def _dpmm(coords, alpha, null_density, dof, prior_precision, prior_h0,
subjects, sampling_coords=None, n_iter=1000, burnin=100,
co_clust=False):
"""Apply the dpmm analysis to compute clusters from regions coordinates
"""
from nipy.algorithms.clustering.imm import MixedIMM
dim = coords.shape[1]
migmm = MixedIMM(alpha, dim)
migmm.set_priors(coords)
migmm.set_constant_densities(
null_dens=null_density, prior_dens=null_density)
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(coords, null_class_proba=prior_h0, niter=burnin, init=False,
kfold=subjects)
# sampling
like, pproba, co_clustering = migmm.sample(
coords, null_class_proba=prior_h0, niter=n_iter, kfold=subjects,
sampling_points=sampling_coords, co_clustering=True)
if co_clust:
return like, 1 - pproba, co_clustering
else:
return like, 1 - pproba
def _dpmm(coords,
alpha,
null_density,
dof,
prior_precision,
prior_h0,
subjects,
sampling_coords=None,
n_iter=1000,
burnin=100,
co_clust=False):
"""Apply the dpmm analysis to compute clusters from regions coordinates
"""
from nipy.algorithms.clustering.imm import MixedIMM
dim = coords.shape[1]
migmm = MixedIMM(alpha, dim)
migmm.set_priors(coords)
migmm.set_constant_densities(null_dens=null_density,
prior_dens=null_density)
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(coords,
null_class_proba=prior_h0,
niter=burnin,
init=False,
kfold=subjects)
# sampling
like, pproba, co_clustering = migmm.sample(coords,
null_class_proba=prior_h0,
niter=n_iter,
kfold=subjects,
sampling_points=sampling_coords,
co_clustering=True)
if co_clust:
return like, 1 - pproba, co_clustering
else:
return like, 1 - pproba
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 compute clusters from regions coordinates
"""
from nipy.algorithms.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
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 compute clusters from regions coordinates
"""
from nipy.algorithms.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