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# 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.

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.

GroupDocs.Metadata for Java is a class library to process metadata associated with various Document, Image, CAD, Audio, Video and Archive formats. Key Features: - Covers most popular metadata standards: XMP, EXIF, IPTC, Image Resource Blocks, ID3, document properties - Covers most popular document formats: Microsoft Word, Microsoft Excel, Microsoft PowerPoint, PDF, Microsoft OneNote, Microsoft Visio, Open Document Format - Covers most popular image formats: BMP, GIF, DjVu, JPEG, PNG, TIFF, PSD, WebP, WMF, EMF, DICOM - Covers most popular email formats: Outlook Message, Email Message - Covers most popular audio formats: Mp3, WAV - Covers most popular video formats: Avi, Mov - Create, modify and remove metadata associated with supported document and image formats with a few lines of code - Manage EXIF metadata in Jpeg and TIFF formats - Manage XMP metadata in image and PDF formats - Manage Image Resource blocks in image formats - Manage audio metadata: ID3 tag (ID3v1, ID3v2), Lyrics3 tag, APE - Utilities to inspect and clean hidden metadata in document formats - Utilities to Search and Compare all metadata - Utilities to Export metadata to Excel/Csv - Metadata cleaner utility - MIME type detection - Read track changes. Accept or reject track changes - Read EXIF maker-notes: Sony, Nikon, Canon, Panasonic For more details on the library, please visit GroupDocs website at: https://products.groupdocs.com/metadata/Java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Metadata for Java library, please request a free 30-day temporary license.

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.