javaxsx

Some simple and lightweight tools and utilities in Java.

Usage

Maven dependency:

Nexus Repository Manager

Maven Central Repository

<dependency>
    <groupId>com.eroelf</groupId>
    <artifactId>javaxsx</artifactId>
    <version>${version}</version>
</dependency>

Manual

behavior

This package provides some basic definition for an event happened at a specified time, say, History.

If a History is related to a subjective action, it becomes a Behavior. As many websites are concerned, users is a basic dimension, so there comes UserBehavior.

Behaviors class defines some basic methods to deal with behaviors.

captcha

Captcha interfaces and a simple implementation.

Captcha captcha=new SimpleCaptcha(captchaLength, captchaImageWidth, captchaImageHeight);
String text=captcha.generateText();
BufferedImage bi=captcha.generateImage(text);

concurrent

Concurrent utilities.

ProducerConsumer

Iterator<Product> iterator;// The producer.
int queueCapacity;// The capacity of the queue holding products that waiting to be consumed.
int nThreads;// Number of consumers.
long timeout;// Time to wait for the threads to join.
TimeUnit unit;// The unit of timeout.
Consumer<Product> handler=p -> System.out.println(p.toString());// Define how do consumers process those products.

ProducerConsumer pc=new ProducerConsumer();
pc.consume(iterator, queueCapacity, nThreads, timeout, unit, handler);

db

Provides some simple APIs for database requests. There is NO support for connection pools and standing connections.

For example:

Simple query

DoDb doDb=new DoDb();
doDb.setConnection(connection);
doDb.executeQuery(true, "select ...");
doDb.executeUpdate(false, "insert into ...", obj1, obj2, ...);
doDb.close();

Batch

doDb.prepareStatement(true, "insert into person (id, name, age, learning_subject) values (?, ?, ?, ?)");
while(...)
{
    ...
    String id=...
    String name=...
    int age=...
    String learningSubject=...
    doDb.addBatch(id, name, age, learningSubject);
    ...
}
doDb.executeBatch();

Convert to list

public class Person
{
    public String id;
    public String name;
    public int age;
    public String learningSubject;
}

List<Person> list=doDb.fromQuery(Person.class, true, true, "select id, name, age, learning_subject from person");

DataSource

A simple and lightweight database connection pool. Support transactions.

DataSource dataSource=new DataSource(...);

dataSource.executeQuery(...);
dataSource.executeUpdate(...);
List<...> list=dataSource.fromQuery(...);
dataSource.executeTransaction(conn -> {
    try
    {
        conn.executeUpdate(...);
        conn.executeUpdate(...);
    }
    catch(SQLException e)
    {
        throw new RuntimeException(e);
    }
});

Database updater

Provide APIs that deal with necessary working flows to update information of a database.

DbUpdater<Person> dbUpdater1=new DbInserter {
    @Override
    public Iterable<Object[]> process(Person data)
    {
        return new Collection.singleton(new Object[]{data.name, data.age});
    }
};
DbUpdater<Person> dbUpdater2=new DbUpdater {
    //...
};

String inputFileName;
Gson gson=new Gson();
UpdateTask task=new UpdateTask();
task.consume(Iterators.transform(new FileIterator(inputFileName), line -> gson.fromJson(line, Person.class)), dbUpdater1, dbUpdater2);

geo

Provides some simple API for geography.

Convert coordinates

System.out.println(CoordinateConverter.fromWGS84ToGCJ02(39.9671454379, 116.3281085168));
System.out.println(CoordinateConverter.fromGCJ02ToWGS84(CoordinateConverter.fromWGS84ToGCJ02(39.9671454379, 116.3281085168)));
System.out.println(CoordinateConverter.fromWGS84ToBD09(39.9684488626, 116.3342346822));
System.out.println(CoordinateConverter.fromBD09ToGCJ02(39.97411842293451, 116.34085726246536));

Output:

\> (39.96845939748989, 116.3342501022455, NaN)
\> (39.9671336261829, 116.32809425470623, NaN)
\> (39.975428838226456, 116.34701964033485, NaN)
\> (39.968449623723544, 116.33423475270888, NaN)

Ground distance

System.out.println(GeoUtil.distance(39.2635, 116.8098, 39.6572, 116.3422));

Output:

\> 59414.14058762891

group

This package provides APIs for grouping some certain set of objects. Includes methods for configuring, updating from some source, and schedule auto-updating.

Simple usage

GroupingUtil.configFacet("GROUPING_TEST", "_grouping_test", "A,B,C,Z".split(","), "0.25,0.75,1".split(","), (id) -> true, new DigestHashGetter("md5", 2));
Group group=GroupingUtil.getGroupFromIdentifier("GROUPING_TEST", "390QSJPOSFN0543XF0WJ");
System.out.println(group.getFacetName());
System.out.println(group.getGroupName());

Output:

\> GROUPING_TEST
\> A

One may like to delegate a Group object to apply a special strategy on objects in this group. Just assign a GroupTask functional interface to the Group object.

Classes in package updater are helpful for update grouping configures from a specified source, e.g., a database.

gson

Gson Type Adapters.

index

APIs for a simple form to index a bunch of objects, implemented by using HashMap.

io

Reading files

String fileName="data.gz";
new FileReader(fileName).lines();
FileReaderPlus.readAFile(fileName, (line) -> {
    ...
    return true;
});
new FileIterator(fileName).lines();
new DirFileIterator("/").lines();

Support text, gzip, bzip2, zstd, and zip format. Extend InputHelper class and override getCompressType and convert methods to modify compress type judgment and to support more file types.

Compressor

The Compressor class provides methods to compress and decompress data. Override CompressorBuilder::setCompression method to support more compress types.

math

Some mathematics related methods. Such as mapping, combination, and statistics.

