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found more than 414 distributions - search limited to the first 2001 files matching your query
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AI-MXNetCAPI
view release on metacpan or search on metacpan
META.json
META.yml
t/AI-MXNetCAPI.t
lib/AI/MXNetCAPI.pm
mxnet.i
mxnet_typemaps.i
AI-MaxEntropy
view release on metacpan or search on metacpan
inc/Module/AutoInstall.pm view on Meta::CPAN
$UnderCPAN = _check_lock(); # check for $UnderCPAN
if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
require Config;
print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";
# make an educated guess of whether we'll need root permission.
print " (You may need to do that as the 'root' user.)\n"
if eval '$>';
}
AI-MegaHAL
view release on metacpan or search on metacpan
0.08 = 0.07_01 2015-04-12
0.07_01 Apr 14 2012
- mention Hailo.pm in docs
- use 'unsigned int' as 32 bit data type
- mingw64 supported
0.07 = 0.06_01 Jul 12 2008
0.06_01 Jan 24 2008
- corrected documentation (typo found by ZOFFIX)
AI-MicroStructure
view release on metacpan or search on metacpan
lib/AI/MicroStructure/RemoteList.pm view on Meta::CPAN
my $ua = LWP::UserAgent->new( env_proxy => 1 );
foreach my $src (@srcs) {
my $request = HTTP::Request->new(
ref $src
? ( POST => $src->[0],
[ content_type => 'application/x-www-form-urlencoded' ],
$src->[1]
)
: ( GET => $src )
);
AI-NNEasy
view release on metacpan or search on metacpan
lib/AI/NNEasy.pm view on Meta::CPAN
sub NNEasy {
my $this = ref($_[0]) ? shift : undef ;
my $CLASS = ref($this) || __PACKAGE__ ;
my $file = shift(@_) ;
my @out_types = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
my $error_ok = shift(@_) ;
my $in = shift(@_) ;
my $out = shift(@_) ;
my @layers = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
my $conf = shift(@_) ;
lib/AI/NNEasy.pm view on Meta::CPAN
foreach my $layers_i ( @layers ) {
$layers_i = $this->_layer_conf( { decay=>$decay } , $layers_i ) ;
}
my $nn_conf = {random_connections=>0 , networktype=>'feedforward' , random_weights=>1 , learning_algorithm=>'backprop' , learning_rate=>0.1 , bias=>1} ;
foreach my $Key ( keys %$nn_conf ) { $$nn_conf{$Key} = $$conf{$Key} if exists $$conf{$Key} ;}
$this->{NN_ARGS} = [[ $nn_in , @layers , $nn_out ] , $nn_conf] ;
$this->{NN} = AI::NNEasy::NN->new( @{$this->{NN_ARGS}} ) ;
$this->{FILE} = $file ;
@out_types = (0,1) if !@out_types ;
@out_types = sort {$a <=> $b} @out_types ;
$this->{OUT_TYPES} = \@out_types ;
if ( $error_ok <= 0 ) {
my ($min_dif , $last) ;
my $i = -1 ;
foreach my $out_types_i ( @out_types ) {
++$i ;
if ($i > 0) {
my $dif = $out_types_i - $last ;
$min_dif = $dif if !defined $min_dif || $dif < $min_dif ;
}
$last = $out_types_i ;
}
$error_ok = $min_dif / 2 ;
$error_ok -= $error_ok*0.1 ;
}
lib/AI/NNEasy.pm view on Meta::CPAN
my $CLASS = ref($this) || __PACKAGE__ ;
my $out = $this->run(@_) ;
foreach my $out_i ( @$out ) {
$out_i = $this->out_type_winner($out_i) ;
}
return $out ;
}
sub out_type_winner {
my $this = ref($_[0]) ? shift : undef ;
my $CLASS = ref($this) || __PACKAGE__ ;
my $val = shift(@_) ;
my ($out_type , %err) ;
foreach my $types_i ( @{ $this->{OUT_TYPES} } ) {
my $er = $types_i - $val ;
$er *= -1 if $er < 0 ;
$err{$types_i} = $er ;
}
my $min_type_err = (sort { $err{$a} <=> $err{$b} } keys %err)[0] ;
$out_type = $min_type_err ;
return $out_type ;
}
my $INLINE_INSTALL ; BEGIN { use Config ; my @installs = ($Config{installarchlib} , $Config{installprivlib} , $Config{installsitelib}) ; foreach my $i ( @installs ) { $i =~ s/[\\\/]/\//gs ;} $INLINE_INSTALL = 1 if ( __FILE__ =~ /\.pm$/ && ( join(" ",...
lib/AI/NNEasy.pm view on Meta::CPAN
__END__
=head1 NAME
AI::NNEasy - Define, learn and use easy Neural Networks of different types using a portable code in Perl and XS.
=head1 DESCRIPTION
The main purpose of this module is to create easy Neural Networks with Perl.
The module was designed to can be extended to multiple network types, learning algorithms and activation functions.
