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src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngPLS.py   AZOrange(Download)
                self._updateDFVExtremes(DFV)
            else:
                y_hat = self.classVar(value)
                score = Orange.statistics.distribution.Continuous(self.classVar)
                score[y_hat] = 1.0

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngRF.py   AZOrange(Download)
                        DFV = float(prediction.value)
                        self._updateDFVExtremes(DFV)
                    y_hat = self.classVar(prediction)
                    probabilities = Orange.statistics.distribution.Continuous(self.classVar)
                    probabilities[y_hat] = 1.0

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvSVM.py   AZOrange(Download)
                    dist[res]=1
                else:
                    y_hat = self.classVar(res)
                    dist = Orange.statistics.distribution.Continuous(self.classVar)
                    dist[y_hat] = 1.0

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngCvANN.py   AZOrange(Download)
            DFV = res.value
            self._updateDFVExtremes(DFV)
            y_hat = self.classVar(res.value)
            dist = Orange.statistics.distribution.Continuous(self.classVar)
            dist[y_hat] = 1.0

src/a/z/AZOrange-HEAD/azorange/trainingMethods/AZorngConsensus.py   AZOrange(Download)
                predictions.append(c(origExample))
            DFV = predicted = sum(predictions,0.0) / len(predictions)
            y_hat = self.classVar(predicted)
            probabilities = Orange.statistics.distribution.Continuous(self.classVar)
            probabilities[y_hat] = 1.0
 
                DFV = predicted = result
                y_hat = self.classVar(predicted)
                probabilities = Orange.statistics.distribution.Continuous(self.classVar)
                probabilities[y_hat] = 1.0