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Never liked hard-coding your property keys? The maven-properties-enum-plugin allows you to generate enum types, holding all valid keys including javadoc. Your advantages: * You can't access on non-existing keys as it would cause compile errors * As it's an enum, you can use your IDE's auto-completion * You'll never have to check your properties keys again * You can see the property's value in the enum field's javadoc

The project provides Result-type similar to Result-type in Rust that allows to return either successfull result or otherwise some kind of error. In Java native way of reporting errors are exceptions, either checked or unchecked, but Result-type provides a way to pass error enformation as a first-class value through the code written in functional style. Routines are provided for interoperability of normal code that uses exception and functional code that uses Result-type, so that exceptions can be catched and propagated as errors in Result-type and then rethrown again later in the control-flow.

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.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.