blocks|key|2145545|text|在Python3.2中，对随机模块进行了一些重构，以使输出跨体系结构(给定相同的种子)一致，请参见第7889期。shuffle()方法被转换为使用Random._randbelow()。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2145546|但是，_randbelow()方法也进行了调整，因此仅仅复制3.5版的shuffle()并不足以解决这个问题。|2145547|尽管如此，如果您传递自己的random()函数，Python3.5中的实现将与2.7版本保持不变，因此可以绕过这一限制：|2145548|random.shuffle(l,+random.random)|code-block|syntax|javascript|2145549|但是请注意，与现在相比，您受到了#7889试图解决的旧的32位对64位架构差异的影响。|2145550|忽略几个优化和特殊情况，如果包含_randbelow()，3.5版本可以支持如下：|2145551|import+random
import+sys

if+sys.version_info+>=+(3,+2):
++++newshuffle+=+random.shuffle
else:
++++try:
++++++++xrange
++++except+NameError:
++++++++xrange+=+range

++++def+newshuffle(x):
++++++++def+_randbelow(n):
++++++++++++"Return+a+random+int+in+the+range+[0,n).++Raises+ValueError+if+n==0."
++++++++++++getrandbits+=+random.getrandbits
++++++++++++k+=+n.bit_length()++#+don't+use+(n-1)+here+because+n+can+be+1
++++++++++++r+=+getrandbits(k)++++++++++#+0+<=+r+<+2**k
++++++++++++while+r+>=+n:
++++++++++++++++r+=+getrandbits(k)
++++++++++++return+r

++++++++for+i+in+xrange(len(x)+-+1,+0,+-1):
++++++++++++#+pick+an+element+in+x[:i%2B1]+with+which+to+exchange+x[i]
++++++++++++j+=+_randbelow(i%2B1)
++++++++++++x[i],+x[j]+=+x[j],+x[i]|2145552|这使您在2.7上的输出与3.5相同：|2145553|>>>+random.seed(42)
>>>+print(random.random())
0.639426798458
>>>+l+=+list(range(20))
>>>+newshuffle(l)
>>>+print(l)
[3,+5,+2,+15,+9,+12,+16,+19,+6,+13,+18,+14,+10,+1,+11,+4,+17,+7,+8,+0]|2145554|entityMap|0|LINK|mutability|MUTABLE|url|http://bugs.python.org/issue7889^0|1K|9|21|J|1D|6|0|0|3|C|Z|9|0|D|8|0|0|0|G|C|0|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|F|3|G|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1J|8|@$9|1K|A|1L|B|C]]|D|@]|E|$]]|$1|J|3|K|5|L|7|1M|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1N|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1O|8|@$9|1P|A|1Q|B|C]]|D|@]|E|$]]|$1|S|3|T|5|L|7|1R|8|@]|D|@]|E|$M|N]]|$1|U|3|V|5|6|7|1S|8|@]|D|@]|E|$]]|$1|W|3|X|5|L|7|1T|8|@]|D|@]|E|$M|N]]|$1|Y|3|-4|5|6|7|1U|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15]]]]

In Python 3.2 the random module was refactored a little to make the output uniform across architectures (given the same seed), see <a href="http://bugs.python.org/issue7889" rel="noreferrer">issue #7889</a>. The <code>shuffle()</code> method was switched to using <code>Random._randbelow()</code>.

However, the <code>_randbelow()</code> method was also adjusted, so simply copying the 3.5 version of <code>shuffle()</code> is not enough to fix this.

That said, if you pass in your own <code>random()</code> function, the implementation in Python 3.5 is unchanged from the 2.7 version, and thus lets you bypass this limitation:

<pre><code>random.shuffle(l, random.random)
</code></pre>

Note however, than now you are subject to the old 32-bit vs 64-bit architecture differences that #7889 tried to solve.

