What does numpy random seed 0 do

Question

What does np random seed do in the below code from a Scikit-Learn tutorial  I m not very familiar with NumPy s random state generator stuff  so I d really appreciate a layman s terms explanation of this   np random seed 0  indices   np random permutation len iris X

User · Answer

A random seed specifies the start point when a computer generates a random number sequence.

For example, let’s say you wanted to generate a random number in Excel (Note: Excel sets a limit of 9999 for the seed). If you enter a number into the Random Seed box during the process, you’ll be able to use the same set of random numbers again. If you typed “77” into the box, and typed “77” the next time you run the random number generator, Excel will display that same set of random numbers. If you type “99”, you’ll get an entirely different set of numbers. But if you revert back to a seed of 77, then you’ll get the same set of random numbers you started with.

For example, “take a number x, add 900 +x, then subtract 52.” In order for the process to start, you have to specify a starting number, x (the seed). Let’s take the starting number 77:

Add 900 + 77 = 977 Subtract 52 = 925 Following the same algorithm, the second “random” number would be:

900 + 925 = 1825 Subtract 52 = 1773 This simple example follows a pattern, but the algorithms behind computer number generation are much more complicated

User · Answer

There is a nice explanation in Numpy docs  https   docs scipy org doc numpy-1 15 1 reference generated numpy random RandomState html it refers to Mersenne Twister pseudo-random number generator   More details on the algorithm here  https   en wikipedia org wiki Mersenne Twister

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All the answers above show the implementation of np random seed   in code  I ll try my best to explain briefly why it actually happens  Computers are machines that are designed based on predefined algorithms  Any output from a computer is the result of the algorithm implemented on the input  So when we request a computer to generate random numbers  sure they are random but the computer did not just come up with them randomly   So when we write np random seed any number here  the algorithm will output a particular set of numbers that is unique to the argument any number here  It s almost like a particular set of random numbers can be obtained if we pass the correct argument  But this will require us to know about how the algorithm works which is quite tedious    So  for example if I write np random seed 10  the particular set of numbers that I obtain will remain the same even if I execute the same line after 10  years unless the algorithm changes

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I hope to give a really short answer  seed make  the next series  random numbers predictable  You can think every time after you call seed  it pre-defines series numbers and numpy random keeps the iterator of it  then every time you get a random number it just gonna call get next  e g   np random seed 2  np random randn 2    array  -0 41675785  -0 05626683   np random randn 1    array  -1 24528809    np random seed 2  np random randn 1    array  -0 41675785   np random randn 2    array  -0 05626683  -1 24528809    You can notice when I set the same seed  no matter how many random number you request from numpy each time  it always gives the same series of numbers  in this case which is array  -0 41675785  -0 05626683  -1 24528809

User · Answer

All the random numbers generated after setting particular seed value are same across all the platforms systems

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As noted  numpy random seed 0  sets the random seed to 0  so the pseudo random numbers you get from random will start from the same point  This can be good for debuging in some cases   HOWEVER  after some reading  this seems to be the wrong way to go at it  if you have threads because it is not thread safe  from differences-between-numpy-random-and-random-random-in-python   For numpy random seed    the main difficulty is that it is not thread-safe - that is  it s not safe to use if you have many different threads of execution  because it s not guaranteed to work if two different threads are executing the function at the same time  If you re not using threads  and if you can reasonably expect that you won t need to rewrite your program this way in the future  numpy random seed   should be fine for testing purposes  If there s any reason to suspect that you may need threads in the future  it s much safer in the long run to do as suggested  and to make a local instance of the numpy random Random class  As far as I can tell  random random seed   is thread-safe  or at least  I haven t found any evidence to the contrary    example of how to go about this  from numpy random import RandomState prng   RandomState   print prng permutation 10  prng   RandomState   print prng permutation 10  prng   RandomState 42  print prng permutation 10  prng   RandomState 42  print prng permutation 10   may give    3 0 4 6 8 2 1 9 7 5   1 6 9 0 2 7 8 3 5 4   8 1 5 0 7 2 9 4 3 6   8 1 5 0 7 2 9 4 3 6   Lastly  note that there might be cases where initializing to 0  as opposed to a seed that has not all bits 0  may result to non-uniform distributions for some few first iterations because of the way xor works  but this depends on the algorithm  and is beyond my current worries and the scope of this question

