blocks|key|270566|text|沿着所需的尺寸切片，如下例所示。要进行相反的操作，您可以使用None作为任何维度的切片，这些维度应该被视为单一维度，但这是使形状工作所必需的。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|270567|In+[786]:+yy+=+np.asarray([[11],[7]])

In+[787]:+yy
Out[787]:
array([[11],
+++++++[7]])

In+[788]:+yy.shape
Out[788]:+(2,+1)

In+[789]:+yy[:,0]
Out[789]:+array([11,+7])

In+[790]:+yy[:,0].shape
Out[790]:+(2,)

In+[791]:+y1+=+yy[:,0]

In+[792]:+y1.shape
Out[792]:+(2,)

In+[793]:+y1[:,None]
Out[793]:
array([[11],
+++++++[7]])

In+[794]:+y1[:,None].shape
Out[794]:+(2,+1)|code-block|syntax|javascript|270568|或者，您可以使用reshape|270569|In+[795]:+yy.reshape((2,))
Out[795]:+array([11,++7])|270570|entityMap^0|U|4|0|0|8|7|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

Slice along the dimension you want, as in the example below. To go in the reverse direction, you can use <code>None</code> as the slice for any dimension that should be treated as a singleton dimension, but which is needed to make shapes work.

<pre><code>In [786]: yy = np.asarray([[11],[7]])

In [787]: yy
Out[787]:
array([[11],
 [7]])

In [788]: yy.shape
Out[788]: (2, 1)

In [789]: yy[:,0]
Out[789]: array([11, 7])

In [790]: yy[:,0].shape
Out[790]: (2,)

In [791]: y1 = yy[:,0]

In [792]: y1.shape
Out[792]: (2,)

In [793]: y1[:,None]
Out[793]:
array([[11],
 [7]])

In [794]: y1[:,None].shape
Out[794]: (2, 1)
</code></pre>

Alternatively, you can use <code>reshape</code>:

<pre><code>In [795]: yy.reshape((2,))
Out[795]: array([11, 7])
</code></pre>

blocks|key|270601|text|reshape可以做到这一点|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|270602|a++=+np.arange(3)++++++++#+a.shape++=+(3,)
b++=+a.reshape((3,1))++++#+b.shape++=+(3,1)
b2+=+a.reshape((-1,1))+++#+b2.shape+=+(3,1)
c++=+b.reshape((3,))+++++#+c.shape++=+(3,)
c2+=+b.reshape((-1,))++++#+c2.shape+=+(3,)|code-block|syntax|javascript|270603|还要注意，reshape不会复制数据，除非它需要为新形状复制数据(这里不需要这样做)：|270604|a.__array_interface__['data']+++#+(22356720,+False)
b.__array_interface__['data']+++#+(22356720,+False)
c.__array_interface__['data']+++#+(22356720,+False)|270605|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html^0|0|7|0|7|0|0|0|5|7|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@$9|14|A|15|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|17|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html" rel="noreferrer"><code>reshape</code></a> works for this

<pre><code>a = np.arange(3) # a.shape = (3,)
b = a.reshape((3,1)) # b.shape = (3,1)
b2 = a.reshape((-1,1)) # b2.shape = (3,1)
c = b.reshape((3,)) # c.shape = (3,)
c2 = b.reshape((-1,)) # c2.shape = (3,)
</code></pre>

note also that <code>reshape</code> doesn't copy the data unless it needs to for the new shape (which it doesn't need to do here):

<pre><code>a.__array_interface__['data'] # (22356720, False)
b.__array_interface__['data'] # (22356720, False)
c.__array_interface__['data'] # (22356720, False)
</code></pre>

blocks|key|270645|text|相反的翻译可以通过以下方式进行：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|270646|np.atleast_2d(y).T|code-block|syntax|javascript|270647|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

the opposite translation can be made by:

<pre><code>np.atleast_2d(y).T
</code></pre>

blocks|key|270659|text|使用numpy.squeeze|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|270660|>>>+x+=+np.array([[[0],+[1],+[2]]])
>>>+x.shape
(1,+3,+1)
>>>+np.squeeze(x).shape
(3,)
>>>+np.squeeze(x,+axis=(2,)).shape
(1,+3)|code-block|syntax|javascript|270661|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.squeeze.html^0|2|D|2|D|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

Use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.squeeze.html" rel="noreferrer"><code>numpy.squeeze</code></a>:

<pre><code>&gt;&gt;&gt; x = np.array([[[0], [1], [2]]])
&gt;&gt;&gt; x.shape
(1, 3, 1)
&gt;&gt;&gt; np.squeeze(x).shape
(3,)
&gt;&gt;&gt; np.squeeze(x, axis=(2,)).shape
(1, 3)
</code></pre>

I have a question regarding the conversion between (N,) dimension arrays and (N,1) dimension arrays. For example, y is (2,) dimension.

<pre><code>A=np.array([[1,2],[3,4]])

x=np.array([1,2])

y=np.dot(A,x)

y.shape
Out[6]: (2,)
</code></pre>

But the following will show y2 to be (2,1) dimension. 

<pre><code>x2=x[:,np.newaxis]

y2=np.dot(A,x2)

y2.shape
Out[14]: (2, 1)
</code></pre>

What would be the most efficient way of converting y2 back to y without copying?

Thanks,
Tom

Numpy Vector (N,1) dimension -> (N,) dimension conversion

我有一个关于(N，)维数组和(N,1)维数组之间转换的问题。例如，y是(2，)维。A=np.array([[1,2],[3,4]])x=np.array([1,2])y=np.dot(A,x)y.shapeOut[6]: (2,)但下面将显示y2为(2,1)维。x2=x[:,np.newaxis]y2=np.dot(A...

问Numpy Vector (N,1)维-> (N，)维转换
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