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Implemented fahlman constant. This eliminates the 'flat spot'
problem, and gets the network to converge more reliably. XOR
seems to never get stuck with this set to 0.1. as a bonus, its 
also about 50% faster (do you sense a theme developing here?)

Removed momentum module (well, removed backprop module and 
renamed momentum module in fact). There were few code differences
and its easier to maintain this way. Default option is vanilla
backprop, momentum & fahlman adjustments are only implemented if
specified in the network config.

Bundled all the maths functions into AI::NNFlex::Mathlib

Implemented calls to error transformation function, as per
Fahlman. Testing, it doesn't seem to make much difference, but
at least now the facility is there.

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0.20
20050308

v0.17 was never released, as I rejigged the whole lot for
object inheritance before I got around to uploading it to CPAN.
Why? I hear you ask, when it worked OK already.
1) its faster, a lot faster.
2) feedforward isn't the only kind of network, and I wanted to
be free to overload some of the methods (especially init) to
simplify writing a Hopfield module (in progress)
3) its more theoretically correct

So now, AI::NNFlex is the base class for the other types of 
networks, and you should never need to call AI::NNFlex class
directly - you should call the constructor of the subclass, such
as:
my $network = AI::NNFlex::momentum->new(params);

The upshot of that is that the network type and learning algorithm
parameters are now obsolete.

###

Removed draw.pm from the distribution - it really wasn't very useful
and hadn't been kept up to date with newer revisions of the code. It
will be put back later (perhaps) or incorporated into a separate GUI
frontend.

Removed feedforward_pdl.pm from the distribution - it shouldn't have
been there in the first place!

Fixed the lesion subs so they expect parameter=>value pairs instead
of an anonymous hash (left over from when I didn't know you could do
that).

Fixed an error - random weights was bounded by 1, not the parameter
'randomweights'. Its now positive only. Some benchmarking needed as
it appears that positive random starting weights rather than a mix
of positive and negative make the network quicker to converge, at
least with momentum.

weights now defaults to rand(1) instead of 0 - at least for backprop
type nets, a default 0 weight will never work. For other types of nets
the random weights can be overridden with the 'fixedweights' parameter.

Fixed load_state to correctly read weights from the bias node






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0.17
2005030x

Implemented a rounding network property in output

Fixed a bug that allowed activation to flow through a node 
even if it was inactive

Altered the syntax for output to be param=>value instead of
an anonymous hash

As per Scott Fahlmans comments about neural net benchmarking,
(Fahlman S.E., (1988) 'An empirical study of learning speed in back-propagation networks'. Tech. Rep. CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, PA.) , I've started using a more realistic benchmark than xor.
The 'cars' subfolder in examples contains the learning code
for this, drawn from
 ftp://ftp.ics.uci.edu/pub/machine-learning-databases/car/





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0.16
20050218

Changed the way weights are identified from a hash (keyed on
stringified node object ref) to an array. Cleaner that way,
and makes it easier to turn the weights into a matrix for
compatibility with the upcoming PDL based modules.

Altered the syntax for calls to load_state and dump_state,
to get rid of the nasty hashref thing.

Cleaned up a bit of debugging in ::init


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0.15
20050206

I've rushed another release out because I found a big fat bug
in feedforward. Instead of applying the transfer function to
the summed node inputs, it was applying it to each input in
turn, then again to the sum. Wups! The network is now more
stable and a lot faster.

I also altered the debug calls, so in normal use without debug
the overhead is a lot smaller.