我只是Python新手
我在互联网上搜索使用 scikit 执行 K 均值的代码,我尝试修改代码以可视化图 3d 并为每个集群(3 个集群)着色,但结果是所有集群具有相同的颜色,代码和想象如下:
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()
最佳答案
现在color = np.random.rand(cluster_num)
正在生成三个随机数并且在ax.scatter(X[i][0], X[i][1], X[i][2], c=color[labels[i]])
中您正在尝试将这些随机数指定为颜色。
相反,您可以更改 color = ["g", "r", "b"]
这样第一个簇将是绿色,第二个簇将是红色,第三个簇将是蓝色。
对于聚类中心,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)
关于python - 如何用每个簇独特的颜色可视化输出簇,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/36438857/