This architecture was 1st based in the module L<AI::NNFlex>, than I have rewrited it to fix some
serialization bugs, and have otimized the code and added some XS functions to get speed
in the learning process. Finally I have added an intuitive inteface to create and use the NN,
and added a winner algorithm to the output.
lib/AI/NNEasy.pm view on Meta::CPAN
my $ERR_OK = 0.1 ;
## Create the NN:
my $nn = AI::NNEasy->new(
'xor.nne' , ## file to save the NN.
[0,1] , ## Output types of the NN.
$ERR_OK , ## Maximal error for output.
2 , ## Number of inputs.
1 , ## Number of outputs.
[3] , ## Hidden layers. (this is setting 1 hidden layer with 3 nodes).
) ;
lib/AI/NNEasy.pm view on Meta::CPAN
=item ERROR_OK
The maximal error of the calculated output.
If not defined ERROR_OK will be calculated by the minimal difference between 2 types at
@OUTPUT_TYPES dived by 2:
@OUTPUT_TYPES = [0 , 0.5 , 1] ;
ERROR_OK = (1 - 0.5) / 2 = 0.25 ;
lib/AI/NNEasy.pm view on Meta::CPAN
Conf can be used to define special parameters of the NN:
Default:
{networktype=>'feedforward' , random_weights=>1 , learning_algorithm=>'backprop' , learning_rate=>0.1 , bias=>1}
Options:
=over 4
=item networktype
The type of the NN. For now only accepts I<'feedforward'>.
=item random_weights
Maximum value for initial weight.
lib/AI/NNEasy.pm view on Meta::CPAN
Here's a completly example of use:
my $nn = AI::NNEasy->new(
'xor.nne' , ## file to save the NN.
[0,1] , ## Output types of the NN.
0.1 , ## Maximal error for output.
2 , ## Number of inputs.
1 , ## Number of outputs.
[3] , ## Hidden layers. (this is setting 1 hidden layer with 3 nodes).
{random_connections=>0 , networktype=>'feedforward' , random_weights=>1 , learning_algorithm=>'backprop' , learning_rate=>0.1 , bias=>1} ,
) ;
And a simple example that will create a NN equal of the above:
my $nn = AI::NNEasy->new('xor.nne' , [0,1] , 0.1 , 2 , 1 ) ;
lib/AI/NNEasy.pm view on Meta::CPAN
=head1 AUTHOR
Graciliano M. P. <gmpassos@cpan.org>
I will appreciate any type of feedback (include your opinions and/or suggestions). ;-P
Thanks a lot to I<Charles Colbourn <charlesc at nnflex.g0n.net>>, that is the
author of L<AI::NNFlex>, that 1st wrote it, since NNFlex was my starting point to
do this NN work, and 2nd to be in touch with the development of L<AI::NNEasy>.
AI-NNFlex
view release on metacpan or search on metacpan
examples/reinforceTest.pl view on Meta::CPAN
# this is /really/ experimental - see perldoc NNFlex::reinforce
use AI::NNFlex;
my $object = AI::NNFlex->new([{"nodes"=>2,"persistent activation"=>0,"decay"=>0.0,"random activation"=>0,"threshold"=>0.0,"activation function"=>"tanh","random weights"=>1},
{"nodes"=>2,"persistent activation"=>0,"decay"=>0.0,"random activation"=>0,"threshold"=>0.0,"activation function"=>"tanh","random weights"=>1},
{"nodes"=>1,"persistent activation"=>0,"decay"=>0.0,"random activation"=>0,"threshold"=>0.0,"activation function"=>"linear","random weights"=>1}],{'random connections'=>0,'networktype'=>'feedforward', 'random weights'=>1,'learn...
