**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