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29% acc, stopwords, fixed bayes?

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This commit is contained in:
Ulf Gebhardt 2013-06-16 19:22:13 +02:00
parent 17dfb87a67
commit 9acfc59dae
2 changed files with 117 additions and 116 deletions
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210
ss2013/1_Web Mining/Uebungen/4_Uebung/G22_predictions.txt
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23
ss2013/1_Web Mining/Uebungen/4_Uebung/code/naive_bayes.py
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@ -115,13 +115,14 @@ class multiclassClassifier:
f = open(pathToFile, 'r')
lines = f.readlines();
for line in lines:
thisline = line.split(" ");
for word in thisline:
thisline = line.split(" ");
for word in thisline:
word = self.clean_word(word)
if dictonary.has_key(word):
dictonary[str(word)] += 1
else:
dictonary[str(word)] = 1
if word <> "":
if dictonary.has_key(word):
dictonary[str(word)] += 1
else:
dictonary[str(word)] = 1
f.close()
return dictonary
@ -134,11 +135,11 @@ class multiclassClassifier:
word = self.clean_word(word)
if word <> "":
#result = 1.0
for word in text:
if termfrequenciesOfClasses.has_key(str(word)):
result += math.log((termfrequenciesOfClasses[word]+1.)/(termCount+1))
else:
result += math.log(1./(termCount+1))
#for word in text:
if termfrequenciesOfClasses.has_key(str(word)):
result += math.log((termfrequenciesOfClasses[word]+1.)/(termCount+1))
else:
result += math.log(1./(termCount+1))
result += math.log(percentage)
return result