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AI-MaxEntropy
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef SvPV_force_nomg
# define SvPV_force_nomg SvPV_force
#endif
#ifndef SvPV_nomg
AI-MegaHAL
view release on metacpan or search on metacpan
-------
If you find a bug, please report it to the author along with the
following information:
* version of Perl (output of 'perl -V' is best)
* version of AI::MegaHAL
* operating system type and version
* exact text of error message or description of problem
* example model files/data being classified
AI-MicroStructure
view release on metacpan or search on metacpan
lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN
our %EXPORT_TAGS = ( 'all' => [ qw( getStopWords ) ] );
our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
sub getStopWordsSmall{
my @search = ("a","a's","able","about","above","according","accordingly","across","actually","after","afterwards","again","against","ain't","all","allow","allows","almost","alone","along","already","also","although","always","am","among","amongst","a...
return @search;
}
sub getStopWords {
if ( @_ and $_[0] eq 'UTF-8' ) {
# adding U0 causes the result to be flagged as UTF-8
my %stoplist = map { ( pack("U0a*", $_), 1 ) } qw(
a able about above according accordingly across actually after afterwards again against aint all allow allows almost alone along already also although always am among amongst an and another any anybody anyhow anyone anything anyway anyways anywhere a...
b be became because become becomes becoming been before beforehand behind being believe below beside besides best better between beyond both brief but by
c came can cannot cant cant cause causes certain certainly changes clearly cmon co com come comes concerning consequently consider considering contain containing contains corresponding could couldnt course cs currently
d definitely described despite did didnt different do does doesnt doing done dont down downwards during
e each edu eg eight either else elsewhere enough entirely especially et etc even ever every everybody everyone everything everywhere ex exactly example except
f far few fifth first five followed following follows for former formerly forth four from further furthermore
g get gets getting given gives go goes going gone got gotten greetings
lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN
ain't all allow allows almost alone along already also although always
am among amongst an and another any anybody anyhow anyone anything
anyway anyways anywhere apart appear appreciate appropriate are aren't
around as aside ask asking associated at available away awfully b be
became because become becomes becoming been before beforehand behind
being believe below beside besides best better between beyond both brief
but by c c'mon c's came can can't cannot cant cause causes certain
certainly changes clearly co com come comes concerning consequently
consider considering contain containing contains corresponding could
couldn't course currently d definitely described despite did didn't
different do does doesn't doing don't done down downwards during e each
lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN
relatively respectively right s said same saw say saying says second secondly see seeing seem seemed seeming seems seen self selves sensible sent serious seriously seven
several shall she should shouldn't since six
so some somebody somehow someone something sometime sometimes somewhat somewhere soon sorry specified specify specifying still sub such sup sure t t's take taken tell tends th than thank thanks thanx that that's thats the their theirs them themselves...
qqq rrr sss ttt uuu vvv www xxx yyy zzz .... unsere ihrer uns wurde wer gegen diesem bis nur wieder unserem einer war man bei wir einen vom einem unter jeder werden wie als durch zum hat vor unseres email bel ihnen unseren bzw lieber uft kommen nicht...
anderweitigen anderweitiger anderweitiges anerkannt anerkannte anerkannter anerkanntes anfangen anfing angefangen angesetze angesetzt angesetzten angesetzter ans anscheinend ansetzen ansonst ansonsten anstatt anstelle arbeiten auch auf aufgehört aufg...
bessere besserem besseren besserer besseres bestehen besteht bestenfalls bestimmt bestimmte bestimmtem bestimmten bestimmter bestimmtes betraechtlich betraechtliche betraechtlichem betraechtlichen betraechtlicher betraechtliches betreffend betreffend...
diesseitiges diesseits dinge dir direkt direkte direkten direkter doch doppelt dort dorther dorthin dran drauf drei dreißig drin dritte drueber drum drunter drüber du dunklen durch durchaus durchweg durchwegs durfte durften dürfen dürfte eben ebenfal...
entsprechender entsprechendes entweder er ergo ergänze ergänzen ergänzte ergänzten erhalten erhielt erhielten erhält erneut erst erste erstem ersten erster erstere ersterem ersteren ersterer ersteres erstes eröffne eröffnen eröffnet eröffnete eröffne...
