如何将轮廓得分添加到列表中

Question

我想在循环的列表中添加剪影分数。你知道吗

        from sklearn.cluster import KMeans
        from sklearn.metrics import silhouette_score

        ks = range(1, 11) # for 1 to 10 clusters
        #sse = []
        sil = []

        for k in ks:
             # Create a KMeans instance with k clusters: model
             kmeans = KMeans(n_clusters = k)
             # Fit model to samples
             #kmeans.fit(X)
             cluster_labels = kmeans.fit_predict(X) #X is dataset that preprocess already.
             silhouette = silhouette_score(X, cluster_labels)


             # Append the inertia to the list of inertias
             #sse.append(kmeans.inertia_)

             #Append silhouette to the list
             sil.append(silhouette)

但是，当我用剪影评分设置剪影时，我在第21行得到以下错误

       ValueError                   Traceback (most recent call last)
       <ipython-input-12-2570ccf62502> in <module>()
       18     #kmeans.fit(X)
       19     cluster_labels = kmeans.fit_predict(X)
   --->20     silhouette = silhouette_score(X, cluster_labels)
       21 
       22 

Answer 1

这是全部代码还是部分代码？如果在此之前没有代码，那么很明显在赋值之前没有定义或使用X。你知道吗

所以把这行放在分配X的地方，事情应该会很顺利。你知道吗

否则，请将完整跟踪添加到错误

Answer 2

； from sklearn.datasets import make_blobs from sklearn.cluster import KMeans from sklearn.metrics import silhouette_samples, silhouette_score X, y = make_blobs(n_samples=500, n_features=2, centers=4, cluster_std=1, center_box=(-10.0, 10.0), shuffle=True, random_state=1) sil=[] #start the cluster range from 2 range_n_clusters = range(2,10) for n_clusters in range_n_clusters: clusterer = KMeans(n_clusters=n_clusters, random_state=10) cluster_labels = clusterer.fit_predict(X) silhouette_avg = silhouette_score(X, cluster_labels) print("For n_clusters =", n_clusters, "The average silhouette_score is :", silhouette_avg) sil.append(silhouette_avg) ；

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