##############################################################################
#
# Copyright (c) 2001 Zope Corporation and Contributors. All Rights Reserved.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.0 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE
#
##############################################################################
from BTrees.IIBTree import IIBucket
from BTrees.IIBTree import weightedIntersection,weightedUnion,difference
from BTrees.OOBTree import OOSet,union
class ResultList:
def __init__(self, d, words, index, TupleType=type(())):
self._index = index
if type(words) is not OOSet: words=OOSet(words)
self._words = words
if (type(d) is TupleType):
d = IIBucket((d,))
elif type(d) is not IIBucket:
d = IIBucket(d)
self._dict=d
self.__getitem__=d.__getitem__
try: self.__nonzero__=d.__nonzero__
except: pass
self.get=d.get
def __nonzero__(self):
return not not self._dict
def bucket(self): return self._dict
def keys(self): return self._dict.keys()
def has_key(self, key): return self._dict.has_key(key)
def items(self): return self._dict.items()
def __and__(self, x):
return self.__class__(
weightedIntersection(self._dict, x._dict)[1],
union(self._words, x._words),
self._index,
)
def and_not(self, x):
return self.__class__(
difference(self._dict, x._dict),
self._words,
self._index,
)
def __or__(self, x):
return self.__class__(
weightedUnion(self._dict, x._dict)[1],
union(self._words, x._words),
self._index,
)
# return self.__class__(result, self._words+x._words, self._index)
def near(self, x):
result = IIBucket()
dict = self._dict
xdict = x._dict
xhas = xdict.has_key
positions = self._index.positions
for id, score in dict.items():
if not xhas(id): continue
p=(map(lambda i: (i,0), positions(id,self._words))+
map(lambda i: (i,1), positions(id,x._words)))
p.sort()
d = lp = 9999
li = None
lsrc = None
for i,src in p:
if i is not li and src is not lsrc and li is not None:
d = min(d,i-li)
li = i
lsrc = src
if d==lp: score = min(score,xdict[id]) # synonyms
else: score = (score+xdict[id])/d
result[id] = score
return self.__class__(
result, union(self._words, x._words), self._index)
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