--- a/src/HOL/Tools/Sledgehammer/MaSh/src/sparseNaiveBayes.py Thu Jan 17 17:55:02 2013 +0100
+++ b/src/HOL/Tools/Sledgehammer/MaSh/src/sparseNaiveBayes.py Thu Jan 17 17:55:03 2013 +0100
@@ -19,13 +19,11 @@
An updateable naive Bayes classifier.
'''
- def __init__(self,defaultPriorWeight = 20.0,posWeight = 20.0,defVal = -15.0,useSinePrior = False,sineWeight = 100.0):
+ def __init__(self,defaultPriorWeight = 20.0,posWeight = 20.0,defVal = -15.0):
'''
Constructor
'''
self.counts = {}
- self.sinePrior = useSinePrior
- self.sineWeight = sineWeight
self.defaultPriorWeight = defaultPriorWeight
self.posWeight = posWeight
self.defVal = defVal
@@ -100,19 +98,11 @@
Returns a ranking of the accessibles.
"""
predictions = []
- fSet = set([f for f,_w in features])
for a in accessibles:
posA = self.counts[a][0]
fA = set(self.counts[a][1].keys())
fWeightsA = self.counts[a][1]
- prior = posA
- if self.sinePrior:
- triggerFeatures = dicts.triggerFeatures[a]
- triggeredFeatures = fSet.intersection(triggerFeatures)
- for f in triggeredFeatures:
- posW = dicts.featureCountDict[f]
- prior += self.sineWeight / posW
- resultA = log(prior)
+ resultA = log(posA)
for f,w in features:
# DEBUG
#w = 1
@@ -131,12 +121,12 @@
def save(self,fileName):
OStream = open(fileName, 'wb')
- dump((self.counts,self.defaultPriorWeight,self.posWeight,self.defVal,self.sinePrior,self.sineWeight),OStream)
+ dump((self.counts,self.defaultPriorWeight,self.posWeight,self.defVal),OStream)
OStream.close()
def load(self,fileName):
OStream = open(fileName, 'rb')
- self.counts,self.defaultPriorWeight,self.posWeight,self.defVal,self.sinePrior,self.sineWeight = load(OStream)
+ self.counts,self.defaultPriorWeight,self.posWeight,self.defVal = load(OStream)
OStream.close()