Download JAR files tagged by backward with all dependencies

Common components for Asciidoctor Maven Plugin and Asciidoctor Doxia Module(s). These should not depend on Doxia libraries, only shared Maven dependencies used by all and provided as runtime. This is an internal library, backward compatibility is not guaranteed.

This module is dedicated to the analysis of a set of existential rules. It can check if this set belongs to a known decidable class of rules, either directly or by means of its Graph of Rule Dependencies (GRD). This module allows to analyze the properties of the strongly connected components in the GRD, which allows to determine properties of the rule set with respect to decidability as well as the kind of paradigm (forward or backward chaining) ensuring decidability.

A scalable, concurrent, in-memory Key Value (KV) map. Oak implements a concurrent Key-Value map that can keep all keys and values off-heap. This enables working with bigger heap sizes than JVM's managed heap. OakMap implements an API similar to the industry standard Java8 ConcurrentNavigableMap API. It provides strong (atomic) semantics for read, write, and read-modify-write, as well as (non-atomic) range query (scan) operations, both forward and backward. OakMap is optimized for big keys and values, in particular, for incremental maintenance of objects (update in-place). It is faster and scales better with additional CPU cores than the popular Java ConcurrentNavigableMap ConcurrentSkipListMap

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.

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.