这是一种很难绘制m
-维数据。一种方法是通过Principal Component Analysis (PCA)映射到2d空间。一旦我们完成了,我们可以用matplotlib把它们扔到一个plot上(基于this answer)。
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import mlab
import Pycluster as pc
# make fake user data
users = np.random.normal(0, 10, (20, 5))
# cluster
clusterid, error, nfound = pc.kcluster(users, nclusters=3, transpose=0,
npass=10, method='a', dist='e')
centroids, _ = pc.clustercentroids(users, clusterid=clusterid)
# reduce dimensionality
users_pca = mlab.PCA(users)
cutoff = users_pca.fracs[1]
users_2d = users_pca.project(users, minfrac=cutoff)
centroids_2d = users_pca.project(centroids, minfrac=cutoff)
# make a plot
colors = ['red', 'green', 'blue']
plt.figure()
plt.xlim([users_2d[:,0].min() - .5, users_2d[:,0].max() + .5])
plt.ylim([users_2d[:,1].min() - .5, users_2d[:,1].max() + .5])
plt.xticks([], []); plt.yticks([], []) # numbers aren't meaningful
# show the centroids
plt.scatter(centroids_2d[:,0], centroids_2d[:,1], marker='o', c=colors, s=100)
# show user numbers, colored by their cluster id
for i, ((x,y), kls) in enumerate(zip(users_2d, clusterid)):
plt.annotate(str(i), xy=(x,y), xytext=(0,0), textcoords='offset points',
color=colors[kls])
如果你想绘制数字以外的东西,只是改变了第一个参数annotate
。例如,您可能可以执行用户名或其他操作。
请注意,在这个空间中,簇可能看起来有点“错误”(例如,15看起来接近红色而不是绿色),因为它不是发生聚集的实际空间。在这种情况下,前两个主要组件保留61%的差异:
>>> np.cumsum(users_pca.fracs)
array([ 0.36920636, 0.61313708, 0.81661401, 0.95360623, 1. ])