8e958d70fa54c0b249f7e6de27fd57cbaa7563d4,algorithm/rating/SlopeOne.py,SlopeOne,predict,#SlopeOne#Any#Any#,29

Before Change

    def predict(self,u,i):
        itemDict = {}
        // check if the user existed in trainSet or not
        if self.dao.containsUser(u):
            for item in self.dao.row(u).valuse():
                if item.values() > 0:
                    itemDict[item] = item.values()
                else:
                    continue
            sum = 0
            freqSum = 0
            for item2 in itemDict.keys():
                sum = sum + ((itemDict[item2] + self.diffAverage[u][item2]) * self.freq[u][item2])
                freqSum = freqSum + self.freq[u][item2]
            pred = sum/freqSum

        else:
            pred = self.dao.itemMeans[u]

After Change

            sum = 0
            freqSum = 0
            itemRated,ratings = self.dao.userRated(u)
            for item,rating in zip(itemRated,ratings):
                diff = self.diffAverage[i][item]
                count = self.freq[i][item]
                sum += (rating + diff) * count
                freqSum += count
            try:
                pred = float(sum)/freqSum
            except ZeroDivisionError:
                pred = self.dao.userMeans[u]

Instances