blocks|key|914044|text|只需为随机数生成器设定一个固定值，例如|type|unstyled|depth|inlineStyleRanges|entityRanges|data|914045|numpy.random.seed(42)|code-block|syntax|javascript|914046|这样，您将始终得到相同的随机数序列。|914047|此函数将作为全局默认随机数生成器的种子，对numpy.random中函数的任何调用都将使用并更改其状态。这对于许多简单的用例都很好，但它是全局状态的一种形式，具有全局状态带来的所有问题。要获得更干净的解决方案，请参阅下面Robert+Kern的答案。|offset|length|style|CODE|914048|entityMap^0|0|0|0|L|C|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

Simply seed the random number generator with a fixed value, e.g.
<pre><code>numpy.random.seed(42)
</code></pre>
This way, you'll always get the same random number sequence.
This function will seed the global default random number generator, and any call to a function in <code>numpy.random</code> will use and alter its state. This is fine for many simple use cases, but it's a form of global state with all the problems global state brings. For a cleaner solution, see Robert Kern's answer below.

blocks|key|697952|text|使用您选择的种子创建您自己的numpy.random.RandomState()实例。除了在不允许传递自己的RandomState实例的不灵活的库之外，不要使用numpy.random.seed()。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|697953|[~]
%7C1>+from+numpy.random+import+RandomState

[~]
%7C2>+prng+=+RandomState(1234567890)

[~]
%7C3>+prng.randint(-1,+2,+size=10)
array([+1,++1,+-1,++0,++0,+-1,++1,++0,+-1,+-1])

[~]
%7C4>+prng2+=+RandomState(1234567890)

[~]
%7C5>+prng2.randint(-1,+2,+size=10)
array([+1,++1,+-1,++0,++0,+-1,++1,++0,+-1,+-1])|code-block|syntax|javascript|697954|entityMap^0|E|Q|1I|B|28|J|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|U|8|@]|D|@]|E|$]]]|L|$]]

Create your own instance of <code>numpy.random.RandomState()</code> with your chosen seed. Do not use <code>numpy.random.seed()</code> except to work around inflexible libraries that do not let you pass around your own <code>RandomState</code> instance.

<pre><code>[~]
|1&gt; from numpy.random import RandomState

[~]
|2&gt; prng = RandomState(1234567890)

[~]
|3&gt; prng.randint(-1, 2, size=10)
array([ 1, 1, -1, 0, 0, -1, 1, 0, -1, -1])

[~]
|4&gt; prng2 = RandomState(1234567890)

[~]
|5&gt; prng2.randint(-1, 2, size=10)
array([ 1, 1, -1, 0, 0, -1, 1, 0, -1, -1])
</code></pre>

blocks|key|914140|text|如果您正在使用依赖于随机状态的其他函数，则不能只设置和总种子，而应该创建一个函数来生成随机数字列表，并将种子设置为函数的参数。这不会干扰代码中的任何其他随机生成器：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|914141|#+Random+states
def+get_states(random_state,+low,+high,+size):
++++rs+=+np.random.RandomState(random_state)
++++states+=+rs.randint(low=low,+high=high,+size=size)
++++return+states

#+Call+function
states+=+get_states(random_state=42,+low=2,+high=28347,+size=25)|code-block|syntax|javascript|914142|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|$]]

If you are using other functions relying on a random state, you can't just set and overall seed, but should instead create a function to generate your random list of number and set the seed as a parameter of the function. This will not disturb any other random generators in the code: 

<pre><code># Random states
def get_states(random_state, low, high, size):
 rs = np.random.RandomState(random_state)
 states = rs.randint(low=low, high=high, size=size)
 return states

# Call function
states = get_states(random_state=42, low=2, high=28347, size=25)
</code></pre>

blocks|key|698068|text|理解什么是随机生成器的种子以及何时/如何在代码中设置它是很重要的(例如，查看here可以很好地解释种子的数学含义)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|698069|为此，您需要通过执行以下操作来设置种子：|698070|random_state+=+np.random.RandomState(seed=your_favorite_seed_value)|code-block|syntax|javascript|698071|因此，重要的是从random_state而不是从np.random生成随机数。也就是说，你应该做：|698072|random_state.randint(...)|698073|而不是|698074|np.random.randint(...)+|698075|它将创建一个新的RandomState()实例，并且基本上使用您的计算机内部时钟来设置种子。|698076|entityMap|0|LINK|mutability|MUTABLE|url|https://blog.sicara.com/how-numpy-generate-pseudo-random-number-46f705b390e3^0|12|4|0|0|0|0|0|0|0|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]]|$1|O|3|P|5|6|7|1A|8|@]|9|@]|C|$]]|$1|Q|3|R|5|H|7|1B|8|@]|9|@]|C|$I|J]]|$1|S|3|T|5|6|7|1C|8|@]|9|@]|C|$]]|$1|U|3|-4|5|6|7|1D|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]]]

