def dpmm(gfc, alpha, g0, g1, dof, prior_precision, gf1, sub, burnin,
spatial_coords=None, nis=1000, co_clust=False, verbose=False):
"""
Apply the dpmm analysis to the data: python version
"""
from nipy.neurospin.clustering.imm import MixedIMM
dim = gfc.shape[1]
migmm = MixedIMM(alpha, dim)
migmm.set_priors(gfc)
migmm.set_constant_densities(null_dens=g0, prior_dens=g1)
migmm._prior_dof = dof
migmm._prior_scale = np.diag(prior_precision[0]/dof)
migmm._inv_prior_scale_ = [np.diag(dof*1./(prior_precision[0]))]
migmm.sample(gfc, null_class_proba=1-gf1, niter=burnin, init=False,
kfold=sub)
if verbose:
print 'number of components: ', migmm.k
#sampling
if co_clust:
like, pproba, co_clust = migmm.sample(
gfc, null_class_proba=1-gf1, niter=nis,
sampling_points=spatial_coords, kfold=sub, co_clustering=co_clust)
if verbose:
print 'number of components: ', migmm.k
return like, 1-pproba, co_clust
else:
like, pproba = migmm.sample(
gfc, null_class_proba=1-gf1, niter=nis,
sampling_points=spatial_coords, kfold=sub, co_clustering=co_clust)
if verbose:
print 'number of components: ', migmm.k
return like, 1-pproba
