AI-NeuralNet-Mesh
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# Print it
print "Forgetfullness: $f%";</PRE>
<P></P>
<P>This is a snippet from the example script examples/finance.pl, which demonstrates DOW average
prediction for the next month. A more simple set defenition would be as such:</P>
<PRE>
my @data = (
[ 0,1 ], [ 1 ],
[ 1,0 ], [ 0 ]
);
$net->learn_set(\@data);</PRE>
<P>Same effect as above, but not the same data (obviously).</P>
<P></P>
<DT><STRONG><A NAME="item_run">$net->run($input_map_ref);</A></STRONG><BR>
<DD>
This method will apply the given array ref at the input layer of the neural network, and
it will return an array ref to the output of the network. <A HREF="#item_run"><CODE>run()</CODE></A> will now automatically <A HREF="#item_crunch"><CODE>crunch()</CODE></A>
a string given as an input (See the <A HREF="#item_crunch"><CODE>crunch()</CODE></A> method for info on crunching).
<P>Example Usage:
</P>
<PRE>
my $inputs = [ 1,1,0,1 ];
my $outputs = $net->run($inputs);</PRE>
<P>You can also do this with a string:
</P>
<PRE>
my $outputs = $net->run('cloudy - wind is 5 MPH NW');</PRE>
<P>See also <A HREF="#item_run_uc"><CODE>run_uc()</CODE></A> and <A HREF="#item_run_set"><CODE>run_set()</CODE></A> below.</P>
<P></P>
<DT><STRONG><A NAME="item_run_uc">$net->run_uc($input_map_ref);</A></STRONG><BR>
<DD>
This method does the same thing as this code:
<PRE>
$net->uncrunch($net->run($input_map_ref));</PRE>
<P>All that <A HREF="#item_run_uc"><CODE>run_uc()</CODE></A> does is that it automatically calls <A HREF="#item_uncrunch"><CODE>uncrunch()</CODE></A> on the output, regardless
of whether the input was <A HREF="#item_crunch"><CODE>crunch()</CODE></A> -ed or not.</P>
<P></P>
<DT><STRONG><A NAME="item_run_set">$net->run_set($set);</A></STRONG><BR>
<DD>
<P>This takes an array ref of the same structure as the learn_set() method, above. It returns
an array ref. Each element in the returned array ref represents the output for the corresponding
element in the dataset passed. Uses run() internally.</P>
<DT><STRONG><A NAME="item_get_outs">$net->get_outs($set);</A></STRONG><BR>
<DD>
Simple utility function which takes an array ref of the same structure as the <A HREF="#item_learn_set"><CODE>learn_set()</CODE></A> method,
above. It returns an array ref of the same type as <A HREF="#item_run_set"><CODE>run_set()</CODE></A> wherein each element contains an
output value. The output values are the target values specified in the $set passed. Each element
in the returned array ref represents the output value for the corrseponding row in the dataset
passed. (A row is two elements of the dataset together, see <A HREF="#item_learn_set"><CODE>learn_set()</CODE></A> for dataset structure.)
<P></P>
<DT><STRONG><A NAME="item_load_set">$net->load_set($file,$column,$seperator);</A></STRONG><BR>
<DD>
Loads a CSV-like dataset from disk
<P>Returns a data set of the same structure as required by the
<A HREF="#item_learn_set"><CODE>learn_set()</CODE></A> method. $file is the disk file to load set from.
$column an optional variable specifying the column in the
data set to use as the class attribute. $class defaults to 0.
$seperator is an optional variable specifying the seperator
character between values. $seperator defaults to ',' (a single comma).
NOTE: This does not handle quoted fields, or any other record
seperator other than ``\n''.</P>
<P>The returned array ref is suitable for passing directly to
<A HREF="#item_learn_set"><CODE>learn_set()</CODE></A> or get_outs().</P>
<P></P>
<DT><STRONG><A NAME="item_range">$net->range();</A></STRONG><BR>
<DD>
See CUSTOM ACTIVATION FUNCTIONS for information on several included activation functions.
<P></P>
<DT><STRONG><A NAME="item_benchmark">$net->benchmark();</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_benchmarked">$net->benchmarked();</A></STRONG><BR>
<DD>
This returns a benchmark info string for the last <A HREF="#item_learn"><CODE>learn()</CODE></A> call.
