Download JAR files tagged by used with all dependencies

DKPro Lab is a lightweight framework for parameter sweeping experiments. It allows to set up experiments consisting of multiple interdependent tasks in a declarative manner with minimal overhead. Parameters are injected into tasks using via annotated class fields. Data produced by a task for any particular parameter configuration is stored and re-used whenever possible to avoid the needless recalculation of results. Reports can be attached to each task to post-process the experimental results and present them in a convenient manner, e.g. as tables or charts.

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

This plugin provides integration between Spring and Maven. Plugin goals support loading a Spring context XML file as part of the Maven build lifecycle. The XML file can be on the local file system or be accessible via any URL Spring's resource loading mechanism can understand. Spring's "classpath:context.xml" style notation is supported. Annotated Java classes can also be used to load a Spring context. Maven properties are injected into the Spring context (both XML and annotation style) as a java.util.Properties bean named "mavenProperties". Maven properties are also registered as a top level PropertySource so that Spring's placeholder resolution framework automatically considers them. See Project Reports -> Plugin Documentation for details on plugin goals. By default, the profile "maven" is set as an active Spring profile along with any other active Maven profiles.

An low latency, lock free, primitive bounded blocking queue backed by an int[]. This class mimics the interface of {@linkplain java.util.concurrent.BlockingQueue BlockingQueue}, however works with primitive ints rather than objects, so is unable to actually implement the BlockingQueue. This class takes advantage of the Unsafe.putOrderedObject, which allows us to create non-blocking code with guaranteed writes. These writes will not be re-orderd by instruction reordering. Under the covers it uses the faster store-store barrier, rather than the the slower store-load barrier, which is used when doing a volatile write. One of the trade off with this improved performance is we are limited to a single producer, single consumer.

JSON is a light-weight, language independent, data interchange format. See http://www.JSON.org/ The files in this package implement JSON encoders/decoders in Java. It also includes the capability to convert between JSON and XML, HTTP headers, Cookies, and CDL. This is a reference implementation. There is a large number of JSON packages in Java. Perhaps someday the Java community will standardize on one. Until then, choose carefully. The license includes this restriction: "The software shall be used for good, not evil." If your conscience cannot live with that, then choose a different package. The package compiles on Java 1.2 thru Java 1.4.

Jadex XML is an XML data binding framework for Java and also for other representations. The main idea of Jadex XML is that neither the XML-Schema on the one side nor the Java classes on the other side should define the binding. Instead, a separate mapping between both is used as a mediation. This allows designing the XML representation independent of the Java side but still being able to connect both as desired. This idea was first put forward by the JiBX data binding framework. Jadex XML pushes it further by combining it with the configuration by exception principle. The framework can detect obvious correspondences between both sides automatically and only needs configuration information when translations are necessary. The configuration information is currently specified directly in form of Java configuration classes.

This is an endorsed version of the original Deployment Client for Oracle GlassFish Open Source Edition. The purpose of the endorsement is to provide a shaded JAR which has the OSGI meta data removed because it causes the original Deployment Client to fail when used within a module running within a GlassFish container due to some class loader configuration conflicts between the container and the deployed module. This issue has been reported to the GlassFish project at https://java.net/jira/browse/GLASSFISH-20856 . It is hoped that it can get fixed so that this workaround project can get deprecated soon.