häufige häufigem häufigen häufiger häufigere häufigeren häufigerer häufigeres höchst höchstens ich igitt ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres ihretwegen im immer immerhin immerwaehrend immerwaehrende immerwaehrendem immerwaehrenden immerwae...
jeglichen jeglicher jegliches jemals jemand jene jenem jenen jener jenes jenseitig jenseitigem jenseitiger jenseits jetzt jährig jährige jährigem jährigen jähriges kaeumlich kam kann kannst kaum kein keine keinem keinen keiner keinerlei keines keines...
naechste naemlich nahm naturgemaess naturgemaeß naturgemäss naturgemäß natürlich neben nebenan nehmen nein neu neue neuem neuen neuer neuerdings neuerlich neuerliche neuerlichem neuerlicher neuerliches neues neulich neun nicht nichts nichtsdestotrotz...
lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN
return \%stoplist;
}
else {
my %stoplist = map { ( $_, 1 ) } qw(
a able about above according accordingly across actually after afterwards again against aint all allow allows almost alone along already also although always am among amongst an and another any anybody anyhow anyone anything anyway anyways anywhere a...
b be became because become becomes becoming been before beforehand behind being believe below beside besides best better between beyond both brief but by
c came can cannot cant cant cause causes certain certainly changes clearly cmon co com come comes concerning consequently consider considering contain containing contains corresponding could couldnt course cs currently
d definitely described despite did didnt different do does doesnt doing done dont down downwards during
e each edu eg eight either else elsewhere enough entirely especially et etc even ever every everybody everyone everything everywhere ex exactly example except
f far few fifth first five followed following follows for former formerly forth four from further furthermore
g get gets getting given gives go goes going gone got gotten greetings
lib/AI/MicroStructure/WordBlacklist.pm view on Meta::CPAN
because as until while of at by for with about against between
into through during before after above below to from up down in
out on off over under again further then once here there when
where why how all any both each few more most other some such
no nor not only own same so than too very
a a's able about above according accordingly across actually after afterwards again against ain't all allow allows almost alone along already also although always am among amongst an and another any anybody anyhow anyone anything anyway anyways anywh...
qqq rrr sss ttt uuu vvv www xxx yyy zzz .... unsere ihrer uns wurde wer gegen diesem bis nur wieder unserem einer war man bei wir einen vom einem unter jeder werden wie als durch zum hat vor unseres email bel ihnen unseren bzw lieber uft kommen nicht...
anderweitigen anderweitiger anderweitiges anerkannt anerkannte anerkannter anerkanntes anfangen anfing angefangen angesetze angesetzt angesetzten angesetzter ans anscheinend ansetzen ansonst ansonsten anstatt anstelle arbeiten auch auf aufgehört aufg...
bessere besserem besseren besserer besseres bestehen besteht bestenfalls bestimmt bestimmte bestimmtem bestimmten bestimmter bestimmtes betraechtlich betraechtliche betraechtlichem betraechtlichen betraechtlicher betraechtliches betreffend betreffend...
diesseitiges diesseits dinge dir direkt direkte direkten direkter doch doppelt dort dorther dorthin dran drauf drei dreißig drin dritte drueber drum drunter drüber du dunklen durch durchaus durchweg durchwegs durfte durften dürfen dürfte eben ebenfal...
entsprechender entsprechendes entweder er ergo ergänze ergänzen ergänzte ergänzten erhalten erhielt erhielten erhält erneut erst erste erstem ersten erster erstere ersterem ersteren ersterer ersteres erstes eröffne eröffnen eröffnet eröffnete eröffne...
häufige häufigem häufigen häufiger häufigere häufigeren häufigerer häufigeres höchst höchstens ich igitt ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres ihretwegen im immer immerhin immerwaehrend immerwaehrende immerwaehrendem immerwaehrenden immerwae...
jeglichen jeglicher jegliches jemals jemand jene jenem jenen jener jenes jenseitig jenseitigem jenseitiger jenseits jetzt jährig jährige jährigem jährigen jähriges kaeumlich kam kann kannst kaum kein keine keinem keinen keiner keinerlei keines keines...
naechste naemlich nahm naturgemaess naturgemaeß naturgemäss naturgemäß natürlich neben nebenan nehmen nein neu neue neuem neuen neuer neuerdings neuerlich neuerliche neuerlichem neuerlicher neuerliches neues neulich neun nicht nichts nichtsdestotrotz...
