上述lstm的输入的数据格式为csv 第一列为时间和日期 第二列为浮点型数据 请写出代码

以下是一个简单的Python代码示例，用于从CSV文件中读取时间序列数据，将其转换为LSTM模型的输入格式，并将其拟合到LSTM模型中进行训练：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 读取CSV文件
data = pd.read_csv('data.csv', header=None, index_col=0)

# 将数据转换为LSTM模型的输入格式
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset)-look_back-1):
        x = dataset[i:(i+look_back), 0]
        X.append(x)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

# 设定随机数种子，以确保可重复性
np.random.seed(42)

# 划分训练集和测试集
train_size = int(len(data) * 0.7)
test_size = len(data) - train_size
train, test = data.iloc[0:train_size, :], data.iloc[train_size:len(data), :]

# 将数据转换为模型的输入格式
look_back = 3
train_X, train_Y = create_dataset(train.values, look_back)
test_X, test_Y = create_dataset(test.values, look_back)

# 将输入数据重塑为LSTM模型所需的形状
train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))
test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))

# 构建LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)

# 在测试集上进行预测
predictions = model.predict(test_X)