AI-NeuralNet-BackProp
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BackProp.pm view on Meta::CPAN
# Rounds a floating-point to an integer with int() and sprintf()
sub intr {
shift if(substr($_[0],0,4) eq 'AI::');
try { return int(sprintf("%.0f",shift)) }
catch { return 0 }
}
# Used to format array ref into columns
# Usage:
# join_cols(\@array,$row_length_in_elements,$high_state_character,$low_state_character);
# Can also be called as method of your neural net.
# If $high_state_character is null, prints actual numerical values of each element.
sub join_cols {
no strict 'refs';
shift if(substr($_[0],0,4) eq 'AI::');
my $map = shift;
my $break = shift;
my $a = shift;
my $b = shift;
my $x;
BackProp.pm view on Meta::CPAN
$x++;
if($x>$break-1) {
print "\n";
$x=0;
}
}
print "\n";
}
# Returns percentage difference between all elements of two
# array refs of exact same length (in elements).
# Now calculates actual difference in numerical value.
sub pdiff {
no strict 'refs';
shift if(substr($_[0],0,4) eq 'AI::');
my $a1 = shift;
my $a2 = shift;
my $a1s = $#{$a1}; #AI::NeuralNet::BackProp::_FETCHSIZE($a1);
my $a2s = $#{$a2}; #AI::NeuralNet::BackProp::_FETCHSIZE($a2);
my ($a,$b,$diff,$t);
$diff=0;
#return undef if($a1s ne $a2s); # must be same length
for my $x (0..$a1s) {
$a = $a1->[$x];
$b = $a2->[$x];
if($a!=$b) {
if($a<$b){$t=$a;$a=$b;$b=$t;}
$a=1 if(!$a);
$diff+=(($a-$b)/$a)*100;
}
}
$a1s = 1 if(!$a1s);
BackProp.pm view on Meta::CPAN
Run returns a refrence with $size elements (Remember $size? $size
is what you passed as the second argument to the network
constructor.) This array contains the results of the mapping. If
you ran the example exactly as shown above, $result would probably
contain (1,1) as its elements.
To make the network learn a new pattern, you simply call the learn
method with a sample input and the desired result, both array
refrences of $size length. Example:
use AI;
my $net = new AI::NeuralNet::BackProp(2,2);
my @map = (0,1);
my @res = (1,0);
$net->learn(\@map,\@res);
my $result = $net->run(\@map);
BackProp.pm view on Meta::CPAN
$net->learn('corn', 'cob');
# Before update, you have had to do this:
# $net->learn($net->crunch('corn'), $net->crunch('cob'));
Note, the old method of calling crunch on the values still works just as well.
UPDATED: You can now learn inputs with a 0 value. Beware though, it may not learn() a 0 value
in the input map if you have randomness disabled. See NOTES on using a 0 value with randomness
disabled.
The first two arguments may be array refs (or now, strings), and they may be of different lengths.
Options should be written on hash form. There are three options:
inc => $learning_gradient
max => $maximum_iterations
error => $maximum_allowable_percentage_of_error
$learning_gradient is an optional value used to adjust the weights of the internal
connections. If $learning_gradient is ommitted, it defaults to 0.20.
BackProp.pm view on Meta::CPAN
=item $net->load($filename);
This will load from disk any network saved by save() and completly restore the internal
state at the point it was save() was called at.
=item $net->join_cols($array_ref,$row_length_in_elements,$high_state_character,$low_state_character);
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 join() ,
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 join_cols() will just print the numerical value of each element seperated
by a null character (\0). join_cols() defaults to the latter behaviour.
=item $net->pdiff($array_ref_A, $array_ref_B);
the network hasn't ``learned'' any patterns yet. But this
illustrates the call. Run now allows strings to be used as
input. See <A HREF="#item_run"><CODE>run()</CODE></A> for more information.</P>
<P>Run returns a refrence with $size elements (Remember $size? $size
is what you passed as the second argument to the network
constructor.) This array contains the results of the mapping. If
you ran the example exactly as shown above, $result would probably
contain (1,1) as its elements.</P>
<P>To make the network learn a new pattern, you simply call the learn
method with a sample input and the desired result, both array
refrences of $size length. Example:</P>
<PRE>
use AI;
my $net = new AI::NeuralNet::BackProp(2,2);
my @map = (0,1);
my @res = (1,0);
$net->learn(\@map,\@res);
my $result = $net->run(\@map);</PRE>
<P><B>UPDATED:</B> You can now specify strings as inputs and ouputs to learn, and they will be crunched
automatically. Example:</P>
<PRE>
$net->learn('corn', 'cob');
# Before update, you have had to do this:
# $net->learn($net->crunch('corn'), $net->crunch('cob'));</PRE>
<P>Note, the old method of calling crunch on the values still works just as well.</P>
<P><B>UPDATED:</B> You can now learn inputs with a 0 value. Beware though, it may not <A HREF="#item_learn"><CODE>learn()</CODE></A> a 0 value
in the input map if you have randomness disabled. See NOTES on using a 0 value with randomness
disabled.</P>
<P>The first two arguments may be array refs (or now, strings), and they may be of different lengths.</P>
<P>Options should be written on hash form. There are three options:
</P>
<PRE>
inc => $learning_gradient
max => $maximum_iterations
error => $maximum_allowable_percentage_of_error</PRE>
<P>$learning_gradient is an optional value used to adjust the weights of the internal
connections. If $learning_gradient is ommitted, it defaults to 0.20.
</P>
<P>
This will save the complete state of the network to disk, including all weights and any
words crunched with <A HREF="#item_crunch"><CODE>crunch()</CODE></A> . Also saves any output ranges set with <A HREF="#item_range"><CODE>range()</CODE></A> .
<P>This has now been modified to use a simple flat-file text storage format, and it does not
depend on any external modules now.</P>
<P></P>
<DT><STRONG><A NAME="item_load">$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
( run in 0.860 second using v1.01-cache-2.11-cpan-65fba6d93b7 )