# Python Program illustrating
# numpy.random.rand() method

import numpy as geek

# 1D Array
array = geek.random.rand(5)
print("1D Array filled with random values : \n", array)

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

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

import numpy as geek

# 1D Array
array = geek.random.normal(0.0, 1.0, 5)
print("1D Array filled with random values "
    "as per gaussian distribution : \n", array)
# 3D array
array = geek.random.normal(0.0, 1.0, (2, 1, 2))
print("\n\n3D Array filled with random values "
    "as per gaussian distribution : \n", array)

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]]]

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

import numpy as geek
import matplotlib.pyplot as plot

# 1D Array as per 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 : \n", 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 constructed Randomly
random_array = geek.random.rand(5)
print("1D Array filled with random values : \n", random_array)

plot.plot(random_array)
plot.show()

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]