AI-NNEasy
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lib/AI/NNEasy.pm view on Meta::CPAN
I<backprop> as learning algorithm. With I<backprop> it pastes the inputs through
the Neural Network and adjust the I<weights> using random numbers until we find
a set of I<weights> that give to us the right output.
The secret of a NN is the number of hidden layers and nodes/neurons for each layer.
Basically the best way to define the hidden layers is 1 layer of (INPUT_NODES+OUTPUT_NODES).
So, a layer of 2 input nodes and 1 output node, should have 3 nodes in the hidden layer.
This definition exists because the number of inputs define the maximal variability of
the inputs (N**2 for bollean inputs), and the output defines if the variability is reduced by some logic restriction, like
int the XOR example, where we have 2 inputs and 1 output, so, hidden is 3. And as we can see in the
logic we have 3 groups of inputs:
AI-NaiveBayes
view release on metacpan or search on metacpan
lib/AI/NaiveBayes.pm view on Meta::CPAN
# Classify a feature vector
my $result = $classifier->classify({bar => 3, blurp => 2});
# $result is now a AI::NaiveBayes::Classification object
my $best_category = $result->best_category;
=head1 DESCRIPTION
This module implements the classic "Naive Bayes" machine learning
algorithm. This is a low level class that accepts only pre-computed feature-vectors
AI-Nerl
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-NeuralNet-BackProp
view release on metacpan or search on metacpan
BackProp.pm view on Meta::CPAN
# is an example. 3 and 2 are set by the new() constructor.
# Flag values:
# 0 - (default) -
# My feed-foward style: Each neuron in layer X is connected to one input of every
# neuron in layer Y. The best and most proven flag style.
#
# ^ ^ ^
# O\ O\ /O Layer Y
# ^\\/^/\/^
# | //|\/\|
BackProp.pm view on Meta::CPAN
The optional parameter, $topology_flag, defaults to 0 when not used. There are
three valid topology flag values:
B<0> I<default>
My feed-foward style: Each neuron in layer X is connected to one input of every
neuron in layer Y. The best and most proven flag style.
^ ^ ^
O\ O\ /O Layer Y
^\\/^/\/^
| //|\/\|
AI-NeuralNet-Kohonen
view release on metacpan or search on metacpan
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
selects a target from the input array (see L</PRIVATE METHOD _select_target>);
=item 2
finds the best-matching unit (see L</METHOD find_bmu>);
=item 3
adjusts the neighbours of the BMU (see L</PRIVATE METHOD _adjust_neighbours_of>);
lib/AI/NeuralNet/Kohonen.pm view on Meta::CPAN
=item -
Fixed-point qualifier: e.g. fixed=2,5. The map unit defined by
the fixed-point coordinates (x = 2; y = 5) is selected instead of
the best-matching unit for training. (See below for the definition
of coordinates over the map.) If several inputs are forced to
known locations, a wanted orientation results in the map.
=back
AI-NeuralNet-Mesh
view release on metacpan or search on metacpan
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.
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
AI-NeuralNet-SOM
view release on metacpan or search on metacpan
lib/AI/NeuralNet/SOM.pm view on Meta::CPAN
my @veggies = @_; # make a local copy, that will be destroyed in the loop
while (@veggies) {
my $sample = splice @veggies, int (rand (scalar @veggies) ), 1; # find (and take out)
my @bmu = $self->bmu ($sample); # find the best matching unit
push @mes, $bmu[2] if wantarray;
my $neighbors = $self->neighbors ($sigma, @bmu); # find its neighbors
map { _adjust ($self, $l, $sigma, $_, $sample) } @$neighbors; # bend them like Beckham
}
}
lib/AI/NeuralNet/SOM.pm view on Meta::CPAN
=item I<bmu>
(I<$x>, I<$y>, I<$distance>) = I<$nn>->bmu (I<$vector>)
This method finds the I<best matching unit>, i.e. that neuron which is closest to the vector passed
in. The method returns the coordinates and the actual distance.
=cut
sub bmu { die; }
AI-PBDD
view release on metacpan or search on metacpan
lib/AI/PBDD.pm view on Meta::CPAN
Same as above but with a window size of 3.
