Ejemplo n.º 1
0
 def FindUsers(self, UserData, finallist):
     instance = Methods.MethodRepository()
     EvolveRes = instance.EvolveForUser(finallist, UserData, self.T)
     Total = len(self.Tset) - 1
     POfClasses = instance.findClassP(Total, self.D)
     # ZeroFrequency =Instance.ZeroFreProblem(EvolveRes)
     Prediction = instance.Predict(EvolveRes, POfClasses)
     Result = instance.GivePrediction(Prediction)
     return Result
Ejemplo n.º 2
0
    def FileRead(self):
        file = 'csv/DataForDT.csv'
        #Getting reccords from CSV file
        lines = csv.reader(open(file))
        #Making a list of reccords
        lisst = list(lines)
        Instance = Methods.MethodRepository()
        TraininD, TestD = Instance.MadeChunks(lisst)

        return [TraininD, TestD]
Ejemplo n.º 3
0
    def excute(self):
        FinalList = []
        Instance = Methods.MethodRepository()
        File = FileReader.File()
        lisst = File.FileRead()
        TraininD = lisst[0]
        TestD = lisst[1]
        TestSet = TestD
        TrainSet = TraininD
        self.Tset = TrainSet
        x = len(TraininD[0])
        Test = Decimal(1)
        Data = []
        Class = Instance.UniqueList(TraininD)
        for increment in range(x - 1):
            #Spilting Data i.e in training and testing

            #Count repetition of attribute per class
            Data = Instance.CountRepInClass(TraininD)
            self.D = Data
            AttributeList = Instance.Findings(TraininD, increment, Class)
            AttributeList = Instance.Filter(AttributeList, Class)

            Difference = Instance.CountDifferent(TraininD, increment)
            AttributeList = Instance.ZeroFreProblem(AttributeList, Difference)
            Total = Instance.Total(TraininD)
            self.T = Total
            Structure = Instance.MakeStruc(AttributeList, TraininD, increment)
            Difference = Instance.CountDifferent(TraininD, increment)
            Attributes = Difference
            Difference = len(Difference)
            C = Instance.PredictC(Structure, Total, Difference)
            X = Instance.PredictX(Structure, TraininD, Difference)
            XC = Instance.PredictXC(Structure, Data, Difference)
            PosterierP = Instance.PosterierP(C, XC, X, Attributes)
            FinalStruc = Instance.FinalStructure(PosterierP, Attributes)
            FinalList.append(FinalStruc)
        # print(AttributeList)
        #have to look here
        EvolveRes = Instance.EvolveAttP(FinalList, TestSet, Total)
        Total = len(TrainSet) - 1
        POfClasses = Instance.findClassP(Total, Data)
        #ZeroFrequency =Instance.ZeroFreProblem(EvolveRes)
        Prediction = Instance.Predict(EvolveRes, POfClasses)
        Result = Instance.GivePrediction(Prediction)
        Accuracy = Instance.Accuracy(TestD, Result)
        #print ("Accuray:%s " %Accuracy)
        return Accuracy, FinalList