AI-NeuralNet-BackProp
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words crunched with <A HREF="#item_crunch"><CODE>crunch()</CODE></A> . Also saves any output ranges set with <A HREF="#item_range"><CODE>range()</CODE></A> .
<P>This has now been modified to use a simple flat-file text storage format, and it does not
depend on any external modules now.</P>
<P></P>
<DT><STRONG><A NAME="item_load">$net->load($filename);</A></STRONG><BR>
<DD>
This will load from disk any network saved by <A HREF="#item_save"><CODE>save()</CODE></A> and completly restore the internal
state at the point it was <A HREF="#item_save"><CODE>save()</CODE></A> was called at.
<P></P>
<DT><STRONG><A NAME="item_join_cols">$net->join_cols($array_ref,$row_length_in_elements,$high_state_character,$low_state_character);</A></STRONG><BR>
<DD>
This is more of a utility function than any real necessary function of the package.
Instead of joining all the elements of the array together in one long string, like <CODE>join()</CODE> ,
it prints the elements of $array_ref to STDIO, adding a newline (\n) after every $row_length_in_elements
number of elements has passed. Additionally, if you include a $high_state_character and a $low_state_character,
it will print the $high_state_character (can be more than one character) for every element that
has a true value, and the $low_state_character for every element that has a false value.
If you do not supply a $high_state_character, or the $high_state_character is a null or empty or
undefined string, it <A HREF="#item_join_cols"><CODE>join_cols()</CODE></A> will just print the numerical value of each element seperated
by a null character (\0). <A HREF="#item_join_cols"><CODE>join_cols()</CODE></A> defaults to the latter behaviour.
<P></P>
<DT><STRONG><A NAME="item_pdiff">$net->pdiff($array_ref_A, $array_ref_B);</A></STRONG><BR>
<DD>
This function is used VERY heavily internally to calculate the difference in percent
between elements of the two array refs passed. It returns a %.10f (sprintf-format)
percent sting.
<P></P>
<DT><STRONG><A NAME="item_p">$net->p($a,$b);</A></STRONG><BR>
<DD>
Returns a floating point number which represents $a as a percentage of $b.
<P></P>
<DT><STRONG><A NAME="item_intr">$net->intr($float);</A></STRONG><BR>
<DD>
Rounds a floating-point number rounded to an integer using <CODE>sprintf()</CODE> and <CODE>int()</CODE> , Provides
better rounding than just calling <CODE>int()</CODE> on the float. Also used very heavily internally.
<P></P>
<DT><STRONG><A NAME="item_high">$net->high($array_ref);</A></STRONG><BR>
<DD>
Returns the index of the element in array REF passed with the highest comparative value.
<P></P>
<DT><STRONG><A NAME="item_low">$net->low($array_ref);</A></STRONG><BR>
<DD>
Returns the index of the element in array REF passed with the lowest comparative value.
<P></P>
<DT><STRONG><A NAME="item_show">$net->show();</A></STRONG><BR>
<DD>
This will dump a simple listing of all the weights of all the connections of every neuron
in the network to STDIO.
<P></P>
<DT><STRONG><A NAME="item_crunch">$net->crunch($string);</A></STRONG><BR>
<DD>
<B>UPDATED:</B> Now you can use a variabled instead of using qw(). Strings will be split internally.
Do not use <CODE>qw()</CODE> to pass strings to crunch.
<P>This splits a string passed with /[\s\t]/ into an array ref containing unique indexes
to the words. The words are stored in an intenal array and preserved across <A HREF="#item_load"><CODE>load()</CODE></A> and <A HREF="#item_save"><CODE>save()</CODE></A>
calls. This is designed to be used to generate unique maps sutible for passing to <A HREF="#item_learn"><CODE>learn()</CODE></A> and
<A HREF="#item_run"><CODE>run()</CODE></A> directly. It returns an array ref.</P>
<P>The words are not duplicated internally. For example:</P>
<PRE>
$net->crunch("How are you?");</PRE>
<P>Will probably return an array ref containing 1,2,3. A subsequent call of:</P>
<PRE>
$net->crunch("How is Jane?");</PRE>
<P>Will probably return an array ref containing 1,4,5. Notice, the first element stayed
the same. That is because it already stored the word ``How''. So, each word is stored
only once internally and the returned array ref reflects that.</P>
<P></P>
<DT><STRONG><A NAME="item_uncrunch">$net->uncrunch($array_ref);</A></STRONG><BR>
<DD>
Uncrunches a map (array ref) into an scalar string of words seperated by ' ' and returns the
string. This is ment to be used as a counterpart to the <A HREF="#item_crunch"><CODE>crunch()</CODE></A> method, above, possibly to
<A HREF="#item_uncrunch"><CODE>uncrunch()</CODE></A> the output of a <A HREF="#item_run"><CODE>run()</CODE></A> call. Consider the below code (also in ./examples/ex_crunch.pl):
<PRE>
use AI::NeuralNet::BackProp;
my $net = AI::NeuralNet::BackProp->new(2,3);
