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Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

A NSGA-II implementation using Java. This implementation of NSGA-II algorithm is in pure reference to the original published paper. This is not an effort to convert the originally implemented C code in Java. The original C code by the authors has not be referred to while writing this implementation. This is a fully customizable implementation of the NSGA-II algorithm, made as generic as possible. This documentation assumes you have basic understanding of the NSGA-II algorithm. Apart from the core concepts of the algorithm, everything else in this package can be implemented as per the user's choice and plugged into the algorithm dynamically. Since NSGA-II is more like a set of protocols to follow as an algorithm rather than a concrete implementation of every aspect, this package has been re-written from scratch keeping complete customizability in mind. Apart from the core concepts of the algorithm, everything is considered to be a plugin external to the algorithm that can be implemented by the user and dynamically plugged into the algorithm during runtime as needed. This opens up the possibility of the package to be used simply as a PoC or be converted into something much more complex according to the users needs.

Xypron Statistics is a Java library which was developped with supply chain simulation in mind. The normal, the exponential and the gamma distribution have been included. Methods to calculate fill rate and order rate service levels as well as safety factors are provided. The Mersenne Twister algorithm is used to provide high quality random number generation. Some functions for the gamma distribution where adopted from http://www.ssfnet.org/download/ssfnet_raceway-2.0.tar.gz. For these the following applies: Copyright 1999 CERN - European Organization for Nuclear Research. Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.

Sirix is a versioned, treebased storage system. It provides an ID-less diff-algorithm to import differences between two versions. Furthermore an ID-based diff-algorithm facilitates the comparison of versions stored within Sirix. A GUI with several visualizations for comparing these versions visually is available to aid an analyst. Versions are stored using well known versioning strategies (full, incremental, differential). The architecture is especially well suited for flash-disks because of a COW-principle. In the future we aim to provide throughout security as well as a replaced page-structure to speedup our architecture. A brackit(.org) binding will enable XQuery and the XQuery Update Facility. Temporal XPath axis and possibly diff-functions will help analysts to gain quick knowledge from the stored data.

Sirix is a versioned, treebased storage system. It provides an ID-less diff-algorithm to import differences between two versions (currently XML-documents). Furthermore an ID-based diff-algorithm facilitates the comparison of versions stored within Sirix. A GUI with several visualizations for comparing these versions visually is available to aid an analyst. Versions are stored using well known versioning strategies (full, incremental, differential). The architecture is especially well suited for flash-disks because of a COW-principle. In the future we aim to provide throughout security as well as a replaced page-structure to speedup our architecture. A brackit(.org) binding will enable XQuery and the XQuery Update Facility. Temporal XPath axis and possibly diff-functions will help analysts to gain quick knowledge from the stored data.

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.

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.

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.

Jadex Rules is a small lightweight rule engine, which currently employs the well-known Rete algorithm for highly efficient rule matching. Jadex rules is therefore similar to other rule engines like JESS and Drools. Despite the similarities there are also important differences between these systems: * Jadex Rules is very small and intended to be used as component of other software. Even though rules can be specified in a Java dialect as well as (a small variation of) the CLIPS language its primary usage is on the API level. Jadex Rules is currently the core component of the Jadex BDI reasoning engine. * Jadex Rules cleanly separates between state and rule representation. This allows the state implementation as well as the matcher to be flexibly exchanged. Some experiments have e.g. been conducted with a Jena representation. Regarding the matcher, it is planned to support also the Treat algorithm, which has a lower memory footprint than Rete. * Jadex Rules pays close attention to rule debugging. The state as well as the rete engine can be observed at runtime. The rule debugger provides functionalities to execute a rule program stepwise and also use rule breakpoints to stop the execution at those points.