47f8fe9404071e5d30ef5aea8fb96539eb0a82c3,fancyimpute/solver.py,Solver,project_result,#Solver#,125

Before Change

        if self.normalizer is not None:
            X = self.normalizer.inverse_transform(X)
        if self.min_value is not None:
            X[X < self.min_value] = self.min_value
        if self.max_value is not None:
            X[X > self.max_value] = self.max_value
        return X

    def solve(self, X, missing_mask):
        

After Change

            X[X > self.max_value] = self.max_value
        return X

    def project_result(self, X):
        
        First undo normaliztion and then clip to the user-specified min/max
        range.
        
        X = np.asarray(X)
        if self.normalizer is not None:
            X = self.normalizer.inverse_transform(X)
        return self.clip(X)

    def solve(self, X, missing_mask):
        
        Given an initialized matrix X and a mask of where its missing values

Instances