$object->run([1,0]);
$output = $object->output();
foreach (@$output)
AI-NNVMCAPI
view release on metacpan or search on metacpan
META.yml
README
t/AI-NNVMCAPI.t
lib/AI/NNVMCAPI.pm
nnvm.i
nnvm_typemaps.i
AI-NaiveBayes
view release on metacpan or search on metacpan
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) 19xx name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the
appropriate parts of the General Public License. Of course, the
commands you use may be called something other than `show w' and `show
c'; they could even be mouse-clicks or menu items--whatever suits your
AI-NaiveBayes1
view release on metacpan or search on metacpan
NaiveBayes1.pm view on Meta::CPAN
real_stat => {},
numof_instances => 0,
stat_labels => {},
stat_attributes => {},
smoothing => {},
attribute_type => {},
}, $package;
}
sub set_real {
my ($self, @attr) = @_;
foreach my $a (@attr) { $self->{attribute_type}{$a} = 'real' }
}
sub import_from_YAML {
my $package = shift;
my $yaml = shift;
NaiveBayes1.pm view on Meta::CPAN
foreach my $a (@_) {
my @tmp = grep { $a ne $_ } @{ $self->{attributes} };
$self->{attributes} = \@tmp;
delete($self->{attvals}{$a});
delete($self->{stat_attributes}{$a});
delete($self->{attribute_type}{$a});
delete($self->{real_stat}{$a});
delete($self->{smoothing}{$a});
}
} # end of drop_attributes
NaiveBayes1.pm view on Meta::CPAN
if (scalar(keys(%{ $self->{stat_attributes} })) == 0) {
foreach my $a (keys(%{$params{attributes}})) {
$self->{stat_attributes}{$a} = {};
push @{ $self->{attributes} }, $a;
$self->{attvals}{$a} = [ ];
$self->{attribute_type}{$a} = 'nominal' unless defined($self->{attribute_type}{$a});
}
} else {
foreach my $a (keys(%{$self->{stat_attributes}}))
{ die "attribute not given in instance: $a"
unless exists($params{attributes}{$a}) }
NaiveBayes1.pm view on Meta::CPAN
$self->{numof_instances} }
$m->{condprob} = {};
$m->{condprobe} = {};
foreach my $att (keys(%{$self->{stat_attributes}})) {
next if $self->{attribute_type}{$att} eq 'real';
$m->{condprob}{$att} = {};
$m->{condprobe}{$att} = {};
foreach my $label (keys(%{$self->{stat_labels}})) {
my $total = 0; my @attvals = ();
foreach my $attval (keys(%{$self->{stat_attributes}{$att}})) {
NaiveBayes1.pm view on Meta::CPAN
}
# For real-valued attributes, we use Gaussian distribution
# let us collect statistics
foreach my $att (keys(%{$self->{stat_attributes}})) {
next unless $self->{attribute_type}{$att} eq 'real';
print STDERR "Smoothing ignored for real attribute $att!\n" if
defined($self->{smoothing}{att}) and $self->{smoothing}{att};
$m->{real_stat}->{$att} = {};
foreach my $attval (keys %{$self->{stat_attributes}{$att}}){
foreach my $label (keys %{$self->{stat_attributes}{$att}{$attval}}){
NaiveBayes1.pm view on Meta::CPAN
my %scores;
my @labels = @{ $self->{labels} };
$scores{$_} = $m->{labelprob}{$_} foreach (@labels);
foreach my $att (keys(%{ $newattrs })) {
if (!defined($self->{attribute_type}{$att})) { die "Unknown attribute: `$att'" }
next if $self->{attribute_type}{$att} eq 'real';
die unless exists($self->{stat_attributes}{$att});
my $attval = $newattrs->{$att};
die "Unknown value `$attval' for attribute `$att'."
unless exists($self->{stat_attributes}{$att}{$attval}) or
exists($self->{smoothing}{$att});
NaiveBayes1.pm view on Meta::CPAN
}
}
foreach my $att (keys %{$newattrs}){
next unless $self->{attribute_type}{$att} eq 'real';
my $sum=0; my %nscores;
foreach my $label (@labels) {
die unless exists $m->{real_stat}{$att}{$label}{mean};
$nscores{$label} =
0.398942280401433 / $m->{real_stat}{$att}{$label}{stddev}*
NaiveBayes1.pm view on Meta::CPAN
@lines = ( "category ", '-' );
my @lines1 = ( "$att ", '-' );
my @lines2 = ( "P( $att | category ) ", '-' );
my @attvals = sort keys(%{ $m->{condprob}{$att} });
foreach my $label (@labels) {
if ( $self->{attribute_type}{$att} ne 'real' ) {
foreach my $attval (@attvals) {
next unless exists($m->{condprob}{$att}{$attval}{$label});
push @lines, "$label ";
push @lines1, "$attval ";
NaiveBayes1.pm view on Meta::CPAN
=item C<{attributes}>
List of attribute names.
=item C<{attribute_type}{$a}>
Attribute types - 'real', or not (e.g., 'nominal')
=item C<{labels}>
List of labels.
AI-Nerl
view release on metacpan or search on metacpan
examples/digits/deep_digits.pl view on Meta::CPAN
my $labels = rfits('t10k-labels-idx1-ubyte.fits');
my $y = identity(10)->range($labels->transpose)->sever;
say 't10k data loaded';
my $nerl = AI::Nerl->new(
# type => image,dims=>[28,28],...
scale_input => 1/256,
);
$nerl->init_network(l1 => 784, l3=>10, l2=>7);#method=batch,hidden=>12345,etc
AI-NeuralNet-BackProp
view release on metacpan or search on metacpan
BackProp.pm view on Meta::CPAN
# Initialzes the base for a new neural network.
# It is recomended that you call learn() before run()ing a pattern.
# See documentation above for usage.
sub new {
no strict;
my $type = shift;
my $self = {};
my $layers = shift;
my $size = shift;
my $out = shift || $size;
my $flag = shift || 0;
bless $self, $type;
# If $layers is a string, then it will be nummerically equal to 0, so try to load it
# as a network file.
if($layers == 0) {
# We use a "1" flag as the second argument to indicate that we want load()
BackProp.pm view on Meta::CPAN
# load_pcx() wrapper package
package AI::NeuralNet::BackProp::PCX;
# Called by load_pcx in AI::NeuralNet::BackProp;
sub new {
my $type = shift;
my $self = {
parent => $_[0],
file => $_[1]
};
my (@a,@b)=load_pcx($_[1]);
$self->{image}=\@a;
$self->{palette}=\@b;
bless \%{$self}, $type;
}
# Returns a rectangular block defined by an array ref in the form of
# [$x1,$y1,$x2,$y2]
# Return value is an array ref
BackProp.pm view on Meta::CPAN
results from any run() call after range() .
