IMP  2.0.1
The Integrative Modeling Platform
kmeans.py
1 ## \example statistics/kmeans.py
2 ## Clustering is very simple. The example generates some random points in
3 ## clusters and extracts the clusters. To cluster density, configurations
4 ## or particles, replace the IMP.statistics.Vector3DEmbedding with a
5 ## IMP.statistics.Vector3DEmbedding with a
6 ## IMP::statistics::HighDensityEmbedding,
7 ## IMP::statistics::ConfigurationSetXYZEmbedding or a
8 ## IMP::statistics::ParticleEmbedding.
9 
10 import IMP.algebra
11 import IMP.statistics
12 
13 # generate some clusters of points
14 vs= []
15 centers=(IMP.algebra.Vector3D(0,0,0),
16  IMP.algebra.Vector3D(10,15,20),
17  IMP.algebra.Vector3D(60,30,12))
18 for i in range(0,3):
19  for j in range(0,100):
20  vs.append(IMP.algebra.get_random_vector_in(IMP.algebra.Sphere3D(centers[i], 10)))
21 
22 # cluster them into 3 clusters
25  3, 1000)
26 
27 # print out the cluster results
28 print c.get_cluster_center(0)
29 print c.get_cluster_center(1)
30 print c.get_cluster_center(2)