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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.