In this article, we will look into the principal difference between the Numpy.random.rand() method and the Numpy.random.normal() method in detail.
- About random: For random we are taking .rand()
numpy.random.rand(d0, d1, …, dn) :
creates an array of specified shape and
fills it with random values.
Parameters :d0, d1, ..., dn : [int, optional] Dimension of the returned array we require, If no argument is given a single Python float is returned.
Return :
Array of defined shape, filled with random values.
- About normal: For random we are taking .normal()
numpy.random.normal(loc = 0.0, scale = 1.0, size = None) : creates an array of specified shape and fills it with random values which is actually a part of Normal(Gaussian)Distribution. This is Distribution is also known as Bell Curve because of its characteristics shape.
Parameters :loc : [float or array_like]Mean of the distribution. scale : [float or array_like]Standard Derivation of the distribution. size : [int or int tuples]. Output shape given as (m, n, k) then m*n*k samples are drawn. If size is None(by default), then a single value is returned.
Return :
Array of defined shape, filled with random values following normal distribution.
Code 1 : Randomly constructing 1D array
# Python Program illustrating# numpy.random.rand() method import numpy as geek # 1D Arrayarray = geek.random.rand(5)print("1D Array filled with random values : \n", array) |
Output :
1D Array filled with random values : [ 0.84503968 0.61570994 0.7619945 0.34994803 0.40113761]
Code 2 : Randomly constructing 1D array following Gaussian Distribution
# Python Program illustrating# numpy.random.normal() method import numpy as geek # 1D Arrayarray = geek.random.normal(0.0, 1.0, 5)print("1D Array filled with random values " "as per gaussian distribution : \n", array)# 3D arrayarray = geek.random.normal(0.0, 1.0, (2, 1, 2))print("\n\n3D Array filled with random values " "as per gaussian distribution : \n", array) |
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 illustrating graphical representation of random vs normal in NumPy
# Python Program illustrating# graphical representation of # numpy.random.normal() method# numpy.random.rand() method import numpy as geekimport matplotlib.pyplot as plot # 1D Array as per Gaussian Distributionmean = 0 std = 0.1array = geek.random.normal(0, 0.1, 1000)print("1D Array filled with random values " "as per gaussian distribution : \n", array); # Source Code : # generated/numpy-random-normal-1.pycount, 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 constructed Randomlyrandom_array = geek.random.rand(5)print("1D Array filled with random values : \n", 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]
1D Array filled with random values :
[ 0.72654409 0.26955422 0.19500427 0.37178803 0.10196284]
Important :
In code 3, plot 1 clearly shows Gaussian Distribution as it is being created from the values generated through random.normal() method thus following Gaussian Distribution.
plot 2 doesn’t follow any distribution as it is being created from random values generated by random.rand() method.
Note :
Code 3 won’t run on online-ID. Please run them on your systems to explore the working.
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