def __init__(self, nddistr, rvs=[]):
if not isinstance(nddistr, NDFun):
nddistr = NDProductDistr(nddistr)
self.nddistr = nddistr
self._segint = nddistr._segint
free_rvs = list(self.nddistr.Vars)
# fetch all necessary RVs
dep_rvs = set(rvs)
for rv in rvs:
dep_rvs.update(rv.getParentsDep())
self.free_rvs = free_rvs
self.dep_rvs = list(sorted(dep_rvs, key=_get_str_sym_name))
self.all_vars = list(
sorted(set(free_rvs) | dep_rvs, key=_get_str_sym_name))
self.sym_to_rv = {}
for rv in self.all_vars:
self.sym_to_rv[rv.getSymname()] = rv
self.rv_to_equation = {}
for rv in self.dep_rvs:
self.rv_to_equation[rv] = rv.getSym()
U.append(UniformDistr(-0.1, 0.1, sym="U{0}".format(i)))
# U will be conditioned on, so in effect constant
if i == 0:
Y.append(Y0 * K + h * U[i])
else:
Y.append(Y[i - 1] * K + h * U[i])
Y[-1].setSym("Y" + str(i + 1))
ei = NormalDistr(0.0, 0.1) | Between(-0.4, 0.4)
ei.setSym("E{0}".format(i))
E.append(ei)
O.append(Y[-1] + E[-1])
O[-1].setSym("O{0}".format(i))
#!
#! Model
#! -----
P = NDProductDistr([A, Y0] + E + U)
M = Model(P, O)
print M
M.eliminate_other(E + Y + O + [A, Y0] + U)
print M
M.toGraphwiz(f=open('bn.dot', mode="w+"))
#!
#! Joint distribution of initial condition and parameter of equation
#! -----------------------------------------------------------------
i = 0
ay0 = []
ui = [0.0] * n
figure()
for yend in [0.25, 1.25, 2.25]:
A = BetaDistr(3, 3, sym="A")
B = BetaDistr(2, 4, sym="A")
Y0 = BetaDistr(2, 2, sym="Y0")
n = 5
h = 1.0 / n
K = (1 + h * A)
K.setSym("K")
Y = [Y0] * (n + 1)
for i in xrange(n + 1):
if i == 0:
pass
else:
Y[i] = Y[i - 1] * K
Y[i].setSym("Y" + str(i))
P = NDProductDistr([Factor1DDistr(A), Factor1DDistr(Y[0])])
M = Model(P, Y[1:])
M.eliminate_other([K] + Y)
#M2 = M.inference2([Y[0], A], [Y[n]], [1])
#M2.plot(); print M2; show()
#M2 = M.inference2([Y[0]], [Y[n]], [0.5])
#figure()
#M2.plot(); print M2;
figure()
Y[-1].plot(color='r', linewidth=5)
M3 = M.inference([Y[-1]], [], [])
M3.plot()
print M3
#M2 = M.inference(wanted_rvs = [X], cond_rvs = [S], cond_X = [2.5])
M2 = M.inference(wanted_rvs=[X, Y], cond_rvs=[S], cond_X=[2.5]).plot()
print "===", M2
#M.plot()
show()
0 / 0
figure()
N = X * Y
N.setSym("N")
D = X + Y
D.setSym("D")
R = N / D
R.setSym("R")
P = NDProductDistr([X, Y])
M = Model(P, [N, D, R])
print M
M2 = M.inference(wanted_rvs=[R])
M2.plot()
0 / 0
M.varschange(X, N)
M.eliminate(X)
print M
M.plot()
show()
0 / 0
X1 = UniformDistr(1.5, 2.5, sym="x1")
X2 = UniformDistr(1.5, 2.5, sym="x2")
X3 = UniformDistr(1.5, 2.5, sym="x3")