=item $net->range($bottom..$top);
This is a common form often used in a C<for my $x (0..20)> type of for() constructor. It works
the exact same way. It will allow all numbers from $bottom to $top, inclusive, to be given
as outputs of the network. No other values will be possible, other than those between $bottom
and $top, inclusive.
AI-NeuralNet-FastSOM
view release on metacpan or search on metacpan
0.15 Wed Jul 11 00:13:02 2012
- tidy up build a bit
- fixed warnings from CODE blocks using RETVAL without OUTPUT
blocks in newer perls
- yet another typemap workaround. this time we have a 5.6.2 with
a new ParseXS and an old xsubpp. i wont even mention the problem
i found in old Test::More finding this. i hope it never becomes
an issue. (Note: since this is an almost 3 year old issue and
haven't seen any more cases, we'll assume it was isolated
to a single user to start with and the whole mess is fixed
now.)
0.14 Fri Aug 21 12:52:32 2009
- work around some sort of ExtUtils::ParseXS bug in 5.6.2,
not picking up typemap files unless specifically named "typemap"
0.13 Mon Aug 17 08:42:37 2009
- fixed perl version check in Makefile.PL
0.12 Sat Aug 15 14:24:50 2009
- bumped perl require back up to 5.8.0 for now
- defined PERL_MAGIC_tied for older perls
- changed hv_fetchs() to hv_fetch() for older perls
- hacked in defines for Newx() and friends for older perls
- changed newSVpvs() to newSVpvn() for older perls
- created seperate typemap for older perls, along with Makefile.PL
modification to use it before 5.8.0
- added requirement for Storable which is non-core in older perls
- moved perl require back down to 5.6.2
0.11 Sun Aug 9 10:04:19 2009
- casting newSVpvs() to SV* to satisfy at least one platform
- added 'const char *' to typemap for older perls
- removed a few unneeded casts to internal types
- moved DESTROY methods to superclass, thus fixing missing
Hexa::DESTROY and consolidating common code
- consolidated neighbors code
- general housekeeping
- consolidated bmu code in ::FastSOM
0.08 Fri Jul 31 16:17:32 2009
- removed leading underscore from struct member names
- removed all // comments, just in case...
- changed all native type (int,double) to perl types (IV,NV)
- fixed couple of instances of calling back to perl to get
stuff from c structs
- reworked Storable support
0.07 Sat Jul 25 14:18:03 2009
AI-NeuralNet-Kohonen-Demo-RGB
view release on metacpan or search on metacpan
for make use of TK in a very slow demonstration of how a SOM can
classify RGB colours. See the SYNOPSIS manpage.
INSTALLATION
To install this module type the following:
perl Makefile.PL
make
make test
make install
AI-NeuralNet-Kohonen-Visual
view release on metacpan or search on metacpan
# http://module-build.sourceforge.net/META-spec.html
#XXXXXXX This is a prototype!!! It will change in the future!!! XXXXX#
name: AI-NeuralNet-Kohonen-Visual
version: 0.3
version_from: lib/AI/NeuralNet/Kohonen/Visual.pm
installdirs: site
requires:
distribution_type: module
generated_by: ExtUtils::MakeMaker version 6.21
AI-NeuralNet-Kohonen
view release on metacpan or search on metacpan
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
warn "Could not open file for writing <$path>: $!";
return undef;
}
#- Dimensionality of the vectors (integer, compulsory).
print OUT ($self->{weight_dim}+1)," "; # Perl indexing
#- Topology type, either hexa or rect (string, optional, case-sensitive).
if (not defined $self->{display}){
print OUT "rect ";
} else { # $self->{display} eq 'hex'
print OUT "hexa ";
}
#- Map dimension in x-direction (integer, optional).
print OUT $self->{map_dim_x}." ";
#- Map dimension in y-direction (integer, optional).
print OUT $self->{map_dim_y}." ";
#- Neighborhood type, either bubble or gaussian (string, optional, case-sen- sitive).
print OUT "gaussian ";
# End of header
print OUT "\n";
# Format input data
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
chomp @_;
my @specs = split/\s+/,(shift @_);
#- Dimensionality of the vectors (integer, compulsory).
