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Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance

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

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.

XApi is a dependency injection, service-oriented, polyglot java framework. It is comprised of standalone modules for use in web, desktop and mobile java applications. This codebase enables complete reuse of all code across your java stack, as well as the ability to easily define your own Platform, so you can build your own customized runtime environment to extend or replace existing platforms. It is currently geared towards servlets and gwt clients, but the target we are currently developing include: java, gwt [webkit, firefox, ie], javafx, playn [android, ios, flash], appengine & vert.x For Google Web Toolkit, we also include reflection support, more complete emulation of java.lang.Class, zero-overhead dependency injection, a range of code generation utilities, and even "magic-method injection" (for production compiles any method in the app can be swapped out with others based on configuration properties, or any AST mangling / code generating methods you wish to write). There is also a bytecode transformer in the works which will replace calls into the DI utility with direct references to static final factories, to allow the java compiler to completely erase all injection overhead in all java runtimes.

This project aims to build a command line tool that can create HTML view with syntax highlighted source code. It uses Jedit syntax highlighting engine and support all languages that are supported in JEdit. Which are currently: ActionScript, Ada 95, ANTLR, Apache HTTPD, APDL, AppleScript, ASP, Aspect-J, Assembly, AWK, B formal method, Batch, BBj, BCEL, BibTeX, C, C++, C#, CHILL, CIL, COBOL, ColdFusion, CSS, CVS Commit, D, DOxygen, DSSSL, Eiffel, EmbPerl, Erlang, Factor, Fortran, Foxpro, FreeMarker, Fortran, Gettext, Groovy, Haskell, HTML, Icon, IDL, Inform, INI, Inno Setup, Informix 4GL, Interlis, Io, Java, JavaScript, JCL, JHTML, JMK, JSP, Latex, Lilypond, Lisp, LOTOS, Lua, Makefile, Maple, ML, Modula-3, MoinMoin, MQSC, NetRexx, NQC, NSIS2, Objective C, ObjectRexx, Occam, Omnimark, Parrot, Pascal, Patch, Perl, PHP, Pike, PL-SQL, PL/I, Pop11, PostScript, Povray, PowerDynamo, Progress 4GL, Prolog, Properties, PSP, PV-WAVE, Pyrex, Python, REBOL, Redcode, Relax-NG, RelationalView, Rest, Rib, RPM spec, RTF, Ruby, Ruby-HTML, RView, S+, S#, SAS, Scheme, SDL/PL, SGML, Shell Script, SHTML, Smalltalk, SMI MIB, SQR, Squidconf, SVN Commit, Swig, TCL, TeX, Texinfo, TPL, Transact-SQL, UnrealScript, VBScript, Velocity, Verilog, VHDL, XML, XSL, ZPT

# pact-jvm-consumer-java8 Provides a Java8 lambda based DSL for use with Junit to build consumer tests. # A Lambda DSL for Pact This is an extension for the pact DSL provided by [pact-jvm-consumer](../pact-jvm-consumer). The difference between the default pact DSL and this lambda DSL is, as the name suggests, the usage of lambdas. The use of lambdas makes the code much cleaner. ## Why a new DSL implementation? The lambda DSL solves the following two main issues. Both are visible in the following code sample: ```java new PactDslJsonArray() .array() # open an array .stringValue("a1") # choose the method that is valid for arrays .stringValue("a2") # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .numberValue(1) # choose the method that is valid for arrays .numberValue(2) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .object() # now we work with an object .stringValue("foo", "Foo") # choose the method that is valid for objects .closeObject() # close the object and we're back in the array .closeArray() # close the array ``` ### The existing DSL is quite error-prone Methods may only be called in certain states. For example `object()` may only be called when you're currently working on an array whereas `object(name)` is only allowed to be called when working on an object. But both of the methods are available. You'll find out at runtime if you're using the correct method. Finally, the need for opening and closing objects and arrays makes usage cumbersome. The lambda DSL has no ambiguous methods and there's no need to close objects and arrays as all the work on such an object is wrapped in a lamda call. ### The existing DSL is hard to read When formatting your source code with an IDE the code becomes hard to read as there's no indentation possible. Of course, you could do it by hand but we want auto formatting! Auto formatting works great for the new DSL! ```java array.object((o) -> { o.stringValue("foo", "Foo"); # an attribute o.stringValue("bar", "Bar"); # an attribute o.object("tar", (tarObject) -> { # an attribute with a nested object tarObject.stringValue("a", "A"); # attribute of the nested object tarObject.stringValue("b", "B"); # attribute of the nested object }) }); ``` ## Installation ### Maven ``` <dependency> <groupId>au.com.dius</groupId> <artifactId>pact-jvm-consumer-java8_2.12</artifactId> <version>${pact.version}</version> </dependency> ``` ## Usage Start with a static import of `LambdaDsl`. This class contains factory methods for the lambda dsl extension. When you come accross the `body()` method of `PactDslWithProvider` builder start using the new extensions. The call to `LambdaDsl` replaces the call to instance `new PactDslJsonArray()` and `new PactDslJsonBody()` of the pact library. ```java io.pactfoundation.consumer.dsl.LambdaDsl.* ``` ### Response body as json array ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonArray; ... PactDslWithProvider builder = ... builder.given("some state") .uponReceiving("a request") .path("/my-app/my-service") .method("GET") .willRespondWith() .status(200) .body(newJsonArray((a) -> { a.stringValue("a1"); a.stringValue("a2"); }).build()); ``` ### Response body as json object ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonBody; ... PactDslWithProvider builder = ... builder.given("some state") .uponReceiving("a request") .path("/my-app/my-service") .method("GET") .willRespondWith() .status(200) .body(newJsonBody((o) -> { o.stringValue("foo", "Foo"); o.stringValue("bar", "Bar"); }).build()); ``` ### Examples #### Simple Json object When creating simple json structures the difference between the two approaches isn't big. ##### JSON ```json { "bar": "Bar", "foo": "Foo" } ``` ##### Pact DSL ```java new PactDslJsonBody() .stringValue("foo", "Foo") .stringValue("bar", "Bar") ``` ##### Lambda DSL ```java newJsonBody((o) -> { o.stringValue("foo", "Foo"); o.stringValue("bar", "Bar"); }).build(); ``` #### An array of arrays When we come to more complex constructs with arrays and nested objects the beauty of lambdas become visible! ##### JSON ```json [ ["a1", "a2"], [1, 2], [{"foo": "Foo"}] ] ``` ##### Pact DSL ```java new PactDslJsonArray() .array() .stringValue("a1") .stringValue("a2") .closeArray() .array() .numberValue(1) .numberValue(2) .closeArray() .array() .object() .stringValue("foo", "Foo") .closeObject() .closeArray(); ``` ##### Lambda DSL ```java newJsonArray((rootArray) -> { rootArray.array((a) -> a.stringValue("a1").stringValue("a2")); rootArray.array((a) -> a.numberValue(1).numberValue(2)); rootArray.array((a) -> a.object((o) -> o.stringValue("foo", "Foo"))); }).build(); ``` `object` is a reserved word in Kotlin. To allow using the DSL without escaping, a Kotlin extension `newObject` is available: ```kotlin newJsonArray { rootArray -> rootArray.array { a -> a.stringValue("a1").stringValue("a2") } rootArray.array { a -> a.numberValue(1).numberValue(2) } rootArray.array { a -> a.newObject { o -> o.stringValue("foo", "Foo") } } }.build(); ```