如何在numpy中收集特定索引的元素？

Question

我想在指定轴上收集指定索引的元素，如下所示。

x = [[1,2,3], [4,5,6]]
index = [[2,1], [0, 1]]
x[:, index] = [[3, 2], [4, 5]]

这本质上是 pytorch 中的收集操作，但如您所知，这在 numpy 中是无法通过这种方式实现的。我想知道numpy中是否有这样的“收集”操作？

Answer 1

numpy.take_along_axis是我需要的，根据索引取元素。它可以像 PyTorch 中的 gather 方法一样使用。

这是手册中的示例：

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

Answer 2

我前段时间写这个是为了gather在 Numpy 中复制 PyTorch 。在这种情况下self，您的x

def gather(self, dim, index):
    """
    Gathers values along an axis specified by ``dim``.

    For a 3-D tensor the output is specified by:
        out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
        out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
        out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2

    Parameters
    ----------
    dim:
        The axis along which to index
    index:
        A tensor of indices of elements to gather

    Returns
    -------
    Output Tensor
    """
    idx_xsection_shape = index.shape[:dim] + \
        index.shape[dim + 1:]
    self_xsection_shape = self.shape[:dim] + self.shape[dim + 1:]
    if idx_xsection_shape != self_xsection_shape:
        raise ValueError("Except for dimension " + str(dim) +
                         ", all dimensions of index and self should be the same size")
    if index.dtype != np.dtype('int_'):
        raise TypeError("The values of index must be integers")
    data_swaped = np.swapaxes(self, 0, dim)
    index_swaped = np.swapaxes(index, 0, dim)
    gathered = np.choose(index_swaped, data_swaped)
    return np.swapaxes(gathered, 0, dim)

这些是测试用例：

# Test 1
    t = np.array([[65, 17], [14, 25], [76, 22]])
    idx = np.array([[0], [1], [0]])
    dim = 1
    result = gather(t, dim=dim, index=idx)
    expected = np.array([[65], [25], [76]])
    print(np.array_equal(result, expected))

# Test 2
    t = np.array([[47, 74, 44], [56, 9, 37]])
    idx = np.array([[0, 0, 1], [1, 1, 0], [0, 1, 0]])
    dim = 0
    result = gather(t, dim=dim, index=idx)
    expected = np.array([[47, 74, 37], [56, 9, 44.], [47, 9, 44]])
    print(np.array_equal(result, expected))

Answer 3

>>> x = np.array([[1,2,3], [4,5,6]])
>>> index = np.array([[2,1], [0, 1]])
>>> x_axis_index=np.tile(np.arange(len(x)), (index.shape[1],1)).transpose() 
>>> print x_axis_index
[[0 0]
 [1 1]]
>>> print x[x_axis_index,index]
[[3 2]
 [4 5]]