Download JAR files tagged by point with all dependencies

The Java Advanced Imaging API extends the Java 2 platform by allowing sophisticated, high-performance image processing to be incorporated into Java applets and applications. It is a set of classes providing imaging functionality beyond that of Java 2D and the Java Foundation classes, though it is designed for compatibility with those APIs. This API implements a set of core image processing capabilities including image tiling, regions of interest, deferred execution and a set of core image processing operators, including many common point, area, and frequency domain operators.

Use the Branch SDK (branch.io) to create and power the links that point back to your apps for all of these things and more. Branch makes it incredibly simple to create powerful deep links that can pass data across app install and open while handling all edge cases (using on desktop vs. mobile vs. already having the app installed, etc). Best of all, it is really simple to start using the links for your own app: only 2 lines of code to register the deep link router and one more line of code to create the links with custom data.

Use the Branch SDK (branch.io) to create and power the links that point back to your apps for all of these things and more. Branch makes it incredibly simple to create powerful deep links that can pass data across app install and open while handling all edge cases (using on desktop vs. mobile vs. already having the app installed, etc). Best of all, it is really simple to start using the links for your own app: only 2 lines of code to register the deep link router and one more line of code to create the links with custom data.

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.

This library is the the backend for the Fitzhì solution. Fitzhì aims to gather all technical data regarding your application portfolio into ONE single portal. Fitzhì provides also analysis and assessments at the project scope, but its main target is the portfolio. The main ambition is to provide a global, and dynamic vision of the technical risk for your IT. The dream functionality for Fitzhì should be the ONE single dashboard, one Single Point Point Of Quality, available for the CTO, or CIO, and ready to resume in one second, one view, the current situation and the historic trend.

This library is the the backend for the Fitzhì solution. Fitzhì aims to gather all technical data regarding your application portfolio into ONE single portal. Fitzhì provides also analysis and assessments at the project scope, but its main target is the portfolio. The main ambition is to provide a global, and dynamic vision of the technical risk for your IT. The dream functionality for Fitzhì should be the ONE single dashboard, one Single Point Point Of Quality, available for the CTO, or CIO, and ready to resume in one second, one view, the current situation and the historic trend.

Suji is a converter library from Japanese number notation to numerical value, and from numerical notation to Japanese Kansuji notation. Converter.values(src) to convert from Japanese number notation to numerical value: Japanese number notation can include Kansuji. The String "１つの価格が二兆30万五千十7円になります。" will be converted to two BigDecimal, 1 and 2000000005017. And also, 打率は三割二部五厘です。 will be a 0.325. The return value is a list of Suji Numeral objects. If the input string has no number notation, Suji returns a empty list. The Numeral object has three methods: value(), begin(), and end(): value(): a BigDecimal instance of a numerical value for the number notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Converter.kansujis(src) to convert from numeric notation to Japanese Kansuji notation: The String 20兆30万五千十7円になります。 will be converted to the Kansuji string, 二十兆三十万五千十七. The boolean flag one is interpreted as whether to display the first character 一 or not. The output of Converter.kansujis('1000万', true) will be converted to 一千万, and the output of Converter.kansujis('1000万', false) will be converted to 千万. Note that kansujis does not support numerical notation after the decimal point. If the input string is 32.01, the output will 三十二, not 三十二割一厘. The return value is a list of Kansuji objects. If the input string has no number notation, Suji returns a empty list. The Kansuji object has three methods: value(), begin(), and end(): value(): a String instance of a Kansuji notation. begin(): the begin index (int) of the found number notation at the input string. end(): the end index (int) of the found number notation. Suji is a one-pass parser. That is, Suji parse a source text from the head to the end only once.

JavaUtil is a utility library to speed the development of Java software. We developed it over multiple years during our internal development efforts, and it has reached a point of stability where we have decided to make it available to the outside world. The JavaUtil package aims to fill in the gaps of the Apache Commons and similar utility libraries out there. Features include convenience classes for string, collections, arrays, dates, iterators, colors, logging, unit testing, a little bit of basic statistics, database queries, caching and more.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

JAXB 2.1.x XJC Boolean-Getter Plugin This plugin causes getter methods for Boolean Objects to be called "getXX" instead of "isXX". Downloaded from http://fisheye5.atlassian.com/browse/~raw,r=1.1/jaxb2-commons/www/boolean-getter/index.html on 18/03/10 Yiannis Paschalidis Sanity4J was created to simplify running multiple static code analysis tools on the Java projects. It provides a single entry point to run all the selected tools and produce a consolidated report, which presents all findings in an easily accessible manner.