Ignoring several optimisations and special cases, if you include <code>_randbelow()</code> the 3.5 version can be backported as:

<pre><code>import random
import sys

if sys.version_info &gt;= (3, 2):
 newshuffle = random.shuffle
else:
 try:
 xrange
 except NameError:
 xrange = range

 def newshuffle(x):
 def _randbelow(n):
 "Return a random int in the range [0,n). Raises ValueError if n==0."
 getrandbits = random.getrandbits
 k = n.bit_length() # don't use (n-1) here because n can be 1
 r = getrandbits(k) # 0 &lt;= r &lt; 2**k
 while r &gt;= n:
 r = getrandbits(k)
 return r

 for i in xrange(len(x) - 1, 0, -1):
 # pick an element in x[:i+1] with which to exchange x[i]
 j = _randbelow(i+1)
 x[i], x[j] = x[j], x[i]
</code></pre>

which gives you the same output on 2.7 as 3.5:

<pre><code>&gt;&gt;&gt; random.seed(42)
&gt;&gt;&gt; print(random.random())
0.639426798458
&gt;&gt;&gt; l = list(range(20))
&gt;&gt;&gt; newshuffle(l)
&gt;&gt;&gt; print(l)
[3, 5, 2, 15, 9, 12, 16, 19, 6, 13, 18, 14, 10, 1, 11, 4, 17, 7, 8, 0]
</code></pre>

blocks|key|1495371|text|详细阐述Martijn的出色回答和评论，在这个讨论上，我终于找到了一个解决办法，这个解决方案可以说没有回答我的问题，但同时也不需要进行深刻的更改。总括而言：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1495372|random.seed实际上使每个random函数都是确定性的，但不一定在不同版本之间产生相同的输出；|unordered-list-item|style|CODE|1495373|将PYTHONHASHSEED设置为0将禁用字典和集合的哈希随机化，默认情况下，这会在Python3中引入一个不确定因素。|1495374|因此，在启动Python+3测试的bash脚本中，我添加了：|1495375|export+PYTHONHASHSEED=0|code-block|syntax|javascript|1495376|然后，我暂时更改了我的测试函数，以便强制使用整数种子，它将在Python+3中复制Python+2中预期的结果。最后，我返回更改并替换了行：|1495377|seed(42)|1495378|就像这样：|1495379|seed(42+if+sys.version_info.major+==+2+else+299)|1495380|没有什么值得吹嘘的，但正如俗语所说，有时实用性胜过纯洁;)|1495381|这个快速的解决方案对于想要在不同版本的Python上测试相同的随机代码的人可能很有用！|1495382|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/30585108/disable-hash-randomization-from-within-python-program?noredirect=1&lq=1^0|N|2|0|0|0|B|H|6|0|1|E|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1B|8|@]|9|@$A|1C|B|1D|1|1E]]|C|$]]|$1|D|3|E|5|F|7|1F|8|@$A|1G|B|1H|G|H]|$A|1I|B|1J|G|H]]|9|@]|C|$]]|$1|I|3|J|5|F|7|1K|8|@$A|1L|B|1M|G|H]]|9|@]|C|$]]|$1|K|3|L|5|6|7|1N|8|@]|9|@]|C|$]]|$1|M|3|N|5|O|7|1O|8|@]|9|@]|C|$P|Q]]|$1|R|3|S|5|6|7|1P|8|@]|9|@]|C|$]]|$1|T|3|U|5|O|7|1Q|8|@]|9|@]|C|$P|Q]]|$1|V|3|W|5|6|7|1R|8|@]|9|@]|C|$]]|$1|X|3|Y|5|O|7|1S|8|@]|9|@]|C|$P|Q]]|$1|Z|3|10|5|6|7|1T|8|@]|9|@]|C|$]]|$1|11|3|12|5|6|7|1U|8|@]|9|@]|C|$]]|$1|13|3|-4|5|6|7|1V|8|@]|9|@]|C|$]]]|14|$15|$5|16|17|18|C|$19|1A]]]]

Elaborating on Martijn Pieters excellent answer and comments, and on this <a href="https://stackoverflow.com/questions/30585108/disable-hash-randomization-from-within-python-program?noredirect=1&amp;lq=1">discussion</a>, I finally found a workaround, which arguably doesn't answer my very question, but at the same time doesn't require deep changes. To sum up:

<ul>
<li><code>random.seed</code> actually makes every <code>random</code> function deterministic, but doesn't necessarily produces the same output across versions;</li>
<li>setting <code>PYTHONHASHSEED</code> to 0 disables hash randomization for dictionaries and sets, which by default introduces a factor of non-determinism in Python 3.</li>
</ul>

So, in the bash script which launches the Python 3 tests, I added:

<pre><code>export PYTHONHASHSEED=0
</code></pre>

Then, I temporarily changed my test functions in order to brute-force my way to an integer seed which would reproduces in Python 3 the results expected in Python 2. Lastly, I reverted my changes and replaced the lines:

<pre><code>seed(42)
</code></pre>

by something like that:

