基于convlstm的地铁客流预测归一化代码

以下是基于convlstm的地铁客流预测归一化代码示例：

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv('data.csv')

# 对数据进行归一化
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

# 定义函数对数据进行reshape
def reshape_data(data, look_back, look_forward):
    X, Y = [], []
    for i in range(len(data) - look_back - look_forward + 1):
        X.append(data[i:(i + look_back)])
        Y.append(data[(i + look_back):(i + look_back + look_forward)])
    return np.array(X), np.array(Y)

# 定义输入输出的时间步
look_back = 12
look_forward = 6

# 对数据进行reshape
X, Y = reshape_data(scaled_data, look_back, look_forward)

# 将数据集分为训练集和测试集
train_size = int(len(X) * 0.8)
test_size = len(X) - train_size
train_X, train_Y = X[:train_size], Y[:train_size]
test_X, test_Y = X[train_size:], Y[train_size:]

# 将数据集reshape为3D形状
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1], train_X.shape[2]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1], test_X.shape[2]))

# 输出结果
print(train_X.shape, train_Y.shape, test_X.shape, test_Y.shape)

该代码中，首先读取数据，并使用`MinMaxScaler`对数据进行归一化。接着定义函数`reshape_data`对数据进行reshape，将其转换为3D形式。最后将数据集分为训练集和测试集，并将其reshape为3D形状。