python numpy.random random sampling

simple random data

1. rand(d0, d1, …, dn)

   • random values in a given shape

       In [2]: np.random.rand(3)
       Out[2]: array([ 0.2978406 ,  0.18461514,  0.24704652])
     
       In [3]: np.random.rand(3, 5)
       Out[3]: 
       array([[ 0.15870093,  0.49928607,  0.39069335,  0.7445641 ,  0.3208757 ],
              [ 0.03554006,  0.11208914,  0.4263552 ,  0.19699995,  0.17269604],
              [ 0.393527  ,  0.33498196,  0.8278391 ,  0.22020756,  0.97791024]])
     

2. randn(d0, d1, …, dn)

   • return a sample (or samples) from the “standard normal” ditribution

       In [4]: np.random.randn(3)
       Out[4]: array([-0.3169426 , -0.62385199, -0.3950756 ])
     
       In [5]: np.random.randn(3, 5)
       Out[5]: 
       array([[ 0.55238932, -1.46249184,  1.83115131, -0.75392455,  0.34590461],
              [-0.71300575,  0.23139916,  0.42955918, -1.00816456, -0.79046912],
              [ 0.27605609,  2.53116393, -0.15409915, -0.90058645,  0.43619731]])
     

3. randint(low[, high, size])

   • return random integers [low, high)

       In [6]: np.random.randint(3)
       Out[6]: 0
     
       In [7]: np.random.randint(3, 5)
       Out[7]: 4
     
       In [8]: np.random.randint(3, 9)
       Out[8]: 3
     

4. random_integers(low[, high, size])

   • return random integers [low, high]

       In [11]: np.random.random_integers(3)
       Out[11]: 1
     
       In [12]: np.random.random_integers(3, 9)
       Out[12]: 4
     

5. random_sample([size])

   • return random floats in the half-open interval [0.0, 1.0)

       In [13]: np.random.random_sample(3)
       Out[13]: array([ 0.4559776 ,  0.76772504,  0.46774327])
     

6. random([size])

   • return random floats in the half-open interval [0.0, 1.0)

       In [15]: np.random.random(3)
       Out[15]: array([ 0.30355838,  0.18042125,  0.3204727 ])
     

7. ranf([size])

   • return random floats in the half-open interval [0.0, 1.0)

       In [16]: np.random.ranf(3)
       Out[16]: array([ 0.75417093,  0.83522699,  0.47386136])
     

8. sample(size)

   • return random floats in the half-open interval [0.0, 1.0)

       In [17]: np.random.sample(3)
       Out[17]: array([ 0.39996352,  0.89666635,  0.3100504 ])
     

9. choice(a[, size, replace, p])

   • generates a random sample from a given 1-d array

       In [18]: np.random.choice(3)
       Out[18]: 2
     
       In [19]: np.random.choice(3, 5)
       Out[19]: array([2, 2, 2, 1, 0])
     
       In [20]: np.random.choice(3, 5, 2)
       Out[20]: array([2, 2, 0, 2, 2])
     

10. bytes(length)

    • return random bytes

        In [27]: np.random.bytes(3)
        Out[27]: '\xd1\x1a\xec'
      
        In [28]: np.random.bytes(5)
        Out[28]: '8\x10\x1e\x96\xc1'
      

permutations

1. shuffle(x)

   • modify a sequence in-place by shuffling its contents

       In [33]: a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
     
       In [34]: a
       Out[34]: [1, 2, 3, 4, 5, 6, 7, 8, 9]
     
       In [35]: np.random.shuffle(a)
     
       In [36]: a
       Out[36]: [6, 5, 4, 2, 3, 8, 7, 1, 9]
     

2. permutation(x)

   • randomly permute a sequence, or return a permuted range

       In [37]: a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
     
       In [38]: np.random.permutation(a)
       Out[38]: array([7, 6, 2, 5, 8, 3, 9, 1, 4])
     

distributions

1. todo