data = numpy.concatenate((a,b,c,d), axis=0)
# Use mean shift to cluster it...
ms = MeanShift()
ms.set_data(data, 'df')
ms.set_kernel(random.choice(filter(lambda s: s!='fisher', ms.kernels())))
ms.set_spatial(random.choice(ms.spatials()))
modes, indices = ms.cluster()
# Print out basic stats...
print 'kernel = %s; spatial = %s' % (ms.get_kernel(), ms.get_spatial())
print 'exemplars = %i; features = %i' % (ms.exemplars(), ms.features())
print 'quality = %.3f; epsilon = %.3f; iter_cap = %i' % (ms.quality, ms.epsilon, ms.iter_cap)
print
# Print out a grid of cluster assignments...
for j in xrange(20):
for i in xrange(20):
fv = numpy.array([0.25*j, 0.25*i])
c = ms.assign_cluster(fv)
print c,
print
data = numpy.concatenate((a, b, c, d), axis=0)
# Use mean shift to cluster it...
ms = MeanShift()
ms.set_data(data, 'df')
normal_kernels = [
'uniform', 'triangular', 'epanechnikov', 'cosine', 'gaussian', 'cauchy',
'logistic'
]
ms.set_kernel(random.choice(normal_kernels))
ms.set_spatial(random.choice(ms.spatials()))
modes, indices = ms.cluster()
# Print out basic stats...
print 'kernel = %s; spatial = %s' % (ms.get_kernel(), ms.get_spatial())
print 'exemplars = %i; features = %i' % (ms.exemplars(), ms.features())
print 'quality = %.3f; epsilon = %.3f; iter_cap = %i' % (
ms.quality, ms.epsilon, ms.iter_cap)
print
# Print out a grid of cluster assignments...
for j in xrange(20):
for i in xrange(20):
fv = numpy.array([0.25 * j, 0.25 * i])
c = ms.assign_cluster(fv)
print c,
print
