求肘部法则和轮廓系数法的详细python代码

肘部法则和轮廓系数法是聚类分析常用的评估指标，下面是详细的Python代码实现。

肘部法则代码：

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
import numpy as np

# 创建数据集
X = np.random.uniform(0, 1, (100, 2))

# 用肘部法则来确定最优的K值
K = range(1, 10)
mean_distortions = []
for k in K:
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    mean_distortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])

# 画出K值和畸变程度的关系图
plt.plot(K, mean_distortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average distortion')
plt.title('Selecting k with the Elbow Method')
plt.show()

轮廓系数法代码：

from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
import numpy as np

# 创建数据集
X = np.random.uniform(0, 1, (100, 2))

# 用轮廓系数法来确定最优的K值
K = range(2, 10)
silhouette_scores = []
for k in K:
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    silhouette_avg = silhouette_score(X, kmeans.labels_)
    silhouette_scores.append(silhouette_avg)

# 画出K值和轮廓系数的关系图
plt.plot(K, silhouette_scores, 'bx-')
plt.xlabel('k')
plt.ylabel('Silhouette Coefficient')
plt.title('Selecting k with the Silhouette Method')
plt.show()

以上是肘部法则和轮廓系数法的Python代码实现，可以根据需要进行修改和调整。