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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Demo ward clustering on a graph:
various ways of forming clusters and dendrogram
"""
print __doc__
import numpy as np
from numpy.random import randn, rand
import matplotlib.pylab as mp
from nipy.neurospin import graph
from nipy.neurospin.clustering.hierarchical_clustering import ward
# n = number of points, k = number of nearest neighbours
n = 100
k = 5
verbose = 0
X = randn(n,2)
X[:np.ceil(n/3)] += 3
G = graph.WeightedGraph(n)
#G.mst(X)
G.knn(X, 5)
tree = ward(G, X, verbose)
threshold = .5*n
u = tree.partition(threshold)
mp.figure()
mp.subplot(1,2,1)
for i in range(u.max()+1):
mp.plot(X[u==i,0], X[u==i,1],'o', color=(rand(), rand(), rand()))
mp.axis('tight')
mp.axis('off')
mp.title('clustering into clusters of inertia<%f'%threshold)
u = tree.split(k)
mp.subplot(1,2,2)
for e in range(G.E):
mp.plot([X[G.edges[e,0],0], X[G.edges[e,1],0]],
[X[G.edges[e,0],1], X[G.edges[e,1],1]], 'k')
for i in range(u.max()+1):
mp.plot(X[u==i,0], X[u==i,1], 'o', color=(rand(), rand(), rand()))
mp.axis('tight')
mp.axis('off')
mp.title('clustering into 5 clusters')
nl = np.sum(tree.isleaf())
validleaves = np.zeros(n)
validleaves[:np.ceil(n/4)]=1
valid = np.zeros(tree.V, 'bool')
valid[tree.isleaf()] = validleaves.astype('bool')
nv = np.sum(validleaves)
nv0 = 0
while nv>nv0:
nv0= nv
for v in range(tree.V):
if valid[v]:
valid[tree.parents[v]]=1
nv = np.sum(valid)
#ax = tree.fancy_plot_(valid)
#ax.axis('off')
ax = tree.plot()
ax.set_visible(True)
mp.show()
if verbose:
print 'List of sub trees'
print tree.list_of_subtrees()
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