差
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))