def get_normed_x(self, rows=None, cols=None):
        """ Returns the normalized input data requested by the user
            @ In, rows, a list of non-negative integers specifying the
            row indices to return
            @ In, cols, a list of non-negative integers specifying the
            column indices to return
            @ Out, a matrix of floating point values specifying the
            normalized data values used in internal computations
            filtered by the three input parameters.
        """
        if rows is None:
            rows = list(range(0, self.get_sample_size()))
        if cols is None:
            cols = list(range(0, self.get_dimensionality()))

        if not hasattr(rows, "__iter__"):
            rows = [rows]
        rows = sorted(list(set(rows)))

        retValue = self.Xnorm[rows, :]
        return retValue[:, cols]
def get_normed_x ( self , rows = None , cols = None ) : if rows is None : rows = list ( range ( 0 , self . get_sample_size ( ) ) ) if cols is None : cols = list ( range ( 0 , self . get_dimensionality ( ) ) ) if not hasattr ( rows , "__iter__" ) : rows = [ rows ] rows = sorted ( list ( set ( rows ) ) ) retValue = self . Xnorm [ rows , : ] return retValue [ : , cols ]
Returns the normalized input data requested by the user
            @ In, rows, a list of non-negative integers specifying the
            row indices to return
            @ In, cols, a list of non-negative integers specifying the
            column indices to return
            @ Out, a matrix of floating point values specifying the
            normalized data values used in internal computations
            filtered by the three input parameters.