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YET ANOTHER IMPLEMENTATION OF LEVENSHTEIN DISTANCE GenericLevenshtein is an implementation of Minimum Edit Distance, also called Levenshtein Distance, written by Ramon Ziai and Niels Ott. This algorithm is very popular and it is often used to compute the similarity of strings. The difference in the presented implementation is that it can operate on sequences of any Java object implementing equals(Object). So no matter if you want to compare genome sequences or sequences of numbers, or just strings, here you go! Furthermore, the costs of the replace, insert, and delete operations can be customized by implementing the simple WeightCalculator<T> interface. In that case it is not a requirement to rely on equals(Object) as your implementation can do whatever you like it to do in oder to compare objects.

The project provides an implementation of REPOSITORY PATTERN using HIBERNATE for data access. The a goal of this project is to provide an EASY TO USE API that allows to write most of CRUD operations you will need in development of end user applications in ONE LINE OF CODE even for developers who are unfamiliar with Hibernate. We provide a well tested set of CRUD operations which were assembled in more than 4 years of refactoring of projects of our clients. Instead of writing same code over and over again we encourage you to try this API on your own project and see how many lines of code YOU can replace with JUST ONE LINE.

Geronimo integration testsuite. This contains 2 profiles, default and child. The default profile is used by the top level suites directly under this. The child profile is used by the test poms under the suites. This pom defines the basic dependencies needed by most suites. The start-selenium, start-server, invoke, and stop-server goals are globally configured here in the pluginManagement sections. They just need to be bound in the suites where needed and appropriate. The test poms under the suites should have this pom as their parent and set their relativePath appropriately The test poms under the suites inherit an empty 'child' profile from here. But any other build executions within the test poms should all be inside a 'child' profile.

How many classes does it take to make a cache? 60? 80? You might think so if you look at the other caches on the market. Ashkay is a streamlined, simple cache that in many ways is more powerful than the other mainstream caches. Ashkay is a strategy based caching tool. In most cases, you might be using a configuration file to tell the cache what rules it should obey. Or, you might just be setting properties on the cache. I have never liked this model as it limits me, the user of the cache to strategies the developer decided were important. Instead, Ashkay lets you choose the strategy(ies) to use when caching certain objects. Of course, a few of the most handy strategies are pre-packaged, but implementing a new one is a simple as can be. Ashkay is a fork of <a href="http://xot.sf.net">xot</a> caching code. Made more sense on its own.

MyDtt-Plus is a starter of spring-boot, It is an object-oriented Java framework that helps developers increase productivity, "Domain Driven Table" is the concept of DTT, It makes you focus more on domain objects rather than tables. Aims to make it easy to automatically create DB tables based on your Java model with annotation driven. It's also support the ability of create table automatically for MyBatis what Hibernate can do and optionally export SQL to local file，Each table can be added database name concat with table name and fully comments, It can work with ORM frameworks such as MyBatis-Plus and MyBatis with little learning and usage costs. It's worth mentioning that DTT can be MyBatis-Plus is integrated in a 0-code way, just like JPA. However, MyDtt-Plus and MyBatis-Plus may be easier to expand and use than JPA. In addition to supporting the functions of JPA, DDT provides multi-table associated SQL DDL based on MyBatis operation, DTT support databases server for MYSQL, ORACLE, DB2, SQLSERVER, MARIADB, POSTGRESQL and embedded database for H2, HSQL, DERBY.

This module defines the interface for AlignmentAlgorithms as well as some helper classes. An AlignmentAlgorithm computes an Alignment of two given input sequences, given a Comparator that works in these sequences. More details on the AlignmentAlgorithm can be found in the respective interface. More information on Comparators can be found in the comparators module. The resulting 'Alignment' may be just a real-valued dissimilarity between the input sequence or may incorporate additional information, such as a full Alignment, a PathList, a PathMap or a CooptimalModel. If those results support the calculation of a Gradient, they implement the DerivableAlignmentDistance interface. In more detail, the Alignment class represents the result of a backtracing scheme, listing all Operations that have been applied in one co-optimal Alignment. A classic AlignmentAlgorithm does not result in a differentiable dissimilarity, because the minimum function is not differentiable. Therefore, this package also contains utility functions for a soft approximation of the minimum function, namely Softmin. For faster (parallel) computation of many different alignments or gradients we also provide the ParallelProcessingEngine, the SquareParallelProcessingEngine and the ParallelGradientEngine.

--------------------------------------------------------------------------- Version for VIVO 1.8 This uses the source and test code from the Version for VIVO 1.10, edited to make it compatible with VIVO 1.8. It also borrows some code from VIVO 1.10 itself, changes the package name to avoid conflicts, and adds it here. * Vitro 1.8 uses Jena 2, not Jena 3, and Commons Lang 2, not 3, so modify the "import" statements in the Data Distribution code accordingly. * Vitro 1.8 does not have a "...webapp.utils.sparqlrunner" package, so borrow the source from Vitro 1.10, move it to a different package (just for compatibility with the 1.9 version), modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this sparqlrunner package. * Vitro 1.9 has an earlier version of "...webapp.utils.configuration" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this configuration package. Make these modifications, compile, JAR it up, and deploy. --------------------------------------------------------------------------- Because VIVO 1.8 is not available as a Maven package, we have extracted the class files, JARed them up, and stored them in a file-based repository within this project. But that doesn't include any transitive dependencies, so any package that this code requires must be explicitly listed as a dependency. ---------------------------------------------------------------------------

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.

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!

The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page. The library already provides specific strategies for common tasks (for example: news article extraction) and may also be easily extended for individual problem settings. Extracting content is very fast (milliseconds), just needs the input document (no global or site-level information required) and is usually quite accurate. Boilerpipe is a Java library written by Christian Kohlschütter. It is released under the Apache License 2.0. The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al., presented at WSDM 2010 -- The Third ACM International Conference on Web Search and Data Mining New York City, NY USA.