如何从每个质心KMeans绘制n个最远点

Question

我正试图在Python中的iris数据集上训练一个kmeans模型。

有没有一种方法可以在Python中使用kmeans绘制离每个质心最远的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

您所做的是np.argsort(X_dist[:3])

它已经从未排序的X_dist中获取前三个值，因此您可以

尝试服用x=np.argsort(x_dist)和

排序完成后，你可以尝试

x[:3]

请随意提问，如果不起作用

欢呼