Download JAR files tagged by attribute with all dependencies

Search methods contributed by Adrian Pino (ScatterSearchV1, TabuSearch). ScatterSearch: Performs an Scatter Search through the space of attribute subsets. Start with a population of many significants and diverses subset stops when the result is higher than a given treshold or there's not more improvement. For more information see: Felix Garcia Lopez (2004). Solving feature subset selection problem by a Parallel Scatter Search. Elsevier. Tabu Search: Abdel-Rahman Hedar, Jue Wangy, Masao Fukushima (2006). Tabu Search for Attribute Reduction in Rough Set Theory.

Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.

Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

This subproject is an extension of the Struts tag library. Each JSP custom tag in this library is a subclass of an associated tag in the Struts tag library. One difference is that this tag library does not use "rtexprvalues", it uses the expression evaluation engine in the Jakarta Taglibs implementation of the JSP Standard Tag Library (version 1.0) to evaluate attribute values. In addition, some of the Struts tags were not ported to this library, as it was determined that their functionality was entirely supplied by the JSTL. These particular Struts tags, and the reason for their non-porting will be described in the documentation for this library. In order to fully understand the correct utilization of this library, you must understand the use and operation of the Struts tag library, and the use and operation of the JavaServer Pages Standard Tag Library (hereafter called the "JSTL"), along with the expression language (sometimes called the "EL") used for evaluating attribute values.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

EasyConfig provides simple way to overview and apply settings to file or folder based collections of files. Synonyms to "setting" are property, attribute, value while throughout application "setting" is used. The settings are groupped in "configuration" that is collection of settings from various sources. Main design concepts are: * minimalistic way to describe configuration * pluggable support for data types (validation), setting sources, source handlers Sample use case: An application is deployed in multiple locations. We need to quickly check key settings/parameters and optionally modify some of them. These values are located in different places: - in files directly in file structure - in files inside archive files (optionally nested archives) - values in DB tables - values accessible via URLs - other sources (just guessed: SSH/telnet connection+some command(s), UPnP devices, proprietary protocols, etc) We gather info from any supported (extendable) source and can modify and apply changes if supported by source (e.g. we can't update value that is count of rows in DB table, but we can read that value).

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.

# 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</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() ```

# 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(); ``` ##### Kotlin Lambda DSL ```kotlin newJsonArray { newArray { stringValue("a1") stringValue("a2") } newArray { numberValue(1) numberValue(2) } newArray { newObject { stringValue("foo", "Foo") } } } ```

# 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</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() ```