=item BDD_REORDER_SIFT
Each block is moved through all possible positions. The best of these is then used as the new position. Potentially a very slow but good method.
=item BDD_REORDER_RANDOM
Select a random position for each variable.
AI-PSO
view release on metacpan or search on metacpan
lib/AI/PSO.pm view on Meta::CPAN
# A particle updates its position according the Euler integration equation for physical motion:
# Xi(t) = Xi(t-1) + Vi(t)
# The velocity portion of this contains the stochastic elements of PSO and is defined as:
# Vi(t) = Vi(t-1) + P1*[pi - Xi(t-1)] + P2*[pg - Xi(t-1)]
# where P1 and P2 add are two random values who's sum adds up to the PSO random range (4.0)
# and pi is the individual's best location
# and pg is the global (or neighborhoods) best position
#
# The velocity vector is obviously updated before the position vector...
#
#
my @particles = ();
lib/AI/PSO.pm view on Meta::CPAN
return &swarm();
}
#
# pso_get_solution_array
# - returns the array of parameters corresponding to the best solution so far
sub pso_get_solution_array() {
return @solution;
}
lib/AI/PSO.pm view on Meta::CPAN
#
sub initialize_particles() {
for(my $p = 0; $p < $numParticles; $p++) {
$particles[$p] = {}; # each particle is a hash of arrays with the array sizes being the dimensionality of the problem space
$particles[$p]{nextPos} = []; # nextPos is the array of positions to move to on the next positional update
$particles[$p]{bestPos} = []; # bestPos is the position of that has yielded the best fitness for this particle (it gets updated when a better fitness is found)
$particles[$p]{currPos} = []; # currPos is the current position of this particle in the problem space
$particles[$p]{velocity} = []; # velocity ... come on ...
for(my $d = 0; $d < $dimensions; $d++) {
$particles[$p]{nextPos}[$d] = &random($deltaMin, $deltaMax);
$particles[$p]{currPos}[$d] = &random($deltaMin, $deltaMax);
$particles[$p]{bestPos}[$d] = &random($deltaMin, $deltaMax);
$particles[$p]{velocity}[$d] = &random($deltaMin, $deltaMax);
}
}
}
lib/AI/PSO.pm view on Meta::CPAN
sub dump_particle($) {
$| = 1;
my ($index) = @_;
print STDERR "[particle $index]\n";
print STDERR "\t[bestPos] ==> " . &compute_fitness(@{$particles[$index]{bestPos}}) . "\n";
foreach my $pos (@{$particles[$index]{bestPos}}) {
print STDERR "\t\t$pos\n";
}
print STDERR "\t[currPos] ==> " . &compute_fitness(@{$particles[$index]{currPos}}) . "\n";
foreach my $pos (@{$particles[$index]{currPos}}) {
print STDERR "\t\t$pos\n";
lib/AI/PSO.pm view on Meta::CPAN
$particles[$p]{currPos}[$d] = $particles[$p]{nextPos}[$d];
}
## test _current_ fitness of position
my $fitness = &compute_fitness(@{$particles[$p]{currPos}});
# if this position in hyperspace is the best so far...
if($fitness > &compute_fitness(@{$particles[$p]{bestPos}})) {
# for each dimension, set the best position as the current position
for(my $d2 = 0; $d2 < $dimensions; $d2++) {
$particles[$p]{bestPos}[$d2] = $particles[$p]{currPos}[$d2];
}
}
## check for exit criteria
if($fitness >= $exitFitness) {
#...write solution
print "Y:$iter:$p:$fitness\n";
&save_solution(@{$particles[$p]{bestPos}});
&dump_particle($p);
return 0;
} else {
if($verbose == 1) {
print "N:$iter:$p:$fitness\n"
