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Java implementation of the Ziggurat algorithm for generating Gaussian distributed pseudorandom numbers. The Ziggurat algorithm is significantly faster than the more commonly encountered Polar method, and has some other desirable statistical properties. The ZigguratGaussian class is a Java port of the GNU Scientific Library's C implementation (Voss, 2005) of the Ziggurat method. In porting to Java, we have made several optimizations, the details of which can be found in the source code comments, which highlights any differences between this Java implementation and the C implementation on which it is based. This package also includes an implementation of the Polar Method, included to enable comparing speed advantage of the Ziggurat algorithm.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

A simple repository to salt a secret key and verify its authenticity. Developed to be used in mobile applications while communicating with a server side using an Api. Instead of sending an apikey straight on the request, SaltedKey can generate a temporary public key, valid for one time use and during a specific time frame, default time is set to 60 seconds. This public key can be sent on request and on server side SaltedKey can verify its authenticity. The Salt is based on time millis and uses the algorithm SHA-256 to create the temporary public key. The public key base will change every time it exceeds the time frame. This library can increase the API security. Even if the public key used on request is exposed, no one will be able to use it again! As it is a one time use only.

In C++, writing a loop that iterates over a sequence is tedious. We can either use iterators, which requires a considerable amount of boiler-plate, or we can use the std::for_each() algorithm and move our loop body into a predicate, which requires no less boiler-plate and forces us to move our logic far from where it will be used. In contrast, some other languages, like Perl, provide a dedicated "foreach" construct that automates this process. BOOST_FOREACH is just such a construct for C++. It iterates over sequences for us, freeing us from having to deal directly with iterators or write predicates.

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

<html>IJ Plugins Toolkit is a set of ImageJ plugins grouped into:<ul> <li>3D IO - import and export of data in 3D formats.</li> <li>3D Toolkit - operations on stacks interpreted as 3D images, including morphological operations.</li> <li>Color - color space conversion, color edge detection (color and multi-band images).</li> <li>Filters - fast median filters and various anisotropic diffusion filters.</li> <li>Graphics - Texture Synthesis - A plugin to perform texture synthesis using the image quilting algorithm of Efros and Freeman.</li> <li>Segmentation - image segmentation through clustering, thresholding, and region growing.</li></ul></html>