如何使用每个群集的唯一颜色可视化输出群集

Question

我只是在python中的newb

我在互联网上搜索代码执行K-means使用scikit，我已经尝试修改代码以可视化绘图3d并为每个群集着色（3个群集），但结果是针对所有具有相同颜色的群集，代码和可视化如下：

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from sklearn.cluster import KMeans
from collections import Counter
from mpl_toolkits.mplot3d import Axes3D
from pylab import *

X = np.array([[1, 2, 5],
              [5, 8, 2],
              [1.5, 1.8, 6],
              [8, 8, 9],
              [1, 0.6, 10],
              [2.5, 3.8, 6],
              [2.5, 5.8, 9],
              [5, 8, 3],
              [4, 0.6, 7],
              [2.5, 1.8, 4.6],
              [6.5, 1.8, 12],
              [7, 8, 9],
              [2, 0.6, 7],
              [5.5, 1.8, 4],
              [4.8, 6.9, 6],
              [4.9, 9.8, 2],
              [9, 11, 12]])


cluster_num = 3

kmeans = KMeans(n_clusters=cluster_num)
kmeans.fit(X)

centroids = kmeans.cluster_centers_
labels = kmeans.labels_

print "centroids : "
print centroids
print "labels : "
print labels

colors = ["g.","r.","c.","y."]

color = np.random.rand(cluster_num)

c = Counter(labels)


fig = figure()
ax = fig.gca(projection='3d')


for i in range(len(X)):
    print("coordinate:",X[i], "label:", labels[i])
    print "i : ",i
    print "color[labels[i]] : ",color[labels[i]]
    ax.scatter(X[i][0], X[i][1], X[i][2], c=color[labels[i]])


for cluster_number in range(cluster_num):
  print("Cluster {} contains {} samples".format(cluster_number, c[cluster_number]))

ax.scatter(centroids[:, 0],centroids[:, 1], centroids[:, 2], marker = "x", s=150, linewidths = 5, zorder = 100)

plt.show()

我怎样才能使每个集群的可视化都有自己的颜色？ THX

Answer 1

现在color = np.random.rand(cluster_num)生成三个随机数，在ax.scatter(X[i][0], X[i][1], X[i][2], c=color[labels[i]])中，您尝试将这些随机数分配为颜色。

相反，您可以更改color = ["g", "r", "b"]，以便第一个群集为绿色，第二个群体为红色，第三个群体为蓝色。

对于集群中心，传递相同的参数：

ax.scatter(centroids[:, 0],centroids[:, 1], centroids[:, 2], marker = "x", s=150, linewidths = 5, zorder = 100, c=color)