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src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngPLS.py   AZOrange(Download)
                probOf1 = score[self.classVar.values[1]]
                DFV = -(probOf1-0.5)
                self._updateDFVExtremes(DFV)
            else:
                y_hat = self.classVar(value)
                score = Orange.statistics.distribution.Continuous(self.classVar)
                score[y_hat] = 1.0
                if not value.isSpecial():
                    DFV = float(value.value)
                    self._updateDFVExtremes(DFV)

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
                    #On Regression models assume the DVF as the value predicted
                    DFV = res 
                self._updateDFVExtremes(DFV)
                res = dataUtilities.CvMat2orangeResponse(res,self.classVar)
            else:
                    #On Regression models assume the DVF as the value predicted
                    DFV = res
                self._updateDFVExtremes(DFV)
                res = dataUtilities.CvMat2orangeResponse(res,self.classVar)
 

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
                # Subtract 0.5 so that the threshold is 0 as all learners DFV
                DFV -= 0.5
                self._updateDFVExtremes(DFV)    
 
            # Retrun the desired quantity
            #On Regression models, assume the DFV as the value predicted
            DFV = res.value
            self._updateDFVExtremes(DFV)
            y_hat = self.classVar(res.value)
            dist = Orange.statistics.distribution.Continuous(self.classVar)

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
                    if not prediction.isSpecial():
                        DFV = float(prediction.value)
                        self._updateDFVExtremes(DFV)
                    y_hat = self.classVar(prediction)
                    probabilities = Orange.statistics.distribution.Continuous(self.classVar)
    def convert2DFV(self,probOf1):
        # Subtract 0.5 so that the threshold is 0 and invert the signal as all learners have standard DFV:
        # Positive Values for the first element of the class attributes, and negatove values to the second
        DFV = -(probOf1-0.5)
        self._updateDFVExtremes(DFV)

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBoost.py   AZOrange(Download)
                probOf1 = probabilities[self.classVar.values[1]]
                DFV = -(probOf1-0.5)
                self._updateDFVExtremes(DFV)
 
        else:
            #On Regression models assume the DVF as the value predicted
            DFV = prediction
            self._updateDFVExtremes(DFV)

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvBayes.py   AZOrange(Download)
            #On Regression models assume the DVF as the value predicted
            DFV = prediction
            self._updateDFVExtremes(DFV)
 
        if resultType == orange.GetBoth:

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngConsensus.py   AZOrange(Download)
    def _convert2DFV(self,probOf1):
        # Subtract 0.5 so that the threshold is 0 and invert the signal as all learners have standard DFV:
        # Positive Values for the first element of the class attributes, and negatove values to the second
        DFV = -(probOf1-0.5)
        self._updateDFVExtremes(DFV)