lib/AI/PSO.pm view on Meta::CPAN
## so we turn to our neighbors for help.
## (we see if they are doing any better than we are,
## and if so, we try to fly over closer to their position)
for(my $p = 0; $p < $numParticles; $p++) {
my $n = &get_index_of_best_fit_neighbor($p);
my @meDelta = (); # array of self position updates
my @themDelta = (); # array of neighbor position updates
for(my $d = 0; $d < $dimensions; $d++) {
if($useModifiedAlgorithm) { # this if shold be moved out much further, but i'm working on code refactoring first
my $meFactor = $meWeight * &random($meMin, $meMax);
my $themFactor = $themWeight * &random($themMin, $themMax);
$meDelta[$d] = $particles[$p]{bestPos}[$d] - $particles[$p]{currPos}[$d];
$themDelta[$d] = $particles[$n]{bestPos}[$d] - $particles[$p]{currPos}[$d];
my $delta = ($meFactor * $meDelta[$d]) + ($themFactor * $themDelta[$d]);
$delta += $particles[$p]{velocity}[$d];
# do the PSO position and velocity updates
$particles[$p]{velocity}[$d] = &clamp_velocity($delta);
$particles[$p]{nextPos}[$d] = $particles[$p]{currPos}[$d] + $particles[$p]{velocity}[$d];
} else {
my $rho1 = &random(0, $psoRandomRange);
my $rho2 = $psoRandomRange - $rho1;
$meDelta[$d] = $particles[$p]{bestPos}[$d] - $particles[$p]{currPos}[$d];
$themDelta[$d] = $particles[$n]{bestPos}[$d] - $particles[$p]{currPos}[$d];
my $delta = ($rho1 * $meDelta[$d]) + ($rho2 * $themDelta[$d]);
$delta += $particles[$p]{velocity}[$d];
# do the PSO position and velocity updates
$particles[$p]{velocity}[$d] = &clamp_velocity($delta);
lib/AI/PSO.pm view on Meta::CPAN
}
}
#
# at this point we have exceeded the maximum number of iterations, so let's at least print out the best result so far
#
print STDERR "MAX ITERATIONS REACHED WITHOUT MEETING EXIT CRITERION...printing best solution\n";
my $bestFit = -1;
my $bestPartIndex = -1;
for(my $p = 0; $p < $numParticles; $p++) {
my $endFit = &compute_fitness(@{$particles[$p]{bestPos}});
if($endFit >= $bestFit) {
$bestFit = $endFit;
$bestPartIndex = $p;
}
}
&save_solution(@{$particles[$bestPartIndex]{bestPos}});
&dump_particle($bestPartIndex);
return 1;
}
#
# save solution
lib/AI/PSO.pm view on Meta::CPAN
return ($particleIndex + $neighborNum) % $numParticles;
}
#
# get_index_of_best_fit_neighbor
# - returns the index of the neighbor with the best fitness (when given a particle index)...
#
sub get_index_of_best_fit_neighbor($) {
my ($particleIndex) = @_;
my $bestNeighborFitness = 0;
my $bestNeighborIndex = 0;
my $particleNeighborIndex = 0;
for(my $neighbor = 0; $neighbor < $numNeighbors; $neighbor++) {
$particleNeighborIndex = &get_index_of_neighbor($particleIndex, $neighbor);
if(&compute_fitness(@{$particles[$particleNeighborIndex]{bestPos}}) > $bestNeighborFitness) {
$bestNeighborFitness = &compute_fitness(@{$particles[$particleNeighborIndex]{bestPos}});
$bestNeighborIndex = $particleNeighborIndex;
}
}
# TODO: insert error checking code / defensive programming
return $particleNeighborIndex;
}
lib/AI/PSO.pm view on Meta::CPAN
sub custom_fitness_function(@input) {
# this is a callback function.
# @input will be passed to this, you do not need to worry about setting it...
# ... do something with @input which is an array of floats
# return a value in [0,1] with 0 being the worst and 1 being the best
}
pso_set_params(\%params);
pso_register_fitness_function('custom_fitness_function');
pso_optimize();
lib/AI/PSO.pm view on Meta::CPAN
The velocity of a particle determines how quickly it changes location.
Larger velocities provide more coverage of hyperspace at the cost of
solution precision. With large velocities, a particle may come close
to a maxima but over-shoot it because it is moving too quickly. With
smaller velocities, particles can really hone in on a local solution
and find the best position but they may be missing another, possibly
even more optimal, solution because a full search of the hyperspace
was not conducted. Techniques such as simulated annealing can be
applied in certain areas so that the closer a partcle gets to a
solution, the smaller its velocity will be so that in bad areas of
the hyperspace, the particles move quickly, but in good areas, they
lib/AI/PSO.pm view on Meta::CPAN
you registered with pso_register_fitness_function()
=item pso_get_solution_array()
By default, pso_optimize() will print out to STDERR the first
solution, or the best solution so far if the max iterations were
reached. This function will simply return an array of the winning
(or best so far) position of the entire swarm system. It is an
array of floats to be used how you wish (like weights in a
neural network!).
