Download JAR files tagged by general with all dependencies

This is the tonic look-and-feel packaged to be distributed with the SQuirreLSQL client. This pluggable look and feel is a free substitute for the default native look and feel of Swing, 'Metal', distributed under the GNU Lesser General Public License. Metal lacks both in usability and aesthetics. It contains considerable graphical noise, distracting the user from the key elements of the GUI. Tonic, on the other hand, tries to provide a clean, balanced look and an improved feel. Tonic is available free of charge both for commercial and non-commercial applications. It lends a professional touch and a very tidy and clean interface to your Swing based applications.

`Romaji` is a converter library to romanize Japanese hiragana/katakana string by standard and IME typing style. Even though [icu::Transliterator](http://userguide.icu-project.org/transforms/general) already has provided the same functions, and returns only one romanized string. However, there exists several different romanization systems, so one hiragana/katakana string has so many romanize string. For example, `"ちゃ"` can be romanized as `"cha"`, `"tya"`, `"chixya"`, `"tixya"`, `"chilya"`, or `"tilya"`. `Romaji` provides romanized strings as many as possible. If an input string contained non hiragana/katakana characters (includes kanji), `Romaji` return the characters as same as the input. For example, `Romaji` converts the input `"お茶の水"` to `"o茶no水"`. The mapping from hiragana/katakana to romaji is based on common IME's system to type Japanese on a computer. Therefor, `Romaji` does not directly implement the standard system like Hepburn, Nihon-shiki or Kunrei-shiki, but includes them.

A bottom-up rewrite machine is a compiler construction tool that is often used in the compiler's back end to convert a tree-structured representation of a program into machine code -- or, in Java's case, bytecode. JBurg can also be used as a general-purpose dynamic programming engine. JBurg is descended from iburg-class BURGs, described in Fraser, Hanson, and Proebsting's paper, "Engineering a Simple, Efficient Code Generator Generator." JBurg brings similar O(N) minimum-cost tree rewriting capabilities to Java, and also allows the programmer to specify transitions between non-terminal states, that are significantly more powerful than iburg's transitive closures: JBurg transformation rules allow the transformation to inject additional program logic, which makes a JBurg specification more like a grammar than like a list of pattern-matching rules.

This module contains a more general approach to construct AlignmentAlgorithms by relying on the theoretical concept of Algebraic Dynamic Programming (ADP) as developed by Giegerich et al. ADP defines four ingredients for an alignment algorithm: 1.) A signature that defines the permitted alignment operations. Operations are just function templates with an associated arity, meaning the number of arguments it takes from the left sequence and from the right sequence. In the TCSAlignmentToolbox we have a fixed signature with the following operations: REPLACEMENT(1, 1), DELETION(1, 0), INSERTION(0, 1), SKIPDELETION(1, 0) and SKIPINSERTION(0, 1) 2.) A regular tree grammar that produces alignments, that is: sequences of operations, in a restricted fashion. 3.) An algebra that can translate such trees to a cost. In the TCSAlignmentToolbox this is a Comparator. 4.) A choice function, in case of the TCSAlignmentToolbox: the strict minimum or the soft minimum. An alignment algorithm in the TCSAlignmentToolbox sense of the word then is the combination of choice function and grammar. While we provide hardcoded versions of these combinations in the main package, the adp package allows you to create your own grammars. You can combine them with a choice function by instantiating one of the Algorithm classes provided in this package with a grammar of your choice. For example: AlignmentAlgorithm algo = new SoftADPScoreAlgorithm(my_grammar, comparator); creates an alignment algorithm that implicitly produces all possible alignments your grammar can construct with the given input, translates them to a cost using the algebra/comparator you provided and applies the soft minimum to return the score. This all gets efficient by dynamic programming. Note that there is runtime overhead when using this method in comparison with the hardcoded algorithms. But for complicated grammars this is a much easier way to go. For more information on the theory, please refer to my master's thesis: "Adaptive Affine Sequence Alignment using Algebraic Dynamic Programming"