$self->{weight_dim} = shift @specs;
$self->{weight_dim}--; # Perl indexing
#- Topology type, either hexa or rect (string, optional, case-sensitive).
my $display = shift @specs;
if (not defined $display and exists $self->{display}){
# Intentionally blank
} elsif (not defined $display){
$self->{display} = undef;
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
$_ = shift @specs;
$self->{map_dim_x} = $_ if defined $_;
#- Map dimension in y-direction (integer, optional).
$_ = shift @specs;
$self->{map_dim_y} = $_ if defined $_;
#- Neighborhood type, either bubble or gaussian (string, optional, case-sen- sitive).
# not implimented
# Format input data
foreach (@_){
$self->_add_input_from_str($_);
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
The first line of the file is reserved for status knowledge of the
entries; in the present version it is used to define the following
items (these items MUST occur in the indicated order):
- Dimensionality of the vectors (integer, compulsory).
- Topology type, either hexa or rect (string, optional, case-sensitive).
- Map dimension in x-direction (integer, optional).
- Map dimension in y-direction (integer, optional).
- Neighborhood type, either bubble or gaussian (string, optional, case-sen-
sitive).
...
Subsequent lines consist of n floating-point numbers followed by an
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
hexa/rect is only visual, and only in the ::Demo::RGB package atm
=item *
I<neighbourhood type> is always gaussian.
=item *
i<x> for missing data.
AI-NeuralNet-Mesh
view release on metacpan or search on metacpan
}
# Package constructor
sub new {
no strict 'refs';
my $type = shift;
my $self = {};
my $layers = shift;
my $nodes = shift;
my $outputs = shift || $nodes;
my $inputs = shift || $nodes;
bless $self, $type;
# If $layers is a string, then it will be numerically equal to 0, so
# try to load it as a network file.
if($layers == 0) {
# We use a "1" flag as the second argument to indicate that we
for (0..$self->{total_layers}){$self->extend_layer($_,$layers->[$_])}
$self->{layers} = $layers;
for (0..$self->{total_layers}){$self->{total_nodes}+= $self->{layers}->[$_]}
}
} else {
$self->{error} = "extend(): Invalid argument type.\n";
return undef;
}
return 1;
}
}
print "\n";
}
}
# Set the activation type of a specific layer.
# usage: $net->activation($layer,$type);
# $type can be: "linear", "sigmoid", "sigmoid_2".
# You can use "sigmoid_1" as a synonym to "sigmoid".
# Type can also be a CODE ref, ( ref($type) eq "CODE" ).
# If $type is a CODE ref, then the function is called in this form:
# $output = &$type($sum_of_inputs,$self);
# The code ref then has access to all the data in that node (thru the
# blessed refrence $self) and is expected to return the value to be used
# as the output for that node. The sum of all the inputs to that node
# is already summed and passed as the first argument.
sub activation {
for($n..$n+$self->{layers}->[$layer]-1) {
$self->{mesh}->[$_]->{activation} = $value;
}
}
# Applies an activation type to a specific node
sub node_activation {
my $self = shift;
my $layer = shift || 0;
my $node = shift || 0;
my $value = shift || 'linear';
use strict;
# Node constructor
sub new {
my $type = shift;
my $self ={
_parent => shift,
_inputs => [],
_outputs => []
};
bless $self, $type;
}
# Receive inputs from other nodes, and also send
# outputs on.
sub input {
package AI::NeuralNet::Mesh::output;
use strict;
sub new {
my $type = shift;
my $self ={
_parent => shift,
_inputs => [],
};
bless $self, $type;
}
sub add_input_node {
my $self = shift;
return (++$self->{_inputs_size})-1;
AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh.
It was designed with accruacy and speed in mind.
This network model is very flexable. It will allow for clasic binary
operation or any range of integer or floating-point inputs you care
to provide. With this you can change activation types on a per node or
per layer basis (you can even include your own anonymous subs as
activation types). You can add sigmoid transfer functions and control
the threshold. You can learn data sets in batch, and load CSV data
set files. You can do almost anything you need to with this module.
This code is deigned to be flexable. Any new ideas for this module?
See AUTHOR, below, for contact info.
This module is designed to also be a customizable, extensable
neural network simulation toolkit. Through a combination of setting
the $Connection variable and using custom activation functions, as
well as basic package inheritance, you can simulate many different
types of neural network structures with very little new code written
by you.
In this module I have included a more accurate form of "learning" for the
mesh. This form preforms descent toward a local error minimum (0) on a
directional delta, rather than the desired value for that node. This allows
=item AI::NeuralNet::Mesh->new(\@array_of_hashes);
Another dandy constructor...this is my favorite. It allows you to tailor the number of layers,
the size of the layers, the activation type (you can even add anonymous inline subs with this one),
and even the threshold, all with one array ref-ed constructor.
Example:
my $net = AI::NeuralNet::Mesh->new([
See CUSTOM ACTIVATION FUNCTIONS for information on several included activation functions
other than the ones listed above.
Three of the activation syntaxes are shown in the first constructor above, the "linear",
"sigmoid" and code ref types.
