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src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
            trainData = trainingData
        else:
            trainData = dataUtilities.getCopyWithoutMeta(trainingData)
        # Impute the data and Convert the ExampleTable to CvMat 
        if self.useBuiltInMissValHandling:
                example = origExample
            else:
                example = dataUtilities.getCopyWithoutMeta(origExample)
 
            if not self.ExFix.ready:
                    impData.append(self.imputer.defaults)
                # Remove the meta attributes from the imputer data. We don't need to store them along with the model
                impData = dataUtilities.getCopyWithoutMeta(impData)
 
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBayes.py   AZOrange(Download)
            trainingData = data
        else:
            trainingData = dataUtilities.getCopyWithoutMeta(data)
        # Create the imputer
        self.imputer = orange.ImputerConstructor_average(trainingData)
            examples = origExamples
        else:
            examples = dataUtilities.getCopyWithoutMeta(origExamples)
        #Check if the examples are compatible with the classifier (attributes order and varType compatibility)
        dataUtilities.verbose = self.verbose
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            impData = dataUtilities.getCopyWithoutMeta(impData)
            impData.save(os.path.join(thePath,"ImputeData.tab"))
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
            trainingData = data
        else:
            trainingData = dataUtilities.getCopyWithoutMeta(data)
        # Create the imputer
        self.imputer = orange.ImputerConstructor_average(trainingData)
            examples = origExamples
        else:
            examples = dataUtilities.getCopyWithoutMeta(origExamples)
 
        #Check if the examples are compatible with the classifier (attributes order and varType compatibility)
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            impData = dataUtilities.getCopyWithoutMeta(impData)
            impData.save(os.path.join(thePath,"ImputeData.tab"))
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBoost.py   AZOrange(Download)
            trainingData = data
        else:
            trainingData = dataUtilities.getCopyWithoutMeta(data)
        # Create the imputer
        self.imputer = orange.ImputerConstructor_average(trainingData)
            examples = origExamples
        else:
            examples = dataUtilities.getCopyWithoutMeta(origExamples)
        #Check if the examples are compatible with the classifier (attributes order and varType compatibility)
        dataUtilities.verbose = self.verbose
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            impData = dataUtilities.getCopyWithoutMeta(impData)
            impData.save(os.path.join(thePath,"ImputeData.tab"))
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
            cleanedData = data
        else:
            cleanedData = dataUtilities.getCopyWithoutMeta(data)
        # Create the imputer
        self.imputer = orange.ImputerConstructor_average(cleanedData)
            examples = origExamples
        else:
            examples = dataUtilities.getCopyWithoutMeta(origExamples)
        #Check if the examples are compatible with the classifier (attributes order and varType compatibility)
        if self.verbose > 1: dataUtilities.verbose = self.verbose
            impData.append(self.imputer.defaults)
            # Remove the meta attributes from the imputer data. We don't need to store them along with the model
            impData = dataUtilities.getCopyWithoutMeta(impData)
            impData.save(os.path.join(thePath,"ImputeData.tab"))
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngPLS.py   AZOrange(Download)
            trainData = trainingData
        else:
            trainData = dataUtilities.getCopyWithoutMeta(trainingData)
 
	# Create the imputer
                examples = origExamples
            else:
                examples = dataUtilities.getCopyWithoutMeta(origExamples)
            #dataUtilities.rmAllMeta(examples) 
 
                impData.append(self.imputer.defaults)
                # Remove the meta attributes from the imputer data. We don't need to store them along with the model
                impData = dataUtilities.getCopyWithoutMeta(impData)
                impData.save(str(filePath)+"/ImputeData.tab")
                #Save the var names orderes the same way the Learner was trained

src/a/z/AZOrange-HEAD/tests/source/AZdataUtilitiesTest.py   AZOrange(Download)
        self.assert_(len(ex.getmetas())>=1,"The initial example had no metas at all")
 
        exNoMeta = dataUtilities.getCopyWithoutMeta(ex)
 
        #The initial example shall not be modified by this method
        self.assert_(len(self.wMetaData.domain.getmetas())>=1,"The initial data had no metas at all")
 
        noMetaData = dataUtilities.getCopyWithoutMeta(self.wMetaData)
 
        #The initial example shall not be modified by this method