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This package contains several example programs of the usage of the JavaPermutationTools (JPT) library. JPT is a Java library that enables representing and generating permutations and sequences, as well as performing computation on permutations and sequences. It includes implementations of a variety of permutation distance metrics as well as distance metrics on sequences (i.e., Strings, arrays, and other ordered data types). In addition to programs demonstrating the usage of the JPT library, the jpt-examples package also contains programs for replicating the experiments from a few published papers that utilized the library or implementations on which the library is based. JPT's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package jpt-examples is to demonstrate usage of the major functionality of the JPT library. You can find out more about the JPT library itself from its website: https://jpt.cicirello.org/.

The ONNX Runtime Mobile package is a size optimized inference library for executing ONNX (Open Neural Network Exchange) models on Android. This package is built from the open source inference engine but with reduced disk footprint targeting mobile platforms. To minimize binary size this library supports a reduced set of operators and types aligned to typical mobile applications. The ONNX model must be converted to ORT format in order to use it with this package. See https://onnxruntime.ai/docs/reference/ort-format-models.html for more details.

Small Java library for generating dynamic proxies on top of [ByteBuddy](https://github.com/raphw/byte-buddy) or [Javassist](https://github.com/jboss-javassist/javassist). You can generate data transfer objects, rich traits or even whole implicit DAO implementations dynamically at runtime easily. This library solves the complex stuff, so you can focus on application logic. Serializability, cloning are already solved by us. We also aim for transparent and easily debuggable proxies, because as we know proxies is usually part of "magic" for the team. Hence, the name of this library - Proxycian as a magician for the proxies ;)

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.