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Module « numpy.matlib »

Fonction argmax - module numpy.matlib

Signature de la fonction argmax

def argmax(a, axis=None, out=None, *, keepdims=<no value>) 

Description

help(numpy.matlib.argmax)

Returns the indices of the maximum values along an axis.

Parameters
----------
a : array_like
    Input array.
axis : int, optional
    By default, the index is into the flattened array, otherwise
    along the specified axis.
out : array, optional
    If provided, the result will be inserted into this array. It should
    be of the appropriate shape and dtype.
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 array.

    .. versionadded:: 1.22.0

Returns
-------
index_array : ndarray of ints
    Array of indices into the array. It has the same shape as ``a.shape``
    with the dimension along `axis` removed. If `keepdims` is set to True,
    then the size of `axis` will be 1 with the resulting array having same
    shape as ``a.shape``.

See Also
--------
ndarray.argmax, argmin
amax : The maximum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
take_along_axis : Apply ``np.expand_dims(index_array, axis)``
                  from argmax to an array as if by calling max.

Notes
-----
In case of multiple occurrences of the maximum values, the indices
corresponding to the first occurrence are returned.

Examples
--------
>>> import numpy as np
>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
       [13, 14, 15]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])

Indexes of the maximal elements of a N-dimensional array:

>>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
>>> ind
(1, 2)
>>> a[ind]
15

>>> b = np.arange(6)
>>> b[1] = 5
>>> b
array([0, 5, 2, 3, 4, 5])
>>> np.argmax(b)  # Only the first occurrence is returned.
1

>>> x = np.array([[4,2,3], [1,0,3]])
>>> index_array = np.argmax(x, axis=-1)
>>> # Same as np.amax(x, axis=-1, keepdims=True)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
array([[4],
       [3]])
>>> # Same as np.amax(x, axis=-1)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1),
...     axis=-1).squeeze(axis=-1)
array([4, 3])

Setting `keepdims` to `True`,

>>> x = np.arange(24).reshape((2, 3, 4))
>>> res = np.argmax(x, axis=1, keepdims=True)
>>> res.shape
(2, 1, 4)

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