JarCastingJava библиотеки
Последняя версия: 0.4.0
Дата:Последняя версия: 0.4.0
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Последняя версия: 1.3.0
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Последняя версия: 1.3.0
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Geomajas Geometry core
org.geomajas.project : geomajas-project-geometry-coreGeomajas Geometry Core: Defines a DTO geometry model that is GWT compatible.
Последняя версия: 1.3.0
Дата:
Geomajas Geometry Project
org.geomajas.project : geomajas-project-geometry-allGeomajas Geometry project. The geometry definition in here is meant as a Data Transfer Object, so be used in combination with a service. The service is based upon the feature specification for SQL. Geomajas is a component framework for building rich Internet applications (RIA) with sophisticated capabilities for the display, analysis and management of geographic information. It is a building block that allows developers to add maps and other geographic data capabilities to their web applications.
Последняя версия: 1.3.0
Дата:denormalize
nz.ac.waikato.cms.weka : denormalizeAn 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.
Последняя версия: 1.0.3
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Последняя версия: 1.0.0-M2
Дата:
Geomajas Expert SLD Editor GWT example
org.geomajas.project : geomajas-project-sld-editor-expert-gwt-exampleExpert SLD Editor GWT example application
Последняя версия: 1.0.0-M2
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Geomajas Expert SLD Editor GWT example code
org.geomajas.project : geomajas-project-sld-editor-expert-gwt-example-jarExpert SLD Editor GWT example application code
Последняя версия: 1.0.0-M2
Дата:
Geomajas Expert SLD GWT client library
org.geomajas.project : geomajas-project-sld-editor-expert-gwtGeomajas expert SLD editor (GWT)
Последняя версия: 1.0.0-M2
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Geomajas Expert SLD Editor common
org.geomajas.project : geomajas-project-sld-editor-expert-commonGeomajas expert SLD editor (Common)
Последняя версия: 1.0.0-M2
Дата:
Geomajas SLD editor project
org.geomajas.project : geomajas-project-sld-editor-allGeomajas is a component framework for building rich Internet applications (RIA) with sophisticated capabilities for the display, analysis and management of geographic information. It is a building block that allows developers to add maps and other geographic data capabilities to their web applications. The SLD project is a standalone project that implements a communication, editing and persistence library for SLD (Styled Layer Descriptor) styles.
Последняя версия: 1.0.0-M2
Дата:Последняя версия: 1.0.6
Дата:jersey-test-framework-provider-default-client
org.glassfish.jersey.test-framework.providers : jersey-test-framework-provider-default-clientJersey Test Framework Default Client Provider
Последняя версия: 2.8
Дата:dagging
nz.ac.waikato.cms.weka : daggingThis meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.
Последняя версия: 1.0.3
Дата:costSensitiveAttributeSelection
nz.ac.waikato.cms.weka : costSensitiveAttributeSelectionThis package provides two meta attribute selection evaluators - one for performing cost-sensitive attribute evaluation (CostSensitiveAttributeEval) and a second for performing cost-sensitive subset evaluation (CostSensitiveSubsetEval). Both methods take a cost matrix and a base evaluator. If the base evaluator can handle instance weights, then the training data is weighted according to the cost matrix, otherwise the training data is sampled according to the cost matrix.
Последняя версия: 1.0.3
Дата:conjunctiveRule
nz.ac.waikato.cms.weka : conjunctiveRuleThis 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.
Последняя версия: 1.0.4
Дата:Sonar Useless Code Tracker Plugin
org.codehaus.sonar-plugins : sonar-useless-code-tracker-pluginReports on source code that can be reduced.
Последняя версия: 1.0
Дата:complementNaiveBayes
nz.ac.waikato.cms.weka : complementNaiveBayesClass for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.
Последняя версия: 1.0.3
Дата:stateless-future-akka
com.qifun : stateless-future-akka_2.11The ultimate control flow solution for Akka.
