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        my $f = $net->learn_set(\@data, 
                                          inc   =>      0.1,    
                                          max   =>      500,
                                         );

        # Print it 
        print &quot;Forgetfullness: $f%&quot;;</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 ]
        );

        $net-&gt;learn_set(\@data);</PRE>
<P>Same effect as above, but not the same data (obviously).</P>
<P></P>
<DT><STRONG><A NAME="item_run">$net-&gt;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 = $net-&gt;run($inputs);</PRE>
<P>You can also do this with a string:
</P>
<PRE>
        my $outputs = $net-&gt;run('cloudy - wind is 5 MPH NW');</PRE>
<P>See also <A HREF="#item_run_uc"><CODE>run_uc()</CODE></A> and <A HREF="#item_run_set"><CODE>run_set()</CODE></A> below.</P>
<P></P>
<DT><STRONG><A NAME="item_run_uc">$net-&gt;run_uc($input_map_ref);</A></STRONG><BR>
<DD>
This method does the same thing as this code:

<PRE>
        $net-&gt;uncrunch($net-&gt;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-&gt;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-&gt;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-&gt;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-&gt;range();</A></STRONG><BR>
<DD>
See CUSTOM ACTIVATION FUNCTIONS for information on several included activation functions.
<P></P>
<DT><STRONG><A NAME="item_benchmark">$net-&gt;benchmark();</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_benchmarked">$net-&gt;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 &quot;Last learn() took &quot;,$net-&gt;benchmark(),&quot;\n&quot;;</PRE>
<P></P>
<DT><STRONG><A NAME="item_verbose">$net-&gt;verbose($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_verbosity">$net-&gt;verbosity($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_v">$net-&gt;v($level);</A></STRONG><BR>
<DD>
<DT><STRONG><A NAME="item_debug">$net-&gt;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-&gt;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.