Use of Rand (), RANDN (), Randint (), random_integers () in Python's NumPy library

1.numpy.random.rand ()
Usage is: Numpy.random.rand (d0,d1,... DN)
Creates an array with the given shape and adds a random sample evenly distributed between [0,1] in the array.
usage and implementation :

>>> Np.random.rand (3,2) Array ([[0.14022471,  0.96360618],  #random       [0.37601032,  0.25528411],  #random       [0.49313049,  0.94909878]]) #random

>>>np.random.rand (5) array ([0.26677034,  0.01680242,  0.5164905,  0.70920141,  0.30438513])

2.numpy.random.randn ()
Usage is: Numpy.random.rand (d0,d1,... DN)
Creates an array of array elements in the given shape to conform to the standard normal distribution N (0,1)
To get a general normal distribution, you can use sigma * NP.RANDOM.RANDN (...) + mu to represent
usage and implementation :

>>> a = NP.RANDOM.RANDN (2, 4) >>> Aarray ([[ -0.29188711],  0.76417681,  1.00922644  , 0.34169581],       [ -0.3652463, -0.9158214,  0.34467129, -0.31121017]]) >>> B = Np.random.randn (2) >> > Barray ([0.37849173,  1.14298464])

3.numpy.random.randint ()
Usage is: numpy.random.randint (low,high=none,size=none,dtype)
Generates an integer value that is uniformly distributed on the semi-open half-closed interval [Low,high] ; if High=none, the range of values becomes [0,low]
usage and implementation
The situation of High=none

>>> a = Np.random.randint (2, size=10) >>> Aarray ([0, 1, 0, 1, 1, 0, 1, 0, 0, 1]) >>> B = Np.rando  M.randint (1, size=10) >>> Barray ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) >>> C =  np.random.randint (5, size= (2, 4)) >>> CArray ([[3, 4, 3, 3],       [3, 0, 0, 1]])

High≠none

D = Np.random.randint (2,high=6,size= (2,4)) >>> Darray ([[5, 2, 4, 2],       [4, 3, 5, 4]])

4.numpy.random.random_integers ()
Usage is: numpy.random.random_integers (low,high=none,size=none)
Generates integer values for discrete uniform distributions on closed intervals [Low,high] ; if High=none, the range of values becomes [1,low]
usage and implementation
The situation of High=none

>>> np.random.random_integers (1, 6,) array ([4, 5, 2, 3, 4, 2, 5, 4, 5, 4]) >>> Np.random.random_integer S (6) 5
>>> np.random.random_integers (6,size= (3,2))
Array ([[1, 3],
[5, 6],
[3, 4]])

The situation of High≠none

>>> C =  np.random.random_integers (6,high=8,size= (3,2)) >>> CArray ([[[7, 8],       [7, 8],       [8, 8])

In addition, to divide the "A, B" interval into equal n, you can also use this function to implement
A + (b-a) * (Numpy.random.random_integers (N)-1)/(N-1)

5.numpy.random_sanmple ()
Usage is: numpy.random.random_sample (size=none)
Returns a random floating-point number between [0,1] in a given shape
usage and implementation

>>> np.random.random_sample () 0.2982524530687424>>> np.random.random_sample ((5,)) Array ([ 0.47989216,  0.12580015,  0.99624494,  0.14867684,  0.56981553]) >>> np.random.random_ Sample ((2,5)) array ([[0.00659559,  0.45824325,  0.13738623,  0.60766919,  0.39234638],       [ 0.6914948,  0.92461145,  0.43289058,  0.63093292,  0.06921928]])

Other functions,numpy.random.random () ; numpy.random.ranf ()
numpy.random.sample () usage and implementation are the same as it

6.numpy.random.choice ()
Usage is: Numpy.random.choice (a,size=none,replace=true,p=none)
If a is an array, the element is selected from a, and if A is a single int type, the number in range (a) is selected
Replace is a bool type, true, the selected element repeats, and no duplicates occur
P is an array, which holds the possibility of selecting each number, that is, the probability
usage and implementation

>>>a =  Np.random.choice (5, 3) >>> Aarray ([4, 3, 1]) >>>b =  Np.random.choice (5, 3, p=[  0.1, 0, 0.3, 0.6, 0]) >>> barray ([2, 3, 3], dtype=int64) >>> C =  np.random.choice (5, 3, Replace=false, p=[0.1, 0, 0.3, 0.6, 0]) >>> carray ([3, 2, 0])

Use of Rand (), RANDN (), Randint (), random_integers () in Python's NumPy library