import torch

def create_db(db_size):
  db = torch.rand(n) >= 0.5
  return db

def modify_db(db,noise=0.5):
  first_coin_flip = (torch.rand(len(db)) < noise).float()
  second_coin_flip = (torch.rand(len(db)) < 0.5).float()

  augmented_database = db.float()*first_coin_flip + (1-first_coin_flip)*second_coin_flip

  #augmented database can also be created using this other way
  '''
  augmented_database = db.copy()
  augmented_database[first_coin_flip == 0] = second_coin_flip[first_coin_flip == 0]
  '''
  return augmented_database

def mean_query(db):
  return db.float().mean()

def analysis(db_size,query,noise_percentage=0.5):
  db = create_db(db_size)
  augmented_db = modify_db(db,noise_percentage)
  true_output = query(db)
  augmented_output = query(augmented_db)
  analysis_output = (augmented_output - (1 - noise_percentage) * 0.5)/noise
  print ("size = %r orginal_db result = %r augmented_db result = %r deskewed analysis result = %r" %(db_size,true_output,augmented_output,analysis_output))


db_size = 100
analysis(db_size,mean_query,0.5)