| Type: | Package |
| Title: | Data Only: Algorithmic Complexity of Short Strings (Computed via Coding Theorem Method) |
| Version: | 1.2 |
| LazyData: | yes |
| LazyDataCompression: | xz |
| Depends: | R (≥ 2.10) |
| Description: | Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs. |
| URL: | https://complexity-calculator.com/methodology.html |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2025-05-19 14:42:58 UTC; singmann |
| Author: | Fernando Soler Toscano [aut], Nicolas Gauvrit [aut], Hector Zenil [aut], Henrik Singmann [aut, cre] |
| Maintainer: | Henrik Singmann <singmann@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-19 23:10:06 UTC |
Data Only: Algorithmic Complexity of Short Strings (Computed via Coding Theorem Method)
Description
Data only package providing the algorithmic complexity of short strings, computed using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states (e.g., 5) and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. This package contains data on 4.5 million strings from length 1 to 12 simulated on TMs with 2, 4, 5, 6, and 9 symbols. The complexity of the string corresponds to the distribution of the halting states of the TMs.
Details
| Package: | acss.data |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2013-04-02 |
| License: GPL (>= 2) | |
| URL: | https://complexity-calculator.com/methodology.html |
This package only contains data. Therefore, this package is not intended to be used directly, but through functions in package acss.
Author(s)
The data in this package was created by Fernando Soler Toscano, Nicolas Gauvrit, and Hector Zenil.
Data was ported to R by Henrik Singmann.
Maintainer: Henrik Singmann <singmann@gmail.com>
References
Delahaye, J.-P., & Zenil, H. (2012). Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness. Applied Mathematics and Computation, 219(1), 63-77. doi:10.1016/j.amc.2011.10.006
Gauvrit, N., Zenil, H., Delahaye, J.-P., & Soler-Toscano, F. (in press). Algorithmic complexity for short binary strings applied to psychology: a primer. Behavior Research Methods. doi:10.3758/s13428-013-0416-0
Soler-Toscano, F., Zenil, H., Delahaye, J.-P., & Gauvrit, N. (2012). Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines. arXiv:1211.1302 [cs.it].
See Also
package acss for functions accessing this data.
acss_data: algorithmic complexity of short strings
Description
Contains the algorithmic complexity for short string, an approximation of the Kolmogorov Complexity of a short string using the coding theorem method. For a given set of symbols in a string, all possible or a large number of random samples of Turing machines (TM) with a given number of states and number of symbols corresponding to the number of symbols in the strings were simulated until they reached a halting state or failed to end. The complexity of the string corresponds to the distribution of the halting states of the TMs.
See https://complexity-calculator.com/methodology.html for more information or references below.
This dataset shouldn't be called directly but rather through the accessor functions in package acss.
Usage
acss_dataFormat
A data frame with 4590267 observations on the following 5 variables.
K.2acss with 2 symbols, computed on all possible Turing machines (TM) with 5 states and 2 symbols.
K.4acss with 4 symbols, computed on a large number of TMs with 4 states and 4 symbols.
K.5acss with 5 symbols, computed on a large number of TMs with 4 states and 5 symbols.
K.6acss with 6 symbols, computed on a large number of TMs with 4 states and 6 symbols.
K.9acss with 9 symbols, computed on a large number of TMs with 4 states and 9 symbols.
Author(s)
Fernando Soler Toscano, Nicolas Gauvrit, and Hector Zenil.
Ported to R by Henrik Singmann.
Source
https://complexity-calculator.com/methodology.html
References
Delahaye, J.-P., & Zenil, H. (2012). Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness. Applied Mathematics and Computation, 219(1), 63-77. doi:10.1016/j.amc.2011.10.006
Gauvrit, N., Zenil, H., Delahaye, J.-P., & Soler-Toscano, F. (in press). Algorithmic complexity for short binary strings applied to psychology: a primer. Behavior Research Methods. doi:10.3758/s13428-013-0416-0
Soler-Toscano, F., Zenil, H., Delahaye, J.-P., & Gauvrit, N. (2012). Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines. arXiv:1211.1302 [cs.it].