python ch指数

CH指数是一种用于评估聚类质量的指标，它衡量了聚类结果中簇内的紧密度和簇间的分离度。在Python中，可以使用sklearn库中的calinski_harabaz_score函数来计算CH指数。这个函数的源码可以参考以下代码：

from sklearn.utils import check_X_y
from sklearn.preprocessing import LabelEncoder
import numpy as np

def calinski_harabaz(X, labels):
    X, labels = check_X_y(X, labels)
    le = LabelEncoder()
    labels = le.fit_transform(labels)
    n_samples, _ = X.shape
    n_labels = len(le.classes_)
    extra_disp, intra_disp = 0., 0.
    mean = np.mean(X, axis=0)
    
    for k in range(n_labels):
        cluster_k = X\[labels == k\]
        mean_k = np.mean(cluster_k, axis=0)
        extra_disp += len(cluster_k) * np.sum((mean_k - mean) ** 2)
        intra_disp += np.sum((cluster_k - mean_k) ** 2)
    
    return (1. if intra_disp == 0. else extra_disp * (n_samples - n_labels) / (intra_disp * (n_labels - 1.)))

你可以将你的数据和对应的标签传递给calinski_harabaz函数，它将返回计算得到的CH指数。请注意，这个函数需要导入sklearn库中的check_X_y和LabelEncoder模块，以及numpy库。希望这个回答对你有帮助！\[3\]
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