new_contents: #! /usr/bin/env python
  
  # Copyright 2021 John Hanley.
  #
  # Permission is hereby granted, free of charge, to any person obtaining a
  # copy of this software and associated documentation files (the "Software"),
  # to deal in the Software without restriction, including without limitation
  # the rights to use, copy, modify, merge, publish, distribute, sublicense,
  # and/or sell copies of the Software, and to permit persons to whom the
  # Software is furnished to do so, subject to the following conditions:
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  # The software is provided "AS IS", without warranty of any kind, express or
  # implied, including but not limited to the warranties of merchantability,
  # fitness for a particular purpose and noninfringement. In no event shall
  # the authors or copyright holders be liable for any claim, damages or
  # other liability, whether in an action of contract, tort or otherwise,
  # arising from, out of or in connection with the software or the use or
  # other dealings in the software.
  from random import gauss
  
  from scipy.optimize import minimize
  import pandas as pd
  
  
  def noisy_line(x: float, m=2, b=3, mu=0, sigma=.1) -> float:
      return m * x + b + gauss(mu, sigma)
  
  def gen_random_dataset(k=12):
      df = pd.DataFrame([
          dict(x=10 * x, y=noisy_line(x))
          for x in range(k)
      ])
      print(minimize(loss, (0, df.mean().y), args=(df,)))
  
  
  def loss(params, df):
      m, b = params
      se = 0
      for i, row in df.iterrows():
          se += (m * row.x + b - row.y) ** 2
      mse = se / len(df)
      return mse
  
  
  if __name__ == '__main__':
      gen_random_dataset()
  
subject: Add linear regression