Den onsdagen den 30:e oktober 2013 kl. 20:46:57 UTC+1 skrev Modulok: > On Wed, Oct 30, 2013 at 12:21 PM, <jonas.t...@gmail.com> wrote: > > > > I am searching for the program or algorithm that makes the best possible of > completly (diffused data/random noise) and wonder what the state of art > compression is. > > > > > I understand this is not the correct forum but since i think i have an > algorithm that can do this very good, and do not know where to turn for such > question i was thinking to start here. > > > > It is of course lossless compression i am speaking of. > > -- > > https://mail.python.org/mailman/listinfo/python-list > > > > > >> I am searching for the program or algorithm that makes the best possible of > >> completly (diffused data/random noise) and wonder what the state of art > > >> compression is. > > > None. If the data to be compressed is truly homogeneous, random noise as you > describe (for example a 100mb file read from cryptographically secure random > > bit generator such as /dev/random on *nix systems), the state-of-the-art > lossless compression is zero and will remain that way for the foreseeable > > future. > > > There is no lossless algorithm that will reduce truly random (high entropy) > data by any significant margin. In classical information theory, such an > > algorithm can never be invented. See: Kolmogorov complexity > > > Real world data is rarely completely random. You would have to test various > > algorithms on the data set in question. Small things such as non-obvious > statistical clumping can make a big difference in the compression ratio from > > one algorithm to another. Data that might look "random", might not actually be > random in the entropy sense of the word. > > > > >> I understand this is not the correct forum but since i think i have an > >> algorithm that can do this very good, and do not know where to turn for > >> such > > >> question i was thinking to start here. > > > Not to sound like a downer, but I would wager that the data you're testing > your > > algorithm on is not as truly random as you imply or is not a large enough body > of test data to draw such conclusions from. It's akin to inventing a perpetual > > motion machine or an inertial propulsion engine or any other classically > impossible solutions. (This only applies to truly random data.) > > > > -Modulok-
Well then i have news for you. -- https://mail.python.org/mailman/listinfo/python-list