for (0..3) { # Note: The four learn() statements below could
# be replaced with learn_set() to do the same thing,
# but use this form here for clarity.
$net->learn($net->crunch("I love chips."), $net->crunch("That's Junk Food!"));
$net->learn($net->crunch("I love apples."), $net->crunch("Good, Healthy Food."));
$net->learn($net->crunch("I love pop."), $net->crunch("That's Junk Food!"));
$net->learn($net->crunch("I love oranges."),$net->crunch("Good, Healthy Food."));
}
my $response = $net->run($net->crunch("I love corn."));
print $net->uncrunch($response),"\n";</PRE>
<P>On my system, this responds with, ``Good, Healthy Food.'' If you try to run <A HREF="#item_crunch"><CODE>crunch()</CODE></A> with
``I love pop.'', though, you will probably get ``Food! apples. apples.'' (At least it returns
that on my system.) As you can see, the associations are not yet perfect, but it can make
for some interesting demos!</P>
<P></P>
<DT><STRONG><A NAME="item_crunched">$net->crunched($word);</A></STRONG><BR>
<DD>
This will return undef if the word is not in the internal crunch list, or it will return the
index of the word if it exists in the crunch list.
<P></P>
<DT><STRONG><A NAME="item_col_width">$net->col_width($width);</A></STRONG><BR>
<DD>
This is useful for formating the debugging output of Level 4 if you are learning simple
bitmaps. This will set the debugger to automatically insert a line break after that many
elements in the map output when dumping the currently run map during a learn loop.
<P>It will return the current width when called with a 0 or undef value.</P>
<P></P>
<DT><STRONG><A NAME="item_random">$net->random($rand);</A></STRONG><BR>
<DD>
This will set the randomness factor from the network. Default is 0.001. When called
with no arguments, or an undef value, it will return current randomness value. When
called with a 0 value, it will disable randomness in the network. See NOTES on learning
a 0 value in the input map with randomness disabled.
<P></P>
<DT><STRONG><A NAME="item_load_pcx">$net->load_pcx($filename);</A></STRONG><BR>
<DD>
Oh heres a treat... this routine will load a PCX-format file (yah, I know ... ancient format ... but
it is the only one I could find specs for to write it in Perl. If anyone can get specs for
any other formats, or could write a loader for them, I would be very grateful!) Anyways, a PCX-format
file that is exactly 320x200 with 8 bits per pixel, with pure Perl. It returns a blessed refrence to
a AI::NeuralNet::BackProp::PCX object, which supports the following routinges/members. See example
it is used internally by AI::NeuralNet::BackProp.
<P></P>
<DT><STRONG><A NAME="item_AI%3A%3ANeuralNet%3A%3ABackProp%3A%3A_run">AI::NeuralNet::BackProp::_run</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_AI%3A%3ANeuralNet%3A%3ABackProp%3A%3A_map">AI::NeuralNet::BackProp::_map</A></STRONG><BR>
<DD>
These two packages, _run and _map are used to insert data into
the network and used to get data from the network. The _run and _map packages
are connected to the neurons so that the neurons think that the IO packages are
just another neuron, sending data on. But the IO packs. are special packages designed
with the same methods as neurons, just meant for specific IO purposes. You will
never need to call any of the IO packs. directly. Instead, they are called whenever
you use the <A HREF="#item_run"><CODE>run()</CODE></A> or <A HREF="#item_learn"><CODE>learn()</CODE></A> methods of your network.
