import random
seed = random.random()
random_seed = random.Random(seed)
random_vec = [ random_seed.random() for i in range(10)]
The above is essentially:
np.random.randn(10)
But I am not able to figure out how to set the seed?
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Why do you want to set the seed? Especially to a random number?abarnert– abarnert2014-12-13 04:04:12 +00:00Commented Dec 13, 2014 at 4:04
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Setting seeds is important for reproducibility in some analysesuser160623– user1606232022-04-25 15:41:30 +00:00Commented Apr 25, 2022 at 15:41
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2 Answers
I'm not sure why you want to set the seed—especially to a random number, even more especially to a random float (note that random.seed wants a large integer).
But if you do, it's simple: call the numpy.random.seed function.
Note that NumPy's seeds are arrays of 32-bit integers, while Python's seeds are single arbitrary-sized integers (although see the docs for what happens when you pass other types).
So, for example:
In [1]: np.random.seed(0)
In [2]: s = np.random.randn(10)
In [3]: s
Out[3]:
array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799,
-0.97727788, 0.95008842, -0.15135721, -0.10321885, 0.4105985 ])
In [4]: np.random.seed(0)
In [5]: s = np.random.randn(10)
In [6]: s
Out[6]:
array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 , 1.86755799,
-0.97727788, 0.95008842, -0.15135721, -0.10321885, 0.4105985 ])
Same seed used twice (I took the shortcut of passing a single int, which NumPy will internally convert into an array of 1 int32), same random numbers generated.
To put it simply random.seed(value) does not work with numpy arrays.
For example,
import random
import numpy as np
random.seed(10)
print( np.random.randint(1,10,10)) #generates 10 random integer of values from 1~10
[4 1 5 7 9 2 9 5 2 4]
random.seed(10)
print( np.random.randint(1,10,10))
[7 6 4 7 2 5 3 7 8 9]
However, if you want to seed the numpy generated values, you have to use np.random.seed(value).
If I revisit the above example,
import numpy as np
np.random.seed(10)
print( np.random.randint(1,10,10))
[5 1 2 1 2 9 1 9 7 5]
np.random.seed(10)
print( np.random.randint(1,10,10))
[5 1 2 1 2 9 1 9 7 5]
