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Module « numpy »

Fonction take_along_axis - module numpy

Signature de la fonction take_along_axis

def take_along_axis(arr, indices, axis) 

Description

help(numpy.take_along_axis)

Take values from the input array by matching 1d index and data slices.

This iterates over matching 1d slices oriented along the specified axis in
the index and data arrays, and uses the former to look up values in the
latter. These slices can be different lengths.

Functions returning an index along an axis, like `argsort` and
`argpartition`, produce suitable indices for this function.

Parameters
----------
arr : ndarray (Ni..., M, Nk...)
    Source array
indices : ndarray (Ni..., J, Nk...)
    Indices to take along each 1d slice of `arr`. This must match the
    dimension of arr, but dimensions Ni and Nj only need to broadcast
    against `arr`.
axis : int
    The axis to take 1d slices along. If axis is None, the input array is
    treated as if it had first been flattened to 1d, for consistency with
    `sort` and `argsort`.

Returns
-------
out: ndarray (Ni..., J, Nk...)
    The indexed result.

Notes
-----
This is equivalent to (but faster than) the following use of `ndindex` and
`s_`, which sets each of ``ii`` and ``kk`` to a tuple of indices::

    Ni, M, Nk = a.shape[:axis], a.shape[axis], a.shape[axis+1:]
    J = indices.shape[axis]  # Need not equal M
    out = np.empty(Ni + (J,) + Nk)

    for ii in ndindex(Ni):
        for kk in ndindex(Nk):
            a_1d       = a      [ii + s_[:,] + kk]
            indices_1d = indices[ii + s_[:,] + kk]
            out_1d     = out    [ii + s_[:,] + kk]
            for j in range(J):
                out_1d[j] = a_1d[indices_1d[j]]

Equivalently, eliminating the inner loop, the last two lines would be::

            out_1d[:] = a_1d[indices_1d]

See Also
--------
take : Take along an axis, using the same indices for every 1d slice
put_along_axis :
    Put values into the destination array by matching 1d index and data slices

Examples
--------
>>> import numpy as np

For this sample array

>>> a = np.array([[10, 30, 20], [60, 40, 50]])

We can sort either by using sort directly, or argsort and this function

>>> np.sort(a, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])
>>> ai = np.argsort(a, axis=1)
>>> ai
array([[0, 2, 1],
       [1, 2, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 20, 30],
       [40, 50, 60]])

The same works for max and min, if you maintain the trivial dimension
with ``keepdims``:

>>> np.max(a, axis=1, keepdims=True)
array([[30],
       [60]])
>>> ai = np.argmax(a, axis=1, keepdims=True)
>>> ai
array([[1],
       [0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[30],
       [60]])

If we want to get the max and min at the same time, we can stack the
indices first

>>> ai_min = np.argmin(a, axis=1, keepdims=True)
>>> ai_max = np.argmax(a, axis=1, keepdims=True)
>>> ai = np.concatenate([ai_min, ai_max], axis=1)
>>> ai
array([[0, 1],
       [1, 0]])
>>> np.take_along_axis(a, ai, axis=1)
array([[10, 30],
       [40, 60]])

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