It is important to understand what is the seed of a random generator and when/how it is set in your code (check e.g. <a href="https://blog.sicara.com/how-numpy-generate-pseudo-random-number-46f705b390e3" rel="nofollow noreferrer">here</a> for a nice explanation of the mathematical meaning of the seed).

For that you need to set the seed by doing:

<pre><code>random_state = np.random.RandomState(seed=your_favorite_seed_value)
</code></pre>

It is then important to generate the random numbers from random_state and not from np.random. I.e. you should do:

<pre><code>random_state.randint(...)
</code></pre>

instead of 

<pre><code>np.random.randint(...) 
</code></pre>

which will create a new instance of RandomState() and basically use your computer internal clock to set the seed.

blocks|key|914243|text|我只是想澄清一些关于@Robert+Kern答案的事情，以防不清楚。即使您确实使用了RandomState，您也必须在每次调用numpy+random方法时初始化它，否则您将获得以下结果。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|914244|Python+3.6.9+%7CAnaconda,+Inc.%7C+(default,+Jul+30+2019,+19:07:31)+
[GCC+7.3.0]+on+linux
Type+"help",+"copyright",+"credits"+or+"license"+for+more+information.
>>>+import+numpy+as+np
>>>+prng+=+np.random.RandomState(2019)
>>>+prng.randint(-1,+2,+size=10)
array([-1,++1,++0,+-1,++1,++1,+-1,++0,+-1,++1])
>>>+prng.randint(-1,+2,+size=10)
array([-1,+-1,+-1,++0,+-1,+-1,++1,++0,+-1,+-1])
>>>+prng.randint(-1,+2,+size=10)
array([+0,+-1,+-1,++0,++1,++1,+-1,++1,+-1,++1])
>>>+prng.randint(-1,+2,+size=10)
array([+1,++1,++0,++0,++0,+-1,++1,++1,++0,+-1])|code-block|syntax|javascript|914245|entityMap^0|16|B|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

I just want to clarify something in regard to @Robert Kern answer just in case that is not clear. Even if you do use the <code>RandomState</code> you would have to initialize it every time you call a numpy random method like in Robert's example otherwise you'll get the following results.

<pre><code>Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) 
[GCC 7.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; prng = np.random.RandomState(2019)
&gt;&gt;&gt; prng.randint(-1, 2, size=10)
array([-1, 1, 0, -1, 1, 1, -1, 0, -1, 1])
&gt;&gt;&gt; prng.randint(-1, 2, size=10)
array([-1, -1, -1, 0, -1, -1, 1, 0, -1, -1])
&gt;&gt;&gt; prng.randint(-1, 2, size=10)
array([ 0, -1, -1, 0, 1, 1, -1, 1, -1, 1])
&gt;&gt;&gt; prng.randint(-1, 2, size=10)
array([ 1, 1, 0, 0, 0, -1, 1, 1, 0, -1])
</code></pre>

I am waiting for another developer to finish a piece of code that will return an np array of shape (100,2000) with values of either -1,0, or 1.

In the meantime, I want to randomly create an array of the same characteristics so I can get a head start on my development and testing. The thing is that I want this randomly created array to be the same each time, so that I'm not testing against an array that keeps changing its value each time I re-run my process.

I can create my array like this, but is there a way to create it so that it's the same each time. I can pickle the object and unpickle it, but wondering if there's another way.

<pre><code>r = np.random.randint(3, size=(100, 2000)) - 1
</code></pre>

Consistently create same random numpy array

Python

我正在等待另一个开发人员完成一段代码，该代码将返回一个形状为(100,2000)的np数组，其值为- 1 ,0或1。同时，我想随机创建一个具有相同特征的数组，这样我就可以在开发和测试中抢先一步。问题是，我希望这个随机创建的数组每次都是相同的，这样我就不会在每次重新运行我的进程时都在测试一个值不断变化的数组。我可以像这样...

问一致地创建相同的随机数组
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