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Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

This project is a resource for the JCoRe Linnaeus Annotator. The dictionaries contained herein are used for the recognition of concrete species names in text, e.g. "human", "mouse", "n. furzeri", "c. elegans" etc as well as species hints, i.e. indirect clues to a species like the word "patient" which most likely refers to a human. Additionally to such rather clear proxies, this project also includes a small dictionary containing maximum-frequency-proxies for genus expressions like "Drosophila" which will be mapped to "D. melanogaster". For the task of only finding concrete species names in text, there is the project jcore-linnaeus-species-ae-species-dict.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

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.

Stopwatch is a free, simple, highly extensible, Java API that allows developers to easily monitor whole application or any part of it. By default Stopwatch generate reports about hits, execution times (total, average, minimum, maximum) as well as load but it can be easily extended to measure anything else by providing custom engine. Out of the box Stopwatch uses an in-memory HSQL database. It is able to persist collected data using a "storage". There is "storage" provided to persist into HSQL database and custom "storage" can be easily integrated.

Axis2 is an effort to re-design and totally re-implement both Axis/Java and (eventually) Axis/C++ on a new architecture. Evolving from the now standard "handler chain" model which Axis1 pioneered, Axis2 is developing a more flexible pipeline architecture which can yet be managed and packaged in a more organized manner. This new design acknowledges the maturing of the Web services space in terms of new protocols such as WS-ReliableMessaging, WS-Security and WS-Addressing that are built on top of the base SOAP system. At the time Axis1 was designed, while it was fully expected that other protocols such as WS-ReliableMessaging would be built on top of it, there was not a proper extension architecture defined to enable clean composition of such layers. Thus, one of the key motivations for Axis2 is to provide a clean and simple environment for like Apache Sandesha and Apache WSS4J to layer on top of the base SOAP system. Another driving force for Axis2 as well as the move away from RPC oriented Web services towards more document-oriented, message style asynchronous service interactions. The Axis2 project is centered on a new representation for SOAP messages called AXIOM (AXIs Object Model). AXIOM consists of two parts: a complete XML Infoset representation and a SOAP Infoset representation on top of that. The XML Infoset representation provides a JDOM-like simple API but is built on a deferred model via a StAX-based (Streaming API for XML) pull parsing API. A key feature of AXIOM is that it allows one to stop building the XML tree and just access the pull stream directly; thus enabling both maximum flexibility and maximum performance. This approach allows us to support multiple levels of abstraction for consuming and offering Web services: using plain AXIOM, using generated code and statically data-bound data types and so on. At the time of Axis1's design, RPC-style, synchronous, request-response interactions were the order of the day for Web services. Today service interactions are much more message -oriented and exploit many different message exchange patterns. The Axis2 engine architecture is careful to not build in any assumptions of request-response patterns to ensure that it can be used easily to support arbitrary message exchange patterns.

