			 LNKnet Basic Documentation

INTRODUCTION

     LNKnet is an integrated neural network and conventional pattern
classification software package.  It is designed to allow a user to quickly
and easily do experiments with a set of data involving many different
algorithms and classification techniques.  Numerous sets of sample data are
provided with the package to help the user get started with the software and
to give examples of the data formats required.

     LNKnet consists of a main program called lnknet that controls the
user interface and a number of other programs that are started.  Each
classification algorithm has a separate executable and there are
programs for various types of plots, feature selection, clustering,
and normalization.

SYSTEM REQUIREMENTS

     LNKnet has a mouse driven interface created using the Builder Xcessory
user interface development system on a Sun Sparc station using the Motif X
Widget set. The software should run on any X windows system to which
it has been ported. Classification programs are not dependent on X
windows.  They are written in the C language and have a command line
style interface. The current executables were compiled on a Sun Sparc 10
running Solaris 2.5. The binaries are staticly linked, to avoid problems
with differing versions of shared libraries. The Motif library is 
OSF Motif version 1.2.4.

     Disk storage requirements for the basic programs and data files follow:
				       MBytes
		LNKnet executables	60.2
		Standard data files      8.1
		Source code		37.3
		other			 4.7
		-----------------------------
		total		       110.3

    To conserve space, the src directory may be deleted.

INSTALLATION

     A typical installation would involve creating a base directory called
lnknet in a location accessable to potential users.  The executables would
be placed in the subdirectory lnknet/bin and the sample data files in the
subdirectory lnknet/data.  

     Each user of lnknet needs a directory from which to run experiments and  
one or more directories for results of experiments.  He/she should also have
a defaults parameter file for lnknet called .lnknetrc in his/her login
directory.  The .lnknetrc file can be set up by a system administrator in
advance or can be created by the user by selecting the SAVE DEFAULTS button
while running lnknet. Users can also set up an application resource file
in their X application resource directory (specified using the
XAPPLRESDIR environment variable).  Example resource files for
lnknet and lnkplot are given in lnknet/XappRes. The character ! is the
comment character in an application resource file. Consult an X windows
manual for more information on how to customize an X program using
application resources.

     Each user must also have the lnknet/bin directory included in their
default path (such as in their .path file) or the programs could be included
in a commonly used bin directory.

			A QUICK LNKnet EXPERIMENT

     To run a quick experiment after installation:
     
1. Type lnknet <Return>				(run lnknet program)

     The lnknet main window should be created.

2. Select Train and Test as the experiment action and select Test as
the Test file.

3. Select a Classifier and set Classifier Parameters

  The classifiers are listed on the Algorithm Menu which is at the top
of the right side of the main window. Classifier parameters are set on
an parameter window which is displayed by selecting the Algorithm
Parameters button below the Algorithm menu. A simple classifier to try
is a Gaussian classifier with separate variances for each clas and a
Full covariance matrix.

4. Select a Data Base from the Data Base

  Display the data base window by selecting the Data Base... button on
the right side of the main window. This displays a window which shows
which data base is currently selected. Select the Data Base Selection
button in the bottom left corner of the Data Base window. A file
selection window will pop up. Set the directory text field at the top
of the window to the LNKnet data base directory. The Directory
Contents scroll box will fill with a list of LNKnet data bases. Data
bases are identified by their defaults files, which specify important
values such as the number of input features and number of
classes. Select XOR.defaults from this list by double clicking on it
or clicking on it once and selecting the Apply button at the bottom of
the window.

5. Press the "START New Experiment" button on the main window.

    A command file named X1gauss.run will be created in the current
directory. This file will be run by lnknet. Results will appear in the
shell window from which lnknet was run and will be stored in a log
file, X1gauss.log. A short version of the results will be stored in a
notebook file, LNKnet.note.

6. Exit lnknet by selecting Quit from the File menu in the top right
hand corner of the interface.

-	-	-	-	-	-	-	-	-	-
PLOTTING RESULTS

     To display the results of an experiment graphically, there is a popup
window initiated by pushing the button labeled "Plots...".  Check boxes 
can be turned on to select the plots that are to be produced. Plots
that are selected when an experiment is run are produced at the end of
the experiment. To generate selected plots after an experiment has been run,
use the "PLOT ONLY" button on the main window.  Some interesting plots
to try are the three decision region and two profile plots found on
the top half of the Plotting Control window.


SELECTING DIFFERENT ALGORITHMS

     Different algorithms can be selected from the algorithm menu. The
algorithms are group by type, neural networks which estimate posterior
probabilities, classifiers which estimate likelihoods, nearest
neighbor classifiers, etc. The clustering algorithms which are used in
combination with some classifiers are also found on this menu.
The currently selected algorithm is displayed as the text of this menu
button.  After selecting an algorithm, parameters related specifically
to that algorithm can be changed in the parameter window initiated by
the "Algorithm Params..." button.

     Algorithms can be compared just by changing the algorithm and pressing
"START New Experiment".