You can also set the activation and threshold values after network creation with the
activation() and threshold() methods.
during learning based on a product of the error difference with several other factors.
$degrade_increment_flag is off by default. Setting $degrade_increment_flag to a true
value turns increment degrading on.
In previous module releases $degrade_increment_flag was not used, as increment degrading
was always on. In this release I have looked at several other network types as well
as several texts and decided that it would be better to not use increment degrading. The
option is still there for those that feel the inclination to use it. I have found some areas
that do need the degrade flag to work at a faster speed. See test.pl for an example. If
the degrade flag wasn't in test.pl, it would take a very long time to learn.
=item $net->get_outs($set);
Simple utility function which takes an array ref of the same structure as the learn_set() method,
above. It returns an array ref of the same type as run_set() 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 learn_set() for dataset structure.)
=item $net->load_set($file,$column,$seperator);
Level 1 ($level = 1) : Displays the activity between nodes, prints what values were
received and what they were weighted to.
Level 2 ($level = 2) : Just prints info from the learn() 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.
Level 3 ($level = 3) : I don't think I included any level 3 debugs in this version.
Level 4 ($level = 4) : This level is the one I use most. It is only used during learning. It
=item $net->save($filename);
This will save the complete state of the network to disk, including all weights and any
words crunched with crunch() . Also saves the layer size and activations of the network.
NOTE: The only activation type NOT saved is the CODE ref type, which must be set again
after loading.
This uses a simple flat-file text storage format, and therefore the network files should
be fairly portable.
=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.
If the file is of an invalid file type, then load() will
return undef. Use the error() method, below, to print the error message.
If there were no errors, it will return a refrence to $net.
UPDATE: $filename can now be a newline-seperated set of mesh data. This enables you
to do $net->load(join("\n",<DATA>)) and other fun things. I added this mainly
for a demo I'm writing but not qutie done with yet. So, Cheers!
=item $net->activation($layer,$type);
This sets the activation type for layer C<$layer>.
C<$type> can be one of four values:
linear ( simply use sum of inputs as output )
sigmoid [ sigmoid_1 ] ( only positive sigmoid )
sigmoid_2 ( positive / 0 /negative sigmoid )
\&code_ref;
a blessed hash refrence to that node.
See CUSTOM ACTIVATION FUNCTIONS for information on several included activation functions
other than the ones listed above.
The activation type for each layer is preserved across load/save calls.
EXCEPTION: Due to the constraints of Perl, I cannot load/save the actual subs that the code
ref option points to. Therefore, you must re-apply any code ref activation types after a
load() call.
=item $net->node_activation($layer,$node,$type);
This sets the activation function for a specific node in a layer. The same notes apply
here as to the activation() method above.
=item $net->extend(\@array_of_hashes);
This allows you to re-apply any activations and thresholds with the same array ref which
you created a network with. This is useful for re-applying code ref activations after a load()
call without having to type the code ref twice.
You can also specify the extension in a simple array ref like this:
$net->extend([2,3,1]);
is to connect one range of nodes in that array to another range of nodes. The calling
function has already calculated the indices into the array, and it passed it to you
as the four arguments after the $self refrence. The first two arguments we will call
$r1a and $r1b. These define the start and end indices of the first range, or "layer." Likewise,
the next two arguemnts, $r2a and $r2b, define the start and end indices of the second
layer. We also grab a refrence to the mesh array so we dont have to type the $self
refrence over and over.
The loop that folows the arguments in the above example is very simple. It opens
a for() loop over the range of numbers, calculating the size instead of just going
$r1a..$r1b because we use the loop index with the next layer up as well.
=item AI::NeuralNet::Mesh::node
This is the worker package of the mesh. It implements all the individual nodes of the mesh.
It might be good to look at the source for this package (in the Mesh.pm file) if you
plan to do a lot of or extensive custom node activation types.
=item AI::NeuralNet::Mesh::cap
This is applied to the input layer of the mesh to prevent the mesh from trying to recursivly
adjust weights out throug the inputs.
AI-NeuralNet-SOM
view release on metacpan or search on metacpan
# http://module-build.sourceforge.net/META-spec.html
#XXXXXXX This is a prototype!!! It will change in the future!!! XXXXX#
name: AI-NeuralNet-SOM
version: 0.07
version_from: lib/AI/NeuralNet/SOM.pm
installdirs: site
requires:
distribution_type: module
generated_by: ExtUtils::MakeMaker version 6.30_01
AI-NeuralNet-Simple
view release on metacpan or search on metacpan
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
The "activation function" is a special function that is applied to the inputs
to generate the actual output. There are a variety of activation functions
available with three of the most common being the linear, sigmoid, and tahn
activation functions. For technical reasons, the linear activation function
cannot be used with the type of network that C<AI::NeuralNet::Simple> employs.
This module uses the sigmoid activation function. (More information about
these can be found by reading the information in the L<SEE ALSO> section or by
just searching with Google.)
