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.

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.

**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.

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 $D(4,9)$ dataset in CSV format $D(4,10)$ dataset in CSV format

## Mining and Exploitation Tools

Mathematica

API notebook

## Content on this site is licensed under a |
## Contact info: hector.zenil at algorithmicnaturelab dot org## If you use results from the OACC in a publication, please visit How to Cite. |