Pact consumer ============= Pact Consumer is used by projects that are consumers of an API. Most projects will want to use pact-consumer via one of the test framework specific projects. If your favourite framework is not implemented, this module should give you all the hooks you need. Provides a DSL for use with Java to build consumer pacts. ## Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer_2.11` ## DSL Usage Example in a JUnit test: ```java import au.com.dius.pact.model.MockProviderConfig; import au.com.dius.pact.model.PactFragment; import org.junit.Test; import java.io.IOException; import java.util.HashMap; import java.util.Map; import static org.junit.Assert.assertEquals; public class PactTest { @Test public void testPact() { PactFragment pactFragment = ConsumerPactBuilder .consumer("Some Consumer") .hasPactWith("Some Provider") .uponReceiving("a request to say Hello") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .toFragment(); MockProviderConfig config = MockProviderConfig.createDefault(); VerificationResult result = pactFragment.runConsumer(config, new TestRun() { @Override public void run(MockProviderConfig config) { Map expectedResponse = new HashMap(); expectedResponse.put("hello", "harry"); try { assertEquals(new ProviderClient(config.url()).hello("{\"name\": \"harry\"}"), expectedResponse); } catch (IOException e) {} } }); if (result instanceof PactError) { throw new RuntimeException(((PactError)result).error()); } assertEquals(ConsumerPactTest.PACT_VERIFIED, result); } } ``` The DSL has the following pattern: ```java .consumer("Some Consumer") .hasPactWith("Some Provider") .given("a certain state on the provider") .uponReceiving("a request for something") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .uponReceiving("another request for something") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") . . . .toFragment() ``` You can define as many interactions as required. Each interaction starts with `uponReceiving` followed by `willRespondWith`. The test state setup with `given` is a mechanism to describe what the state of the provider should be in before the provider is verified. It is only recorded in the consumer tests and used by the provider verification tasks. ### Building JSON bodies with PactDslJsonBody DSL The body method of the ConsumerPactBuilder can accept a PactDslJsonBody, which can construct a JSON body as well as define regex and type matchers. For example: ```java PactDslJsonBody body = new PactDslJsonBody() .stringType("name") .booleanType("happy") .hexValue("hexCode") .id() .ipAddress("localAddress") .numberValue("age", 100) .timestamp(); ``` #### DSL Matching methods The following matching methods are provided with the DSL. In most cases, they take an optional value parameter which will be used to generate example values (i.e. when returning a mock response). If no example value is given, a random one will be generated. | method | description | |--------|-------------| | string, stringValue | Match a string value (using string equality) | | number, numberValue | Match a number value (using Number.equals)\* | | booleanValue | Match a boolean value (using equality) | | stringType | Will match all Strings | | numberType | Will match all numbers\* | | integerType | Will match all numbers that are integers (both ints and longs)\* | | decimalType | Will match all real numbers (floating point and decimal)\* | | booleanType | Will match all boolean values (true and false) | | stringMatcher | Will match strings using the provided regular expression | | timestamp | Will match string containing timestamps. If a timestamp format is not given, will match an ISO timestamp format | | date | Will match string containing dates. If a date format is not given, will match an ISO date format | | time | Will match string containing times. If a time format is not given, will match an ISO time format | | ipAddress | Will match string containing IP4 formatted address. | | id | Will match all numbers by type | | hexValue | Will match all hexadecimal encoded strings | | uuid | Will match strings containing UUIDs | _\* Note:_ JSON only supports double precision floating point values. Depending on the language implementation, they may parsed as integer, floating point or decimal numbers. #### Ensuring all items in a list match an example (2.2.0+) Lots of the time you might not know the number of items that will be in a list, but you want to ensure that the list has a minimum or maximum size and that each item in the list matches a given example. You can do this with the `arrayLike`, `minArrayLike` and `maxArrayLike` functions. | function | description | |----------|-------------| | `eachLike` | Ensure that each item in the list matches the provided example | | `maxArrayLike` | Ensure that each item in the list matches the provided example and the list is no bigger than the provided max | | `minArrayLike` | Ensure that each item in the list matches the provided example and the list is no smaller than the provided min | For example: ```java DslPart body = new PactDslJsonBody() .minArrayLike("users") .id() .stringType("name") .closeObject() .closeArray(); ``` This will ensure that the users list is never empty and that each user has an identifier that is a number and a name that is a string. #### Matching JSON values at the root (Version 3.2.2/2.4.3+) For cases where you are expecting basic JSON values (strings, numbers, booleans and null) at