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-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=Sorry for the bad quality of this README file. It was generated from thedvi-file of the documentation. Please have a look at the correspondinghtml- or PostScript file. Thank you, Hartmut Loos.-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= DemoGNG v1.5 Hartmut S. Loos 1 and Bernd Fritzke y2 1Systems Biophysics Institute for Neural Computation Ruhr-Universit"at Bochum 2Neural Computation Group Artifical Intelligence Institute Computer Science Department Technical University Dresden October 19, 1998 Abstract DemoGNG, a Java applet, implements several methods related to com- petitive learning. It is possible to experiment with the methods using various data distributions and observe the learning process. A common terminology is used to make it easy to compare one method to the other. Thanks to the Java programming language the implementations run on a very large number of platforms without the need of compilation or local adaptation. Hopefully, the experimentation with the models will increase the intuitive understanding and make it easier to judge their particular strengths and weaknesses._____________________________________ http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/loos yhttp://pikas.inf.tu-dresden.de/ fritzke 1 Contents1 Introduction 32 System Requirements 33 Installation 34 Starting the Java Applet 45 Manual 4 5.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 5.2 Drawing Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 5.3 General Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5.3.1 Buttons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5.3.2 Checkboxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 5.3.3 Pull-Down Menus . . . . . . . . . . . . . . . . . . . . . . . . 8 5.4 Model Specific Options . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5.4.1 LBG, LBG-U . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5.4.2 Hard Competitive Learning . . . . . . . . . . . . . . . . . . . 9 5.4.3 Neural Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.4.4 Competitive Hebbian Learning . . . . . . . . . . . . . . . . . 11 5.4.5 Neural Gas with Competitive Hebbian Learning . . . . . . . 11 5.4.6 Growing Neural Gas, Growing Neural Gas with Utility . . . . 11 5.4.7 Self-Organizing Map . . . . . . . . . . . . . . . . . . . . . . . 12 5.4.8 Growing Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Wishlist 13References 147 Change log 15List of Figures 1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Pull-down menu with all available models . . . . . . . . . . . . . . . 5 3 Example screenshots of the available models . . . . . . . . . . . . . . 6 4 Drawing Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5 Options for all models . . . . . . . . . . . . . . . . . . . . . . . . . . 7 6 Some examples for activated checkboxes. . . . . . . . . . . . . . . . . 8 7 General pull-down menus . . . . . . . . . . . . . . . . . . . . . . . . 8 8 Overview of the available probability distributions . . . . . . . . . . 10 2 1 IntroductionIn the area of competitive learning a rather large number of models exist which havesimilar goals but differ considerably in the way they work. A common goal of thosealgorithms is to distribute a certain number of vectors in a possibly high-dimensionalspace. The distribution of these vectors should reflect (in one of several possibleways) the distribution of input signals which in general is not given explicitly butonly through sample vectors.2 System RequirementsTo run DemoGNG, a Web browser with Java enhancements (e.g. Netscape 2.0 ornewer, Microsofts Internet Explorer, ...) is needed or, e.g. the Sun Microsystemsappletviewer from the Java Developers Kit (JDK).3 InstallationDownload the compressed tar version1, save it to a file, say DemoGNG-1.5.tar.gz,and then extract the files with% gunzip DemoGNG-1.5.tar.gz% tar xvf DemoGNG-1.5.tar Instead, you can download DemoGNG in zip-format2. After uncompressing and unpacking you should have the following:_____________________________________1 ftp://ftp.neuroinformatik.ruhr-uni-bochum.de/pub/software/NN/DemoGNG/DemoGNG-1.5.tar.gz2 ftp://ftp.neuroinformatik.ruhr-uni-bochum.de/pub/software/NN/DemoGNG/DemoGNG-1.5.zip 3 COPYING copy of the GNU General Public License Changes change history for DemoGNG ComputeGNG.java Java source for class ComputeGNG DemoGNG.java Java source for class DemoGNG EdgeGNG.java Java source for class EdgeGNG EdgeVoronoi.java Java source for class EdgeVoronoi FPoint.java Java source for class FPoint GraphGNG.java Java source for class GraphGNG GridNodeGNG.java Java source for class GridNodeGNG HalfEdgeVoronoi.java Java source for class HalfEdgeVoronoi LineGNG.java Java source for class LineGNG ListElem.java Java source for class ListElem ListGNG.java Java source for class ListGNG NodeGNG.java Java source for class NodeGNG SelGraphics.java Java source for class SelGraphics SiteVoronoi.java Java source for class SiteVoronoi DemoGNGcode.html starting page for source documentation GenerateHTML.tcl a tcl-script to generate some HTML-pages with different parameters Makefile Makefile for Unix make README the ASCII-version of this document SwitchGNG.html the main HTML-page audio/ subdirectory containing audio files doc/ subdirectory containing the javadoc generated documenta- tion of DemoGNG with the needed images tex/ subdirectory containing the LaTeX source of the manual with the DVI-, PS- and HTML-format Type make world to generate some necessary files (all these files are includedin the archive). *.html needed