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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.

<p> MMBase Utils is a part of MMBase, but can also be used in other projects. It contains several kind of utility classes.</p> <p>Highlights:</p> <ul> <li>An implementation of 'events'. Threads can use this to communicate certain things. Using mmbase-clustering, these events can also be sent to other servers.</li> <li>A logging framework</li> <li>A framework for string and byte array transformeration, plus a whole lot of implemetentations for that.</li> <li>Several java.util like classes (collections, dateparsing)</li> <li>File type recognition ('magicfile')</li> <li>The MMBase resourceloader, a generic fall-back mechanism for configuration files and similar resources.</li> <li>...</li> </ul>

This is an open source library that allows generating performance indicators easily for java based application. This library is configurable through an xml file (similar to log4j) in order to define the performance object types, dimensions, counters, the output format and statistics generation periodicity. Those statistics can be saved as a file (xml/json) or injected directly in a database. Then they can be correlated and analyzed through any Business Intelligence solution in order to check the application performance and quality: number of transactions, ratio of successful and failure ...

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.