It is easily printed as a string, as following:
<PRE>
print "Last learn() took ",$net->benchmark(),"\n";</PRE>
<P></P>
<DT><STRONG><A NAME="item_verbose">$net->verbose($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_verbosity">$net->verbosity($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_v">$net->v($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_debug">$net->debug($level)</A></STRONG><BR>
<DD>
Note: verbose(), verbosity(), and <A HREF="#item_v"><CODE>v()</CODE></A> are all functional aliases for debug().
<P>Toggles debugging off if called with $level = 0 or no arguments. There are several levels
of debugging.</P>
<P>NOTE: Debugging verbosity has been toned down somewhat from AI::NeuralNet::BackProp,
but level 4 still prints the same amount of information as you were used to. The other
levels, however, are mostly for advanced use. Not much explanation in the other
levels, but they are included for those of you that feel daring (or just plain bored.)</P>
<P>Level 0 ($level = 0) : Default, no debugging information printed. All printing is
left to calling script.</P>
<P>Level 1 ($level = 1) : Displays the activity between nodes, prints what values were
received and what they were weighted to.</P>
<P>Level 2 ($level = 2) : Just prints info from the <A HREF="#item_learn"><CODE>learn()</CODE></A> loop, in the form of ``got: X, wanted Y''
type of information. This is about the third most useful debugging level, after level 12 and
level 4.</P>
<P>Level 3 ($level = 3) : I don't think I included any level 3 debugs in this version.</P>
<P>Level 4 ($level = 4) : This level is the one I use most. It is only used during learning. It
displays the current error (difference between actual outputs and the target outputs you
asked for), as well as the current loop number and the benchmark time for the last learn cycle.
Also printed are the actual outputs and the target outputs below the benchmark times.</P>
<P>Level 12 ($level = 12) : Level 12 prints a dot (period) [.] after each learning loop is
complete. This is useful for letting the user know that stuff is happening, but without
having to display any of the internal variables. I use this in the ex_aln.pl demo,
as well as the ex_agents.pl demo.</P>
<P>Toggles debuging off when called with no arguments.</P>
<P></P>
<DT><STRONG><A NAME="item_save">$net->save($filename);</A></STRONG><BR>
<DD>
This will save the complete state of the network to disk, including all weights and any
words crunched with <A HREF="#item_crunch"><CODE>crunch()</CODE></A> . Also saves the layer size and activations of the network.
<P>NOTE: The only activation type NOT saved is the CODE ref type, which must be set again
after loading.</P>
<P>This uses a simple flat-file text storage format, and therefore the network files should
be fairly portable.</P>
<P>This method will return undef if there was a problem with writing the file. If there is an
called with a 0 value, it will disable randomness in the network. The randomness factor
is preserved across <A HREF="#item_load"><CODE>load()</CODE></A> and <A HREF="#item_save"><CODE>save()</CODE></A> calls.
<P></P>
<DT><STRONG><A NAME="item_const">$net->const($const);</A></STRONG><BR>
<DD>
This sets the run const. for the network. The run const. is a value that is added
to every input line when a set of inputs are <A HREF="#item_run"><CODE>run()</CODE></A> or <A HREF="#item_learn"><CODE>learn()</CODE></A> -ed, to prevent the
network from hanging on a 0 value. When called with no arguments, it returns the current
const. value. It defaults to 0.0001 on a newly-created network. The run const. value
is preserved across <A HREF="#item_load"><CODE>load()</CODE></A> and <A HREF="#item_save"><CODE>save()</CODE></A> calls.
<P></P>
<DT><STRONG><A NAME="item_error">$net->error();</A></STRONG><BR>
<DD>
Returns the last error message which occured in the mesh, or undef if no errors have
occured.
<P></P>
<DT><STRONG><A NAME="item_load_pcx">$net->load_pcx($filename);</A></STRONG><BR>
<DD>
NOTE: To use this function, you must have PCX::Loader installed. If you do not have
PCX::Loader installed, it will return undef and store an error for you to retrive with
the <A HREF="#item_error"><CODE>error()</CODE></A> method, below.
<P>This is 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 PCX::Loader object, which
supports the following routinges/members. See example files ex_pcx.pl and ex_pcxl.pl in
the ./examples/ directory.</P>
<P>See <CODE>perldoc PCX::Loader</CODE> for information on the methods of the object returned.</P>
<P>You can download PCX::Loader from <A HREF="http://www.josiah.countystart.com/modules/get.pl?pcx-loader:mpod">http://www.josiah.countystart.com/modules/get.pl?pcx-loader:mpod</A></P>
<P></P></DL>
<P>
<HR>
<H1><A NAME="custom activation functions">CUSTOM ACTIVATION FUNCTIONS</A></H1>
<P>Included in this package are four custom activation functions meant to be used
as a guide to create your own, as well as to be useful to you in normal use of the
module. There is only one function exported by default into your namespace, which
is the <A HREF="#item_range"><CODE>range()</CODE></A> functions. These are not meant to be used as methods, but as functions.