<P></P></DL>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="bugs">BUGS</A></H1>
<P>This is an alpha release of <CODE>AI::NeuralNet::BackProp</CODE>, and that holding true, I am sure
there are probably bugs in here which I just have not found yet. If you find bugs in this module, I would
appreciate it greatly if you could report them to me at <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM>,
or, even better, try to patch them yourself and figure out why the bug is being buggy, and
send me the patched code, again at <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM>.</P>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="author">AUTHOR</A></H1>
<P>Josiah Bryan <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM></P>
<P>Copyright (c) 2000 Josiah Bryan. All rights reserved. This program is free software;
you can redistribute it and/or modify it under the same terms as Perl itself.</P>
<P>The <CODE>AI::NeuralNet::BackProp</CODE> and related modules are free software. THEY COME WITHOUT WARRANTY OF ANY KIND.</P>
<P></P>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="thanks">THANKS</A></H1>
<P>Below is a list of people that have helped, made suggestions, patches, etc. No particular order.</P>
<PRE>
Tobias Bronx, tobiasb@odin.funcom.com
Pat Trainor, ptrainor@title14.com
Steve Purkis, spurkis@epn.nu
Rodin Porrata, rodin@ursa.llnl.gov
Daniel Macks dmacks@sas.upenn.edu</PRE>
<P>Tobias was a great help with the initial releases, and helped with learning options and a great
many helpful suggestions. Rodin has gave me some great ideas for the new internals, as well
as disabling Storable. Steve is the author of AI::Perceptron, and gave some good suggestions for
weighting the neurons. Daniel was a great help with early beta testing of the module and related
ideas. Pat has been a great help for running the module through the works. Pat is the author of
the new Inter game, a in-depth strategy game. He is using a group of neural networks internally
which provides a good test bed for coming up with new ideas for the network. Thankyou for all of
your help, everybody.</P>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="download">DOWNLOAD</A></H1>
<P>You can always download the latest copy of AI::NeuralNet::BackProp
from <A HREF="http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl">http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl</A></P>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="mailing list">MAILING LIST</A></H1>
<P>A mailing list has been setup for AI::NeuralNet::BackProp for discussion of AI and
neural net related topics as they pertain to AI::NeuralNet::BackProp. I will also
announce in the group each time a new release of AI::NeuralNet::BackProp is available.</P>
<P>The list address is at: <A HREF="mailto:ai-neuralnet-backprop@egroups.com">ai-neuralnet-backprop@egroups.com</A></P>
<P>To subscribe, send a blank email to: <A HREF="mailto:ai-neuralnet-backprop-subscribe@egroups.com">ai-neuralnet-backprop-subscribe@egroups.com</A></P>
<P>
<HR SIZE=1 COLOR=BLACK>
<H1><A NAME="what can it do">WHAT CAN IT DO?</A></H1>
<P>Rodin Porrata asked on the ai-neuralnet-backprop malining list,
"What can they [Neural Networks] do?". In regards to that questioin,
consider the following:</P>
<P>Neural Nets are formed by simulated neurons connected together much the same
way the brain's neurons are, neural networks are able to associate and
generalize without rules. They have solved problems in pattern recognition,
robotics, speech processing, financial predicting and signal processing, to
name a few.</P>
<P>One of the first impressive neural networks was NetTalk, which read in ASCII
text and correctly pronounced the words (producing phonemes which drove a
speech chip), even those it had never seen before. Designed by John Hopkins
biophysicist Terry Sejnowski and Charles Rosenberg of Princeton in 1986,
this application made the Backprogagation training algorithm famous. Using
the same paradigm, a neural network has been trained to classify sonar
returns from an undersea mine and rock. This classifier, designed by
Sejnowski and R. Paul Gorman, performed better than a nearest-neighbor
classifier.</P>
<P>The kinds of problems best solved by neural networks are those that people
are good at such as association, evaluation and pattern recognition.
Problems that are difficult to compute and do not require perfect answers,
just very good answers, are also best done with neural networks. A quick,
very good response is often more desirable than a more accurate answer which
takes longer to compute. This is especially true in robotics or industrial
controller applications. Predictions of behavior and general analysis of
data are also affairs for neural networks. In the financial arena, consumer
loan analysis and financial forecasting make good applications. New network
designers are working on weather forecasts by neural networks (Myself
included). Currently, doctors are developing medical neural networks as an
aid in diagnosis. Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.</P>
<P>Neural networks are poor at precise calculations and serial processing. They
are also unable to predict or recognize anything that does not inherently
contain some sort of pattern. For example, they cannot predict the lottery,
since this is a random process. It is unlikely that a neural network could
be built which has the capacity to think as well as a person does for two
reasons. Neural networks are terrible at deduction, or logical thinking and
the human brain is just too complex to completely simulate. Also, some
problems are too difficult for present technology. Real vision, for
example, is a long way off.</P>
<P>In short, Neural Networks are poor at precise calculations, but good at
association, evaluation, and pattern recognition.
</P>
<P>
<HR SIZE=1 COLOR=BLACK>
<A HREF="http://www.josiah.countystart.com/modules/AI/rec.pl?docs.htm">AI::NeuralNet::BackProp</a> - <i>Written by Josiah Bryan, <<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></I>
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