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Skeletal implementation of interface javax.lang.model.util.Types, plus concrete realization backed by core Java Reflection API, akin to JEP 119. This module implements those methods in interface javax.lang.model.util.Types that pertain to the Java type system, plus it provides a method for resolving formal type parameters to actual type arguments. These methods have no equivalent in the Java Reflection API. The abstract skeletal implementation in this module is meant to be specialized for a particular javax.lang.model implementation. The abstraction makes this module well-suited to be used in projects that contain javax.lang.model as part of their domain model (for instance, implementations of alternative languages on top of the JVM).

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

Provides basic support of servlet filter. Boilerplate code etc. It provides a boilerplate implementation of the Filter interface. The GenericFilter provides a getter and setter for the FilterConfig object and ensures that this is set when init is called. The init method also calls the configureFilter method after the FilterConfig object is set and can be used by subclasses to process the filter config during initiation. It also provides an empty implementation of the destory method. The only method that requires implementation is the doFilter method. The GenericHTTPFilter extends the GenericFilter class, it provides a doFilter method that attempts to cast the ServletRequest and ServletResponse objects to HttpServletRequest and HttpServletResponse objects. If these objects can be cast successfuly then it calls the doHTTPFilter method and passes the cast objects references. If the objects cannot be cast a ServletException is thrown. The doHTTPFilter method is abstract and must be implemented by subclasses.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

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 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.