• Examples of using class DataPreProcessor:

  • Basic statistics and scaling

    DataPreProcessor dataPreProcessor=new DataPreProcessor(new double[]{2, 4, 4, 6}, false);
    System.out.println(dataPreProcessor.getOriginalMean());
    System.out.println(dataPreProcessor.getOriginalStd());
    System.out.println(dataPreProcessor.getOriginalSum());
    System.out.println(dataPreProcessor.getOriginalMax());
    System.out.println(dataPreProcessor.getOriginalMin());
    System.out.println("--------------------------------");
    dataPreProcessor.scale(2, 1);
    dataPreProcessor.toUniform();
    System.out.println(dataPreProcessor.getProcessedMean());
    System.out.println(dataPreProcessor.getProcessedStd());
    System.out.println(dataPreProcessor.getProcessedSum());
    System.out.println(dataPreProcessor.getProcessedMax());
    System.out.println(dataPreProcessor.getProcessedMin());
    System.out.println("--------------------------------");
    dataPreProcessor.reScale(2, 1);
    dataPreProcessor.reScale(-0.5, -1);
    System.out.println(dataPreProcessor.getProcessedMean());
    System.out.println(dataPreProcessor.getProcessedStd());
    System.out.println(dataPreProcessor.getProcessedSum());
    System.out.println(dataPreProcessor.getProcessedMax());
    System.out.println(dataPreProcessor.getProcessedMin());

    Output:

    \> 4.0
    \> 1.632993161855452
    \> 16.0
    \> 6.0
    \> 2.0
    \> --------------------------------
    \> 0.5
    \> 0.30618621784789724
    \> 2.0
    \> 0.875
    \> 0.125
    \> --------------------------------
    \> -2.0
    \> 0.30618621784789724
    \> -8.0
    \> -1.625
    \> -2.375
    

  • Histogram for console

    DataPreProcessor dataPreProcessor=new DataPreProcessor(new double[]{2, 4, 4, 6}, false);
    HistInfo histInfo=dataPreProcessor.getOriginalHistogram(new double[]{0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5}, true);
    String histStr=DataPreProcessor.formatHistogram(histInfo, 100);
    System.out.println(histStr);

    Output:

    \> [0.5, 1.5)|0.00000000|
    \> [1.5, 2.5)|0.25000000|>>>>>>>>>>>>>>>>>>>>>>>>>
    \> [2.5, 3.5)|0.00000000|
    \> [3.5, 4.5)|0.50000000|>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
    \> [4.5, 5.5)|0.00000000|
    \> [5.5, 6.5)|0.25000000|>>>>>>>>>>>>>>>>>>>>>>>>>
    \>     others|0.00000000|
    

ml

This package is an abstract core working flow of any certain recall, recommender, estimating, scoring, and ranking system.

There is a demo for a simple usage.

• feature package

  For Item feature storing and scoring.

  • model package

    A Modelable object can be "modeled", say, filling features, by one or more Modeler objects. That means the Modelable object contains features of a specified thing while those Modeler objects can be anything like strategies or descriptors to describe the Modelable object.

  • score package

    A Scoreable object can be "scored" by a Scorer object and hold the final score until next modification. Scoreable objects are Comparable by their scores in descending order, which implies the higher the score the "better" the Scoreable object is. This is consistent to general ranking and recommender projects.

  • strategy package

    A Strategy object is a Modeler object but with method to recall candidates under its definition.

  • Item class

    An Item object is both a Modelable and a Scoreable object. This means an Item object is very suitable to represent a specified "product" in a well defined ranking and recommender system.

• flow package

  This package defines general working flows for ranking and recommender systems in both enumerable candidate situation and innumerable candidate situation. Note that innumerable candidate situation can also be used in enumerable candidate situation.

  • controller package

    This package defines many detailed behaviors of both the two working flow mentioned above.

  • convert package

    Provide a Converter class for converting a back-end product data structure (Item) list to a front-end product data structure (Info) list, as well as logging necessary information for each Info object to a logger data structure (InfoLog).

  • estimate package

    The top-most interface for both the enumerable and the innumerable candidate working flow. Call the generate method to get the scored but unordered product (Item) list. Using Collections.sort to sort the list if necessary.

    Please note that in most cases the generate method need NOT to be overridden.

  • Item, Info, and InfoLog

    As described above, objects of all the three classes are "products" in a specified ranking and recommender system. Item class is used for back-end data, Info class is used for front-end data, and InfoLog class is used for logging necessary information for Info objects.

monitor

General data structure for a server monitoring.

net

Provide APIs for constructing, parsing, and requesting URLs.

ParamInfo class

Help to repeatedly construct URLs for a specified domain and pre-defined parameters with different values. See the Javadoc for detail.

UrlUtil class

Help to construct and parse URLs.

RequestUrl class

Help to request URLs. There is NO support for connection pools and standing connections.

HttpRequesterClient class

Implemented an HTTP requester connection pool client.

reflect

Java reflection utilities.

ClassWrapper

Wrap runtime objects to make it possible to call their methods by method name strings.

This is useful when you have a bunch of objects containing methods with identical signatures but with no common interface.

ClassWrapper wrappedObject=new ClassWrapper(obj);
wrappedObject.invoke('methodName', ...);

trie

Implemented HashMapTrie and DoubleArrayTrie, and a simple trie division algorithm in IndexedTrie.

others

ArrayUtil

CollectionUtil

FileSysUtil

NumBytes

PackageUtil

StdLoggers

Strings

TimeLasted

TimeUtil

UUIDs

Authors

• ZHONG Weikun

License

This project is released under the Apache license 2.0.

Copyright 2018 ZHONG Weikun.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    https://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.