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Maven plugin for small application config support. Allows you to define configuration for your application at one place and generate config files and java class. Configuration can be instantiate by var args, file config or environment variables or combination of all. ISSUES: -v potomkovi mam rucne psanou pomocnou property, ta se nesmi dostat do property descriptoru, pak je tam s klicem null -pokud reloadArgs byposition vrati null, vyhodit hned vyjimku - confDef = byOrder.get(iPos++); TODO: - boolean opts -- is/get -- option without value true - make def enum public - static instance to access from everywhere without pass an instance -static enum Def -more executions with different configuration has conflict in options and names

It helps to quickly make settings for class and its fields of with properties XML. Instead of create a lot of files to describe every class and its fields it use describing by type, by name. E.g. setting "java.lang.Integer"-"INTEGER NOT NULL" will be assigned fo every field of this type. Of course it can be overriden by settings for field name and so on. It is used by beige-web to describe how to render an entity and convert from HTML value. It is used by beige-orm to describe how to map an entity to a DBMS. It make simple settings - Map<String, String>, e.g. "title"-"Beige ORM 1.1", class settings Map<String, Map<String, String>>, e.g. "org.mode.Customer"-["defaultOrder"-"ITSNAME", "wdgFilterOrder"-"filterOrderStd"], fields settings Map<String, Map<String, Map<String, String>>>, e.g. "org.mode.Customer"-["itsId"["wdgNew"-"null", "wdgFilter"-"filterNumber"]]

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.

Bond is a small library that can be used to spy values and mock functions during tests. Spying is a replacement for writing the assertEquals in your test, which are tedious to write and even more tedious to update whem your test setup or code inevitably changes. With Bond, you separate what is being verified, e.g., the variable named output, from what value it should have. This way you can quickly spy several variables, even have structured values such as lists or dictionaries, and these values are saved into an observation log that is saved for future reference. If the test observations are different you have the option to interact with a console or visual tool to see what has changed, and whether the reference set of observations need to be updated.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

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.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is now versioned in the main HermiT version repository, although not officially supported by the HermiT developers. The version number of this package is a composite of the HermiT version and an value representing releases of this packaged version. So, 1.3.7.1 is the first release of the mavenized version of HermiT based on the 1.3.7 release of HermiT. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.