Function assert_equal

torch/_numpy/testing/utils.py:132–282

Raises an AssertionError if two objects are not equal. Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised at the first conflicting values. When one of `actual` and `desired` is a

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

def assert_equal(actual, desired, err_msg="", verbose=True):
    """
    Raises an AssertionError if two objects are not equal.

    Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
    check that all elements of these objects are equal. An exception is raised
    at the first conflicting values.

    When one of `actual` and `desired` is a scalar and the other is array_like,
    the function checks that each element of the array_like object is equal to
    the scalar.

    This function handles NaN comparisons as if NaN was a "normal" number.
    That is, AssertionError is not raised if both objects have NaNs in the same
    positions.  This is in contrast to the IEEE standard on NaNs, which says
    that NaN compared to anything must return False.

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The expected object.
    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.

    Examples
    --------
    >>> np.testing.assert_equal([4,5], [4,6])
    Traceback (most recent call last):
        ...
    AssertionError:
    Items are not equal:
    item=1
     ACTUAL: 5
     DESIRED: 6

    The following comparison does not raise an exception.  There are NaNs
    in the inputs, but they are in the same positions.

    >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])

    """
    __tracebackhide__ = True  # Hide traceback for py.test

    num_nones = sum([actual is None, desired is None])
    if num_nones == 1:
        raise AssertionError(f"Not equal: {actual} != {desired}")
    elif num_nones == 2:
        return True
    # else, carry on