These functions return code refs to a Perl closure which does the actual work when
the time comes.</P>
<DL>
<DT><STRONG>range(0..X);</STRONG><BR>
<DD>
<DT><STRONG>range(@range);</STRONG><BR>
<DD>
<DT><STRONG>range(A,B,C);</STRONG><BR>
<DD>
<A HREF="#item_range"><CODE>range()</CODE></A> returns a closure limiting the output
of that node to a specified set of values.
Good for use in output layers.
<P>Usage example:
$net->activation(4,range(0..5));
or (in the <A HREF="#item_new"><CODE>new()</CODE></A> hash constructor form):
..
{
nodes => 1,
activation => range 5..2
}
..
You can also pass an array containing the range
values (not array ref), or you can pass a comma-
seperated list of values as parameters:</P>
<PRE>
$net->activation(4,range(@numbers));
$net->activation(4,range(6,15,26,106,28,3));</PRE>
<P>Note: when using a <A HREF="#item_range"><CODE>range()</CODE></A> activatior, train the
net TWICE on the data set, because the first time
the <A HREF="#item_range"><CODE>range()</CODE></A> function searches for the top value in
the inputs, and therefore, results could flucuate.
The second learning cycle guarantees more accuracy.</P>
<P>The actual code that implements the range closure is
a bit convulted, so I will expand on it here as a simple
tutorial for custom activation functions.</P>
<PRE>
= line 1 = sub {
= line 2 = my @values = ( 6..10 );
= line 3 = my $sum = shift;
= line 4 = my $self = shift;
= line 5 = $self->{top_value}=$sum if($sum>$self->{top_value});
= line 6 = my $index = intr($sum/$self->{top_value}*$#values);
= line 7 = return $values[$index];
= line 8 = }</PRE>
<P>Now, the actual function fits in one line of code, but I expanded it a bit
here. Line 1 creates our array of allowed output values. Lines two and
three grab our parameters off the stack which allow us access to the
internals of this node. Line 5 checks to see if the sum output of this
node is higher than any previously encountered, and, if so, it sets
the marker higher. This also shows that you can use the $self refrence
to maintain information across activations. This technique is also used
in the <A HREF="#item_ramp"><CODE>ramp()</CODE></A> activator. Line 6 computes the index into the allowed
values array by first scaling the $sum to be between 0 and 1 and then
expanding it to fit smoothly inside the number of elements in the array. Then
we simply round to an integer and pluck that index from the array and
use it as the output value for that node.</P>
<P>See? It's not that hard! Using custom activation functions, you could do
just about anything with the node that you want to, since you have
access to the node just as if you were a blessed member of that node's object.</P>
<P></P>
<DT><STRONG><A NAME="item_ramp">ramp($r);</A></STRONG><BR>
<DD>
<A HREF="#item_ramp"><CODE>ramp()</CODE></A> preforms smooth ramp activation between 0 and 1 if $r is 1,
or between -1 and 1 if $r is 2. $r defaults to 1.
<P>You can get this into your namespace with the ':acts' export
tag as so:
</P>
<PRE>
use AI::NeuralNet::Mesh ':acts';</PRE>
<P>Note: when using a <A HREF="#item_ramp"><CODE>ramp()</CODE></A> activatior, train the
net at least TWICE on the data set, because the first
time the <A HREF="#item_ramp"><CODE>ramp()</CODE></A> function searches for the top value in
the inputs, and therefore, results could flucuate.
The second learning cycle guarantees more accuracy.</P>
<P>No code to show here, as it is almost exactly the same as range().</P>
<P></P>
<DT><STRONG><A NAME="item_and_gate">and_gate($threshold);</A></STRONG><BR>
<DD>
Self explanitory, pretty much. This turns the node into a basic AND gate.
$threshold is used to decide if an input is true or false (1 or 0). If
an input is below $threshold, it is false. $threshold defaults to 0.5.
<P>You can get this into your namespace with the ':acts' export
tag as so:
( run in 1.001 second using v1.01-cache-2.11-cpan-2e0ccfb7a10 )