=back
AI-ParticleSwarmOptimization-MCE
view release on metacpan or search on metacpan
example/PSOTest-MultiCore.pl view on Meta::CPAN
-fitFunc => \&calcFit,
-dimensions => 10,
-iterations => 10,
-numParticles => 1000,
# only for many-core version # the best if == $#cores of your system
# selecting best value if undefined
-workers => 4,
);
my $beg = time;
$pso->init();
my $fitValue = $pso->optimize ();
my ( $best ) = $pso->getBestParticles (1);
my ( $fit, @values ) = $pso->getParticleBestPos ($best);
my $iters = $pso->getIterationCount();
printf "Fit %.4f at (%s) after %d iterations\n", $fit, join (', ', map {sprintf '%.4f', $_} @values), $iters;
warn "\nTime: ", time - $beg, "\n\n";
#=======================================================================
AI-ParticleSwarmOptimization-Pmap
view release on metacpan or search on metacpan
example/PSOTest-MultiCore.pl view on Meta::CPAN
-fitFunc => \&calcFit,
-dimensions => 10,
-iterations => 10,
-numParticles => 1000,
# only for many-core version # the best if == $#cores of your system
# selecting best value if undefined
-workers => 4,
);
my $beg = time;
$pso->init();
my $fitValue = $pso->optimize ();
my ( $best ) = $pso->getBestParticles (1);
my ( $fit, @values ) = $pso->getParticleBestPos ($best);
my $iters = $pso->getIterationCount();
printf "Fit %.4f at (%s) after %d iterations\n", $fit, join (', ', map {sprintf '%.4f', $_} @values), $iters;
warn "\nTime: ", time - $beg, "\n\n";
#=======================================================================
AI-ParticleSwarmOptimization
view release on metacpan or search on metacpan
Samples/PSOPlatTest.pl view on Meta::CPAN
);
$pso->init ();
my $fitValue = $pso->optimize ();
my ($best) = $pso->getBestParticles (1);
my ($fit, @values) = $pso->getParticleBestPos ($best);
my $iters = $pso->getIterationCount ();
print $pso->getSeed();
printf ",# Fit %.5f at (%s) after %d iterations\n",
$fit, join (', ', map {sprintf '%.4f', $_} @values), $iters;
AI-Pathfinding-AStar-Rectangle
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef sv_catpvn_nomg
# define sv_catpvn_nomg sv_catpvn
#endif
#ifndef sv_catsv_nomg
AI-Pathfinding-AStar
view release on metacpan or search on metacpan
lib/AI/Pathfinding/AStar.pm view on Meta::CPAN
=item * Heuristic
=back
Basically you should return an array reference like this: C<[ [$node1, $cost1, $h1], [$node2, $cost2, $h2], [...], ...];> For more information on heuristics and the best ways to calculate them, visit the links listed in the I<SEE ALSO> section below...
As mentioned earlier, AI::Pathfinding::AStar provides two routines named C<findPath> and C<findPathIncr>. C<findPath> requires as input the starting and target node identifiers. It is unimportant what format you choose for your node IDs. As long a...
