Function assert_almost_equal

torch/_numpy/testing/utils.py:326–448

Raises an AssertionError if two items are not equal up to desired precision. .. note:: It is recommended to use one of `assert_allclose`, `assert_array_almost_equal_nulp` or `assert_array_max_ulp` instead of this function for more consistent floating point

(actual, desired, decimal=7, err_msg="", verbose=True)

def assert_almost_equal(actual, desired, decimal=7, err_msg="", verbose=True):
    """
    Raises an AssertionError if two items are not equal up to desired
    precision.

    .. note:: It is recommended to use one of `assert_allclose`,
              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    The test verifies that the elements of `actual` and `desired` satisfy.

        ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``

    That is a looser test than originally documented, but agrees with what the
    actual implementation in `assert_array_almost_equal` did up to rounding
    vagaries. An exception is raised at conflicting values. For ndarrays this
    delegates to assert_array_almost_equal

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The expected object.
    decimal : int, optional
        Desired precision, default is 7.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    >>> from torch._numpy.testing import assert_almost_equal
    >>> assert_almost_equal(2.3333333333333, 2.33333334)
    >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not almost equal to 10 decimals
     ACTUAL: 2.3333333333333
     DESIRED: 2.33333334

    >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
    ...                     np.array([1.0,2.33333334]), decimal=9)
    Traceback (most recent call last):