(self, data, sigmas=None)
return recovered_data
Take matrix data and output raw data.

        Output uses the same type as input to the transform function.
        Either np array or pd dataframe.
Take matrix data and output raw data.
Take matrix data and output raw data .
def inverse_transform(self, data, sigmas=None):
        """Take matrix data and output raw data.

        Output uses the same type as input to the transform function.
        Either np array or pd dataframe.
        """
        st = 0
        recovered_column_data_list = []
        column_names = []
        for column_transform_info in self._column_transform_info_list:
            dim = column_transform_info.output_dimensions
            column_data = data[:, st:st + dim]

            if column_transform_info.column_type == 'continuous':
                recovered_column_data = self._inverse_transform_continuous(
                    column_transform_info, column_data, sigmas, st)
            else:
                assert column_transform_info.column_type == 'discrete'
                recovered_column_data = self._inverse_transform_discrete(
                    column_transform_info, column_data)

            recovered_column_data_list.append(recovered_column_data)
            column_names.append(column_transform_info.column_name)
            st += dim

        recovered_data = np.column_stack(recovered_column_data_list)
        recovered_data = (pd.DataFrame(recovered_data, columns=column_names)
                          .astype(self._column_raw_dtypes))
        if not self.dataframe:
            recovered_data = recovered_data.to_numpy()

        return recovered_data
def inverse_transform ( self , data , sigmas = None ) : st = 0 recovered_column_data_list = [ ] column_names = [ ] for column_transform_info in self . _column_transform_info_list : dim = column_transform_info . output_dimensions column_data = data [ : , st : st + dim ] if column_transform_info . column_type == 'continuous' : recovered_column_data = self . _inverse_transform_continuous ( column_transform_info , column_data , sigmas , st ) else : assert column_transform_info . column_type == 'discrete' recovered_column_data = self . _inverse_transform_discrete ( column_transform_info , column_data ) recovered_column_data_list . append ( recovered_column_data ) column_names . append ( column_transform_info . column_name ) st += dim recovered_data = np . column_stack ( recovered_column_data_list ) recovered_data = ( pd . DataFrame ( recovered_data , columns = column_names ) . astype ( self . _column_raw_dtypes ) ) if not self . dataframe : recovered_data = recovered_data . to_numpy ( ) return recovered_data