def runTest(self):
# construct factors of various sizes with no data
for sz in xrange(6):
vars = ['V'+str(i) for i in xrange(sz)]
vals = dict([(v,[0,1]) for v in vars])
data = (vars,vals,vars,[])
for v_on in subsetn(vars, sz):
inst = []
for v in vars:
if v in v_on:
inst.append(1)
else:
inst.append(0)
data[3].append(tuple(inst+[0]))
d = CompactFactor(data,domain=Domain())
x2 = X2Separator(d)
g2 = G2Separator(d)
for a,b in pairs(vars):
for s in powerset(set(vars) - set([a,b])):
x2p, x2s, x2d = x2.test_independ(a, b, set(s))
g2p, g2s, g2d = g2.test_independ(a, b, set(s))
# one degree of freedom
self.assertEquals(x2d, 0)
self.assertEquals(g2d, 0)
# default to independent
self.assertEquals(x2p, 1)
self.assertEquals(g2p, 1)
# zero statistics (no data)
self.assertEquals(x2s, 0)
self.assertEquals(g2s, 0)
def _ic_discovery(self, separator):
n = 0
self._skel.complete(self._skel.vertices())
skel = self._skel
too_many_adjacent = True
while too_many_adjacent:
too_many_adjacent = False
# find a pair (x,y) such that the cardinality of the neighbourhood
# exceeds n
for x, y in pairs(self.variables()):
if x not in self._must_have and y not in self._must_have:
continue
n_x = set(skel.neighbours(x))
n_y = set(skel.neighbours(y))
if y not in n_x:
continue
if x not in n_y:
raise RuntimeError,'inconsistent neighbourhoods'
# separators must be potential ancestors of both variables.
# constrain the neighbourhood to contain only potential ancestors
cond = n_x & self.potential_ancestors(x)
cond |= n_y & self.potential_ancestors(y)
cond -= frozenset([x,y])
# if the neighbourhood is too small, try the next pair
if n > len(cond):
continue
# find an untested subset s of neighbours of x of cardinality n
for s in subsetn(tuple(cond), n):
s = frozenset(s)
self.num_tests += 1
# test for x _|_ y | s
if separator.separates(x, y, s):
# see if we can find a more probable separator
s = self.hill_climb_cond(separator,x,y,s,cond)
# record independence since found
self._add_independence(x,y,s)
skel.remove_line(x, y)
break
# increment required neighbourhood minimum size
n += 1
# see if we've found all the CIs
too_many_adjacent = False
for x in self.variables():
too_many_adjacent |= len(skel.neighbours(x)) > n
if too_many_adjacent:
break