the root level of the body and need to use matchers, you can use the `PactDslJsonRootValue` class. It has all the DSL matching methods for basic values that you can use. For example: ```java .consumer("Some Consumer") .hasPactWith("Some Provider") .uponReceiving("a request for a basic JSON value") .path("/hello") .willRespondWith() .status(200) .body(PactDslJsonRootValue.integerType()) ``` #### Root level arrays that match all items (version 2.2.11+) If the root of the body is an array, you can create PactDslJsonArray classes with the following methods: | function | description | |----------|-------------| | `arrayEachLike` | Ensure that each item in the list matches the provided example | | `arrayMinLike` | Ensure that each item in the list matches the provided example and the list is no bigger than the provided max | | `arrayMaxLike` | Ensure that each item in the list matches the provided example and the list is no smaller than the provided min | For example: ```java PactDslJsonArray.arrayEachLike() .date("clearedDate", "mm/dd/yyyy", date) .stringType("status", "STATUS") .decimalType("amount", 100.0) .closeObject() ``` This will then match a body like: ```json [ { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 }, { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 }, { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 } ] ``` #### Matching arrays of arrays (version 3.2.12/2.4.14+) For the case where you have arrays of arrays (GeoJSON is an example), the following methods have been provided: | function | description | |----------|-------------| | `eachArrayLike` | Ensure that each item in the array is an array that matches the provided example | | `eachArrayWithMaxLike` | Ensure that each item in the array is an array that matches the provided example and the array is no bigger than the provided max | | `eachArrayWithMinLike` | Ensure that each item in the array is an array that matches the provided example and the array is no smaller than the provided min | For example (with GeoJSON structure): ```java new PactDslJsonBody() .stringType("type","FeatureCollection") .eachLike("features") .stringType("type","Feature") .object("geometry") .stringType("type","Point") .eachArrayLike("coordinates") // coordinates is an array of arrays .decimalType(-7.55717) .decimalType(49.766896) .closeArray() .closeArray() .closeObject() .object("properties") .stringType("prop0","value0") .closeObject() .closeObject() .closeArray() ``` This generated the following JSON: ```json { "features": [ { "geometry": { "coordinates": [[-7.55717, 49.766896]], "type": "Point" }, "type": "Feature", "properties": { "prop0": "value0" } } ], "type": "FeatureCollection" } ``` and will be able to match all coordinates regardless of the number of coordinates. #### Matching any key in a map (3.3.1/2.5.0+) The DSL has been extended for cases where the keys in a map are IDs. For an example of this, see [#313](https://github.com/DiUS/pact-jvm/issues/131). In this case you can use the `eachKeyLike` method, which takes an example key as a parameter. For example: ```java DslPart body = new PactDslJsonBody() .object("one") .eachKeyLike("001", PactDslJsonRootValue.id(12345L)) // key like an id mapped to a matcher .closeObject() .object("two") .eachKeyLike("001-A") // key like an id where the value is matched by the following example .stringType("description", "Some Description") .closeObject() .closeObject() .object("three") .eachKeyMappedToAnArrayLike("001") // key like an id mapped to an array where each item is matched by the following example .id("someId", 23456L) .closeObject() .closeArray() .closeObject(); ``` For an example, have a look at [WildcardKeysTest](src/test/java/au/com/dius/pact/consumer/WildcardKeysTest.java). **NOTE:** The `eachKeyLike` method adds a `*` to the matching path, so the matching definition will be applied to all keys of the map if there is not a more specific matcher defined for a particular key. Having more than one `eachKeyLike` condition applied to a map will result in only one being applied when the pact is verified (probably the last). ### Matching on paths (version 2.1.5+) You can use regular expressions to match incoming requests. The DSL has a `matchPath` method for this. You can provide a real path as a second value to use when generating requests, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .matchPath("/transaction/[0-9]+") // or .matchPath("/transaction/[0-9]+", "/transaction/1234567890") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") ``` ### Matching on headers (version 2.2.2+) You can use regular expressions to match request and response headers. The DSL has a `matchHeader` method for this. You can provide an example header value to use when generating requests and responses, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .path("/hello") .method("POST") .matchHeader("testreqheader", "test.*value") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .matchHeader("Location", ".*/hello/[0-9]+", "/hello/1234") ``` ### Matching on query parameters (version 3.3.7+) You can use regular expressions to match request query parameters. The DSL has a `matchQuery` method for this. You can provide an example value to use when generating requests, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .path("/hello") .method("POST") .matchQuery("a", "\\d+", "100") .matchQuery("b", "[A-Z]", "X") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") ```