<pre><code>seed(42 if sys.version_info.major == 2 else 299)
</code></pre>

Nothing to brag about, but as the saying goes, sometimes practicality beats purity ;)

This quick workaround may be useful to somebody who wants to test the same stochastic code across different versions of Python!

blocks|key|1495420|text|如果我错了，可能有人会纠正我，但numpy.random模块似乎不会在python2和3之间更改。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1495421|>>>+import+numpy+as+np
>>>+l+=+list(range(20))
>>>+np.random.RandomState(42).shuffle(l)
>>>+l
[0,+17,+15,+1,+8,+5,+11,+3,+18,+16,+13,+2,+9,+19,+4,+12,+7,+10,+14,+6]|code-block|syntax|javascript|1495422|我在Python2.7(np1.12.1)和3.7+(np1.14.5)中得到了相同的结果。|1495423|文档还声明生成了数字版本之间应该是相同的。。|1495424|兼容性保证使用相同参数的固定种子和对“RandomState”方法的一系列固定调用将始终产生相同的结果，直到舍入错误，除非值不正确。将修复不正确的值，并在相关的docstring中注明进行修复的NumPy版本。只要以前的行为保持不变，就允许扩展现有的参数范围和添加新的参数。|blockquote|1495425|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.RandomState.html#numpy.random.RandomState^0|G|C|0|0|0|A|B|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|14|8|@]|D|@$9|15|A|16|1|17]]|E|$]]|$1|O|3|P|5|Q|7|18|8|@]|D|@]|E|$]]|$1|R|3|-4|5|6|7|19|8|@]|D|@]|E|$]]]|S|$T|$5|U|V|W|E|$X|Y]]]]

Someone may correct me if I'm wrong but it seems that <code>numpy.random</code> module do not change between python 2 and 3.

<pre><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; l = list(range(20))
&gt;&gt;&gt; np.random.RandomState(42).shuffle(l)
&gt;&gt;&gt; l
[0, 17, 15, 1, 8, 5, 11, 3, 18, 16, 13, 2, 9, 19, 4, 12, 7, 10, 14, 6]
</code></pre>

I got the same result in both Python 2.7 (with np 1.12.1) and 3.7 (with np 1.14.5).

The doc also states that generated numbers <a href="https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.RandomState.html#numpy.random.RandomState" rel="nofollow noreferrer">should be the same between versions</a>.

<blockquote>
 Compatibility Guarantee A fixed seed and a fixed series of calls to
 ‘RandomState’ methods using the same parameters will always produce
 the same results up to roundoff error except when the values were
 incorrect. Incorrect values will be fixed and the NumPy version in
 which the fix was made will be noted in the relevant docstring.
 Extension of existing parameter ranges and the addition of new
 parameters is allowed as long the previous behavior remains unchanged.
</blockquote>

I am writing a program compatible with both Python 2.7 and 3.5. Some parts of it rely on stochastic process. My unit tests use an arbitrary seed, which leads to the same results across executions and languages... except for the code using <code>random.shuffle</code>.

Example in Python 2.7:

<pre><code>In[]: import random
 random.seed(42)
 print(random.random())
 l = list(range(20))
 random.shuffle(l)
 print(l)
Out[]: 0.639426798458
 [6, 8, 9, 15, 7, 3, 17, 14, 11, 16, 2, 19, 18, 1, 13, 10, 12, 4, 5, 0]
</code></pre>

Same input in Python 3.5:

<pre><code>In []: import random
 random.seed(42)
 print(random.random())
 l = list(range(20))
 random.shuffle(l)
 print(l)
Out[]: 0.6394267984578837
 [3, 5, 2, 15, 9, 12, 16, 19, 6, 13, 18, 14, 10, 1, 11, 4, 17, 7, 8, 0]
</code></pre>

Note that the pseudo-random number is the same, but the shuffled lists are different. As expected, reexecuting the cells does not change their respective output.

How could I write the same test code for the two versions of Python?

Difference between Python 2 and 3 for shuffle with a given seed

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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我正在编写一个与Python2.7和3.5兼容的程序。它的某些部分依赖随机过程。我的单元测试使用了任意的种子，这导致了跨执行和语言的相同结果..。除了使用random.shuffle的代码。Python 2.7中的示例：In[]:   import random        random.seed(42)      ...

问Python 2和3与给定种子洗牌的区别
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问Python 2和3与给定种子洗牌的区别EN