Random Sampling



In [2]:

    
import numpy as np



In [3]:

    
np.__version__









    Out[3]:





'1.11.2'



In [5]:

    
__author__ = 'kyubyong. longinglove@nate.com'

Simple random data

Q1. Create an array of shape (3, 2) and populate it with random samples from a uniform distribution over [0, 1).



In [49]:

    
np.random.rand(3, 2) 
# Or np.random.random((3,2))









    Out[49]:





array([[ 0.13879034,  0.71300174],
       [ 0.08121322,  0.00393554],
       [ 0.02349471,  0.56677474]])

Q2. Create an array of shape (1000, 1000) and populate it with random samples from a standard normal distribution. And verify that the mean and standard deviation is close enough to 0 and 1 repectively.



In [42]:

    
out1 = np.random.randn(1000, 1000)
out2 = np.random.standard_normal((1000, 1000))
out3 = np.random.normal(loc=0.0, scale=1.0, size=(1000, 1000))
assert np.allclose(np.mean(out1), np.mean(out2), atol=0.1)
assert np.allclose(np.mean(out1), np.mean(out3), atol=0.1)
assert np.allclose(np.std(out1), np.std(out2), atol=0.1)
assert np.allclose(np.std(out1), np.std(out3), atol=0.1)
print np.mean(out3)
print np.std(out1)









    



-0.00110028519551
0.999683483393

Q3. Create an array of shape (3, 2) and populate it with random integers ranging from 0 to 3 (inclusive) from a discrete uniform distribution.



In [44]:

    
np.random.randint(0, 4, (3, 2))









    Out[44]:





array([[1, 3],
       [3, 0],
       [0, 0]])

Q4. Extract 1 elements from x randomly such that each of them would be associated with probabilities .3, .5, .2. Then print the result 10 times.



In [58]:

    
x = [b'3 out of 10', b'5 out of 10', b'2 out of 10']



In [60]:

    
for _ in range(10):
    print np.random.choice(x, p=[.3, .5, .2])









    



2 out of 10
5 out of 10
3 out of 10
5 out of 10
5 out of 10
5 out of 10
2 out of 10
2 out of 10
5 out of 10
5 out of 10

Q5. Extract 3 different integers from 0 to 9 randomly with the same probabilities.



In [66]:

    
np.random.choice(10, 3, replace=False)









    Out[66]:





array([5, 4, 0])

Permutations

Q6. Shuffle numbers between 0 and 9 (inclusive).



In [86]:

    
x = np.arange(10)
np.random.shuffle(x)
print x









    



[2 3 8 4 5 1 0 6 9 7]



In [88]:

    
# Or
print np.random.permutation(10)









    



[5 2 7 4 1 0 6 8 9 3]

Random generator

Q7. Assign number 10 to the seed of the random generator so that you can get the same value next time.



In [91]:

    
np.random.seed(10)