def _eval_weight_gradients_per_var(self, v_w, nodes):
wgts = []
Hcond = []
for node in nodes:
sc = self.mg.message_graph.node[node]['sc']
mu_p = self.mg.message_graph.node[node]['pseudo_belief'].marginal(sc)
# mu_p = self.mg.message_graph.node[node]['pseudo_belief']
elimvars_in_node = sorted(sc, key=lambda x: self.elim_order.index(x))
v_w_ind = elimvars_in_node.index(v_w)
mu_p_temp1 = mu_p.marginal(elimvars_in_node[v_w_ind:]) # P(xi, xi+1~)
if type(mu_p_temp1) is not Factor:
mu_p_temp1 = Factor({}, mu_p_temp1)
if np.all(mu_p_temp1.t == 0): # when mu_u_temp1 is all zero factor, raise assertion error
H1_p = 0.0
else:
H1_p = mu_p_temp1.entropy()
if v_w_ind + 1 < len(elimvars_in_node):
mu_p_temp2 = mu_p.marginal(elimvars_in_node[v_w_ind + 1:]) # P(xi+1 , xi+2 ~)
if type(mu_p_temp2) is not Factor:
mu_p_temp2 = Factor({}, mu_p_temp2)
if np.all(mu_p_temp2.t == 0):
H2_p = 0.0
else:
H2_p = mu_p_temp2.entropy()
else:
H2_p = 0.0
wgts.append(self.mg.message_graph.node[node]['w'][v_w])
Hcond.append(H1_p - H2_p) # conditional entropy
Hbar = 0.0
for i in range(len(wgts)):
Hbar += wgts[i] * Hcond[i]
gradient = [w_i*(Hcond[ind] - Hbar) for ind, w_i in enumerate(wgts)]
if self.verbose:
print('wgts:', wgts)
print('Hcond:', Hcond)
print('Hbar:', Hbar)
print('grad:', gradient)
if debug:
assert np.all(np.isfinite(gradient))
return gradient
def _eval_weight_gradients_per_var(self, v_w, nodes):
wgts = []
Hcond_u = []
Hcond_p = []
for node in nodes:
sc = self.mg.message_graph.node[node]['sc']
mu_p = self.mg.message_graph.node[node][
'pseudo_belief'].prob.marginal(sc)
mu_u = self.mg.message_graph.node[node][
'pseudo_belief'].util.marginal(sc)
elimvars_in_node = sorted(sc,
key=lambda x: self.elim_order.index(x))
v_w_ind = elimvars_in_node.index(v_w)
mu_p_temp1 = mu_p.marginal(
elimvars_in_node[v_w_ind:]) # P(xi, xi+1~)
if type(mu_p_temp1) is not Factor:
mu_p_temp1 = Factor({}, mu_p_temp1)
H1_p = mu_p_temp1.entropy()
mu_u_temp1 = mu_u.marginal(
elimvars_in_node[v_w_ind:]) # P(xi, xi+1~)
if type(mu_u_temp1) is not Factor:
mu_u_temp1 = Factor({}, mu_u_temp1)
if np.all(
mu_u_temp1.t == 0
): # when mu_u_temp1 is all zero factor, raise assertion error
H1_u = 0.0
else:
H1_u = mu_u_temp1.entropy()
if v_w_ind + 1 < len(elimvars_in_node):
mu_p_temp2 = mu_p.marginal(
elimvars_in_node[v_w_ind + 1:]) # P(xi+1 , xi+2 ~)
if type(mu_p_temp2) is not Factor:
mu_p_temp2 = Factor({}, mu_p_temp2)
H2_p = mu_p_temp2.entropy()
mu_u_temp2 = mu_u.marginal(
elimvars_in_node[v_w_ind + 1:]) # P(xi+1 , xi+2 ~)
if type(mu_u_temp2) is not Factor:
mu_u_temp2 = Factor({}, mu_u_temp2)
if np.all(mu_u_temp2.t == 0):
H2_u = 0.0
else:
H2_u = mu_u_temp2.entropy()
else:
H2_p = 0.0
H2_u = 0.0
wgts.append(self.mg.message_graph.node[node]['w'][v_w])
H_p_cond = H1_p - H2_p
H_u_cond = H1_u - H2_u
Hcond_p.append(H_p_cond) # conditional entropy
Hcond_u.append(H_u_cond) # conditional entropy
Hbar_u = 0.0
Hbar_p = 0.0
for m, node in enumerate(nodes):
Hbar_u += wgts[m] * Hcond_u[m] * self._eval_util_bound_norms(node)
Hbar_p += wgts[m] * Hcond_p[m] * (
self._eval_util_bound_norms() -
self._eval_util_bound_norms(node))
gradient = []
for m, node in enumerate(nodes):
grad = Hcond_u[m] * self._eval_util_bound_norms(node) - Hbar_u
grad += Hcond_p[m] * (self._eval_util_bound_norms() -
self._eval_util_bound_norms(node)) - Hbar_p
grad *= wgts[m]
grad *= self._eval_prob_bound_norms(
) # todo pull out this term with objective?
gradient.append(grad)
if self.verbose:
print('wgts:', wgts)
print('Hcond_p:', Hcond_p)
print('Hcond_u:', Hcond_p)
print('Hbar_u:', Hbar_u)
print('Hbar_p:', Hbar_p)
print('grad:', gradient)
if debug:
assert np.all(np.isfinite(gradient))
return gradient