The installation of GroovyLab is very simple:
-
Step 1 Download and unzip the .zip download.
-
Step 2 Run the proper script for your platform, i.e. the .sh scripts for Unix like systems and the .bat scripts for Windows.
The GroovyLab zip download contains both the sources and all the relevant libraries to build GroovyLab with ant.
To build GroovyLab is very simple:
-
Unzip the zipped file
-
Go to .\GroovyLabPr folder and build with ant, i.e. type
ant
The executable is built in the dist folder
Of course, better is to use the Netbeans where you can load the corresponding project.
Building with Gradle and support for fat jar that supports the DeepLearning4j library and Apache Spark
To build GroovyLab with Gradle, unzip the zipped file, go to the root folder where the contents are unzipped and type:
-
gradle clean
-
gradle build
The executable should be placed in the build/libs folder
You can also build a fat jar, the GroovyLab-all.jar, that supports the DeepLearning4j effective Java library for Deep Learning (it provides very fast native numerical processing operations). Also, it supports Apache Spark for Big Data Processing. The Gradle command is:
- gradle fatJar
a fat jar is produced with all the DeepLearning4j and Apache Spark functionality!
Therefore we can run Deep Learning an d Apache Spark algorithms in scripting mode, with either Groovy or the JShell of Java 9+.
Note that F6 executes the selected script code with GroovyShell, and F7 with the JShell.
Java machine learning examples of the project DeepLearning4jExamples can be executed easily.
For example, at the current source code of GroovyLab, at the folder groovySciExamples/Jshell.DeepLearning4j the application example:
SVMLightExampleJava.gsci
is adapted from these examples.
The GroovyLab environment aims to provide a MATLAB/Scilab like scientific computing platform that is supported by a scripting engine implemented in Groovy language. The GroovyLab user can work either with a MATLAB-like command console, or with a flexible editor based on the rsyntaxarea component, that offers more convenient code development. GroovyLab supports extensive plotting facilities and can exploit effectively a lot of powerful Java scientific libraries, as JLAPACK , Apache Common Maths , EJML , MTJ , NUMAL translation to Java , Numerical Recipes Java translation , Colt etc. Also, GroovyLab supports Computer Algebra based on the symja (http://code.google.com/p/symja/) project.
GroovyLab scripts can be easily combined with MATLAB, thus the scientist can combine the flexibility of Groovy with the computational effectiveness of MATLAB.
GroovyLab provides the GroovySci compiled scripting engine which is based mainly on GroovyShell , with extensions to provide MATLAB-like matrix operations.
GroovyLab supports indy (invoke dynamic) since it utilizes the indy version of Groovy. Also, by default performs computations using doubles instead of BigDecimals for speed. These settings are configurable. Also, very convenient for scripting, is the import and code buffering facility, described in ScriptingWithImportBuffering wiki page.
The main advantages of GroovySci that equip it with great potential are:
-
The flexibility of the Groovy language, that offer many opportunities to implement convenient high-level scientific operators. 1. The vast Java based scientific libraries with excellent code for many application domains. These libraries are directly available from GroovySci with the dynamic loading of the corresponding Java toolboxes.
-
The user friendly Matlab-like environment of GroovyLab and the high quality scientific plotting support.
-
The syntax of Groovy is much like Java, and Java code can be easily mixed with Groovy, therefore engineers with Java background can become productive in short time.
-
The speed of Groovy based scripting, with the invoke dynamic implementation, dynamic code comes generally close to Java code.
We started to write documentation with a draft user guide and at the wiki pages of the project, please see them.
GroovyLab provides a lot of on-line help and demo examples. The philosophy of GroovyLab is to exploit high quality Java scientific software. Although any Java scientific library can be utilized as toolbox, GroovyLab includes by default some excellent Java scientific libraries. Work is in progress to utilize more effectively these libraries by designing Groovy based high level interfaces. These integrated libraries are:
-
The Java translation of Numerical Recipes, Third Edition, C++ code by Huang Wen Hui 1. The NUMAL library described in the book: A Numerical Library in Java for Scientists & Engineers, Hang T. Lau, Chapman & Hall/CRC, 2004
-
The Efficient Matrix Java Library, http://code.google.com/p/efficient-java-matrix-library/
-
The Matrix Toolkit for Java, http://code.google.com/p/matrix-toolkits-java/
-
The Parallel Colt Library, http://sites.google.com/site/piotrwendykier/software/parallelcolt
-
Jsci - A Science API for Java, http://jsci.sourceforge.net/, provides mostly Wavelet Analysis Routines
-
The JFreeChart plotting system, http://www.jfree.org/jfreechart/, provides plots, for some of which already exists Matlab-like interface
http://groups.google.com/group/jlabgroovy
Stergios Papadimitriou
Technology Education Institute of East Macedonia and Thraki
Dept of Informatics and Computer Engineering
Greece
email: sterg@teiemt.gr