如何绘制离每个质心 KMeans 最远的 n 个点

Question

我正在尝试在 Python 中的 iris 数据集上训练 kmeans 模型。

有没有办法在 Python 中使用 kmeans 绘制距离每个质心最远的 n 个点？

这是一个完整的工作代码：

from sklearn import datasets
from sklearn.cluster import KMeans
import numpy as np

# import iris dataset
iris = datasets.load_iris()
X = iris.data[:, 2:5] # use two variables

# plot the two variables to check number of clusters
import matplotlib.pyplot as plt
plt.scatter(X[:, 0], X[:, 1]) 
 
# kmeans
km = KMeans(n_clusters = 2, random_state = 0) # Chose two clusters
y_pred = km.fit_predict(X)

X_dist = kmeans.transform(X) # get distances to each centroid

## Stuck at this point: How to make a function that extracts three points that are furthest from the two centroids
max3IdxArr = []
for label in np.unique(km.labels_):
    X_label_indices = np.where(y_pred == label)[0]
    # max3Idx = X_label_indices[np.argsort(X_dist[:3])] # This part is wrong
    max3Idx = X_label_indices[np.argsort(X_dist[:3])] # This part is wrong
    max3IdxArr.append(max3Idx)
  
max3IdxArr

# plot
plt.scatter(X[:, 0].iloc[max3IdxArr], X[:, 1].iloc[max3IdxArr])

Answer 1

你所做的是np.argsort(X_dist[:3])

它已经从未排序的 X_dist 中获取了前三个值，因此您可以

尝试服用 x=np.argsort(x_dist) 和

排序完成后，您可以尝试

x[:3]

欢迎提问，如果这不起作用

干杯

如何绘制离每个质心 KMeans 最远的 n 个点

How To Plot n Furthest Points From Each Centroid KMeans

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python-3.x