for SwitchGNG.html *.class compiled classes doc/*.html javadoc generated HTML-files4 Starting the Java AppletStart the Web browser with the main HTML-page.Example:% netscape SwitchGNG.htmlImportant: Make sure you have Java support enabled. Instead, you can also use an appletviewer with one of the generated HTML-files.Example:% appletviewer GNG.htmlImportant: The appletviewer requires an applet included in the Web page, so youmust choose one of the generated HTML-files.5 ManualAfter the Java applet has been launched, the DemoGNG main window appears.This window can be divided into four regions: 1. Network Model 4 Figure 1: Overview 2. Drawing Area 3. General Options 4. Model Specific Options5.1 Network Model Figure 2: Pull-down menu with all available modelsThe first part shows the actual algorithm. To select another algorithm or restartthe current, click on the pull-down menu and choose the desired algorithm. Thefollowing algorithms are available: fflLBG (Linde et al., 1980) fflLBG-U (Fritzke, 1997b) fflHard Competitive Learning (standard algorithm) - with constant learning rate - with decreasing learning rate fflNeural Gas (Martinetz and Schulten, 1991) fflCompetitive Hebbian Learning (Martinetz and Schulten, 1991; Martinetz, 1993) 5 fflNeural Gas with Competitive Hebbian Learning (Martinetz and Schulten, 1991; Martinetz and Schulten, 1994) fflGrowing Neural Gas (Fritzke, 1994; Fritzke, 1995a) fflGrowing Neural Gas with Utility (Fritzke, 1997a) fflSelf-Organizing Map (Kohonen, 1982) fflGrowing Grid (Fritzke, 1995b) LBG, LBG-U Hard Competitive Learning Neural Gas Competitive Hebbian Learning Neural Gas with CHL Growing Neural Gas, GNG-U Self-Organizing Map Growing Grid Figure 3: Example screenshots of the available models5.2 Drawing AreaThe drawing area shows the network for the selected algorithm. The geometricfigure in the background of the area reflects the probability distribution (see Section 6 Figure 4: Drawing Area5.3.3 for more information). In every phase of the algorithm you can select a nodeand drag it to an arbitrary location within the drawing area. Additional informationis displayed in the corners of this region: upper left Number of input signals occured so far upper right Version number lower left Number of nodes lower right Some additional information5.3 General Options Figure 5: Options for all modelsThis region contains the non-specific parameters for all algorithms. There are threekinds of interface elements: buttons, checkboxes and pull-down-menus.5.3.1 Buttons Start Starts/Continues a stopped run. Stop Stops a calculation to modify parameters or move nodes (these modifications can also be done while the simulator runs). Reset Resets the algorithm, but leaves all parameters unchanged (to restart an algorithm with default parameters see Section 5.1 for more information). 7 Teach mode Voronoi diagram Error graph Figure 6: Some examples for activated checkboxes. 5.3.2 Checkboxes Teach Toggles teach mode. In the teach mode the algorithm is slowed down and more information is displayed depending on the algo- rithm (default: off). Signals Toggles display of signals. The most recently generated signals are shown (default: off). Voronoi Toggles display of Voronoi diagram (default: off). Delaunay Toggles display of Delaunay triangulation (default: off). Error Graph Toggles the error graph. It is displayed in a separate window and shows the mean square error of the selected model (default: off). Nodes Toggles display of nodes (default: on). Edges Toggles display of edges (default: on). Random Init If this is switched on, initial node positions can lie outside the region of positive probability density (default: off). White Switches the background of the drawing area to white. This is useful for making hardcopies of the screen (default: off). Sound Toggles sound (default: off). 5.3.3 Pull-Down MenusProbability Distribution (max.) Nodes Display Speed Figure 7: General pull-down menus 8 prob. Distrib. Selects one of the available probability distributions. The choosen distribution is displayed in the drawing area (see Section 5.2 for more information). The following distributions are provided: The convex uniform ones Rectangle, Circle and Ring. The clustered uniform ones UNI, Small Spirals, Large Spirals and UNIT. The non-uniform HiLo Density which consists of a small and a large rectangle. Each of these rectangles gets 50% of the signals. The discrete distribution Discrete, which consists of 500 data vectors generated from a number of Gaussian Kernels. And the non-stationary uniform distributions Move & Jump, Move, Jump and Right MouseB. The first three distributions are moving automatically, for the last one click on the right mouse button and place the distribution where you want. Or hold the right mouse button and the distribution will follow the mouse pointer. (max.) Nodes Selects the number of nodes (Growing Neural Gas and Grow- ing Grid: maximum number of nodes). Display Selects the update interval for the display. Speed Selects an individual speed depending on the machine and/or browser. Select a slow speed for good interaction with the program and slower program execution. Select a fast speed for slow interaction and fast program execution. The most suitable setting depends on your local hard- and soft- ware.5.4 Model Specific OptionsThis region shows the model specific parameters. Each time a new model is selected,the necessary parameters are displayed. For a complete description of the modelsand their parameters look at the technical report Some Competitive Learning Meth-⌨️ 快捷键说明
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