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A wrapper for an SLF4J Logger that includes methods allowing logging to a level decided at run, rather than compile, time.

Module that contains dependencies to all modules normally needed for a basic Cocoon webapp. Cocoon blocks should normally depend on this modules, as the exact content of the more fine grained sitemap, pipeline and support modules is not stable yet. This module also contains some classes, configuration files and tests that we not have decided where to move yet.

This tool receives as input a workflow name, and a range of times, to export Conductor Workflows into any format, e.g. csv. Historically, this tool run to only generate CSV, but then I needed other formats, like series for each task, and decided to extend this tool for supporting custom external exporters, which can be added using configuration.

The ASN.1 subproject attempts to isolate the ASN.1 libraries and tools for encoding ASN.1 in BER/DER/CER/PER encodings. The LDAP and X.500 aspects of the directory project impose the need for ASN.1 and BER codecs. Kerberos requires DER. Rather than implement highly specific and britle code for these needs we decided to separate out the APIs and implementations used for dealing with ASN.1 codecs for any ASN.1 defined protocol.

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 plugin gives SQuirreLSQL client the ability to configure alternate LaFs provided by the plugin as well as others that the user can provide. All dependencies listed with a groupId of net.sf.squirrel-sql and artifactId starting with squirrelsql-lf- are externally available open-source look and feels whose project maintainers have decided to not use maven, or those which were built and maintained only before the introduction of maven. Maven's central repository policy requires any pom that is deployed there to reference only artifacts which are also hosted in central. And a project may only use the groupId that is related to that project. So, this only leaves us with one option which is to host these external third-party artifacts using our groupId. The hope is that someday these projects will start using maven and the need for this will no longer be.

How many classes does it take to make a cache? 60? 80? You might think so if you look at the other caches on the market. Ashkay is a streamlined, simple cache that in many ways is more powerful than the other mainstream caches. Ashkay is a strategy based caching tool. In most cases, you might be using a configuration file to tell the cache what rules it should obey. Or, you might just be setting properties on the cache. I have never liked this model as it limits me, the user of the cache to strategies the developer decided were important. Instead, Ashkay lets you choose the strategy(ies) to use when caching certain objects. Of course, a few of the most handy strategies are pre-packaged, but implementing a new one is a simple as can be. Ashkay is a fork of <a href="http://xot.sf.net">xot</a> caching code. Made more sense on its own.

This is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.