Once the activation function is applied, the output is then sent through the
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
1 1 0 1
1 0 0 1
0 1 0 1
0 0 1 0
The type of network we use is a forward-feed back error propagation network,
referred to as a back-propagation network, for short. The way it works is
simple. When we feed in our input, it travels from the input to hidden layers
and then to the output layers. This is the "feed forward" part. We then
compare the output to the expected results and measure how far off we are. We
then adjust the weights on the "output to hidden" synapses, measure the error
lib/AI/NeuralNet/Simple.pm view on Meta::CPAN
"Naturally Intelligent Systems", by Maureen Caudill and Charles Butler,
copyright (c) 1990 by Massachussetts Institute of Technology.
This book is a decent introduction to neural networks in general. The forward
feed back error propogation is but one of many types.
=head1 AUTHORS
Curtis "Ovid" Poe, C<ovid [at] cpan [dot] org>
AI-Ollama-Client
view release on metacpan or search on metacpan
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $queue = Future::Queue->new( prototype => 'Future::Mojo' );
$res->done( $queue );
my $ct = $resp->headers->content_type;
return unless $ct;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/x-ndjson' ) {
# we only handle ndjson currently
my $handled_offset = 0;
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
if( $msg->{state} eq 'finished' ) {
$queue->finish();
}
});
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d", $resp->code ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $queue = Future::Queue->new( prototype => 'Future::Mojo' );
$res->done( $queue );
my $ct = $resp->headers->content_type;
return unless $ct;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/x-ndjson' ) {
# we only handle ndjson currently
my $handled_offset = 0;
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
if( $msg->{state} eq 'finished' ) {
$queue->finish();
}
});
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d", $resp->code ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $ct = $resp->headers->content_type;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/json' ) {
my $payload = $resp->json();
$self->validate_response( $payload, $tx );
$res->done(
AI::Ollama::GenerateEmbeddingResponse->new($payload),
);
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d: %s", $resp->code, $resp->body ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $queue = Future::Queue->new( prototype => 'Future::Mojo' );
$res->done( $queue );
my $ct = $resp->headers->content_type;
return unless $ct;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/x-ndjson' ) {
# we only handle ndjson currently
my $handled_offset = 0;
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
if( $msg->{state} eq 'finished' ) {
$queue->finish();
}
});
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d", $resp->code ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $queue = Future::Queue->new( prototype => 'Future::Mojo' );
$res->done( $queue );
my $ct = $resp->headers->content_type;
return unless $ct;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/x-ndjson' ) {
# we only handle ndjson currently
my $handled_offset = 0;
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
if( $msg->{state} eq 'finished' ) {
$queue->finish();
}
});
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d", $resp->code ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $ct = $resp->headers->content_type;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/json' ) {
my $payload = $resp->json();
$self->validate_response( $payload, $tx );
$res->done(
AI::Ollama::PushModelResponse->new($payload),
);
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d: %s", $resp->code, $resp->body ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $ct = $resp->headers->content_type;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/json' ) {
my $payload = $resp->json();
$self->validate_response( $payload, $tx );
$res->done(
AI::Ollama::ModelInfo->new($payload),
);
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d: %s", $resp->code, $resp->body ), $resp);
}
lib/AI/Ollama/Client/Impl.pm view on Meta::CPAN
my $resp = $tx->res;
$self->emit(response => $resp);
# Should we validate using OpenAPI::Modern here?!
if( $resp->code == 200 ) {
# Successful operation.
my $ct = $resp->headers->content_type;
$ct =~ s/;\s+.*//;
if( $ct eq 'application/json' ) {
my $payload = $resp->json();
$self->validate_response( $payload, $tx );
$res->done(
AI::Ollama::ModelsResponse->new($payload),
);
} else {
# Unknown/unhandled content type
$res->fail( sprintf("unknown_unhandled content type '%s'", $resp->content_type), $resp );
}
} else {
# An unknown/unhandled response, likely an error
$res->fail( sprintf( "unknown_unhandled code %d: %s", $resp->code, $resp->body ), $resp);
}
AI-PBDD
view release on metacpan or search on metacpan
INSTALLATION
First of all set the environment variable BUDDYPATH to the directory where you unzipped buddy-2.4, e.g.:
export BUDDYPATH=/my/buddy/home
Then type the following:
perl Makefile.PL
make
make test
make install
AI-PSO
view release on metacpan or search on metacpan
installdirs: site
requires:
Callback: 0
Math::Random: 0
distribution_type: module
generated_by: ExtUtils::MakeMaker version 6.30
AI-ParticleSwarmOptimization-MCE
view release on metacpan or search on metacpan
},
"release_status" : "stable",
"resources" : {
"homepage" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-MCE",
"repository" : {
"type" : "git",
"url" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-MCE.git",
"web" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-MCE"
}
},
"version" : "1.006",
{
"class" : "Dist::Zilla::Plugin::Prereqs",
"config" : {
"Dist::Zilla::Plugin::Prereqs" : {
"phase" : "runtime",
"type" : "requires"
}
},
"name" : "Prereqs",
"version" : "6.017"
},
AI-ParticleSwarmOptimization-Pmap
view release on metacpan or search on metacpan
},
"release_status" : "stable",
"resources" : {
"homepage" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-Pmap",
"repository" : {
"type" : "git",
"url" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-Pmap.git",
"web" : "https://github.com/d-strzelec/AI-ParticleSwarmOptimization-Pmap"
}
},
"version" : "1.008",
{
"class" : "Dist::Zilla::Plugin::Prereqs",
"config" : {
"Dist::Zilla::Plugin::Prereqs" : {
"phase" : "runtime",
"type" : "requires"
}
},
"name" : "Prereqs",
"version" : "6.017"
},
AI-Pathfinding-AStar-Rectangle
view release on metacpan or search on metacpan
- added clone.
