def marginals(self, *args):
self.do_inference()
n = self.n
proj = Gauss.Map(np.zeros((0, n))) # n -> 0
for t in args:
proj = Gauss.copy(n).then(Gauss.tensor(proj, self.eval_term(t)))
return self.state.then(proj)
def do_inference(self):
n_eqs = len(self.equations)
if n_eqs > 0:
n = self.n
joint = Gauss.Map(np.zeros((0, n))) # n -> 0
for (s, t) in self.equations:
diff = self.eval_term(s + (-1.0) * t)
joint = Gauss.copy(n).then(Gauss.tensor(joint, diff))
zz = np.zeros((n_eqs, 1))
zeros = Gauss.Map(np.zeros((n_eqs, 0)), zz) # 0 -> n_eqs
if not Gauss.in_support(self.state.then(joint), zz):
raise InferenceError("conditions cannot be satisfied")
else:
dis = self.state.disintegrate(joint)
self.state = dis.dot(zeros)
self.equations = []
def eval_term(self, term):
(A, b) = self.eval_term_rec(term)
return Gauss.Map(A, np.array([[b]]))