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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 package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

A simple templating system for java codegen. This library includes an object-oriented, fluent source writing api, for a chained, intuitive codegen api. It also includes a template processor which can take java source files formatted with "magic comments", to allow simple mapping of GeneratorClass#generatorMethod() to inject dynamic source content. //@generateWith(GeneratorClass)// -Sets the current generator. You may use as many as you please //generatorMethod(arbitrary-string-data-you-may-want-to-parse)// -invokes the named method on the current injector, with any text (between brackets) sent as a string This allows you to markup default source with hooks where you may want to inject arbitrary code. It is especially useful for generating super-source in gwt compiles; changes to the default class are propagated to overrides.

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 ServiceMix Drools component provides JBI integration to the Drools Rules Engine. It can be used to deploy a rules set that will implement a router or an actual service. A router will mostly act as a transparent proxy between the consumer and the target service provider mad will mostly be implemented by the jbi.route(uri) method below. This method creates a new exchange identical to the one received by the component and will send it to the specified destination. You can also send back a Fault if needed. A router can also be implemented by using directly the JBI Apis (available with the jbi helper) by using the provided client.

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

"Tests with Parameters" allows you to simply add parameters to your JUnit test methods. TwiP calls such methods with all possible combinations of their parameters... or at least some reasonable subset of commonly failing values in the case of Integers, etc. You can further reduce these values with an assume expression in an annotation, e.g. ">= 0". Alternatively you can specify a static method or field to provide the values for your test method(s), if you want to test with other than the default values. By using TwiP you change the semantics of your tests from existence to for-all quantifiers, i.e. you specify "all ravens are black" instead of "Abraxas is black", "Toni is black", etc. This moves your tests closer to an executable specification, so TwiP is a very nice addition to BDD.

MulTEx is a simple, but powerful framework for organizing exceptions and messages in a multi-tier Java software system. It offers the key features: Causal chains/trees as a means to capture low-level error information Redundancy-free stack traces in the case of indirectly caused exceptions Internationalizable message texts and parameters for exceptions Services for reporting an exception with its causal chain/tree onto streams and screens A standard way for writing method bodies with regard to exceptions. MulTEx depends on Java >= 1.4

Avalon Excalibur's resource management code allows you to be notified when a resource has changed. There are two methods of resource management: active and passive. Passive resource management acts as a holder for resources, and after the resource has been modified through it's normal API, notification goes to all listeners. Active resource management does the same, but it also polls the resources periodically to see if the resource was modified through an external method. Active resource management is perfect for monitoring files because they can be modified by external programs, and your program will be notified when the change occurs instead of constantly polling it.