29% acc, stopwords, fixed bayes?

ss2013/1_Web Mining/Uebungen/4_Uebung/G22_predictions.txt

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ss2013/1_Web Mining/Uebungen/4_Uebung/code/naive_bayes.py

@@ -115,13 +115,14 @@ class multiclassClassifier:
             f = open(pathToFile, 'r')
             lines = f.readlines();
             for line in lines:
-                thisline = line.split(" ");
+                thisline = line.split(" ");                
-                for word in thisline:
+                for word in thisline:                    
                     word = self.clean_word(word)
-                    if dictonary.has_key(word):
+                    if word <> "":
-                        dictonary[str(word)] += 1
+                        if dictonary.has_key(word):
-                    else:
+                            dictonary[str(word)] += 1
-                        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:
+                        #for word in text:
-                            if termfrequenciesOfClasses.has_key(str(word)):
+                        if termfrequenciesOfClasses.has_key(str(word)):
-                                result += math.log((termfrequenciesOfClasses[word]+1.)/(termCount+1))
+                            result += math.log((termfrequenciesOfClasses[word]+1.)/(termCount+1))
-                            else:
+                        else:
-                                result += math.log(1./(termCount+1))
+                            result += math.log(1./(termCount+1))
                         result += math.log(percentage)
             return result