Classe « ufunc »

Méthode numpy.ufunc.reduce

Signature de la méthode reduce

Description

reduce.__doc__

reduce(array, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)

    Reduces `array`'s dimension by one, by applying ufunc along one axis.

    Let :math:`array.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
    :math:`ufunc.reduce(array, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
    the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
    ufunc to each :math:`array[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
    For a one-dimensional array, reduce produces results equivalent to:
    ::

     r = op.identity # op = ufunc
     for i in range(len(A)):
       r = op(r, A[i])
     return r

    For example, add.reduce() is equivalent to sum().

    Parameters
    ----------
    array : array_like
        The array to act on.
    axis : None or int or tuple of ints, optional
        Axis or axes along which a reduction is performed.
        The default (`axis` = 0) is perform a reduction over the first
        dimension of the input array. `axis` may be negative, in
        which case it counts from the last to the first axis.

        .. versionadded:: 1.7.0

        If this is None, a reduction is performed over all the axes.
        If this is a tuple of ints, a reduction is performed on multiple
        axes, instead of a single axis or all the axes as before.

        For operations which are either not commutative or not associative,
        doing a reduction over multiple axes is not well-defined. The
        ufuncs do not currently raise an exception in this case, but will
        likely do so in the future.
    dtype : data-type code, optional
        The type used to represent the intermediate results. Defaults
        to the data-type of the output array if this is provided, or
        the data-type of the input array if no output array is provided.
    out : ndarray, None, or tuple of ndarray and None, optional
        A location into which the result is stored. If not provided or None,
        a freshly-allocated array is returned. For consistency with
        ``ufunc.__call__``, if given as a keyword, this may be wrapped in a
        1-element tuple.

        .. versionchanged:: 1.13.0
           Tuples are allowed for keyword argument.
    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 original `array`.

        .. versionadded:: 1.7.0
    initial : scalar, optional
        The value with which to start the reduction.
        If the ufunc has no identity or the dtype is object, this defaults
        to None - otherwise it defaults to ufunc.identity.
        If ``None`` is given, the first element of the reduction is used,
        and an error is thrown if the reduction is empty.

        .. versionadded:: 1.15.0

    where : array_like of bool, optional
        A boolean array which is broadcasted to match the dimensions
        of `array`, and selects elements to include in the reduction. Note
        that for ufuncs like ``minimum`` that do not have an identity
        defined, one has to pass in also ``initial``.

        .. versionadded:: 1.17.0

    Returns
    -------
    r : ndarray
        The reduced array. If `out` was supplied, `r` is a reference to it.

    Examples
    --------
    >>> np.multiply.reduce([2,3,5])
    30

    A multi-dimensional array example:

    >>> X = np.arange(8).reshape((2,2,2))
    >>> X
    array([[[0, 1],
            [2, 3]],
           [[4, 5],
            [6, 7]]])
    >>> np.add.reduce(X, 0)
    array([[ 4,  6],
           [ 8, 10]])
    >>> np.add.reduce(X) # confirm: default axis value is 0
    array([[ 4,  6],
           [ 8, 10]])
    >>> np.add.reduce(X, 1)
    array([[ 2,  4],
           [10, 12]])
    >>> np.add.reduce(X, 2)
    array([[ 1,  5],
           [ 9, 13]])

    You can use the ``initial`` keyword argument to initialize the reduction
    with a different value, and ``where`` to select specific elements to include:

    >>> np.add.reduce([10], initial=5)
    15
    >>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)
    array([14., 14.])
    >>> a = np.array([10., np.nan, 10])
    >>> np.add.reduce(a, where=~np.isnan(a))
    20.0

    Allows reductions of empty arrays where they would normally fail, i.e.
    for ufuncs without an identity.

    >>> np.minimum.reduce([], initial=np.inf)
    inf
    >>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])
    array([ 1., 10.])
    >>> np.minimum.reduce([])
    Traceback (most recent call last):
        ...
    ValueError: zero-size array to reduction operation minimum which has no identity