rand vs normal in Numpy.random in python



 1D Array filled with rand om values: [0.84503968 0.61570994 0.7619945 0.34994803 0.40113761] 

Code 2: random construction of a one-dimensional array using Gaussian distribution

# Python program illustrating
# numpy.random.normal () method

  

import numpy as geek

 
# 1D Array

array = geek.random.normal ( 0.0 , 1.0 , 5 )

print ( < code class = "string"> “1D Array filled with random values”

"as per gaussian distribution: " , array)

# 3D array

array = geek.random.normal ( 0.0 , 1.0 , ( 2 , 1 , 2 ))

print ( "3D Array filled with random values"

  "as per gaussian distribution:" , array)

B Output:

 1D Array filled with random values ​​as per gaussian distribution: [-0.99013172 -1.52521808 0.37955684 0.57859283 1.34336863] 3D Array filled with random values ​​as per gaussian distribution: [[[-0.0320374 2.14977849 ]] [[0.3789585 0.17692125]]] 


Code3: Python program that illustrates graphical representations of random and normal in NumPy

# Python program illustrating
# graphical representation
# numpy .random.normal () method
# numpy.random.rand () method

 

import numpy as geek

import matplotlib.pyplot as plot

 
Array # 1D according to Gaussian distribution

mean = 0  

std = 0.1

array = geek.random.normal ( 0 , 0.1 , 1000 )

print ( "1D Array filled with random values"

"as per gaussian distribution: " , array); 

 
# Source code:
# https://docs.scipy.org/doc/numpy- 1.13.0 / reference /
# generated / numpy-random-normal-1.py

count, bins, ignored = plot.hist (array, 30 , normed = True )

plot.plot (bins, 1 / (std * geek .sqrt ( 2 * geek.pi)) *

  geek.exp ( - (bins - mean) * * 2 / ( 2 * std * * 2 )),

  linewidth = 2 , color = ` r` )

plot.show ()

 

 
# 1D array built randomly 

random_array = geek.random.rand ( 5 )

print ( "1D Array filled with random values:" , random_array)

 
plot.plot (random_array)
plot.show ()

Output:

 1D Array filled with random values ​​as per gaussian distribution: [0.12413355 0.01868444 0.08841698 ..., -0.01523021 -0.14621625 -0.09157214] 
1D Array filled with random values: [0.72654409 0.26955422 0.19500427 0.37178803 0.10196284]

Important:
In Code 3, graph 1 clearly shows the distribution of Gaussian because it is generated from the values ​​generated by the random.normal () method, thus after being Gaussian. 
plot 2 does not follow the distribution, as it is generated from random values ​​generated by the random.rand () method.

Notes:
Code 3 will not work to an online ID. Please run them on your systems to see how they work. 
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This article is provided by Mohit Gupta_OMG