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Fonction argmax - module numpy.matlib

Signature de la fonction argmax

def argmax(a, axis=None, out=None) 

Description

argmax.__doc__

    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.

    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.

    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
    --------
    >>> 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.max(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.max(x, axis=-1)
    >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
    array([4, 3])