def ve(Phi, q, E, Z, m):
for z in Z:
Phi = eliminate(Phi, z, E, m)
q.value = True
S = reduce(operator.mul, [bayesnet.likelihood(n) if not n.name.startswith('tau') else n.lam() for n in Phi])
q.value = False
T = reduce(operator.mul, [bayesnet.likelihood(n) if not n.name.startswith('tau') else n.lam() for n in Phi])
w_X[q.name] = S/(S + T)
return S + T
def weighted_sample(model, evidence, tobn):
w = 1
for node in tobn:
if node in evidence:
w = w * bn.likelihood(model[node])
else:
model[node].random()
return w
def draw_sample():
global m, tobn
f = g = 0
for node in tobn:
if not node.name in e:
node.random()
n = m[node.name]
n.value = node.value
f += math.log(bn.likelihood(node))
g += math.log(bn.likelihood(n))
f = math.exp(f)
g = math.exp(g)
g *= reduce(mul, [bn.likelihood(m[node]) for node in e])
if g == 0:
return bn.as_sample(m), 0, 0
else:
return bn.as_sample(m), g, f
def p(phi, query):
global w_X
result = 0
if phi:
head = bndict[phi.pop(0)]
if str(head) in e:
m[head].value = e[str(head)]
result = bayesnet.likelihood(m[head]) * p(list(phi), query)
else:
m[head].value = True
tmp = bayesnet.likelihood(m[head]) * p(list(phi), query)
m[head].value = False
result = tmp + bayesnet.likelihood(m[head]) * p(list(phi), query)
if m[head].name == query:
w_X[m[head].name][0] = result - tmp
w_X[m[head].name][1] = tmp
return result
else:
return 1
def lam(z=z):
z.value = True
s = reduce(operator.mul, [bayesnet.likelihood(n) if not n.name.startswith('tau') else n.lam() for n in Phi_1])
z.value = False
t = s + reduce(operator.mul, [bayesnet.likelihood(n) if not n.name.startswith('tau') else n.lam() for n in Phi_1])
return t
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
def flip_mb(node):
P = [0]*2
#if node.name == 'alc':
# print bn.likelihood(node)
#print node.name
if node.children:
# P( node | pa( node ) ) * \prod_{c \in ch( node )} P( c | pa( c ) )
node.value = True
# check if this is impossible
if bn.likelihood(node) == 0:
#print "Set " + node.name + " = False: P( "+ node.name + " = " + str(node.value) + " | " + str({ M[p].name: M[p].value for p in node.parents }) + " ) = " + str(bn.likelihood(node))
node.value = False
#print "---> P( "+ node.name + " = " + str(node.value) + " | " + str({ M[p].name: M[p].value for p in node.parents }) + " ) = " + str(bn.likelihood(node))
return 0
else:
P[0] = log(bn.likelihood(node))
# ...also for all children
for n in MB[node.name]:
if bn.likelihood(n) == 0:
#print "Set " + node.name + " = False: P( "+ n.name + " = " + str(n.value) + " | " + str({ M[p].name: M[p].value for p in n.parents }) + " ) = " + str(bn.likelihood(n))
node.value = False
#print "---> P( "+ n.name + " = " + str(n.value) + " | " + str({ M[p].name: M[p].value for p in n.parents }) + " ) = " + str(bn.likelihood(n))
return 1
else:
P[0] += log(bn.likelihood(n))
# for normalization
node.value = False
# check if this is impossible
if bn.likelihood(node) == 0:
#print "Set " + node.name + " = True: P( "+ node.name + " = " + str(node.value) + " | " + str({ M[p].name: M[p].value for p in node.parents }) + " ) = " + str(bn.likelihood(node))
node.value = True
#print "---> P( "+ node.name + " = " + str(node.value) + " | " + str({ M[p].name: M[p].value for p in node.parents }) + " ) = " + str(bn.likelihood(node))
return 2
else:
P[1] = log(bn.likelihood(node))
# ...also for all children
for n in MB[node.name]:
if bn.likelihood(n) == 0:
#print "Set " + node.name + " = True: P( "+ n.name + " = " + str(n.value) + " | " + str({ M[p].name: M[p].value for p in n.parents}) + " ) = " + str(bn.likelihood(n))
node.value = True
#print "---> P( "+ n.name + " = " + str(n.value) + " | " + str({ M[p].name: M[p].value for p in n.parents }) + " ) = " + str(bn.likelihood(n))
return 3
else:
P[1] += log(bn.likelihood(n))
#print iteration
#print "case 4, " + node.name + " " + str(exp(P[0]) + exp(P[1]))
likelihood = exp(P[0]) / (exp(P[0]) + exp(P[1]))
#print "P( "+ node.name + " | " + str({M[p].name: M[p].value for p in node.parents}) + str({c.name: c.value for c in MB[node.name]}) + " ) = " + str(likelihood)
if likelihood > random.random():
node.value = True
else:
node.value = False
return 4
else:
node.random()
return 5
