def search(self, data, order):
# no need to separate out interventional data
ci = PCCI(CBFSeparator(data), constrain_order=order,
hill_climb_cond=self._hill_climb_cond)
skel = ci.skeleton()
g = ADG(vertices=skel.vertices())
for a, b in skel.lines():
if a in ci.potential_ancestors(b):
g.add_arrow(a, b)
else:
g.add_arrow(b, a)
return g
best_score = score
best_parents = parents
except NameError:
best_score = score
best_parents = parents
return best_parents
def sublist(candidates,n):
if not candidates or n == 0:
yield []
else:
first = [candidates[0]]
for sl in sublist(candidates[1:],n-1):
yield first + sl
for sl in sublist(candidates[1:],n):
yield sl
best_adg = ADG(ordering)
for i, v in enumerate(ordering):
if verbose:
print 'Finding best parents for %s' % v
parents = best_parents(v,ordering[:i])
for parent in parents:
best_adg.add_arrow(parent,v)
print 'Best ADG'
print '*********'
print best_adg
best_score = score
best_parents = parents
except NameError:
best_score = score
best_parents = parents
return best_parents
def sublist(candidates, n):
if not candidates or n == 0:
yield []
else:
first = [candidates[0]]
for sl in sublist(candidates[1:], n - 1):
yield first + sl
for sl in sublist(candidates[1:], n):
yield sl
best_adg = ADG(ordering)
for i, v in enumerate(ordering):
if verbose:
print 'Finding best parents for %s' % v
parents = best_parents(v, ordering[:i])
for parent in parents:
best_adg.add_arrow(parent, v)
print 'Best ADG'
print '*********'
print best_adg
