The Online Algorithmic Complexity Calculator
Full Code for the Online Algorithmic Complexity Calculator
Source files for the R Shiny web server are available at Github.
After installing Git, all files can be downloaded from a terminal calling:
git clone https://github.com/algorithmicnaturelab/OACC.git
With RStudio and Shiny installed, the OACC can be run locally by calling runApp() on the server.r or ui.r source files.
Algorithmic Complexity for Short Strings (ACSS) R Package
The acss package can be installed from the Comprehensive R Archive Network (CRAN) via:
install.packages("acss")
and loaded with:
require(acss)
It will work right out of the box, without any software other than R.
Usage
acss_data is a data frame with 4590267 values for the following 5 columns:
K.2 acss with 2 symbols
K.4 acss with 4 symbols
K.5 acss with 5 symbols
K.6 acss with 6 symbols
K.9 acss with 9 symbols
Each representing a set of strings with CTM values K.x estimated by algorithmic probability D.x. in the alphabet of size x.
Calling
acss(string = "ATATATATATAT", alphabet = 2)
will output the CTM value (K) and the algorithmic probability (D) for the string considering only 2 possible symbols in the algorithmic probability distribution.
| K.2 | D.2 | |
| ATATATATATAT | 26.99073 | 7.498626e-09 |
While calling
acss(string = "ATATATATATAT", alphabet = 4)
will output the CTM value (K) and the algorithmic probability (D) for the string considering 4 possible symbols in the algorithmic probability distribution.
| K.4 | D.4 | |
| ATATATATATAT | 27.81547 | 4.233589e-09 |
$K.x$ is always equal to $-\log_{2}(D.x)$
Running example(acss) will give plenty of usage examples.
Full documentation is available at the Comprehensive R Archive Network (CRAN).
For technical details on the Coding Theorem Method, please visit How It Works.
Universal Distribution Approximations
You can download the known algorithmic probability datasets from the Algorithmic Nature Dataverse Repository or you can click on the Download icon below directly.
$D(4,2)$ dataset in CSV format
$D(5,2)$ dataset in CSV format $D(4,2)_{2D}$ dataset in CSV format $D(5,2)_{2D}$ dataset in CSV format $D(4,4)$ dataset in CSV format $D(4,6)$ dataset in CSV format d $D(4,9)$ dataset in CSV format $D(4,10)$ dataset in CSV format
Software
8 of the most popular
programming languages
Mining and Exploitation Tools
API notebook
Data from ~ 3500 participants on the randomness generation project (PLoS CompBio paper and wide media coverage)
file
Content on this site is licensed under a |
Contact info: hector.zenil at algorithmicnaturelab dot orgIf you use results from the OACC in a publication, please visit How to Cite. |
