【更新7】ARCH和GARCH模型

晓阳凤年: ```python import numpy as np from sklearn.datasets import load_wine from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans from sklearn.metrics import homogeneity_score, completeness_score, silhouette_score import matplotlib.pyplot as plt # 加载红酒数据 wine = load_wine() data = wine.data # Z-Score归一化预处理 scaler = StandardScaler() data_scaled = scaler.fit_transform(data) # K-means聚类 kmeans = KMeans(n_clusters=3) kmeans.fit(data_scaled) # 获取聚类结果标签 labels = kmeans.labels_ # 同质性 homogeneity = homogeneity_score(wine.target, labels) print("Homogeneity:", homogeneity) # 完全性 completeness = completeness_score(wine.target, labels) print("Completeness:", completeness) # 轮廓系数 silhouette = silhouette_score(data_scaled, labels) print("Silhouette Coefficient:", silhouette) # 绘制散点图 plt.scatter(data[:, 0], data[:, 1], c=labels) plt.xlabel("Feature 1") plt.ylabel("Feature 2") plt.show() ``` 运行以上代码可以完成红酒数据的K-means聚类，并输出同质性、完全性和轮廓系数分数。最后，根据聚类结果绘制了散点图，不同簇的数据点用不同颜色标记。

勇敢牛牛，不怕困难801: 啊啊啊啊，气死了，还是不行，一堆报错文件

INSIST.166: 在哪儿看视频呀

qq_34706232: 这应该是anaconda非常重要的作用

理永: 这个成了