blocks|key|1409178|text|参见NumPy+for+Matlab+users。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1409179|Matlab：|1409180|repmat(a,+2,+3)|code-block|syntax|javascript|1409181|Numpy：|1409182|numpy.kron(numpy.ones((2,3)),+a)|1409183|Numpy中的Matlib+(numpy.matlib.repmat())：|1409184|numpy.matlib.repmat(a,+2,+3)|1409185|entityMap|0|LINK|mutability|MUTABLE|url|http://www.scipy.org/NumPy_for_Matlab_Users|1|https://numpy.org/doc/stable/reference/generated/numpy.matlib.repmat.html^0|2|M|0|0|0|0|0|0|F|L|1|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|D|3|E|5|6|7|16|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|17|8|@]|9|@]|C|$I|J]]|$1|K|3|L|5|6|7|18|8|@]|9|@]|C|$]]|$1|M|3|N|5|H|7|19|8|@]|9|@]|C|$I|J]]|$1|O|3|P|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|Q|3|R|5|H|7|1E|8|@]|9|@]|C|$I|J]]|$1|S|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]]|T|$U|$5|V|W|X|C|$Y|Z]]|10|$5|V|W|X|C|$Y|11]]]]

See <a href="http://www.scipy.org/NumPy_for_Matlab_Users" rel="nofollow noreferrer">NumPy for Matlab users</a>.
Matlab:
<pre><code>repmat(a, 2, 3)
</code></pre>
Numpy:
<pre><code>numpy.kron(numpy.ones((2,3)), a)
</code></pre>
Matlib in Numpy (<a href="https://numpy.org/doc/stable/reference/generated/numpy.matlib.repmat.html" rel="nofollow noreferrer">numpy.matlib.repmat()</a>):
<pre><code>numpy.matlib.repmat(a, 2, 3)
</code></pre>

blocks|key|1193851|text|请注意，您需要使用MATLAB的repmat的一些原因可以由NumPy的broadcasting机制处理，该机制允许您对形状相似的数组进行各种类型的数学运算。因此，如果您有一个1600x1400x3数组来表示一个3色图像，您可以(按元素)将其乘以[1.0+0.25+0.25]，以减少每个像素上的绿色和蓝色数量。有关更多信息，请参阅上面的链接。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1193852|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html^0|3F|F|10|C|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@$9|Q|A|R|1|S]]|E|$]]|$1|F|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]]]

Note that some of the reasons you'd need to use MATLAB's repmat are taken care of by NumPy's <a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html" rel="noreferrer">broadcasting</a> mechanism, which allows you to do various types of math with arrays of similar shape. So if you had, say, a 1600x1400x3 array representing a 3-color image, you could (elementwise) multiply it by <code>[1.0 0.25 0.25]</code> to reduce the amount of green and blue at each pixel. See the above link for more information.

blocks|key|1409257|text|这里有一个更好的(官方的)+NumPy+for+Matlab+Users链接--恐怕mathesaurus+one已经过时了。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1409258|repmat(a,+m,+n)的numpy等价物是tile(a,+(m,+n))。|style|CODE|1409259|这适用于多个维度，并给出了与matlab相似的结果。(如你所料，Numpy给出了一个3d输出数组--出于某种原因，matlab给出了2d输出--但内容是一样的)。|1409260|Matlab：|1409261|>>+repmat([1;1],[1,1,1])

ans+=
+++++1
+++++1|code-block|syntax|javascript|1409262|Python：|1409263|In+[46]:+a+=+np.array([[1],[1]])
In+[47]:+np.tile(a,+[1,1,1])
Out[47]:+
array([[[1],
++++++++[1]]])|1409264|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.15.0/user/numpy-for-matlab-users.html|1|http://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html^0|E|M|0|0|0|F|P|F|P|F|1|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|D|3|E|5|6|7|18|8|@$A|19|B|1A|F|G]|$A|1B|B|1C|F|G]]|9|@$A|1D|B|1E|1|1F]]|C|$]]|$1|H|3|I|5|6|7|1G|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1H|8|@]|9|@]|C|$]]|$1|L|3|M|5|N|7|1I|8|@]|9|@]|C|$O|P]]|$1|Q|3|R|5|6|7|1J|8|@]|9|@]|C|$]]|$1|S|3|T|5|N|7|1K|8|@]|9|@]|C|$O|P]]|$1|U|3|-4|5|6|7|1L|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]|12|$5|X|Y|Z|C|$10|13]]]]

Here is a much better (official) <a href="https://docs.scipy.org/doc/numpy-1.15.0/user/numpy-for-matlab-users.html" rel="noreferrer">NumPy for Matlab Users</a> link - I'm afraid the mathesaurus one is quite out of date.

