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Java's syntactic and library support for these components tends to cause the above concerns to be mixed with each other, with other concerns, and with low-level implementation details. This means that the code is cluttered, verbose and does not express intent very well. Jedi aims to repair this by providing a library of routines for filtering, iteration, conversion from one collection type to another, first order logic functions, etc. It makes extensive use of higher order functions and closures (also known as 'blocks' or 'lambdas' that can be implemented manually or by annotations.

This subproject is an extension of the Struts tag library. Each JSP custom tag in this library is a subclass of an associated tag in the Struts tag library. One difference is that this tag library does not use "rtexprvalues", it uses the expression evaluation engine in the Jakarta Taglibs implementation of the JSP Standard Tag Library (version 1.0) to evaluate attribute values. In addition, some of the Struts tags were not ported to this library, as it was determined that their functionality was entirely supplied by the JSTL. These particular Struts tags, and the reason for their non-porting will be described in the documentation for this library. In order to fully understand the correct utilization of this library, you must understand the use and operation of the Struts tag library, and the use and operation of the JavaServer Pages Standard Tag Library (hereafter called the "JSTL"), along with the expression language (sometimes called the "EL") used for evaluating attribute values.

# geokey K Dimensional Z-Order curve utils. [](https://travis-ci.org/markrileybot/geokey) [](https://coveralls.io/github/markrileybot/geokey?branch=master) [](https://maven-badges.herokuapp.com/maven-central/io.github.markrileybot/geokey) ## Building ./gradlew build ## Gradle dependency See https://search.maven.org/artifact/io.github.markrileybot/geokey/ ## Using ### Use built in keys to make geohashes ```java import org.geokey.GeoKey; // Make a geo hash key String key = new GeoKey().setLatitude(48.669).setLongitude(-4.329).toString(); // "gbsuv7ztqzpts82uzfwq5e1bp" // parse a geo hash key GeoKey gk = new GeoKey("gbsuv7ztqzpts82uzfwq5e1bp"); ``` ### Make a special purpose K-Dimensional key ```java public class GeoTimeKey extends KDKey { private static final KDKeySpec spec = new KDKeySpec.Builder() .addDim(-180, 180, 1) .addDim(-90, 90, 1) .addDim(0, 1L << 62, 1) .setAlphabet(Alphabet.GEO_TIME_HASH) .build(); public GeoTimeKey() { super(spec); } public GeoTimeKey(String s) { super(spec, s); } public GeoTimeKey(byte[] s) { super(spec, s); } public GeoTimeKey setLatitude(double latitude) { set(1, latitude); return this; } public double getLatitude() { return super.get(1); } public GeoTimeKey setLongitude(double longitude) { set(0, longitude); return this; } public double getLongitude() { return super.get(0); } public GeoTimeKey setTime(long time) { set(2, time); return this; } public long getTime() { return (long) get(2); } } ```

GroupDocs.Signature for Java allows developers to write applications with ability to significantly sign electronic docs and verify them. Signatures can be of different types. API supports multitude of file formats and includes Pdf, Microsoft Office documents, Open Office formats, PowerPoint presentations, Images etc. The quite captivating fact about the API is, its UI less and independent calls can be made. It also provides a useful features to verify signed documents with stored signatures. Signature Types: - Text Signature - Image Signature - Digital Signature - Barcode Signature - QR-Code Signature - Stamp Signature Verification Types: - Text Signature - Digital Signature - Barcode Signature - QR-Code Signature For more details on the library, please visit GroupDocs website at: http://groupdocs.com/products/signature/java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Signature for Java library, please request a free 30-day temporary license.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616 This version allows defining section scopes for sentence segmentation.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616

POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates. It parses dates from most languages and formats out of the box without having to specify which format is expected. Defaults such as time zones, and whether to interpret an internationally ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone and locale and stored in a default config object that can be replaced or overridden. Multiple languages for month names are supported without any additional configuration needed. The net effect the default parser for a server in Paris would have a different automatic configuration from a server in New York. Throw a random local date at either, and it'll parse it as expected. If your server supports customers from multiple locales and time zones, then each can be specified when parsing a date/time to resolve any ambiguities.

Xypron Statistics is a Java library which was developped with supply chain simulation in mind. The normal, the exponential and the gamma distribution have been included. Methods to calculate fill rate and order rate service levels as well as safety factors are provided. The Mersenne Twister algorithm is used to provide high quality random number generation. Some functions for the gamma distribution where adopted from http://www.ssfnet.org/download/ssfnet_raceway-2.0.tar.gz. For these the following applies: Copyright 1999 CERN - European Organization for Nuclear Research. Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty.

Performs one-class classification on a dataset. Classifier reduces the class being classified to just a single class, and learns the datawithout using any information from other classes. The testing stage will classify as 'target'or 'outlier' - so in order to calculate the outlier pass rate the dataset must contain informationfrom more than one class. Also, the output varies depending on whether the label 'outlier' exists in the instances usedto build the classifier. If so, then 'outlier' will be predicted, if not, then the label willbe considered missing when the prediction does not favour the target class. The 'outlier' classwill not be used to build the model if there are instances of this class in the dataset. It cansimply be used as a flag, you do not need to relabel any classes. For more information, see: Kathryn Hempstalk, Eibe Frank, Ian H. Witten: One-Class Classification by Combining Density and Class Probability Estimation. In: Proceedings of the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases and 19th European Conference on Machine Learning, ECMLPKDD2008, Berlin, 505--519, 2008.

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.