Tandon-A

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()
  

Tandon-A

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]

Tandon-A

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))

Tandon-A

import argparse 
import cv2
import numpy as np
import os
import pandas as pd


def parse_args():
    ''' Argument Parser. '''
    parser = argparse.ArgumentParser()