//  [python]
def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False,
                         rtol=None, atol=None):
        """
        Parameters
        ----------
        allow_remove_inf
            If True, when there is an inf in a, we allow any value in b in
            that position. Event -inf
        allow_remove_nan
            If True, when there is a nan in a, we allow any value in b in
            that position. Event +-inf
        rtol
            Relative tolerance, passed to _allclose.
        atol
            Absolute tolerance, passed to _allclose.

        """
        if isinstance(a, numpy.ndarray) and isinstance(b, numpy.ndarray):
            if a.shape != b.shape:
                return False
            if a.dtype != b.dtype:
                return False
            if 'int' in str(a.dtype):
                return numpy.alltrue(a == b)
            else:
                # work around a numpy.allclose bug:
                # http://projects.scipy.org/numpy/ticket/1672
                if a.ndim == 0 and numpy.isinf(a):
                    a = a.reshape(1)
                    b = b.reshape(1)

                cmp = theano.tensor.basic._allclose(a, b, rtol=rtol, atol=atol)
                if cmp:
                    # Numpy claims they are close, this is good enough for us.
                    return True
                # Numpy is unhappy, but it does not necessarily mean that a and
                # b are different. Indeed, Numpy does not like missing values
                # and will return False whenever some are found in a or b.
                # The proper way would be to use the MaskArray stuff available
                # in Numpy. However, it looks like it has been added to Numpy's
                # core recently, so it may not be available to everyone. Thus,
                # for now we use a home-made recipe, that should probably be
                # revisited in the future.
                a_missing = numpy.isnan(a)
                a_inf = numpy.isinf(a)

                if not (a_missing.any() or (allow_remove_inf and a_inf.any())):
                    # There are no missing values in a, thus this is not the
                    # reason why numpy.allclose(a, b) returned False.
                    _logger.info(
                        'numpy allclose failed for abs_err %f and rel_err %f',
                        numpy.max(abs(a - b)),
                        numpy.max(abs(a - b) / (abs(a) + abs(b))))
                    return False
                # The following line is what numpy.allclose bases its decision
                # upon, according to its documentation.
                rtol = 1.0000000000000001e-05
                atol = 1e-8
                cmp_elemwise = (numpy.absolute(a - b) <=
                                (atol + rtol * numpy.absolute(b)))
                # Find places where both a and b have missing values.
                both_missing = a_missing * numpy.isnan(b)

                # Find places where both a and b have inf of the same sign.
                both_inf = a_inf * numpy.isinf(b)

                # cmp_elemwise is weird when we have inf and -inf.
                # set it to False
                cmp_elemwise = numpy.where(
                    both_inf & cmp_elemwise,
                    a == b,
                    cmp_elemwise)

                # check the sign of the inf
                both_inf = numpy.where(both_inf, (a == b), both_inf)

                if allow_remove_inf:
                    both_inf += a_inf
                if allow_remove_nan:
                    both_missing += a_missing

                # Combine all information.
                return (cmp_elemwise + both_missing + both_inf).all()

        return False