class Matrix_(object):
    def init(self, *args, **kwargs):
        # Preprocessing data
        # Making a dict of all non-zero key-value pairs
        if len(args) == 3 and callable(args[2]):
            rows, cols, mat = _from_callable(args)
        elif len(args) == 3 and isinstance(args[2], (list, tuple)):
            rows, cols, mat = _from_list(args)
        else: # Value passing only through lambda and list right now
            raise NotImplementedError("Data type not understood")
        # mat is now a dict of of all non-zero elements

        # Choosing a representation for the matrix
        if 'repr' in kwargs:
            # User-specified representation
            if kwargs['repr'] == 'dok':
                mat = _dict_to_dokmatrix(rows, cols, mat)
            elif kwargs['repr'] == 'lil':
                mat = _dict_to_lilmatrix(rows, cols, mat)
            elif kwargs['repr'] == 'dense':
                mat = _dict_to_densematrix(rows, cols, mat)
            else:
                raise Exception('repr %s not recognized' % kwargs['repr'])
        else:
            # Representation chosen by sparsity check
            sparsity = float(len(mat)) / (rows * cols)
            if sparsity > 0.5:
                mat = _dict_to_densematrix(rows, cols, mat)
            else:
                mat = _dict_to_dokmatrix(rows, cols, mat)

        # mat is now one of the low-level matrices

        self.rows = rows
        self.cols = cols
        self.mat = mat

        # We're done.