def __init__(self, td, n, w):
self.termDocs = td
self.norms = n
self.weight = w
self.scoreCache = [
similarity.tf(i) * self.weight
for i in range(self.SCORE_CACHE_SIZE)
]
def score(self, end):
for d, f in self.termDocs.read():
if d >= end:
break
if f < self.SCORE_CACHE_SIZE:
score = self.scoreCache[f]
else:
# cache miss
score = similarity.tf(f) * self.weight
# normalize for field
score *= similarity.normByte(self.norms[d])
# collect score
yield (d, score)
else:
# close stream
self.termDocs.close()
# set to sentinel value
self.doc = sys.maxint
def score(self, end):
for d, f in self.termDocs.read():
if d >= end:
break
if f < self.SCORE_CACHE_SIZE:
score = self.scoreCache[f]
else:
# cache miss
score = similarity.tf(f) * self.weight
# normalize for field
score *= similarity.normByte(self.norms[d])
# collect score
yield (d, score)
else:
# close stream
self.termDocs.close()
# set to sentinel value
self.doc = sys.maxint
def __init__(self, td, n, w):
self.termDocs = td
self.norms = n
self.weight = w
self.scoreCache = [similarity.tf(i) * self.weight for i in range(self.SCORE_CACHE_SIZE)]