请编写一段基于keras的代码，读取csv文件中的第一列，利用滑动窗口法选择长度为300的子序列，窗口大小为300。从数据的第一个时刻开始，依次向后滑动窗口，选择对长序列来说余弦相似度和代表性综合最高的窗口作为代表性子序列并生成新的csv

以下是一段基于 Keras 的代码，用于读取 CSV 文件中的第一列，使用滑动窗口法选择长度为 300 的子序列，并生成新的 CSV 文件：

import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# 读取 CSV 文件
df = pd.read_csv("data.csv")
data = df.iloc[:, 0].values

# 窗口大小和步长
window_size = 300
stride = 1

# 滑动窗口选择代表性子序列
representative_subsequences = []
max_similarity = 0
for i in range(0, len(data) - window_size + 1, stride):
    subsequence = data[i:i+window_size].reshape(1, -1)
    similarity = 0
    for j in range(len(representative_subsequences)):
        similarity += cosine_similarity(subsequence, representative_subsequences[j])[0][0]
    if similarity >= max_similarity:
        max_similarity = similarity
        representative_subsequence = subsequence
    if i % 100 == 0:
        representative_subsequences.append(representative_subsequence)

# 生成新的 CSV 文件
representative_subsequences = np.vstack(representative_subsequences)
df = pd.DataFrame(representative_subsequences)
df.to_csv("representative_subsequences.csv", index=False)