Classe « ufunc »

Méthode numpy.matlib.ufunc.outer

Signature de la méthode outer

Description

outer.__doc__

outer(A, B, /, **kwargs)

    Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.

    Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
    ``op.outer(A, B)`` is an array of dimension M + N such that:

    .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
       op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])

    For `A` and `B` one-dimensional, this is equivalent to::

      r = empty(len(A),len(B))
      for i in range(len(A)):
          for j in range(len(B)):
              r[i,j] = op(A[i], B[j]) # op = ufunc in question

    Parameters
    ----------
    A : array_like
        First array
    B : array_like
        Second array
    kwargs : any
        Arguments to pass on to the ufunc. Typically `dtype` or `out`.

    Returns
    -------
    r : ndarray
        Output array

    See Also
    --------
    numpy.outer : A less powerful version of ``np.multiply.outer``
                  that `ravel`\ s all inputs to 1D. This exists
                  primarily for compatibility with old code.

    tensordot : ``np.tensordot(a, b, axes=((), ()))`` and
                ``np.multiply.outer(a, b)`` behave same for all
                dimensions of a and b.

    Examples
    --------
    >>> np.multiply.outer([1, 2, 3], [4, 5, 6])
    array([[ 4,  5,  6],
           [ 8, 10, 12],
           [12, 15, 18]])

    A multi-dimensional example:

    >>> A = np.array([[1, 2, 3], [4, 5, 6]])
    >>> A.shape
    (2, 3)
    >>> B = np.array([[1, 2, 3, 4]])
    >>> B.shape
    (1, 4)
    >>> C = np.multiply.outer(A, B)
    >>> C.shape; C
    (2, 3, 1, 4)
    array([[[[ 1,  2,  3,  4]],
            [[ 2,  4,  6,  8]],
            [[ 3,  6,  9, 12]]],
           [[[ 4,  8, 12, 16]],
            [[ 5, 10, 15, 20]],
            [[ 6, 12, 18, 24]]]])