def test_basic():
print '\bb.',
n = Network()
assert_raises(AttributeError, n.set, (0,1,2))
n = network.independent([DiscreteRV(4) for i in range(10)])
n.set((0,1,2,3,0,1,2,3,0,1))
assert n.forward() == (1./4)**10, str(n.forward()) + ',' + str((1./4)**10)
n = network.independent('4cx1')
n.set((0,1,2,3))
assert n.forward() == prod([norm.pdf(x) for x in (0,1,2,3)])
n = network.independent('4d3')
assert_raises(ValueError, n.set, (0,1,2,3))
def test_history():
print '\bh.',
net = network.independent('8d3')
def do_random_op():
# randomly select a product node and child subset
pnl = [n for n in net.pot if n.is_prod() and len(n.children) >=2]
pn = random.choice(pnl)
chl_size = random.choice(range(2, len(pn.children)+1))
chl = list(pn.children)
random.shuffle(chl)
chl = chl[:chl_size]
# perform an operation
mo = ops.MixOp(net, pn, chl)
mo.connect(random.choice(range(2, 5)))
net.pot = None
return mo
opot = list(net.pot)
ops_hist = []
pot_hist = []
for i in range(10):
ops_hist.append( do_random_op() )
pot_hist.append( list(net.pot) )
for i in range(len(pot_hist))[::-1]:
assert len(pot_hist[i]) == len(net.pot)
assert set(pot_hist[i]) == set(net.pot)
ops_hist[i].undo()
assert len(opot) == len(net.pot)
assert set(opot) == set(net.pot)
def seed_network(trn, schema):
net = network.independent(np.array(schema))
for i,cn in enumerate(net.graph.root.children):
train.categorical_node_map_with_dirichlet_prior(cn, trn[:,i], 2*np.ones(cn.masses.size()))
return net