Последняя версия: 0.1.1
Дата:stateless-future-akka
com.qifun : stateless-future-akka_2.10The ultimate control flow solution for Akka.
Последняя версия: 0.1.1
Дата:Calrissian Accumulo Recipes Metrics Store
org.calrissian.accumulorecipes : metrics-storeThe metrics store rolls up counts and statistics for various units of time for different components.
Последняя версия: 0.1
Дата:Последняя версия: 0.0.1
Дата:Последняя версия: 0.0.1
Дата:Последняя версия: 1.1.0
Дата:
Последняя версия: 0.15.0
Дата:enml4j
com.syncthemall : enml4jSimple utility classes to handle ENML (Evernote Markup Language) in Java
Последняя версия: 1.1.0
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Последняя версия: 0.0.1-rc1
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periodic-jobs-controlpanel
nz.ac.auckland.lmz : periodic-jobs-controlpanelDisplays information about current jobs
Последняя версия: 3.3
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Последняя версия: 3.2
Дата:
CloudBees Clickstack Java Agent
com.cloudbees.clickstack : cloudbees-clickstack-javaagentThe CloudBees OSS Parent Project
Последняя версия: 1.2.1
Дата:AutoFixtureGenerator
com.github.autofixture : AutoFixtureGeneratorAn attempt to reimplement core features of a popular .NET anonymous value generator - AutoFixture - in Java
Последняя версия: 0.6.2
Дата:streamql
org.deephacks.streamql : streamqlPredicate and ordering for java.util.stream.Stream using a minimal query language.
Последняя версия: 0.5.8
Дата:Последняя версия: 0.0.6
Дата:
simplespec for Scala 2.11.0
com.simple : simplespec_2.11.0simplespec is a thin Scala wrapper over JUnit, featuring Hamcrest matchers, easy mocks, and literate test descriptions.
Последняя версия: 0.8.4
Дата:Mail Blog
org.vesalainen.mailblog : mailblogGoogle Appengine application for blog site. Blog authoring is done by sending email. The same appengine site can run several blog-sites using different namespaces.
Последняя версия: 1.0.1
Дата:Последняя версия: 1.0.0
Дата:Последняя версия: 0.0.1
Дата:Последняя версия: 1.1.0
Дата:Последняя версия: 4.0.4
Дата:
OPS4J Pax Wicket :: Karaf Feature File
org.ops4j.pax.wicket : pax-wicket-featuresMaven parent POM for all OPS4J Maven projects.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: Karaf Feature File
org.ops4j.pax.wicket.samples : pax-wicket-samples-featuresMaven parent POM for all OPS4J Maven projects.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: View :: Franchisee
org.ops4j.pax.wicket.samples.view : pax-wicket-samples-view-franchiseePax Department Store Franchisee View.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: View :: Floor
org.ops4j.pax.wicket.samples.view : pax-wicket-samples-view-floorPax Department Store Floor View.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: View :: Application
org.ops4j.pax.wicket.samples.view : pax-wicket-samples-view-applicationPax Department Store View Application.
Последняя версия: 0.7.15
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OPS4J Pax Wicket :: Samples :: View :: About
org.ops4j.pax.wicket.samples.view : pax-wicket-samples-view-aboutPax Department Store About Page.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: View :: Parent
org.ops4j.pax.wicket.samples.view : pax-wicket-samples-view-parentMaven parent POM for all OPS4J Maven projects.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: Service :: Alternative
org.ops4j.pax.wicket.samples.service : pax-wicket-samples-service-alternativePax Wicket Samples Departmentstore - Alternative implementation of the business logic.
Последняя версия: 0.7.15
Дата:
OPS4J Pax Wicket :: Samples :: Service :: Basic
org.ops4j.pax.wicket.samples.service : pax-wicket-samples-service-basicPax Wicket Samples Departmentstore - Basic implementation of the business logic.
Последняя версия: 0.7.15
Дата: