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Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

Randoop is an automatic unit test generator for Java. It automatically creates unit tests for your classes, in JUnit format. Randoop generates unit tests using feedback-directed random test generation. In a nutshell, this technique randomly, but smartly, generates sequences of methods and constructor invocations for the classes under test, and uses the sequences to create tests. Randoop executes the sequences it creates, using the results of the execution to create assertions that capture the behavior or your program and that catch bugs. Randoop has created tests that find previously unkwon errors even in widely-used libraries including Sun and IBM's JDKs. A .NET version of Randoop, used internally at Microsoft, has been used successfully by a team of test engineers to find errors in a core .NET component that has been heavily tested for years. Randoop's combination of randomized test generation and test execution results in a highly effective test generation technique.

Module that defines the HttpChannel API. HttpChannels abstract complex download and upload steps into a simple and easy to use NIO Channel. NIO Channels can be wrapped into an InputStream or OutputStream and used in any way you may find possible to. Aside from that, Channels can be used natively in most next-gen libraries, meaning that you don't even need to wrap anything, just start writing or reading data to or from the channel wth a ByteBuffer. Anyone using the library should try to rely on code from this module only and, only if necessary, on configuration classes that are implementation specific. Relying on any other resource or class is considered an error and should NOT be done. One of the most interesting usages of channels for uploads and download is that you can easily copy data straight from one channel to the other, with less than 10 lines of code! Also, channels allows the implementation of a "tee" mechanism, in which data redden from a single channel can be copied to several other channels on the fly!

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

Rouplex-Niossl is a java SPI (service provider interface) for secure (SSL/TLS), selectable, socket channels. Some of the classes in the java.nio.channels package have been extended by secure counterparts that can be used side by side, or replace existing instances of the plain implementations. This package contains just the entry point calls for instantiating such instances, as well as a non-functional, default implementation. For a concrete implementation of these classes you can take a look at Rouplex-Niossl-Spi, which would be included as a separate dependency to your applications. More specifically this library defines SSLSocketChannel class to inherit from SocketChannel, SSLServerSocketChannel to inherit from ServerSocketChannel and SSLSelector to inherit from SSLSelector. One or more instances of SSLSocketChannel can be registered with an (or more) instance of SSLSelector to be selected upon, with the same exact semantics a SocketChannel would expect from registering with a Selector. Further, a mixture of SocketChannels and SSLSocketChannels can be registered simultaneously with an SSLSelector. The secure counterparts abide to the same API and semantics defined for plain channels at https://docs.oracle.com/javase/8/docs/api/java/nio/channels/package-summary.html. This way, the existing products can be easily updated to provide secure communication and new products can achieve security of data in transit by using the already proven and excellent patterns for communication such as nio.

Rouplex-Niossl is a java SPI (service provider interface) for secure (SSL/TLS), selectable, socket channels. Some of the classes in the java.nio.channels package have been extended by secure counterparts that can be used side by side, or replace existing instances of the plain implementations. This package contains just the entry point calls for instantiating such instances, as well as a non-functional, default implementation. For a concrete implementation of these classes you can take a look at Rouplex-Niossl-Spi, which would be included as a separate dependency to your applications. More specifically this library defines SSLSocketChannel class to inherit from SocketChannel, SSLServerSocketChannel to inherit from ServerSocketChannel and SSLSelector to inherit from SSLSelector. One or more instances of SSLSocketChannel can be registered with an (or more) instance of SSLSelector to be selected upon, with the same exact semantics a SocketChannel would expect from registering with a Selector. Further, a mixture of SocketChannels and SSLSocketChannels can be registered simultaneously with an SSLSelector. The secure counterparts abide to the same API and semantics defined for plain channels at https://docs.oracle.com/javase/8/docs/api/java/nio/channels/package-summary.html. This way, the existing products can be easily updated to provide secure communication and new products can achieve security of data in transit by using the already proven and excellent patterns for communication such as nio.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Includes 2 main parts: - Path finding through 2D polygons using the A star algorithm and navigation-mesh generation Field of vision / shadows / line of sight / lighting. The basic polygon and point classes are the KPolygon and KPoint. KPolygon contains a list of KPoints for vertices as well as a center (centroid), area, and radius (circular bound or distance from center to furthest point). KPolygon was born out of the need for a more game-oriented and flexible polygon class than the Path2D class in the standard Java library. KPolygon implements java.awt.geom.Shape so it can be easily drawn and filled by Java2D's Graphics2D object. - This API provides path-finding and field-of-vision. For other complex geometric operations such as buffering (fattening and shrinking) and constructive area geometry (intersections and unions) it is recommended to use the excellent Java Topology Suite (JTS). The standard Java2D library also provides the Area class which can be used for some constructive area geometry operations. Note that there is a utility class PolygonConverter that can quickly convert KPolygons to JTS polygons and vice versa.

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

Jsgen is a Java Source Generation Framework: That means, it should be a valuable tool, if you intend to write a custom generator for Java sources. As such, it is the successor of a previous framework, called JaxMeJS (http://jaxme.sourceforge.net/JaxMeJS/docs/index.html). The predecessor came into being as a standalone project. It was incorporated into the bigger JaxMe project, when the latter was adopted by the Apache Webservices project. And it was buried as part of the bigger project, when the latter was moved to the Apache Attic (http://svn.apache.org/repos/asf/webservices/archive/jaxme/). That was fine for quite some time, because the latest released version (JaxMeJS 0.5.2) did its job quite well. Over the years, however, the Java language has evolved, and the lack of support for features like Generics, or Annotations, became a burden. Hence the Successor: Jsgen picks up, where JaxMeJS ended. It is, however, a complete rewrite with several additional features, that the author considers to be important for modern Java applications: 1. It supports Generics. 2. It supports Annotations. 3. The builder pattern has been adopted. Almost all important classes are implemented as builders. This should make writing the actual source generators much more concise, and maintainable, than it used to be before. 4. The code style is configurable. Code styles allow you to concentrate on the actual work. The resulting Jave source will look nicely formatted, anyways. As of this writing, you can select between two builtin code styles: - The default code style is basically the authors personal free style, roughly comparable to the default code style of the Eclipse Java IDE. - As an alternative, there is also a Maven code style, which is widely used in the Open Source communities. Compared to the default style, it is less concise, if not even a bit verbose. On the other hand, it is widely adopted by projects in the vicinity of {{{https://maven.apache.org}Apache Maven}}. 5. Import lists are created, and sorted, automatically.