Download JAR files tagged by reduced with all dependencies

Apache Empire-db is an Open Source relational data persistence component which allows database vendor independent dynamic query definition as well as safe and simple data retrieval and updating. Compared to most other solutions like e.g. Hibernate, TopLink, iBATIS or JPA implementations, Empire-db takes a considerably different approach, with a special focus on compile-time safety, reduced redundancies and improved developer productivity.

The ONNX Runtime Mobile package is a size optimized inference library for executing ONNX (Open Neural Network Exchange) models on Android. This package is built from the open source inference engine but with reduced disk footprint targeting mobile platforms. To minimize binary size this library supports a reduced set of operators and types aligned to typical mobile applications. The ONNX model must be converted to ORT format in order to use it with this package. See https://onnxruntime.ai/docs/reference/ort-format-models.html for more details.

zlib is designed to be a free, general-purpose, legally unencumbered -- that is, not covered by any patents -- lossless data-compression library for use on virtually any computer hardware and operating system. The zlib data format is itself portable across platforms. Unlike the LZW compression method used in Unix compress(1) and in the GIF image format, the compression method currently used in zlib essentially never expands the data. (LZW can double or triple the file size in extreme cases.) zlib's memory footprint is also independent of the input data and can be reduced, if necessary, at some cost in compression.

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.

This is a fork of jfreechart. The purpose is to the build it with the Java module sytem and Java 17. This to be able to use it in building java applications with reduced runtimes. Three servlet classes have been removed as they depended on the automatic module servlet.api. No functionality has beed added. All credit to the original project. Introduzing the module system has broken the unit tests. Everything seems to work as expected though. If you stumble upon this, then it might be useful if you need to use jfreechart in a Java module application. This project will not be maintained though. It looks like jfreechart is adopting the module system and that it might be released soon. jfreechart with Java 11 and modules for java fx applications was recently released. JFreeChart is a class library, written in Java, for generating charts. Utilising the Java2D API, it supports a wide range of chart types including bar charts, pie charts, line charts, XY-plots, time series plots, Sankey charts and more.