statistics/kmeans.py

Clustering is very simple. The example generates some random points in clusters and extracts the clusters. To cluster density, configurations or particles, replace the IMP.statistics.Vector3DEmbedding with a IMP.statistics.Vector3DEmbedding with a IMP::statistics::HighDensityEmbedding, IMP::statistics::ConfigurationSetXYZEmbedding or a IMP::statistics::ParticleEmbedding.

## \example statistics/kmeans.py
# Clustering is very simple. The example generates some random points in
# clusters and extracts the clusters. To cluster density, configurations
# or particles, replace the IMP.statistics.Vector3DEmbedding with a
# IMP.statistics.Vector3DEmbedding with a
# IMP::statistics::HighDensityEmbedding,
# IMP::statistics::ConfigurationSetXYZEmbedding or a
# IMP::statistics::ParticleEmbedding.

import IMP.algebra
import IMP.statistics

# generate some clusters of points
vs = []
centers = (IMP.algebra.Vector3D(0, 0, 0),
           IMP.algebra.Vector3D(10, 15, 20),
           IMP.algebra.Vector3D(60, 30, 12))
for i in range(0, 3):
    for j in range(0, 100):
        vs.append(IMP.algebra.get_random_vector_in(
            IMP.algebra.Sphere3D(centers[i], 10)))

# cluster them into 3 clusters
e = IMP.statistics.VectorDEmbedding(vs)
c = IMP.statistics.create_lloyds_kmeans(e,
                                        3, 1000)

# print out the cluster results
print(c.get_cluster_center(0))
print(c.get_cluster_center(1))
print(c.get_cluster_center(2))