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消息队列快速使用，可自动从配置中心获取消息队列服务器集群的连接信息，提供工具类发送消息，提供注解方式消费消息，带来更舒适的编程体验， 提供消息发送和消费时的拦截器机制，基于base集成全链路信息（包括tid）跨线程跨线程池跨进程传递的功能 Message queue use quickly, can automatically obtain the connection information of the message queue server cluster from the configuration center, provide tools to send messages, provide annotations to consume messages, and bring more comfortable programming experience. It provides the interceptor mechanism for message sending and consumption, and integrates the function of full-link information (including tid) across threads, across thread pools and across processes based on base

eDelfoi is a research program based on Delphi expert method. It is developed in co-operation with Future Research Center of Turku School of Economics. Delphi technique is used for bringing values, new viewpoints and ideas as a foundation for planning and decision making, i.e. making qualitative research. The program can also be used for making a simple, Survey-type of query. The newest version of the program is called eDelfoi.

Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.

rpc快速使用，可自动从配置中心获取注册中心、线程池、超时时间等配置， 在rpc调用异常时将异常封装成标准响应对象返回给调用方，并自定义rpc telnet指令供开发测试使用， 业务可使用提供注解进行rpc服务的消费和提供，可实现消费和提供者服务批量管理，提供泛化调用工具 RPC use quickly, and the underlying implementation is currently dubbo, which can automatically obtain configurations such as registration center, thread pool, and timeout time from the configuration center. When dubbo calls an exception, it will encapsulate the exception into a standard response object and return it to the caller, and customize the dubbo telnet command. For development and testing, businesses can use annotations to consume and provide RPC services, realize batch management of consumption and provider services, and provide generalized calling tools

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.

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.