https://github.com/gagniuc/entropy-of-strings

This application calculates the entropy of a string. The focus of this implementation is represented by a specialized function called "entropy" which receives a text sequence as a parameter and returns a value that represents the entropy. Entropy is a measure of the uncertainty in a random variable.
https://github.com/gagniuc/entropy-of-strings

code entropy entropy-measures function information javascript js measure random-variable shannon shannon-entropy strings uncertainty

Last synced: 6 months ago
JSON representation

This application calculates the entropy of a string. The focus of this implementation is represented by a specialized function called "entropy" which receives a text sequence as a parameter and returns a value that represents the entropy. Entropy is a measure of the uncertainty in a random variable.

• Host: GitHub
• URL: https://github.com/gagniuc/entropy-of-strings
• Owner: Gagniuc
• License: mit
• Created: 2022-02-08T17:33:07.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-21T10:26:26.000Z (over 3 years ago)
• Last Synced: 2025-03-26T03:41:39.927Z (7 months ago)
• Topics: code, entropy, entropy-measures, function, information, javascript, js, measure, random-variable, shannon, shannon-entropy, strings, uncertainty
• Language: HTML
• Homepage:
• Size: 127 KB
• Stars: 6
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE.md

Awesome Lists containing this project

README

          
# Entropy-of-strings

The current JS application calculates the [entropy of a string](https://gagniuc.github.io/Entropy-of-strings/). The focus of this implementation is represented by a specialized function called "entropy" which receives a text sequence as a parameter and returns a value that represents the entropy. Entropy is a measure of the uncertainty in a random variable. In the context of information theory the term "Entropy" refers to the Shannon entropy:



Which can also be written as:



Where n represents the total number of symbols in the alphabet of a sequence and pi represents the probability of occurrence of a symbol i found in the alphabet. A step-by-step version of the entropy calculation is also shown [here](https://github.com/Gagniuc/Entropy-of-Text). For more detailed information on entropy please see the specialized chapter from the book entitled Algorithms in Bioinformatics: Theory and Implementation.

	

```js

function entropy(c){

	//ALPHABET DETECTION

	var a = [];

	var t = c.split('');

	var k = t.length;

	for(var i=0; i<=k; i++){

		var q = 1;

		for(var j=0; j<=a.length; j++){

			if (t[i] === a[j]) {q = 0;}

		}

		if (q === 1) {a.push(t[i]);}

	}

	var e = 0;

	var r = '';

	var l = '';

	for(var i=0; i<=a.length-1; i++){

				

		r = c.replace(new RegExp(a[i], 'g'),'').length;

					

		l = a[i];

		a[i]=(k-r)/k;

		//e += -(a[i]*Log(2,a[i]));

		e += (a[i]*Log(2,(1/a[i])));

	}

	return e;

}

function Log(n, v) {

	return Math.log(v) / Math.log(n);

}

```

**Live demo**: https://gagniuc.github.io/Entropy-of-strings/

![screenshot](https://github.com/Gagniuc/Entropy-of-strings/blob/main/img/text_entropy.png?raw=true)

# References

- Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.