np.sum

[deepakkumar1984]

Implement np.sum
https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html

[Oceania2018]

I'll expose sum to np. You can use it as extension temporarily.

[Oceania2018]

Oh, I saw you already assigned to yourself. Are you going to PR?

[deepakkumar1984]

The function seems counting if the element of both array is equal. it should be Sum of array elements over a given axis. So the parameter need to be an axis.

[henon]

def sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, initial=np._NoValue):
    """
    Sum of array elements over a given axis.
    Parameters
    ----------
    a : array_like
        Elements to sum.
    axis : None or int or tuple of ints, optional
        Axis or axes along which a sum is performed.  The default,
        axis=None, will sum all of the elements of the input array.  If
        axis is negative it counts from the last to the first axis.
        .. versionadded:: 1.7.0
        If axis is a tuple of ints, a sum is performed on all of the axes
        specified in the tuple instead of a single axis or all the axes as
        before.
    dtype : dtype, optional
        The type of the returned array and of the accumulator in which the
        elements are summed.  The dtype of `a` is used by default unless `a`
        has an integer dtype of less precision than the default platform
        integer.  In that case, if `a` is signed then the platform integer
        is used while if `a` is unsigned then an unsigned integer of the
        same precision as the platform integer is used.
    out : ndarray, optional
        Alternative output array in which to place the result. It must have
        the same shape as the expected output, but the type of the output
        values will be cast if necessary.
    keepdims : bool, optional
        If this is set to True, the axes which are reduced are left
        in the result as dimensions with size one. With this option,
        the result will broadcast correctly against the input array.
        If the default value is passed, then `keepdims` will not be
        passed through to the `sum` method of sub-classes of
        `ndarray`, however any non-default value will be.  If the
        sub-class' method does not implement `keepdims` any
        exceptions will be raised.
    initial : scalar, optional
        Starting value for the sum. See `~numpy.ufunc.reduce` for details.
        .. versionadded:: 1.15.0
    Returns
    -------
    sum_along_axis : ndarray
        An array with the same shape as `a`, with the specified
        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar
        is returned.  If an output array is specified, a reference to
        `out` is returned.
    See Also
    --------
    ndarray.sum : Equivalent method.
    cumsum : Cumulative sum of array elements.
    trapz : Integration of array values using the composite trapezoidal rule.
    mean, average
    Notes
    -----
    Arithmetic is modular when using integer types, and no error is
    raised on overflow.
    The sum of an empty array is the neutral element 0:
    >>> np.sum([])
    0.0
    Examples
    --------
    >>> np.sum([0.5, 1.5])
    2.0
    >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
    1
    >>> np.sum([[0, 1], [0, 5]])
    6
    >>> np.sum([[0, 1], [0, 5]], axis=0)
    array([0, 6])
    >>> np.sum([[0, 1], [0, 5]], axis=1)
    array([1, 5])
    If the accumulator is too small, overflow occurs:
    >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
    -128
    You can also start the sum with a value other than zero:
    >>> np.sum([10], initial=5)
    15
    """
    if isinstance(a, _gentype):
        # 2018-02-25, 1.15.0
        warnings.warn(
            "Calling np.sum(generator) is deprecated, and in the future will give a different result. "
            "Use np.sum(np.fromiter(generator)) or the python sum builtin instead.",
            DeprecationWarning, stacklevel=2)

        res = _sum_(a)
        if out is not None:
            out[...] = res
            return out
        return res

    return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
                          initial=initial)

[deepakkumar1984]

Hi, seems there is an issue with np.sum for axis -1. The result I am getting is SumAll whereas should be sum at axis -1 which for 2D will be 1, 3D will be 2.

Python version:

In NumSharp I get 45 for the same invoke.

[Oceania2018]

OK, -1 means last axis. Will add next release.

[henon]

by the way, many NumSharp methods use -1 for the default axis right now. these will all have to be changed to use int? instead of int for axis and use null instead of -1 for the default axis

[Oceania2018]

Oh, I will fix them one by one.