'KMeans' object has no attribute 'labels'

这个错误通常是因为没有在KMeans对象上调用fit()函数来适应数据，从而没有为其分配标签。要解决这个问题，您需要在使用KMeans对象之前调用fit()函数。以下是一个示例代码：

from sklearn.cluster import KMeans
import numpy as np

# 创建一个KMeans对象
kmeans = KMeans(n_clusters=3)

# 生成一些数据
X = np.array([[1, 2], [1, 4], [1, 0],
              [4, 2], [4, 4], [4, 0]])

# 调用fit()函数适应数据
kmeans.fit(X)

# 使用KMeans对象进行预测
labels = kmeans.labels_

# 输出标签
print(labels)

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 'labels

这个错误通常发生在使用KMeans聚类算法时，可能是因为你的代码中没有对KMeans对象进行训练或者训练不成功，导致没有生成聚类标签。你可以检查以下代码中是否有对KMeans对象进行拟合的语句，例如使用fit()函数对数据进行训练。

如果有，你可以尝试检查训练数据的格式是否正确，或者尝试调整KMeans算法的超参数以获得更好的聚类效果。

如果没有，你需要在使用KMeans对象之前对其进行拟合，例如使用fit()函数对数据进行训练，然后才能使用labels属性获取聚类标签。