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POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates. It parses dates from most languages and formats out of the box without having to specify which format is expected. Defaults such as time zones, and whether to interpret an internationally ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone and locale and stored in a default config object that can be replaced or overridden. Multiple languages for month names are supported without any additional configuration needed. The net effect the default parser for a server in Paris would have a different automatic configuration from a server in New York. Throw a random local date at either, and it'll parse it as expected. If your server supports customers from multiple locales and time zones, then each can be specified when parsing a date/time to resolve any ambiguities.

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.

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph

Show color codes preview per line in a sidebar area of an editor. <h2>Disable / Enable</h2> Check/Uncheck View > Show Colors <h2>Supported color patterns</h2> <ul> <li>Hex color code (e.g. #ffffff, #000)</li> <li>Css rgb/rgba values (e.g. rgb(0,0,0), rgba(255, 255, 255, 0.8))</li> <li>Css hsl/hsla values (e.g. hsl(0, 100%, 50%), hsla(120, 100%, 50%, 0.5))</li> <li>Named colors (e.g. red, blue)</li> <li>Java Color class (e.g. Color.black, new Color(100, 100, 100))</li> </ul> <h2>Multiple colors</h2> <ul> <li>Show top two colors in a sidebar if there are multiple colors in a line.</li> <li>If you want to check all colors, please click a specific rectangle. They will be shown as a list.</li> </ul> <h2>Change a color using the color chooser</h2> <ul> <li>Click a colored rectangle</li> <li>Click a color value of a list</li> <li>Select a new color in the color chooser</li> <li>An old color value will be changed to new one with the same format</li> </ul> <h2>Generate color codes</h2><p>You can generate color codes via a code generator(<kbd>Alt</kbd> + <kbd>Ins</kbd>).</p> <ol> <li>Run a code generator(Alt + Ins)</li> <li>Choose <code>Color...</code></li> <li>Choose format you expect (e.g. <code>new Color(r, g, b)</code>)</li> <li>Choose a color</li> <li>Click the OK button</li> <li>A color code is generated at the caret position</li> </ol> <h2>Options</h2> Tools > Options > Miscellaneous > Color Codes Preview <h3>Regex for enabled mime-types for Hex and CSS colors</h3> Default value is `^text/(x-)?(css|less|sass|scss)$`. If you would like to disable/enable some mime-types, please change the default regex. This pattern is used when the plugin checks a mime-type. <h3>Named Colors</h3> This option is `false` by default. If you would like to show named colors, please check it. <h2>NOTE</h2> <ul> <li>If you would like to show colors of `Color.decode(<hex>)` e.g. `Color.decode(#000000)`, Please add `java` to "Regex for enabled mime-types" of Hex and CSS e.g. (`^text/(x-)?(css|less|sass|scss|java)$`)</li> <li>Colors may be shown if they are not color codes. e.g. "#feature" contains #fea. This plugin recognizes it as a hex color code.</li> <li>If you use the GTK Look and Feel, you cannot change an alpha value in the color chooser.</li> <li>Hsl or hsla color values may not be changed correctly when you use the color chooser. (There may be 1% errors.)</li> </ul>

pact-jvm-consumer-specs2 ======================== ## Specs2 Bindings for the pact-jvm library ## Dependency In the root folder of your project in build.sbt add the line: ```scala libraryDependencies += &quot;au.com.dius&quot; %% &quot;pact-jvm-consumer-specs2&quot; % &quot;3.2.11&quot; ``` or if you are using Gradle: ```groovy dependencies { testCompile &quot;au.com.dius:pact-jvm-consumer-specs2_2.11:3.2.11&quot; } ``` __*Note:*__ `PactSpec` requires spec2 3.x. Also, for spray users there&apos;s an incompatibility between specs2 v3.x and spray. Follow these instructions to resolve that problem: https://groups.google.com/forum/#!msg/spray-user/2T6SBp4OJeI/AJlnJuAKPRsJ ## Usage To author a test, mix `PactSpec` into your spec First we define a service client called `ConsumerService`. In our example this is a simple wrapper for `dispatch`, an HTTP client. The source code can be found in the test folder alongside the `ExamplePactSpec`. Here is a simple example: ``` import au.com.dius.pact.consumer.PactSpec class ExamplePactSpec extends Specification with PactSpec { val consumer = &quot;My Consumer&quot; val provider = &quot;My Provider&quot; override def is = uponReceiving(&quot;a request for foo&quot;) .matching(path = &quot;/foo&quot;) .willRespondWith(body = &quot;{}&quot;) .withConsumerTest { providerConfig =&gt; Await.result(ConsumerService(providerConfig.url).simpleGet(&quot;/foo&quot;), Duration(1000, MILLISECONDS)) must beEqualTo(200, Some(&quot;{}&quot;)) } } ``` This spec will be run along with the rest of your specs2 unit tests and will output your pact json to ``` /target/pacts/&lt;Consumer&gt;_&lt;Provider&gt;.json ``` # Forcing pact files to be overwritten (3.6.5+) By default, when the pact file is written, it will be merged with any existing pact file. To force the file to be overwritten, set the Java system property `pact.writer.overwrite` to `true`.

