print("...DONE...")


# python -m pip install --user --upgrade setuptools wheel
# python -m pip install --user --upgrade twine

# python setup.py sdist bdist_wheel

# python -m twine upload --repository testpypi dist/*  <-- upload to test Pypi
# python -m twine upload dist/*  <-- upload to Pypi




#You can use
#[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
#to write your content with the markdown protocol.




# // Mission Log \\
#
# algocryption.pipeline --> add "save_key" and "load_key"
#
# algocryption --> add to "simple columnar transposition" the option to have a 'chunk_size' as a list
#
#
# algocryption --> add "MD5"
# algocryption --> add "RC4"
# algocryption --> add "RC5"
# algocryption --> add "RC6"
# algocryption --> add "DES"
# algocryption --> add "IDEA"
# algocryption --> add "HMAC"
# algocryption --> add "Rijndael"
# algocryption --> add "TripleDES"
# algocryption --> add "fence gate"
# algocryption --> add "Caesar cipher"
# algocryption --> add "sha algorithms"
# algocryption --> add "Vigenere Cipher"
# algocryption --> add "Playfair Cipher"
# algocryption --> add "monoalphabetic cipher"
# algocryption --> add "MD5 encryption algorithm"
# algocryption --> add "IDEA encryption algorithm"
# algocryption --> add "HMAC encryption algorithm"
# algocryption --> add "shamir secret sharing scheme"
# algocryption --> add "Cryptographic hash functions"
# algocryption --> add "Twofish encryption algorithm"
# algocryption --> add "Blowfish encryption algorithm"
# algocryption --> add "Advanced Encryption Standard (AES)"
# algocryption --> add "ECC Asymmetric Encryption Algorithm"
# algocryption --> add "Elliptic Curve Digital Signature Algorithm" (ECDSA)
#
# pytrainer --> ML_functions // add lambda to Normal_Equation
# pytrainer --> ML_functions // complete Cost_function_Grad -> linear regression \\
# pytrainer --> ML_functions // complete Cost_function -> logistic regression \\
# pytrainer --> ML_functions // complete Cost_function_Grad -> logistic regression \\
#
# pytrainer --> ML_functions // complete gradient_descent \\
# pytrainer --> ML_functions_Training // complete forward_propagation \\ (add forward propagation to neural network)
#
#
# sortalgo --> try to optimize "quick sort"
# sortalgo --> try to use recursion in "quick sort"
#
#
# basic_modules --> in "default_functions" try to optimize "to_single_list"
# basic_modules --> in "default_functions" try to use regression