=head1 PREREQUISITES
AI-Pathfinding-OptimizeMultiple
view release on metacpan or search on metacpan
lib/AI/Pathfinding/OptimizeMultiple.pm view on Meta::CPAN
=head2 $calc_meta_scan->calc_meta_scan()
Calculates the meta-scan after initialisation. See here for the details
of the algorithm:
L<http://www.shlomifish.org/lecture/Freecell-Solver/The-Next-Pres/slides/multi-tasking/best-meta-scan/>
=head2 $self->calc_flares_meta_scan()
This function calculates the flares meta-scan: i.e: assuming that all atomic
scans are run one after the other and the shortest solutions of all
AI-Pathfinding-SMAstar
view release on metacpan or search on metacpan
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
my $num_states_in_queue = $$priority_queue->size();
my $max_extra_states_in_queue = $max_states_in_queue;
$max_states_in_queue = $num_states_in_queue + $max_extra_states_in_queue;
my $max_depth = ($max_states_in_queue - $num_states_in_queue);
my $best; # the best candidate for expansion
if($$priority_queue->is_empty() || !$$priority_queue){
return;
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
# loop over the elements in the priority queue
while(!$$priority_queue->is_empty()){
# determine the current size of the queue
my $num_states_in_queue = $$priority_queue->{_size};
# get the best candidate for expansion from the queue
$best = $$priority_queue->deepest_lowest_cost_leaf_dont_remove();
#------------------------------------------------------
if(!$DEBUG){
my $str = $log_function->($best);
$show_prog_func->($iteration, $num_states_in_queue, $str);
}
else{
my $str = $log_function->($best);
print "best is: " . $str_function->($best) . ", cost: " . $best->{_f_cost} . "\n";
}
#------------------------------------------------------
if($best->$goal_p()) {
# goal achieved! iteration: $iteration, number of
# states in queue: $num_states_in_queue.
return $best;
}
elsif($best->{_f_cost} >= $max_cost){
croak "\n\nSearch unsuccessful. max_cost reached (cost: $max_cost).\n";
}
else{
my $successors_iterator = $best->$successors_func();
my $succ = $successors_iterator->();
if($succ){
# if succ is at max depth and is not a goal node, set succ->fcost to infinity
if($succ->depth() >= $max_depth && !$succ->$goal_p() ){
$succ->{_f_cost} = $max_cost;
}
else{
# calling eval for comparison, and maintaining pathmax property
$succ->{_f_cost} = max($eval_func->($succ), $eval_func->($best));
my $descendant_index = $succ->{_descendant_index};
$best->{_descendant_fcosts}->[$descendant_index] = $succ->{_f_cost};
}
}
# determine if $best is completed, and if so backup values
if($best->is_completed()){
# remove from queue first, back up fvals, then insert back on queue.
# this way, it gets placed in its rightful place on the queue.
my $fval_before_backup = $best->{_f_cost};
# STEPS:
# 1) remove best and all antecedents from queue, but only if they are
# going to be altered by backing-up fvals. This is because
# removing and re-inserting in queue changes temporal ordering,
# and we don't want to do that unless the node will be
# placed in a new cost-bucket/tree.
# 2) then backup fvals
# 3) then re-insert best and all antecedents back on queue.
# Check if need for backup fvals
$best->check_need_fval_change();
my $cmp_func = sub {
my ($str) = @_;
return sub{
my ($obj) = @_;
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
return 0;
}
}
};
my $antecedent = $best->{_antecedent};
my %was_on_queue;
my $i = 0;
# Now remove the offending nodes from queue, if any
if($best->need_fval_change()){
# remove best from the queue
$best = $$priority_queue->deepest_lowest_cost_leaf();
while($antecedent){
my $path_str = $str_function->($antecedent);
if($antecedent->is_on_queue() && $antecedent->need_fval_change()){
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
}
}
# Backup fvals
if($best->need_fval_change()){
$best->$backup_func();
}
# Put everything back on the queue
if($best->need_fval_change()){
$$priority_queue->insert($best);
my $antecedent = $best->{_antecedent};
my $i = 0;
while($antecedent){
if($was_on_queue{$i} && $antecedent->need_fval_change()){
# the antecedent needed fval change too.
$$priority_queue->insert($antecedent);
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
$antecedent = $antecedent->{_antecedent};
$i++;
}
# Again, set need_fval_change back to 0, so it will not be automatically
# seen as needing changed in the future.
$best->{_need_fcost_change} = 0;