The numpy equivalent of <code>repmat(a, m, n)</code> is <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html" rel="noreferrer"><code>tile(a, (m, n))</code></a>. 

This works with multiple dimensions and gives a similar result to matlab. (Numpy gives a 3d output array as you would expect - matlab for some reason gives 2d output - but the content is the same).

Matlab:

<pre><code>&gt;&gt; repmat([1;1],[1,1,1])

ans =
 1
 1
</code></pre>

Python:

<pre><code>In [46]: a = np.array([[1],[1]])
In [47]: np.tile(a, [1,1,1])
Out[47]: 
array([[[1],
 [1]]])
</code></pre>

blocks|key|1193958|text|了解tile和repeat。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1193959|x+=+numpy.arange(5)
print+numpy.tile(x,+2)
print+x.repeat(2)|code-block|syntax|javascript|1193960|entityMap^0|2|4|7|6|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Know both <code>tile</code> and <code>repeat</code>.

<pre><code>x = numpy.arange(5)
print numpy.tile(x, 2)
print x.repeat(2)
</code></pre>

blocks|key|1409317|text|>>>+import+numpy+as+np

>>>+np.repeat(['a','b'],+[2,5])

array(['a',+'a',+'b',+'b',+'b',+'b',+'b'],+dtype='<U1')

>>>+np.repeat([1,2],+[2,5])

array([1,+1,+2,+2,+2,+2,+2])

>>>+np.repeat(np.array([1,2]),+[3]).reshape(2,3)

array([[1,+1,+1],
+++++++[2,+2,+2]])

>>>+np.repeat(np.array([1,2]),+[2,4]).reshape(3,2)

array([[1,+1],
+++++++[2,+2],
+++++++[2,+2]])

>>>+np.repeat(np.matrix('1+2;+3+4'),+[2]).reshape(4,2)

matrix([[1,+1],
++++++++[2,+2],
++++++++[3,+3],
++++++++[4,+4]])|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1409318|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; import numpy as np

&gt;&gt;&gt; np.repeat(['a','b'], [2,5])

array(['a', 'a', 'b', 'b', 'b', 'b', 'b'], dtype='&lt;U1')

&gt;&gt;&gt; np.repeat([1,2], [2,5])

array([1, 1, 2, 2, 2, 2, 2])

&gt;&gt;&gt; np.repeat(np.array([1,2]), [3]).reshape(2,3)

array([[1, 1, 1],
 [2, 2, 2]])

&gt;&gt;&gt; np.repeat(np.array([1,2]), [2,4]).reshape(3,2)

array([[1, 1],
 [2, 2],
 [2, 2]])

&gt;&gt;&gt; np.repeat(np.matrix('1 2; 3 4'), [2]).reshape(4,2)

matrix([[1, 1],
 [2, 2],
 [3, 3],
 [4, 4]])
</code></pre>

blocks|key|1409403|text|numpy.matlib有一个repmat函数，其接口与matlab函数类似。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1409404|from+numpy.matlib+import+repmat
repmat(+np.array([[1],[1]])+,+1,+1)|code-block|syntax|javascript|1409405|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.matlib.repmat.html^0|F|6|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

numpy.matlib has a <a href="https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.matlib.repmat.html" rel="nofollow noreferrer">repmat</a> function with a similar interface as the matlab function

<pre><code>from numpy.matlib import repmat
repmat( np.array([[1],[1]]) , 1, 1)
</code></pre>

I would like to execute the equivalent of the following MATLAB code using NumPy: <code>repmat([1; 1], [1 1 1])</code>. How would I accomplish this?

What is the equivalent of MATLAB's repmat in NumPy

Python

我想使用NumPy：repmat([1; 1], [1 1 1])执行以下MATLAB代码的等价物。我该如何做到这一点呢？

问NumPy中MATLAB的repmat的等价物是什么？
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问NumPy中MATLAB的repmat的等价物是什么？EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问NumPy中MATLAB的repmat的等价物是什么？
EN