绘制聚类结果

def solve():
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_iris
    from sklearn.cluster import KMeans
    import numpy as np
    iris = load_iris()
    X = iris.data
    inertias = []
    K = range(1, 11)
    for k in K:
        km = KMeans(n_clusters=k, random_state=42, n_init=10)
        km.fit(X)
        inertias.append(km.inertia_)
    plt.figure(figsize=(8, 5))
    plt.plot(K, inertias, 'bo-')
    plt.xlabel('Number of clusters')
    plt.ylabel('Inertia')
    plt.title('Elbow Method')
    plt.tight_layout()
    plt.show()
    print(f'{inertias[2]:.2f}')