如何使用seaborn绘制星团的质心？

Question

基本上，我想这样画：

我已经成功地使用

sns.scatterplot(X[:,0], X[:,1], hue=y, palette=['red', 'blue', 'purple', 'green'], alpha=0.5, s=7)

这导致了

如何像前一幅图像一样固定质心

Answer 1

你可以计算每组的平均值，然后在那个位置画一个散点

from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np

N = 1000
X0 = np.random.normal(np.repeat(np.random.uniform(0, 20, 4), N), 1)
X1 = np.random.normal(np.repeat(np.random.uniform(0, 10, 4), N), 1)
X = np.vstack([X0, X1]).T
y = np.repeat(range(4), N)
colors = ['red', 'blue', 'purple', 'green']
ax = sns.scatterplot(X[:, 0], X[:, 1], hue=y, palette=colors, alpha=0.5, s=7)

means = np.vstack([X[y == i].mean(axis=0) for i in range(4)])
ax = sns.scatterplot(means[:, 0], means[:, 1], hue=range(4), palette=colors, s=20, ec='black', legend=False, ax=ax)
plt.show()

或者，Scikit Learns的KMeans可用于计算KMeans标签和平均值：

from sklearn.cluster import KMeans
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns

N = 500
X0 = np.random.normal(np.repeat(np.random.uniform(0, 20, 20), N), 3)
X1 = np.random.normal(np.repeat(np.random.uniform(0, 10, 20), N), 2)
X = np.vstack([X0, X1]).T
num_clusters = 4
kmeans = KMeans(n_clusters=num_clusters).fit(X)

colors = ['red', 'blue', 'purple', 'green']
ax = sns.scatterplot(X[:, 0], X[:, 1], hue=kmeans.labels_, palette=colors, alpha=0.5, s=7)
ax = sns.scatterplot(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1],
                     hue=range(num_clusters), palette=colors, s=20, ec='black', legend=False, ax=ax)
plt.show()