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src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
        self.imputer = orange.ImputerConstructor_average(trainingData)
        # Impute the data 
        trainingData = self.imputer(trainingData)
        if self.scaleData:
            self.scalizer = dataUtilities.scalizer()

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBoost.py   AZOrange(Download)
        self.imputer = orange.ImputerConstructor_average(trainingData)
        # Impute the data 
        self.trainData = self.imputer(trainingData)
 
        impData=self.imputer.defaults

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngPLS.py   AZOrange(Download)
        self.imputer = orange.ImputerConstructor_average(trainData)
	# Impute the data 
	trainData = self.imputer(trainData)
        # Save the Data already imputed to an Orange formated file
	if self.verbose > 1: print time.asctime(), "Saving Orange Data to a tab file..."

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBayes.py   AZOrange(Download)
        self.imputer = orange.ImputerConstructor_average(trainingData)
        # Impute the data 
        trainingData = self.imputer(trainingData)
        if self.scale:
            self.scalizer = dataUtilities.scalizer()

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
            self.imputer = orange.ImputerConstructor_average(trainData)
            impData=self.imputer.defaults
            trainData = self.imputer(trainData)
            CvMatrices = dataUtilities.ExampleTable2CvMat(trainData)
            CvMatrices["missing_data_mask"] = None

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
        self.imputer = orange.ImputerConstructor_average(cleanedData)
        # Impute the data 
        self.trainData = self.imputer(cleanedData)
         # If we are not seetin neither weights init optimization or nEphocs optimization (opencvLayer), the do nto split the data
        if self.stopUPs != 0 or self.nDiffIniWeights > 1: