Path: csiph.com!weretis.net!feeder8.news.weretis.net!reader5.news.weretis.net!news.solani.org!.POSTED!not-for-mail From: Mild Shock Newsgroups: comp.lang.javascript Subject: Re: ANN: Dogelog Player 1.2.6 (Segmented Fileaccess) Date: Thu, 20 Feb 2025 11:20:10 +0100 Message-ID: References: MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8; format=flowed Content-Transfer-Encoding: 8bit Injection-Date: Thu, 20 Feb 2025 10:20:09 -0000 (UTC) Injection-Info: solani.org; logging-data="520815"; mail-complaints-to="abuse@news.solani.org" User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:128.0) Gecko/20100101 Firefox/128.0 SeaMonkey/2.53.20 Cancel-Lock: sha1:jq3+C+Lp7WklRZ6XBqwZHctYfm4= X-User-ID: eJwNwYEBwCAIA7CXQKCTc0Dp/ye4JAyK8zkCHgx2nmIanXJh8Bo5etFQwtdSK9jdyJ5FkZbp3oVfptpoPmGZFWE= In-Reply-To: Xref: csiph.com comp.lang.javascript:124388 We made our remark reality that a binary decision tree can be directly created from the data. Starting from adaptive trees we built a new aggregate that can perform the statistics for a Bayes Classifier using the majority rule. We only use Prolog code! The adaptive tree can be used like a bitwise trie, and allows us to compute some statistics in one pass. From this statistics we can then derive a decision tree using a majority rule. The entropy of the computed output will be inside an 1/2 bit interval of the sample output entropy. See also: Bayes Classifier for SAT Learning https://x.com/dogelogch/status/1892517071730135467 Bayes Classifier for SAT Learning https://www.facebook.com/groups/dogelog Mild Shock schrieb: > An autoencoder learns two functions: an encoding > function that transforms the input data, and a > decoding function that recreates the input data > from the encoded representation. We approach > autoencoders via our already developed SAT Learning > in the Prolog programming language. > > Switching from a marginal maximizer to a conditional > maximizer gives better results but also requires a > more costly and slower optimizer. Maximum entropy > methods were already suggest by Peter Cheeseman in > 1987. Mostlikely flawed since there is not yet a > feedback loop from the decoder to the encoder. > > Maximum Entropy in SAT Autoencoding > https://x.com/dogelogch/status/1890093860782764409 > > Maximum Entropy in SAT Autoencoding > https://www.facebook.com/groups/dogelog > > Mild Shock schrieb: >> Dogelog Player is a Prolog system for JavaScript, >> Python and Java. It is 100% written in Prolog itself. >> We present an enhancement to DCG translation. It uses >> unification spilling to reduce the number of needed >> unify (=)/2 calls and intermediate variables. >> >> Unification spilling can be readily implemented by >> performing unification (=)/2 during DCG translation. >> Careful spilling without breaking steadfastness gave >> us a 10% — 25% speed increase not only for the calculator >> example but also for the Albufeira transpiler. >> >> See also: >> >> DCG Translation with Unification Spilling >> https://x.com/dogelogch/status/1889270444647182542 >> >> DCG Translation with Unification Spilling >> https://www.facebook.com/groups/dogelog >> >