blocks|key|4342281|text|>>>+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]]|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4342282|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>&gt;&gt;&gt; x = np.array([[1,2,3], [4,5,6]])
&gt;&gt;&gt; index = np.array([[2,1], [0, 1]])
&gt;&gt;&gt; x_axis_index=np.tile(np.arange(len(x)), (index.shape[1],1)).transpose() 
&gt;&gt;&gt; print x_axis_index
[[0 0]
 [1 1]]
&gt;&gt;&gt; print x[x_axis_index,index]
[[3 2]
 [4 5]]
</code></pre>

blocks|key|4341877|text|我前段时间写这篇文章是为了在Numpy中复制PyTorch的gather。在本例中，self是您的x|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4341878|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]+%2B+\
++++++++index.shape[dim+%2B+1:]
++++self_xsection_shape+=+self.shape[:dim]+%2B+self.shape[dim+%2B+1:]
++++if+idx_xsection_shape+!=+self_xsection_shape:
++++++++raise+ValueError("Except+for+dimension+"+%2B+str(dim)+%2B
+++++++++++++++++++++++++",+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)|code-block|syntax|javascript|4341879|以下是测试用例：|4341880|#+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))|4341881|entityMap^0|U|6|16|4|1D|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

I wrote this awhile ago to replicate PyTorch's <code>gather</code> in Numpy. In this case <code>self</code> is your <code>x</code>

<pre><code>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)
</code></pre>

These are the test cases:

<pre><code># 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))
</code></pre>

blocks|key|4342373|text|使用numpy.take()函数，该函数具有PyTorch的聚集函数的大部分功能。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4342374|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html^0|2|C|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Use <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html" rel="nofollow noreferrer">numpy.take()</a> function which has most of PyTorch's gather function functionalities.

blocks|key|4342401|text|numpy.take_along_axis是我需要的，根据索引获取元素。它可以像PyTorch中的gather方法一样使用。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4342402|这是手册中的一个示例：|4342403|>>>+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]])|code-block|syntax|javascript|4342404|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.take_along_axis.html^0|0|L|0|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|X|8|@]|9|@]|C|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]]]

<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.take_along_axis.html" rel="noreferrer">numpy.take_along_axis</a> is what I need, take elements according to the index. It can be used like gather method in PyTorch.

This is an example from the manual:

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

</code></pre>

I want to gather elements of specified indices in specified axis like following. 

<pre><code>x = [[1,2,3], [4,5,6]]
index = [[2,1], [0, 1]]
x[:, index] = [[3, 2], [4, 5]]
</code></pre>

This is essentially gather operation in pytorch, but as you know, this is not achievable in numpy this way. I am wondering if there is such a "gather" operation in numpy?

how to gather elements of specific indices in numpy?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我想在指定的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中的聚集操作，但正如您所知道的，这在numpy中是不能用这种方式实现的。我想知道在numpy中是否有...

问如何在numpy中收集特定索引的元素？
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