fitting_straight_line_pytorch___02___generate_synthetic_data.py

# Defining the number of data points to be generated
num_points = 100

# Let's generate the synthetic data points
x_list = []
y_list = []
for _ in range(num_points):
    
    # Selecting a random integer from the predefined range
    x = random.randint(x_range[0] , x_range[1])
    
    # Calculating the dependent valiable 'y' using the formula of a straight line y = mx + c
    y = m_synthetic*x + c_synthetic
    
    # Randomly choosing the deviation (noise) for both the dependent as well as the independent variable, so that the dataset becomes a little noisy
    deviation_x = random.randint(-deviation , deviation)
    deviation_y = random.randint(-deviation , deviation)
    
    # Finally, appending the noisy data points in the respective lists
    x_list.append(x + deviation_x)
    y_list.append(y + deviation_y)

x_min, y_min, x_max, y_max = min(x_list) , min(y_list) , max(x_list) , max(y_list)
print('\nFollowing are the extreme ends of the synthetic data points...')
print('x_min, x_max: {}, {}'.format(x_min, x_max))
print('y_min, y_max: {}, {}'.format(y_min, y_max))

fitting_straight_line_pytorch___02___generate_synthetic_data_output.py

Following are the extreme ends of the synthetic data points...
x_min, x_max: -2022, 1966
y_min, y_max: -1392.3200000000002, 1352.16