- added test for this functions
0.17 September 26 14:34 2010
- begin_x, end_x, last_x, last_y
- added typemap.
- added test 06 for this functions
- remove some duplicated code
0.18 September 26 15:24 2010
- added clone_rect for selection rectangle
AI-Pathfinding-AStar
view release on metacpan or search on metacpan
# http://module-build.sourceforge.net/META-spec.html
#XXXXXXX This is a prototype!!! It will change in the future!!! XXXXX#
name: AI-Pathfinding-AStar
version: 0.10
version_from: lib/AI/Pathfinding/AStar.pm
installdirs: site
requires:
Heap::Binomial: 0
Heap::Elem: 0
Test::More: 0.11
distribution_type: module
generated_by: ExtUtils::MakeMaker version 6.30
AI-Pathfinding-OptimizeMultiple
view release on metacpan or search on metacpan
"bugtracker" : {
"web" : "https://github.com/shlomif/fc-solve/issues"
},
"homepage" : "http://metacpan.org/release/AI-Pathfinding-OptimizeMultiple",
"repository" : {
"type" : "git",
"url" : "ssh://git@github.com/shlomif/fc-solve.git",
"web" : "http://github.com/shlomif/fc-solve"
}
},
"version" : "0.0.17",
{
"class" : "Dist::Zilla::Plugin::Prereqs",
"config" : {
"Dist::Zilla::Plugin::Prereqs" : {
"phase" : "test",
"type" : "requires"
}
},
"name" : "@Filter/TestMoreDoneTesting",
"version" : "6.024"
},
{
"class" : "Dist::Zilla::Plugin::Prereqs",
"config" : {
"Dist::Zilla::Plugin::Prereqs" : {
"phase" : "runtime",
"type" : "requires"
}
},
"name" : "Prereqs",
"version" : "6.024"
},
AI-Pathfinding-SMAstar
view release on metacpan or search on metacpan
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
unless(Scalar::Util::looks_like_number($num_successors)){
croak "Error: Number of state successors is not numeric. Cannot add state to queue.\n";
}
# test out the iterator function to make sure it returns
# an object of the correct type
my $classname = ref($state);
my $test_successor_iterator = $state_obj->{_successors_iterator}->($state);
my $test_successor = $test_successor_iterator->($state);
my $succ_classname = ref($test_successor);
unless($succ_classname eq $classname){
croak "Error: Successor iterator method of object $classname does " .
"not return an object of type $classname.\n";
}
# add this node to the queue
$self->{_priority_queue}->insert($state_obj);
AI-Perceptron-Simple
view release on metacpan or search on metacpan
lib/AI/Perceptron/Simple.pm view on Meta::CPAN
=head1 NERVE PORTABILITY RELATED SUBROUTINES
These subroutines can be imported using the C<:portable_data> tag.
The file type currently supported is YAML. Please be careful with the data as you won't want the nerve data accidentally modified.
=head2 preserve_as_yaml ( ... )
The parameters and usage are the same as C<save_perceptron_yaml>. See the next subroutine.
AI-Perceptron
view release on metacpan or search on metacpan
--- #YAML:1.0
name: AI-Perceptron
version: 1.0
license: perl
distribution_type: module
requires:
accessors: 0.01
recommends: {}
build_requires:
Module::Build: 0.20
AI-PredictionClient
view release on metacpan or search on metacpan
lib/AI/PredictionClient/CPP/PredictionGrpcCpp.pm view on Meta::CPAN
use Inline
CPP => 'DATA',
with => ['Alien::Google::GRPC', 'AI::PredictionClient::Alien::TensorFlowServingProtos'],
version => '0.05',
name => 'AI::PredictionClient::CPP::PredictionGrpcCpp',
TYPEMAPS => getcwd . '/blib/lib/AI/PredictionClient/CPP/Typemaps/more_typemaps_STL_String.txt',
LIBS => '-ldl',
ccflags => '-std=c++11 -pthread';
use 5.010;
use strict;
( run in 2.360 seconds using v1.01-cache-2.11-cpan-df04353d9ac )