On Wed, Feb 27, 2013 at 7:40 AM, ronni montoya <ronni.mont...@gmail.com>wrote:
> Hi, why is not possible? What I mean is using floating point numbers, as an approximation of real numbers. We have a finite number of samples, so it's impossible to work with continuous distributions, except by approximation. However--brainstorming a few methods of approximation is good. I'm not particularly an expert on the subject of entropy, but I enjoy it. > Instead of analysing the real time value of > the signal , maybe i can have a memory or buffer that store the a > piece of signal ( groups of samples) from time to time and then > analize that group of values. > If you're analyzing only pieces you might wonder if the signals behave the same all the time. There are many "bursting" phenomena that are interesting. Those kinds of signals have long-term correlations that have lower entropy--but any small segment does not capture the behavior. > > Maybe it can convert that group of values into a string and then: > > http://www.shannonentropy.netmark.pl/calculate > > That would do something, but may be meaningless--It would be just one way of converting the signal from real numbers to a discrete set of things/symbols that is easier to calculate. Since you brought up the topic---I was reading on wikipedia about how shannon entropy is used to obtain lower bounds on compression ratios. There are some types of audio compression--could you find a connection there? > Other idea : ive seen using shannon entropy for calculating complexity > in terms of spatial configuration. > > Maybe other option could be converting my signal into image for > example using similarity matrix and then analyze that image to get > entropy values. > > > > > cheers > > > R > > > > > > 2013/2/26, Charles Z Henry <czhe...@gmail.com>: > > Hi Ronni > > > > How do you mean to do it? > > > > Shannon entropy is not an independent measurement--the information in a > > observation is relative to the distribution of all it's possible values. > > > > If I just take one sample and it's evenly distributed between -0.98 and 1 > > and it's quantized in 0.02 increments (to make the math easier), then the > > information of any value observed is: > > -0.01*log(0.01) > > > > Then--if I had a signal that's N samples long, I have N times as much > > information. Or perhaps think of it as a rate of information. > > > > But for real numbers and continuous distributions, this doesn't work. > The > > information in a single observation diverges. So, doing that with > floating > > point numbers is not practical. > > > > You often see Shannon entropy describing digital signals. If the signal > > just switches between 0 and 1, we can generate a distribution of the data > > and see what the probability is empirically. The entropy of each new > > sample is relative to the distribution. Likewise, then if you know the > > maximum rate of switching, you can figure out the maximum rate of > > information in the signal. > > > > Just a few thoughts... > > > > Chuck > > > > > > > > On Tue, Feb 26, 2013 at 6:09 AM, ronni montoya > > <ronni.mont...@gmail.com>wrote: > > > >> Hi , i was wondering if anybody have implemented the shannon entropy > >> function in pd? > >> > >> Do anybody have tried measuring entropy of a signal? > >> > >> > >> cheeers > >> > >> > >> > >> R. > >> > >> _______________________________________________ > >> Pd-list@iem.at mailing list > >> UNSUBSCRIBE and account-management -> > >> http://lists.puredata.info/listinfo/pd-list > >> > > >
_______________________________________________ Pd-list@iem.at mailing list UNSUBSCRIBE and account-management -> http://lists.puredata.info/listinfo/pd-list