rand vs normal in Numpy.random in Python
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.
d0, d1, ..., dn : [int, optional] Dimension of the returned array we require, If no argument is given a single Python float is returned.
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.
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.
Array of defined shape, filled with random values following normal distribution.
Code 1 : Randomly constructing 1D array
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
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
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]
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.
Code 3 won’t run on online-ID. Please run them on your systems to explore the working.
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