def __init__(self, lattice=ngc.grid_graph( dim=[N,N] ),
data=zeros((N,N)), tau_x = 1, tau_y = 1, phi = 0.1):
# sanity test
if lattice.number_of_nodes() != data.size:
raise Exception('data and lattice sizes do not match',
'%d vs %d' % (data.size, lattice.number_of_nodes()) );
self.num_nodes = lattice.number_of_nodes();
self.phi = phi;
self.tau_x = 1;
self.tau_y = 1;
#just in case the input decides to give us weights
for e in lattice.edges_iter():
if not lattice.get_edge_data(e[0],e[1]) :
#setting lattice
lattice.edge[e[0]][e[1]] = {'weight':phi};
lattice.edge[e[1]][e[0]] = {'weight':phi};
else:
#keep the data
pass;
self.lattice , self.data = lattice, data;
# convert the lattice into a GMRF precision matrix
self.Lambda = zeros(self.num_nodes,self.num_nodes);
#set up the grid and the data
#@stochastic(dtype=float)
#@def X
# this v is a tuple index of the grid
self.Y = [ Normal('Y_'+str(v), mu=0.5, tau=Sigma_Y**-1,
value=data[v], observed = True )
for v in lattice.nodes_iter() ];
MCMC.__init__(self, [self.Y])
def __init__(self, G=cycle_graph(9), beta=0.0):
self.G, self.beta = G, beta
self.x = [Bernoulli(str(v), 0.5, value=0) for v in G.nodes_iter()]
self.psi = [self.IndepSetPotential(v, G[v]) for v in G.nodes_iter()]
MCMC.__init__(self, [self.x, self.psi])
