def rejection_sampling(X, evidence, model):
global w_X, P, E, s, iteration
topo = bn.topo_ordered(model)
tobn = [model[n] for n in topo]
w_X = {str(x):[0,0] for x in X}
P = { str(x): [] for x in X }
E = {'E': [] }
s = 0
iteration = 0
while True:
iteration +=1
prior_sample(tobn)
if evidence == {n.name:n.value for n in tobn if n.name in evidence}:
s += 1
for x in X:
if model[x].value == True:
w_X[x][0] += 1
else:
w_X[x][1] += 1
if (w_X[x][0] + w_X[x][1]) > 0:
P[x].append(w_X[x][0] / s)
else:
P[x].append(0)
E['E'].append(s/iteration)
def plot_net(model):
G = pgv.AGraph(strict=False,directed=True)
tobn = bn.topo_ordered(model)
G.add_nodes_from(tobn)
for n in tobn:
for c in model[n].children:
G.add_edge(n, c)
G.layout('dot')
G.draw('outputs/net_plot.png')
def variable_elimination(m, Q, E):
global w_X
w_X = {x:0 for x in Q}
Q = [m[q] for q in Q]
topo = bayesnet.topo_ordered(m)
Phi = [m[n] for n in topo]
for n in Phi:
if n.name in E:
n.value = E[n.name]
for q in Q:
Z = [p for p in Phi if not p.name in E and not p.name == q.name]
pe = ve(Phi, q, E, Z, m)
return pe, w_X
def variable_elimination(model, query, evidence):
global w_X
topo = bayesnet.topo_ordered(model)
tbn = [m[n] for n in topo]
roots = bayesnet.roots(model)
w_X = {str(x):[0,0] for x in query}
for q in query:
print q
Z = [n for n in tbn if not n.name in evidence and not n.name == q]
print Z
if x in roots:
phi = [str(n) for n in tbn if str(n) != x]
phi.insert(0,x)
result = p(phi, x)
return result, {x:w_X[x][1]/result for x in w_X if x in roots}
def init(X, evidence, model):
t0 = time.time()
# global types
global N, Y, MB, O, iteration, M, tobn, nonev, mnonev, rnodes, roots, observables, E, M, enodes
N = { str(x): [0, 0] for x in X }
Y = { str(x): [] for x in X }
enodes = [model[e] for e in evidence]
M = model
EC = []
#init important sets
tobn = bn.topo_ordered(model)
nonev = [node for node in tobn if node not in evidence]
mnonev = [model[n] for n in nonev]
rnodes = [model[x] for x in X]
roots = [r for r in bn.roots(model)]
MB = { str(x): [] for x in tobn if x not in evidence }
# init net
# the initial net might be improper as likelihood of evidence nodes can be 0
for e in evidence:
model[e].value = evidence[e]
for p in model[e].parents:
if p in evidence:
continue
par = model[p]
par.value = True
if MB[p] == []:
MB[p] = [model[c] for c in par.children]
if p in nonev:
nonev.remove(p)
#if bn.likelihood(n) == 0:
# print "still something wrong with initialization at node " + node
for node in nonev:
n = model[node]
# P = [0]*2
# prod = 1
# if n.children and MB[node] == []:
# MB[node] = [model[c] for c in n.children]
# if n.children:
# EC = [model[e] for e in n.children if e in evidence]
# if EC == []:
# n.random()
# continue
# else:
# n.value = True
# if bn.likelihood(n) == 0:
# print n.name
# n.value = False
# continue
# print "here"
# P[0] = log(bn.likelihood(n))
# for e in EC:
# if e.name == 'v':
# print bn.likelihood(e)
# if bn.likelihood(e) == 0:
# n.value = False
# break
# P[0] += log(bn.likelihood(e))
# else:
# n.value = False
# if bn.likelihood(n) == 0:
# n.value = True
# continue
# P[1] = log(bn.likelihood(n))
# for e in EC:
# if bn.likelihood(e) == 0:
# n.value = True
# break
# P[1] += log(bn.likelihood(e))
# else:
# likelihood = exp(P[0]) / (exp(P[0]) + exp(P[1]))
# if likelihood > random.random():
# n.value = True
# else:
# n.value = False
# if random.random() > .5:
# n.value = True
# else:
# n.value = False
if n.children != [] and MB[node] == []:
MB[node] = [model[c] for c in n.children]
#n.random()
n.value = True
for node in tobn:
n = model[node]
if node not in evidence:
flip_mb(n)
else:
if bn.likelihood(n) == 0:
print "straaaaaange " + node
print "time to init: " + str(time.time() - t0)
return
