Random data generation

Aim of this notebook is to show how we can generate artificial random data and interact with it using interaction widgets in Python. To enable the interactive feature, open this notebook in google colab.

1. Linear random data

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['figure.figsize'] = (10, 5)   # Change this if figures look ugly. from matplotlib import rcParams

# IPython libraries

from ipywidgets import interactive
from IPython.display import display

training_points = 250    #  Number of training points
noise = 0.1   # Noise level

def generate_linear_data(training_points,noise):
    # generate random data-set
    np.random.seed(0)
    x = np.random.rand(training_points, 1)
    m = 3   # Slope
    c = 1   # Intercept
    y = c + m * x +  np.random.rand(training_points,1) * noise    # y = mx + c + noise
    # plot
    plt.scatter(x,y,s=25, marker = "o")
    plt.xlabel('x')
    plt.ylabel('y')
    plt.title("Generated data")
    plt.show()
    return (x,y)


# This will call the interactive widget with the data generating function, which also plots the data real-time
l=interactive(generate_linear_data,training_points={'50 samples':50,'200 samples':200},noise =(0,1,0.2))
display(l)

2. Polynomial random data

x_min = -5
x_max = 5
noise = 0.1

def generate_poly_data(training_points,x_min,x_max,noise):
    x1 = np.linspace(x_min,x_max,training_points*5)
    x = np.random.choice(x1,size=training_points)
    y = np.sin(x) + noise*np.random.normal(size=training_points)
    plt.scatter(x,y,edgecolors='k',c='red',s=60)
    plt.grid(True)
    plt.show()
    return (x,y)

# This will call the interactive widget with the data generating function, which also plots the data real-time
p=interactive(generate_poly_data,training_points={'50 samples':50,'200 samples':200},noise =(0,1,0.2),x_min=(-5,0,1), x_max=(0,5,1))
display(p)