
Instructions for running sparsenet (for Matlab version 5.0 or higher)
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>Modified for Intel processors running Windows.
>C. H. Anderson
>Dept. Anatomy and Neurobiology
>Washington University School of Medicine
>St. Louis, MO 63110
>cha@shifter.wustl.edd

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Ignore the following

To run sparsenet, you will first need to compile the conjugate
gradient (cgf) routine so that it may be called through matlab.  To do
this, you will first need to build the library libnrfopt.a, which
contains the actual conjugate gradient subroutines.  Go to the nrf/
subdirectory and type

    make libnrfopt.a

Then go back up the to sparsenet directory and compile the cgf routine
so that it may be called by matlab.  First modify the makefile
definition for MEXT to be the proper suffix for the mex file for your
machine (for SGI's it is mexsg).  Then type

    make

If successful, this should create a file named cgf.xxx, where xxx is a
suffix that depends on what machine you are on.

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The libraries have been compiled using Microsofts VC 6.0 
for Pentium 2 or later.

cgf.dll, nrfopt.dll, nrfopt.lib   

The Matlab script setup.m does the following items		      
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At this point, you are in business.  Now startup matlab.

First load the training data:

>> load IMAGES;

This will create a 262144 x 10 matrix whose columns are 512x512
images.  This consumes about 20 Meg, so hopefully you have enough
memory.  If not, email me and I can make you a smaller training set.

The next step is to define a matrix of basis functions, A.  For
example, to learn 64 bases on 8x8 patches, define A as follows:

>> A = rand(64)-0.5;
>> A = A*diag(1./sqrt(sum(A.*A)));

This will create a 64x64 matrix initialized to random values.  Each
column is a different basis function that has unit length.

Set the colormap of Figure 1 to greyscale:

>> figure(1), colormap(gray)

Now simply run the simulation by typing

>> sparsenet


Once the simulation starts running, it will display the bases in
Figure No. 1 every 10 batches, and it will show the variance of the
coefficients in Figure No. 2.  You can stop the simulation at any
point by typing control-c.  You an change the parameters in
sparsenet.m and then resume execution by typing "sparsenet" again.

The learning rate (eta) is initialized to 5.0, which is a large value.
This quickly gets the solution in the right ballpark, but it is too
large to come to a clean stable solution.  Once it looks like you have
something interesting emerging, start reducing eta, eventually to
about 1.0.  A full set of 8x8 bases takes about 15 min. to learn
(depending on how fast your workstation is).


Notation:
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A          Basis functions (Phi in Vis. Res. paper)
X          Input image (I in Vis. Res. paper)
S          Coefficients (a in Vis. Res. paper)
noise_var  noise variance (sigma_N in Vis. Res. paper)
beta       steepness of prior
sigma      scaling parameter for prior
eta        learning rate
tol        tolerance for conjugate gradient routine

VAR_GOAL   variance goal for the coefficients
S_var      actual variance of the coefficients
var_eta    average rate for S_var
alpha      gain adaptation rate
gain       L2 norm of basis functions

