entropy: Information entropy
In obreschkow/cooltools: Practical Tools for Scientific Computations and Visualizations
entropy R Documentation
Information entropy
Description
Computes the information entropy H=sum(p*log_b(p)), also known as Shannon entropy, of a probability vector p.
Usage
entropy(p, b = exp(1), normalize = TRUE)
Arguments
p
vector of probabilities; typically normalized, such that sum(p)=1.
b
base of the logarithm (default is e)
normalize
logical flag. If TRUE (default), the vector p is automatically normalized.
Value
Returns the information entropy in units that depend on b. If b=2, the units are bits; if b=exp(1), the units are nats; if b=10, the units are dits.
Author(s)
Danail Obreschkow
obreschkow/cooltools documentation built on Nov. 16, 2024, 2:46 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| entropy | R Documentation |
Information entropy
Description
Computes the information entropy H=sum(p*log_b(p)), also known as Shannon entropy, of a probability vector p.
Usage
entropy(p, b = exp(1), normalize = TRUE)
Arguments
p |
vector of probabilities; typically normalized, such that sum(p)=1. |
b |
base of the logarithm (default is e) |
normalize |
logical flag. If TRUE (default), the vector p is automatically normalized. |
Value
Returns the information entropy in units that depend on b. If b=2, the units are bits; if b=exp(1), the units are nats; if b=10, the units are dits.
Author(s)
Danail Obreschkow
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.