CouncilDataProject / CouncilDataProject/cdp-data

Look into n-gram entropy

Open
#6 0 comments 0 reactions 0 assignees View on GitHub
enhancement
Dominant language
Jupyter Notebook
Stars
5
Forks
4
PR merge metrics
No merged PRs in 30d

Description

From Bill:

> `(word1, frequncy), (word2, frequency), ...`
> then trying to measure how far that distribution is from uniform
> one simple nice way is entropy
> `P(word1)*log(P(word1)) + P(word2)*log(P(word2)) + ...`
> where P(word1) is just frequency of word1 / total words
>
> It's nice because it measures how "unpredictable" the signal is. If most words are zero, and only a few words are common, then it's predictable. Or, if all words are exactly the same, then it's predictable. But if it's crazy town, then it's not predictable.

This seems like a decent resource: http://normal-extensions.com/2013/08/04/entropy-for-n-grams/

Contributor guide

Open the contributing guide

Assessment

This issue has not been assessed yet.

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.