git-commit-id-plugin is a plugin quite similar to https://fisheye.codehaus.org/browse/mojo/tags/buildnumber-maven-plugin-1.0-beta-4 for example but as buildnumber only supports svn (which is very sad) and cvs (which is even more sad). This plugin makes basic repository information available through maven resources. This can be used to display "what version is this?" or "who has deployed this and when, from which branch?" information at runtime - making it easy to find things like "oh, that isn't deployed yet, I'll test it tomorrow" and making both testers and developers life easier. The data currently exported is like this (that's the end effect from the GitRepositoryState Bean): { "branch" : "testing-maven-git-plugin", "commitTime" : "06.01.1970 @ 16:16:26 CET", "commitId" : "787e39f61f99110e74deed68ab9093088d64b969", "commitUserName" : "Konrad Malawski", "commitUserEmail" : "[email protected]", "commitMessageFull" : "releasing my fun plugin :-) + fixed some typos + cleaned up directory structure + added license etc", "commitMessageShort" : "releasing my fun plugin :-)", "buildTime" : "06.01.1970 @ 16:17:53 CET", "buildUserName" : "Konrad Malawski", "buildUserEmail" : "[email protected]" } Note that the data is exported via maven resource filtering and is really easy to use with spring - which I've explained in detail in this readme https://github.com/ktoso/maven-git-commit-id-plugin

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.

IronPDF Java library offers an extensive compatibility range, making it a go-to solution for a wide array of developers. It fully supports JVM languages like Java, Scala, and Kotlin, making it incredibly versatile. This Java PDF library is also compatible with Java 8 and above, providing optimum performance across multiple platforms. It's been designed with a wide range of users in mind Here's a look at what it supports: JVM Languages: Java, Scala, Kotlin.Platforms: Java 8 and above.Operating Systems: Microsoft Windows, Linux, Docker, Azure, AWS.IDEs: Jetbrains IntelliJ IDEA, Eclipse. You can deploy IronPDF Java across various platforms, including Microsoft Windows, Linux, Docker, Azure, and AWS. It is also fully compatible with popular IDEs like Jetbrains IntelliJ IDEA and Eclipse, facilitating smooth project development and management. Your pom.xml file is essentially the backbone of your project when you're using Maven. It's here where you introduce new dependencies that you wish to include. To make IronPDF Java package a part of your Maven project, you simply need to add the following snippets to your pom.xml: Remember to replace '20xx.xx.xxxx' with the latest version of IronPDF. IronPDF Java simplifies the process of creating PDF files. Convert HTML files, HTML strings, or URLs directly to new PDF documents in a few lines of code. The variety of file formats it handles is vast, as it can even transform images into PDF documents and vice versa. Need to use base 64 encoding, base URLs, or custom file paths? No problem! IronPDF Java has got you coveredFor more detail about installing and using IronPDF Java. When you run your project for the first time post-integration, IronPDF's engine binaries will automatically be downloaded. The engine starts its journey when you call any IronPDF function for the first time and takes a breather when your application is either closed or enters an idle state. It is not an open source java PDF library but here's the best part - IronPDF Java is offering a 30-day free trial. So, why wait? Give it a go and boost your PDF operations today.

pact-jvm-consumer-specs2 ======================== ## Specs2 Bindings for the pact-jvm library ## Dependency In the root folder of your project in build.sbt add the line: ```scala libraryDependencies += &quot;au.com.dius.pact.consumer&quot; %% &quot;specs2&quot; % &quot;4.0.1&quot; ``` or if you are using Gradle: ```groovy dependencies { testCompile &quot;au.com.dius.pact.consumer:specs2_2.13:4.0.1&quot; } ``` __*Note:*__ `PactSpec` requires spec2 3.x. Also, for spray users there&apos;s an incompatibility between specs2 v3.x and spray. Follow these instructions to resolve that problem: https://groups.google.com/forum/#!msg/spray-user/2T6SBp4OJeI/AJlnJuAKPRsJ ## Usage To author a test, mix `PactSpec` into your spec First we define a service client called `ConsumerService`. In our example this is a simple wrapper for `dispatch`, an HTTP client. The source code can be found in the test folder alongside the `ExamplePactSpec`. Here is a simple example: ``` import au.com.dius.pact.consumer.PactSpec class ExamplePactSpec extends Specification with PactSpec { val consumer = &quot;My Consumer&quot; val provider = &quot;My Provider&quot; override def is = uponReceiving(&quot;a request for foo&quot;) .matching(path = &quot;/foo&quot;) .willRespondWith(body = &quot;{}&quot;) .withConsumerTest { providerConfig =&gt; Await.result(ConsumerService(providerConfig.url).simpleGet(&quot;/foo&quot;), Duration(1000, MILLISECONDS)) must beEqualTo(200, Some(&quot;{}&quot;)) } } ``` This spec will be run along with the rest of your specs2 unit tests and will output your pact json to ``` /target/pacts/&lt;Consumer&gt;_&lt;Provider&gt;.json ``` # Forcing pact files to be overwritten (3.6.5+) By default, when the pact file is written, it will be merged with any existing pact file. To force the file to be overwritten, set the Java system property `pact.writer.overwrite` to `true`. # Test Analytics We are tracking anonymous analytics to gather important usage statistics like JVM version and operating system. To disable tracking, set the &apos;pact_do_not_track&apos; system property or environment variable to &apos;true&apos;.