Download JAR files tagged by integers with all dependencies

It is a library to compress and uncompress arrays of integers very fast. The assumption is that most (but not all) values in your array use less than 32 bits.

A Modifiable Variable concept allows for easy runtime modifications of basic data types like integers, booleans, or byte arrays

Automaton is a Free and Open-Source Library dedicated to create and manipulate determinist and non-deterministic finite automaton. Alphabet are solely composed of integers.

Aries is a service for generating unique IDs at high scale with some simple guarantees. These IDs are unique 64-bit unsigned integers, which are based on time, instead of being sequential. The full ID is composed of a timestamp, a project number, a node number, and a sequence number.

Snowflake is a service for generating unique IDs at high scale with some simple guarantees. These IDs are unique 64-bit unsigned integers, which are based on time, instead of being sequential. The full ID is composed of a timestamp, a project number, a node number, and a sequence number.

AuthzForce PDP extensions for JSON value processing, in particular JSONPath evaluation; it provides the XACML datatype 'urn:ow2:authzforce:feature:pdp:datatype:json' for JSON object/array values, and XACML functions that evaluate a JSON path (second parameter of standard string datatype) against an input JSON object/array (first parameter of datatype 'urn:ow2:authzforce:feature:pdp:datatype:json') and return the result of this evaluation. The return datatype depends on the actual function used: 'urn:ow2:authzforce:feature:pdp:function:string-from-json-path' returns a bag of strings, 'urn:ow2:authzforce:feature:pdp:function:integer-from-json-path' returns a bag of integers, 'urn:ow2:authzforce:feature:pdp:function:double-from-json-path' returns a bag of doubles, 'urn:ow2:authzforce:feature:pdp:function:boolean-from-json-path' returns a bag of booleans.

fastutil extends the Java Collections Framework by providing type-specific maps, sets, lists, and queues with a small memory footprint and fast operations; it provides also big (64-bit) arrays, sets, and lists, sorting algorithms, fast, practical I/O classes for binary and text files, and facilities for memory mapping large files. This jar (fastutil-core.jar) contains data structures based on integers, longs, doubles, and objects, only; fastutil.jar contains all classes. If you have both jars in your dependencies, this jar should be excluded.

fastutil extends the Java Collections Framework by providing type-specific maps, sets, lists, and queues with a small memory footprint and fast operations; it provides also big (64-bit) arrays, sets, and lists, sorting algorithms, fast, practical I/O classes for binary and text files, and facilities for memory mapping large files. This jar (fastutil-core.jar) contains data structures based on integers, longs, doubles, and objects, only; fastutil.jar contains all classes. If you have both jars in your dependencies, this jar should be excluded.

"Tests with Parameters" allows you to simply add parameters to your JUnit test methods. TwiP calls such methods with all possible combinations of their parameters... or at least some reasonable subset of commonly failing values in the case of Integers, etc. You can further reduce these values with an assume expression in an annotation, e.g. ">= 0". Alternatively you can specify a static method or field to provide the values for your test method(s), if you want to test with other than the default values. By using TwiP you change the semantics of your tests from existence to for-all quantifiers, i.e. you specify "all ravens are black" instead of "Abraxas is black", "Toni is black", etc. This moves your tests closer to an executable specification, so TwiP is a very nice addition to BDD.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.