'KMeans' object has no attribute 'labels_'

这个错误可能是因为你在使用KMeans聚类算法之前没有先对数据进行拟合。KMeans模型需要对数据进行拟合才能得到聚类标签，即'labels_'属性。你可以尝试在调用KMeans的fit方法后再访问'labels_'属性，例如:

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(data)
labels = kmeans.labels_

这样就可以得到聚类标签了。如果还有问题，可以提供更多的代码细节和上下文信息，便于更好地帮助你解决问题。

相关问题

kmeans object has no attribute labels

The error message "kmeans object has no attribute labels" typically occurs when you try to access the "labels" attribute of a KMeans object in scikit-learn, but the attribute does not exist.

In scikit-learn, the KMeans algorithm does not have a "labels" attribute by default. Instead, you can use the "predict" method to assign labels to new data points based on a trained KMeans model. For example:

from sklearn.cluster import KMeans

# create KMeans object and fit it to data
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

# assign labels to new data points
labels = kmeans.predict(new_data)

If you need to access the cluster labels assigned to the training data by KMeans, you can use the "labels_" attribute instead:

# get cluster labels assigned to training data
train_labels = kmeans.labels_

Note that the "labels_" attribute only exists after the KMeans object has been fit to data.

'KMeans' object has no attribute '_estimate_weighted_centroids'

对不起，看起来你在引用的是`sklearn`库中的`KMeans`类，但在最新版的`scikit-learn`（v1.0及以上）中，`_estimate_weighted_centroids`属性已经被移除，取而代之的是公开的方法`partial_fit`，用于在已有训练数据的基础上更新模型。如果你想要在找到合适的簇后更新中心点，你应该使用`update`或`fit_predict`方法，而不是直接访问私有属性。

以下是修正后的代码段：

kmeans = KMeans(n_clusters=3, random_state=15, init='k-means++')

while True:
    kmeans.partial_fit(X)  # 或者使用 fit_predict 如果你想同时得到聚类标签
    cluster_labels = kmeans.labels_

    # ... (继续剩下的代码，包括检查簇大小和可能的合并)

    # 更新KMeans模型的簇中心
    kmeans.cluster_centers_ = kmeans.cluster_centers_

在这里，`partial_fit`会在每次迭代时对新来的数据进行调整，而`cluster_centers_`将自动更新为新的聚类中心。记得在`break`之前处理完簇大小检查和可能的合并操作。