def _matcher(self, matchercls, q_weight_fn, searcher, weighting=None,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Pull any queries inside a Not() out into their own list
subs, nots = self._split_queries()
if not subs:
return matching.NullMatcher()
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, weighting=weighting) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(matchercls, subms)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(matchercls, w_subms)
# If there were queries inside Not(), make a matcher for them and
# wrap the matchers in an AndNotMatcher
if nots:
if len(nots) == 1:
notm = nots[0].matcher(searcher)
else:
r = searcher.reader()
notms = [(q.estimate_size(r), q.matcher(searcher))
for q in nots]
notm = make_weighted_tree(matching.UnionMatcher, notms)
if notm.is_active():
m = matching.AndNotMatcher(m, notm)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _matcher(self, matchercls, q_weight_fn, searcher, weighting=None,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Pull any queries inside a Not() out into their own list
subs, nots = self._split_queries()
if not subs:
return matching.NullMatcher()
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, weighting=weighting) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(matchercls, subms)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(matchercls, w_subms)
# If there were queries inside Not(), make a matcher for them and
# wrap the matchers in an AndNotMatcher
if nots:
if len(nots) == 1:
notm = nots[0].matcher(searcher)
else:
r = searcher.reader()
notms = [(q.estimate_size(r), q.matcher(searcher))
for q in nots]
notm = make_weighted_tree(matching.UnionMatcher, notms)
if notm.is_active():
m = matching.AndNotMatcher(m, notm)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _tree_matcher(self, subs, mcls, searcher, context, q_weight_fn,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, context) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(mcls, subms, **kwargs)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(mcls, w_subms, **kwargs)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _tree_matcher(self, subs, mcls, searcher, context, q_weight_fn,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, context) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(mcls, subms, **kwargs)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(mcls, w_subms, **kwargs)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
