def matches(self, tree, statemap):
"""Returns the count of this rule's LHS matches in the given tree.
Accounts for pattern matching too."""
count = 0
my_vars = variables_to_paths(self.lhs)
## Check whether, once you replace the right parts of the given tree
## with variables, it's equal to this rule's LHS.
dprint("matching this lhs:", self.lhs)
for path, state in statemap.items():
indexed = tree_index(tree, path)
subtree = deepcopy(indexed)
dprint("subtree:", subtree)
try:
for (var,varpath) in my_vars:
if varpath is ():
subtree = var
break
subtree[varpath] = var
except:
# print("EXCEPTION:", path)
# tree is wrong shape, keep tranglin'.
continue
if self.state == state and subtree == self.lhs:
# print("FOUND A MATCH.")
count += 1
return count
def matches(self, tree, statemap):
"""Returns the count of this rule's LHS matches in the given tree.
Accounts for pattern matching too."""
count = 0
my_vars = variables_to_paths(self.lhs)
## Check whether, once you replace the right parts of the given tree
## with variables, it's equal to this rule's LHS.
dprint("matching this lhs:", self.lhs)
for path, state in statemap.items():
indexed = tree_index(tree, path)
subtree = deepcopy(indexed)
dprint("subtree:", subtree)
try:
for (var, varpath) in my_vars:
if varpath is ():
subtree = var
break
subtree[varpath] = var
except:
# print("EXCEPTION:", path)
# tree is wrong shape, keep tranglin'.
continue
if self.state == state and subtree == self.lhs:
# print("FOUND A MATCH.")
count += 1
return count
def apply(self, tree, statemap):
"""Returns a list of (new tree, new statemap) by applying this rule in
all the places where it works."""
#dprint("APPLYING", self.lhs, "to", tree, statemap)
out = []
my_vars = variables_to_paths(self.lhs)
for path, state in statemap.items():
indexed = tree_index(tree, path)
subtree = deepcopy(indexed)
try:
for (var, vp) in my_vars:
if vp is ():
subtree = var
break
subtree[vp] = var
except:
continue
if self.state == state and subtree == self.lhs:
newtree = deepcopy(tree)
newsubtree = replace(indexed, self.lhs, self.rhs)
newstates_with_prepend = {
tuple(list(path) + list(rulepath)): state
for (rulepath, state) in self.newstates.items()
}
newstates_with_prepend.update(statemap)
del newstates_with_prepend[path]
if path == ():
out.append((newsubtree, newstates_with_prepend))
else:
newtree[path] = newsubtree
out.append((newtree, newstates_with_prepend))
dprint("PRODUCED:", out)
return out
def transduce(tree, rules, initial):
"""Given a tree, a set of rules, and an initial state, return the list of
all the trees that are produced by the transduction."""
# list of the current generation of SearchStates
current = []
complete = []
# give the root the initial state.
statemap = {(): 'q'}
current.append(SearchState(tree, statemap, 1.0))
progress = True
while progress:
nextgen = []
# for every tree that has a state: find every rule that applies to
# that tree. apply that rule to that tree, put the results into the
# nextgen.
progress = False
for ss in current: # (tr, statemap)
if statemap != {}:
for i, rule in enumerate(rules):
if rule.matches(ss.tree, ss.statemap):
results = rule.apply(ss.tree, ss.statemap)
for (newtr, newstatemap) in results:
w = rule.weight * ss.weight
newss = SearchState(newtr, newstatemap, w)
if newstatemap == {}:
complete.append(newss)
else:
nextgen.append(newss)
break
if nextgen:
dprint(nextgen)
progress = True
current = nextgen
return complete
def transduce(tree, rules, initial):
"""Given a tree, a set of rules, and an initial state, return the list of
all the trees that are produced by the transduction."""
# list of the current generation of SearchStates
current = []
complete = []
# give the root the initial state.
statemap = {():'q'}
current.append(SearchState(tree, statemap, 1.0))
progress = True
while progress:
nextgen = []
# for every tree that has a state: find every rule that applies to
# that tree. apply that rule to that tree, put the results into the
# nextgen.
progress = False
for ss in current: # (tr, statemap)
if statemap != {}:
for i,rule in enumerate(rules):
if rule.matches(ss.tree, ss.statemap):
results = rule.apply(ss.tree, ss.statemap)
for (newtr, newstatemap) in results:
w = rule.weight * ss.weight
newss = SearchState(newtr, newstatemap, w)
if newstatemap == {}:
complete.append(newss)
else:
nextgen.append(newss)
if nextgen:
dprint(nextgen)
progress = True
current = nextgen
return complete
