def VB_estimate(self,x,niter = 100,delta = 0.0001):
"""
Estimation of the BGMM using a Variational Bayes approach
Parameters
----------
x array of shape (nbitems,dim) the input data
niter = 100, the maximal number of iterations of the VB algo
delta = 0.0001, the increment in log-likelihood
to declare convergence
Returns
-------
label: array of shape nbitems: resulting MAP labelling
"""
x = self.check_data(x)
# pre_cluster the data (this improves convergence...)
label = np.zeros(x.shape[0])
nit = 10
mean,label,J = fc.kmeans(x,self.k,label,nit)
label, mean, meansc, prec, we, dof, Li = fc.bayesian_gmm (x,self.prior_means,self.prior_precisions,self.prior_shrinkage,self.prior_weights, self.prior_dof,label,niter,delta)
self.estimated = 1
self.means = mean
self.shrinkage = meansc
self.precisions = prec
self.weights = we
self.dof = dof
return label
def VB_estimate_and_sample(self,x,niter = 1000,delta = 0.0001,gd = None,verbose = 0):
"""
Estimation of the BGMM using a Variational Bayes approach,
and sampling of the model on test points in order to have
an estimate of the posterior on these points
Parameters
----------
x array of shape (nbitems,dim) the input data
niter = 100, the maximal number of iterations of the VB algo
delta = 0.0001, the increment in log-likelihood
to declare convergence
gd = None a grid descriptor, i.e.
the grid on chich the model is sampled
if gd==None, x is used as Grid
verbose = 0: the verbosity mode
Returns
-------
Li : array of shape (nbnodes): the average log-posterior
label: array of shape nbitems: resulting MAP labelling
"""
x = self.check_data(x)
# pre_cluster the data (this improves convergence...)
label = np.zeros(x.shape[0])
nit = 10
mean,label,J = fc.kmeans(x,self.k,label,nit)
if gd==None:
grid = x
else:
grid = gd.make_grid()
label, mean, meansc, prec, we,dof,Li = fc.bayesian_gmm (x,
self.prior_means, self.prior_precisions, self.prior_shrinkage,
self.prior_weights, self.prior_dof, label, niter, delta, grid)
self.estimated = 1
self.means = mean
self.shrinkage = meansc
self.precisions = prec
self.weights = we
self.dof = dof
if verbose:
self.show(x,gd,np.exp(Li))
return Li,label
