def setUp(self):
self.BN1 = dag.bayes_net_from_script('''
PCore A {
A = 5
B = A * 4
C = B + B2
D = pow(C)
}
''')
self.BN2 = dag.bayes_net_from_script('''
PCore B2 {
C = B
D = pow(C)
}
''')
def test_clone(self):
script = '''
PCore Regression {
x = 1
y = x + 1
z = y + 1
}
'''
bn = dag.bayes_net_from_script(script)
na = dag.NodeSet('a')
nb = na.new_child('b', as_fixed=['x'])
nb.new_child('c', as_fixed=['z'])
sc = dag.as_simulation_core(bn, na)
pc_a = sc.generate('A')
pc_b = pc_a.breed('B', 'b')
pc_c = pc_b.breed('C', 'c')
pc_aa = pc_a.clone(copy_sc=True, include_children=True)
pc_cc = pc_aa.find_descendant('B@C')
self.assertEqual(pc_c['z'], 3)
self.assertEqual(pc_cc['z'], 3)
pc_aa.impulse({'x': 5})
self.assertEqual(pc_c['z'], 3)
self.assertEqual(pc_cc['z'], 7)
pc_a.impulse({'x': 7})
self.assertEqual(pc_c['z'], 9)
self.assertEqual(pc_cc['z'], 7)
def quick_build_parameter_core(script):
bn = dag.bayes_net_from_script(script)
flt = [d for d in bn.Leaves if bn.is_rv(d)]
ns = NodeSet('Root', as_floating=flt)
sm = as_simulation_core(bn, ns)
return sm.generate('')
def test_div(self):
bn = dag.bayes_net_from_script(script_betabin2)
ns = dag.NodeSet('root')
ns.new_child('a', as_floating=['x1'])
ns.new_child('b', as_floating=['x2'])
sc = dag.as_simulation_core(bn, ns)
pc = sc.generate("T4")
pc_a = pc.breed('A', 'a')
pc_b = pc.breed('B', 'b')
self.assertIn('x1', pc_a.get_samplers())
self.assertIn('x2', pc_b.get_samplers())
import epidag as dag
__author__ = 'TimeWz667'
script = '''
PCore BetaBin {
al = 1
be = 1
p ~ beta(al, be)
x ~ binom(5, p)
}
'''
bn = dag.bayes_net_from_script(script)
ns = dag.NodeSet('root', as_fixed=['al'], as_floating=['p'])
ns.new_child('ag', as_floating=['x'])
sc = dag.as_simulation_core(bn, ns)
sc.deep_print()
pc = sc.generate('a')
p = pc.breed('x', 'ag')
print(p.get_sampler('p'))
def test_no_exo(self):
bn = dag.bayes_net_from_script(script_betabin)
sc = dag.as_simulation_core(bn)
pc = sc.generate("T3")
with self.assertRaises(KeyError):
pc.get_sampler('x')()
def test_random(self):
bn = dag.bayes_net_from_script(script_betabin)
ns = dag.NodeSet('Root', as_floating=['p'])
sc = dag.as_simulation_core(bn, ns)
pc = sc.generate("T2", {'n': 10})
self.assertSetEqual(set(pc.get_samplers().keys()), {'x', 'p'})
def test_simple(self):
bn = dag.bayes_net_from_script(script_betabin)
sc = dag.as_simulation_core(bn)
pc = sc.generate("T1", {'n': 10})
self.assertEqual(pc['n'], 10)
def setUp(self):
self.BN = dag.bayes_net_from_script(scr)
self.DM = BinBeta(self.BN, data)
self.Logger = logging.getLogger(__name__)
BayesianModel.__init__(self, bn, pars=['prob'])
self.Data = data
@property
def has_exact_likelihood(self):
return True
def evaluate_distance(self, pars):
di = 0
for d in self.Data:
pars.update(d)
di += self.BN['x'].evaluate(pars)
return di
def evaluate_likelihood(self, pars):
return -self.evaluate_distance(pars)
data = [{'id': 1, 'n': 10, 'x': 4}, {'id': 2, 'n': 20, 'x': 7}]
bn = bayes_net_from_script('''
PCore test {
prob ~ beta(1, 1)
x ~ binom(n, prob)
}
''')
model = BinBeta(bn, data)
p = model.sample_prior()
print(p)
if self.FloatingNodes:
print('{}|-Floating {}'.format(ind, list(self.FloatingNodes)))
if self.ExoNodes:
print('{}|-Exo {}'.format(ind, list(self.ExoNodes)))
if self.ListeningNodes:
print('{}|-Listening {}'.format(ind, list(self.ListeningNodes)))
for ch in self.__children.values():
ch.print(i + 2)
if __name__ == '__main__':
from epidag import bayes_net_from_script
bn1 = bayes_net_from_script('''
PCore Test {
a = 1
b = a + 3
c = b * 2
d ~ binom(b, 0. 5)
e = d + c
}
''')
ndr = NodeSet('root', as_fixed=['a'])
nds = ndr.new_child('med', as_fixed=['b'], as_floating=['d'])
nde = nds.new_child('leaf', as_fixed=['e'])
ndr.inject_bn(bn1)
ndr.print()