X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/turing4complexity.git/blobdiff_plain/8729d89ff655085d7e4f6342d03df1a6eeff2e5b..refs/heads/master:/turing4complexity.tex?ds=sidebyside diff --git a/turing4complexity.tex b/turing4complexity.tex index e69de29..5753ad8 100644 --- a/turing4complexity.tex +++ b/turing4complexity.tex @@ -0,0 +1,170 @@ +\documentclass{article} + +\title{Using complexity of short strings to define new statistical tests for PRNGs} + + +\begin{document} +\maketitle + + +\begin{abstract} +blabalbabla +\end{abstract} + + +\section{Introduction} + +\section{Algorithmic complexity for short strings} + +In their paper, they introduced a method to compute the complexity of $D(n)$. +With D(4), 5 970 768 960 machines halt. There are 1832 different strings. + +\section{Property: a PRNG must preserve the distribution of short strings} + +In this section, we propose to measure the quality of a PRNG by considering that +a good PRNG must preserve the distribution of short strings. More precisely, the +pseudo-random numbers obtained by a PRNG can form the states of the Rado's +original Busy Beaver. Then, machines obtained by such a construction can be +executed and if the PRNG is good, machines should generate the same strings with +the same distribution than the theoretical Busy Beaver (with all the enumeration +of states). In practice, PRNGs will not generate all the strings with exactly +the same probabilities. Nevertheless, the distance between the theoretical +probabibilities and the obtained probabilities with a PRNG is a good indication +of the quality of the PRNG. + +In the following we report our experiments. Let $S(n)_i$ be the string number +$i$ obtained by the busy beaver $D(n)$, $S(n)$ is the set of strings of +$D(n)$. With $D(4)$ there are 1832 strings, so $|S(4)|=1832$. $N_{bb}(S(n)_i)$ is +the number of occurrences of the string $S(n)_i$ obtained by the Busy Beaver and $N_{PRNG}(S(n)_i)$ is the +number of occurrences of the string $S(n)_i$ obtained with the execution of a given +PRNG. In order to measure the distance between the theoretical result and the +pratical one, we use this measure: +\begin{equation} +Dist(PRNG,BB)=\sum_{i=1}^{|S(n)|} \frac{|N_{bb}S(n)_i-N_{PRNG}S(n)_i|}{N_{bb}S(n)_i} +\end{equation} + +Here are the result for some PRNGS: bbs, lcg, xor128, rand, xorshift, devrandom, +newprng, isaac. These results are reported for experiments with different +numbers of random number generation. + + +\begin{table} +\begin{tabular}{|l|l|} +\hline + & Number of experiments\\ +\hline +Name of PRNG & 500,000,000\\ +\hline +lcg & 491.78\\ +\hline +rand & 382.04\\ +\hline +xor128 & 380.69\\ +\hline +dev random & 376.78\\ +\hline +xorshift & 372.68\\ +\hline +new prng & 367.42\\ +\hline +isaac & 366.91\\ +\hline +BBS & 358.38\\ +\hline + +\end{tabular} +\caption{Distance for some PRNG with different number generations in decreasing ordering} +\end{table} + + + +\begin{table} +\begin{tabular}{|l|l|} +\hline + & Number of experiments\\ +\hline +Name of PRNG & 500,000,000\\ +\hline +lcg & 2652694\\ +\hline +rand & 1511.34\\ +\hline +xor128 & 1489.53\\ +\hline +dev random & 1477.99\\ +\hline +xorshift & 1474.00\\ +\hline +BBS & 1403.50\\ +\hline +isaac & 1397.28\\ +\hline +new prng & 1356.76\\ +\hline + +\end{tabular} +\caption{Chi squared for some PRNG with different number generations in decreasing ordering} +\end{table} + + +\begin{table} +\begin{tabular}{|l|l|} +\hline + & Number of experiments\\ +\hline +Name of PRNG & 50,000,000\\ +\hline +lcg & 1191.87\\ +\hline +rand & 1187.18\\ +\hline +xorshift & 1257.87\\ +\hline +new prng & 1210.77\\ +\hline +dev random & 1190.33\\ +\hline +xor128 & 1190.11\\ +\hline +isaac & 1188.74\\ +\hline +BBS & 1185.77\\ +\hline + +\end{tabular} +\caption{Distance for some PRNG with different number generations in decreasing ordering} +\end{table} + + +\begin{table} +\begin{tabular}{|l|l|} +\hline + & Number of experiments\\ +\hline +Name of PRNG & 50,000,000\\ +\hline +lcg & 2664408\\ +\hline +xorshift & 14904.91\\ +\hline +new prng & 14711.53\\ +\hline +dev random & 14189.61\\ +\hline +BBS & 13857.77\\ +\hline +isaac & 13933.8\\ +\hline +xor128 & 13426.15\\ +\hline +rand & 13339.74\\ +\hline + +\end{tabular} +\caption{Chi squared for some PRNG with different number generations in decreasing ordering} +\end{table} + + + + +\end{document}