SOM_PAK

survey performed by Juha Ikonen, May 21st 1999

SOM_PAK is a collection of tools for implementing the SOM (or Kohonen Network) algorithm. The program package has been developed to demonstrate the implementation of the SOM algorithm and to ease first experiments. It is created by the SOM Programming Team of the Helsinki University of Technology and can be considered as the original SOM implementation.

The programs are fast but a bit difficult to use.
Old-fashion or UNIX-style user interface.
Map parameters are rather limited such as dimensionality, topology.
Lacks visualization functions

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General

Program name	SOM_PAK Version 3.1
Availability	Program is free for scientific purposes and is available at http://www.cis.hut.fi/nnrc/nnrc-programs.html Postal address: SOM Programming Team of the Helsinki University of Technology Laboratory of Computer and Information Science Rakentajanaukio 2 C, SF-02150 Espoo FINLAND
Purpose	An interactive monitoring tool using SOM algorithm.
Operating system	UNIX and MS-DOS. Program is distributed as source code in C language, pre-compiled binaries for MS-DOS are also available.
User interface	Command line interface. The package contains 11 separate programs or tools, user must know which tools to use to achieve desired results. The approach can not be considered user-friendly, although it may be very effective and convenient in many applications.
Documentation	PostScript/text file, which also reveals the basic theory of SOM.

SOM features

map parameters
Teaching algorithm	Standard algorithm. Data vectors can be forced to specified points in resulting map. [is implementation correct?]
Map size	2-dimensional map grid minimum size: 1x1 maximum size: 74x74
Map lattice and shape	Map lattice: rectangular or hexagonal Map shape: rectangular
Neighborhood function	Function type: Bubble or Gaussian
	Neighborhood size (h): Type: linear Parameters: initial value (decreases automatically to one during trainig)
	Learning rate (alpha): Type: linear or inverse (1/t) Parameters: initial value (decreases automatically to zero during training)
Initialization	Data samples
Distance function	Euclidian
Unknown components	Allowed
Teaching length	Explicit
efficiency
Speed [Windows NT 4.0, 333 MHz Pentium II, 128 MB RAM]	for ~3000 13-dim data samples, 4 epochs training time: 36 seconds
Results	seemed ok final average quantization error: 0.885901 final topographic error: not applicable

Usability

preprocessing
Input formats	ASCII, compressed (gzip) ASCII, stdin
Data handling and selection	No data handling and selection tools are provided
postprocessing
Output formats	ASCII or compressed (gzip) ASCII for data, PostScript for images. Output can be sent to stdout instead of a regular file.
Map measures	Quantization error
Labeling	Simple: labels can be included in a calibration data set. BMU's for calibration data samples are sought and labeled with the labels included.
Clustering	No
visualization
Inspection of neurons	Simple: weights of a neuron can be viewed from map file, no visualization tool is provided.
Clusters/map shape	Clusters: not applicable Sammon mapping: output to data file and PostScript image. U-matrix: PostScript image
Correlations	Component planes: single plane or all planes.
Data projections	Single: BMU can be sought for a data vector, corresponding label will be attached if defined. Quantization error is also calculated for the data vector. Projection data is stored in a file, no visualization tool is provided. Alternatively a trajectory of BMU's for a set of data vectors can be formed and saved as a PostScript image.
Markers	Labels

http://www.cis.hut.fi/projects/somtoolbox/links/som_pak.shtml
somtlbx@mail.cis.hut.fi
Monday, 09-Oct-2000 12:53:09 EEST