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 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_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'})
b0r ~ norm(0, .01)
ageA # ~ norm(20, 3)
ageB ~ norm(30, 2)
ps ~ beta(5, 6)
sexA ~ cat({'m': ps, 'f': 1-ps})
muA = b0 + b0r + b1*ageA
bmiA ~ norm(muA, sd)
sdB = sd * 0.5
muB = b0 + b0r + b1*ageB
bmiB ~ norm(muB, sdB)
}
'''
bn = dag.bayes_net_from_script(scr)
root = dag.NodeSet('country')
node_area = root.new_child('area',
as_fixed=['b0r', 'ps'],
as_floating=['foodstore'])
node_area.new_child('agA', as_fixed=['ageA', 'sexA'], as_floating=['bmiA'])
node_area.new_child('agB', as_fixed=['ageB'], as_floating=['bmiB'])
sc = dag.as_simulation_core(bn, root)
root.print()
root.print_samplers()
pc = sc.generate('Taiwan', {'sd': 1})
print(pc.list_actors())
pc_taipei = pc.breed('Taipei', 'area')
pc_taipei.breed('A1', 'agA', {'ageA': 5})
def fn_mean(sim, data):
return -abs(sim - data['X'])
d = {'N': 10, 'K': 10, 'X': 200}
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=['p'])
ns.new_child('ag', as_floating=['x'])
sm = dag.as_simulation_core(bn, ns)
sm.deep_print()
sdm = dag.as_simulation_data_model(sm, d, fn_sim, fn_mean)
fit = dag.fitting.GA(sdm)
fit.fit(100)
print(fit.BestFit)