User · Answer

Imagine you are showing someone how to code something with a bunch of  random  numbers  By using numpy seed they can use the same seed number and get the same set of  random  numbers   So it s not exactly random because an algorithm spits out the numbers but it looks like a randomly generated bunch

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If you set the np random seed a fixed number  every time you call the numpy s other random function  the result will be the same    gt  gt  gt  import numpy as np  gt  gt  gt  np random seed 0    gt  gt  gt  perm   np random permutation 10    gt  gt  gt  print perm   2 8 4 9 1 6 7 3 0 5   gt  gt  gt  np random seed 0    gt  gt  gt  print np random permutation 10    2 8 4 9 1 6 7 3 0 5   gt  gt  gt  np random seed 0    gt  gt  gt  print np random permutation 10    2 8 4 9 1 6 7 3 0 5   gt  gt  gt  np random seed 0    gt  gt  gt  print np random permutation 10    2 8 4 9 1 6 7 3 0 5   gt  gt  gt  np random seed 0    gt  gt  gt  print np random rand 4    0 5488135  0 71518937 0 60276338 0 54488318   gt  gt  gt  np random seed 0    gt  gt  gt  print np random rand 4    0 5488135  0 71518937 0 60276338 0 54488318    However  if you just call it once and use various random functions  the results will still be different    gt  gt  gt  import numpy as np  gt  gt  gt  np random seed 0    gt  gt  gt  perm   np random permutation 10   gt  gt  gt  print perm   2 8 4 9 1 6 7 3 0 5   gt  gt  gt  np random seed 0    gt  gt  gt  print np random permutation 10   2 8 4 9 1 6 7 3 0 5   gt  gt  gt  print np random permutation 10    3 5 1 2 9 8 0 6 7 4   gt  gt  gt  print np random permutation 10    2 3 8 4 5 1 0 6 9 7   gt  gt  gt  print np random rand 4    0 64817187 0 36824154 0 95715516 0 14035078   gt  gt  gt  print np random rand 4    0 87008726 0 47360805 0 80091075 0 52047748

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numpy random seed 0  numpy random randint 10  size 5    This produces the following output  array  5  0  3  3  7   Again if we run the same code we will get the same result   Now if we change the seed value 0 to 1 or others   numpy random seed 1  numpy random randint 10  size 5    This produces the following output  array  5 8 9 5 0   but now the output not the same like above

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np random seed 0  makes the random numbers predictable   gt  gt  gt  numpy random seed 0    numpy random rand 4  array   0 55   0 72   0 6    0 54    gt  gt  gt  numpy random seed 0    numpy random rand 4  array   0 55   0 72   0 6    0 54     With the seed reset  every time   the same set of numbers will appear every time   If the random seed is not reset  different numbers appear with every invocation    gt  gt  gt  numpy random rand 4  array   0 42   0 65   0 44   0 89    gt  gt  gt  numpy random rand 4  array   0 96   0 38   0 79   0 53      pseudo- random numbers work by starting with a number  the seed   multiplying it by a large number  adding an offset  then taking modulo of that sum   The resulting number is then used as the seed to generate the next  random  number   When you set the seed  every time   it does the same thing every time  giving you the same numbers   If you want seemingly random numbers  do not set the seed   If you have code that uses random numbers that you want to debug  however  it can be very helpful to set the seed before each run so that the code does the same thing every time you run it   To get the most random numbers for each run  call numpy random seed     This will cause numpy to set the seed to a random number obtained from  dev urandom or its Windows analog or  if neither of those is available  it will use the clock   For more information on using seeds to generate pseudo-random numbers  see wikipedia

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I have used this very often in neural networks  It is well known that when we start training a neural network we randomly initialise the weights  The model is trained on these weights on a particular dataset  After number of epochs you get trained set of weights    Now suppose you want to again train from scratch or you want to pass the model to others to reproduce your results  the weights will be again initialised to a random numbers which mostly will be different from earlier ones  The obtained trained weights after same number of epochs   keeping same data and other parameters   as earlier one will differ  The problem is your model is no more reproducible that is every time you train your model from scratch it provides you different sets of weights  This is because the model is being initialized by different random numbers every time   What if every time you start training from scratch the model is initialised to the same set of random initialise weights  In this case  your model could become reproducible  This is achieved by numpy random seed 0   By mentioning seed   to a particular number  you are hanging on to same set of random numbers always

[python] What does numpy.random.seed(0) do?

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