ece383696800b9b34854df27a65a3d1d74669952,skrebate/iterrelief.py,IterRelief,fit,#IterRelief#Any#Any#,69

Before Change

            feature_weights = core_fit.feature_importances_
            
            mx = max(feature_weights)
            mn = min(feature_weights)
            rg = mx - mn

            weight_history.append(feature_weights)

            feature_weights = [(x - mn)/(rg) for x in feature_weights]
            distance_weights += feature_weights

After Change

            //normalize and update scores
            //negative numbers -> 0, positive numbers become normalized so maximum is 1.
            for i in range(0, len(feature_weights)):
                if feature_weights[i] <= 0:
                    feature_weights[i] = 0
                else:
                    feature_weights[i] = feature_weights[i] / mx

            distance_weights += feature_weights

            iteration += 1

Instances