def Gibbs_estimate(self,x,niter = 1000,method = 1):
"""
Estimation of the BGMM using Gibbs sampling
Parameters
----------
x array of shape (nbitems,dim) the input data
niter = 1000, the maximal number of iterations of the Gibbs sampling
method = 1: boolean to state whether covariance
are fixed (0 ; normal model) or variable
(1 ; normal-wishart model)
Returns
-------
label: array of shape nbitems, resulting MAP labelling
"""
x = self.check_data(x)
label, mean, meansc, prec, we,dof,Li = fc.gibbs_gmm (x,\
self.prior_means, self.prior_precisions, self.prior_shrinkage,
self.prior_weights, self.prior_dof, niter, method)
self.estimated = 1
self.means = mean
self.shrinkage = meansc
self.precisions = prec
self.weights = we
self.dof = dof
label = np.argmax(label,1)
return label
def test_Gibbs_GMM(self, verbose=0):
k = 2
dim = 2
# prior_means = np.zeros((k,dim),'d')
prior_means = np.concatenate([np.zeros((1,dim)),np.ones((1,dim))])
prior_precision_scale = 1*np.ones((k,dim),'d') # 0.01
prior_mean_scale = 1*np.ones(k,'d')
prior_weights = np.ones(k,'d')
prior_dof = (dim+1)*np.ones(k,'d') # 100
X = nr.randn(100,dim)-1
X[-30:] = X[-30:]+ 4
membership, mean, mean_scale,precision_scale, weights,dof,density = fc.gibbs_gmm(X, prior_means,prior_precision_scale,prior_mean_scale,prior_weights,prior_dof,1000)
expectC = np.array([[-1,-1],[3,3]])
if verbose:
print expectC,mean
self.assert_( np.allclose(expectC, mean,0.3,0.3))
def Gibbs_estimate_and_sample(self, x, niter = 1000, method = 1,
gd = None, nsamp = 1000, verbose=0):
"""
Estimation of the BGMM using Gibbs sampling
and sampling of the posterior on test points
Parameters
----------
x array of shape (nbitems,dim) the input data
niter = 1000, the maximal number of iterations of the Gibbs sampling
method = 1: boolean to state whether covariance
are fixed (0 ; normal model)
or variable (1 ; normal-wishart model)
gd = None, a grid descriptor, i.e.
the grid on chich the model is sampled
if gd==None, x is used as Grid
nsamp = 1000 number of draws of the posterior
verbose = 0: the verboseity level
Returns
-------
Li : array of shape (nbnodes): the average log-posterior
label: array of shape (nbitems): resulting MAP labelling
"""
x = self.check_data(x)
if gd==None:
grid = x
else:
grid = gd.make_grid()
label, mean, meansc, prec, we,dof,Li = fc.gibbs_gmm (x, \
self.prior_means, self.prior_precisions, self.prior_shrinkage,
self.prior_weights, self.prior_dof, niter, method, grid, nsamp)
self.estimated = 1
self.means = mean
self.shrinkage = meansc
self.precisions = prec
self.weights = we
self.dof = dof
if verbose:
self.show(x,gd,Li)
label = np.argmax(label,1)
return np.log(Li),label