def VB_sample(self,gd,x=None):
"""
Sampling of the BGMM model on test points (the 'grid')in order to have
an estimate of the posterior on these points
Parameters
----------
gd = a grid descriptor, i.e.
the grid on chich the BGMM is sampled
x = None: used for plotting (empirical data)
Returns
-------
Li : array of shape (nbnodes,self.k),
the posterior for each node and component
"""
if self.estimated:
grid = gd.make_grid()
Li = fc.bayesian_gmm_sampling(self.prior_means, \
self.prior_precisions, self.prior_shrinkage,
self.prior_weights, self.prior_dof, self.means,
self.precisions, self.shrinkage, self.weights, self.dof, grid)
else:
raise ValueError, "the model has not been estimated"
if x!=None:
self.show(x,gd,np.exp(Li))
return Li.sum(1)
def sample_on_data(self,grid):
"""
Sampling of the BGMM model on test points (the 'grid')in order to have
an estimate of the posterior on these points
Parameters
----------
grid: a set of points from which the posterior should be sampled
Returns
-------
Li : array of shape (nbnodes,self.k),
the posterior for each node and component
"""
if self.estimated:
Li = fc.bayesian_gmm_sampling( self.prior_means,\
self.prior_precisions, self.prior_shrinkage,
self.prior_weights, self.prior_dof, self.means,
self.precisions, self.shrinkage, self.weights, self.dof, grid)
else:
raise ValueError, "the model has not been estimated"
return Li
