maths.entropy

Implementation of entropy of information https://en.wikipedia.org/wiki/Entropy_(information_theory)

Functions

analyze_text(→ tuple[dict, dict])	Convert text input into two dicts of counts.
calculate_prob(→ None)	This method takes path and two dict as argument
main()

Module Contents

maths.entropy.analyze_text(text: str) → tuple[dict, dict]

Convert text input into two dicts of counts. The first dictionary stores the frequency of single character strings. The second dictionary stores the frequency of two character strings.

maths.entropy.calculate_prob(text: str) → None

This method takes path and two dict as argument and than calculates entropy of them. :param dict: :param dict: :return: Prints 1) Entropy of information based on 1 alphabet 2) Entropy of information based on couples of 2 alphabet 3) print Entropy of H(X n|Xn-1)

Text from random books. Also, random quotes. >>> text = (“Behind Winston’s back the voice ” … “from the telescreen was still ” … “babbling and the overfulfilment”) >>> calculate_prob(text) 4.0 6.0 2.0

>>> text = ("The Ministry of Truth—Minitrue, in Newspeak [Newspeak was the official"
...         "face in elegant lettering, the three")
>>> calculate_prob(text)
4.0
5.0
1.0
>>> text = ("Had repulsive dashwoods suspicion sincerity but advantage now him. "
...         "Remark easily garret nor nay.  Civil those mrs enjoy shy fat merry. "
...         "You greatest jointure saw horrible. He private he on be imagine "
...         "suppose. Fertile beloved evident through no service elderly is. Blind "
...         "there if every no so at. Own neglected you preferred way sincerity "
...         "delivered his attempted. To of message cottage windows do besides "
...         "against uncivil.  Delightful unreserved impossible few estimating "
...         "men favourable see entreaties. She propriety immediate was improving. "
...         "He or entrance humoured likewise moderate. Much nor game son say "
...         "feel. Fat make met can must form into gate. Me we offending prevailed "
...         "discovery.")
>>> calculate_prob(text)
4.0
7.0
3.0

maths.entropy.main()