}
}
#
# If best's descendants are all in memory, mark best as completed.
#
if($best->all_in_memory()) {
if(!($best->is_completed())){
$best->is_completed(1);
}
my $cmp_func = sub {
my ($str) = @_;
return sub{
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
return 0;
}
}
};
my $best_str = $str_function->($best);
# If best is not a root node
if($best->{_depth} != 0){
# descendant index is the unique index indicating which descendant
# this node is of its antecedent.
my $descendant_index = $best->{_descendant_index};
my $antecedent = $best->{_antecedent};
$$priority_queue->remove($best, $cmp_func->($best_str));
if($antecedent){
$antecedent->{_descendants_produced}->[$descendant_index] = 0;
}
}
}
# there are no more successors of $best
if(!$succ){
next;
}
my $antecedent;
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
# If the maximum number of states in the queue has been reached,
# we need to remove the shallowest-highest-cost leaf to make room
# for more nodes. That means we have to make sure that the antecedent
# produces this descendant again at some point in the future if needed.
if($num_states_in_queue > $max_states_in_queue){
my $shcl_obj = $$priority_queue->shallowest_highest_cost_leaf($best, $succ, $str_function);
if(!$shcl_obj){
croak "Error while pruning queue: shallowest-highest-cost-leaf was null\n";
}
$antecedent = $shcl_obj->{_antecedent};
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
$num_states_in_queue--;
}
} # end if (num_states_on_queue > max_states)
# if there is a successor to $best, insert it in the priority queue.
if($succ){
$$priority_queue->insert($succ);
$best->{_num_successors_in_mem} = $best->{_num_successors_in_mem} + 1;
}
else{
croak "Error: no successor to insert\n";
}
}
lib/AI/Pathfinding/SMAstar.pm view on Meta::CPAN
SMA* addresses the possibility of running out of memory
by pruning the portion of the search-space that is being examined. It relies on
the I<pathmax>, or I<monotonicity> constraint on I<f(n)> to remove the shallowest
of the highest-cost nodes from the search queue when there is no memory left to
expand new nodes. It records the best costs of the pruned nodes within their
antecedent nodes to ensure that crucial information about the search space is
not lost. To facilitate this mechanism, the search queue is best maintained
as a search-tree of search-trees ordered by cost and depth, respectively.
=head4 Nothing is for free
The pruning of the search queue allows SMA* search to utilize all available
AI-PredictionClient-Alien-TensorFlowServingProtos
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
AI-PredictionClient
view release on metacpan or search on metacpan
lib/AI/PredictionClient.pm view on Meta::CPAN
The design of this client is to be fairly easy for a developer to see how the data is formed and received.
The TensorFlow interface is based on Protocol Buffers and gRPC.
That implementation is built on a complex architecture of nested protofiles.
In this design I flattened the architecture out and where the native data handling of Perl is best,
the modules use plain old Perl data structures rather than creating another layer of accessors.
The Tensor interface is used repetitively so this package includes a simplified Tensor class
to pack and unpack data to and from the models.
AI-Prolog
view release on metacpan or search on metacpan
t/40parser.t view on Meta::CPAN
$parser->getname; # owns
$parser->advance; # skip (
$parser->getname; # owns
$parser->advance; # skip ,
$parser->skipspace;
# not sure of the best way to handle this
is $parser->getname, 'heap o\\\'junk', 'The parser should be able to handle single quoted atoms with escape';
is $parser->current, ')',
'... and the current character should be the first one after the final quote';
is $parser->_start, 38, '... and have the parser should start at the first quote';
is $parser->_posn, 52,
AIIA-GMT
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef SvPV_force_nomg
# define SvPV_force_nomg SvPV_force
#endif
#ifndef SvPV_nomg
AIX-Perfstat
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef SvPV_force_nomg
# define SvPV_force_nomg SvPV_force
#endif
#ifndef SvPV_nomg
ALBD
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
convey the exclusion of warranty; and each file should have at least
ALPM
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#ifndef PERL_MAGIC_ext
# define PERL_MAGIC_ext '~'
#endif
/* That's the best we can do... */
#ifndef sv_catpvn_nomg
# define sv_catpvn_nomg sv_catpvn
#endif
#ifndef sv_catsv_nomg
AMF-Connection
view release on metacpan or search on metacpan
lib/AMF/Connection/Message.pm view on Meta::CPAN
$class->{'bodies'} = [];
$stream->readByte();
my $sent_encoding = $stream->readByte();
# need to make AMF1 returned encoding the same as AMF0 - see more about the bug at http://balazs.sebesteny.com/footprints-in-blazeds/
$class->setEncoding( ( $sent_encoding!=0 and $sent_encoding!=3 ) ? 0 : $sent_encoding );
my $totalHeaders = $stream->readInt();
for(my $i=0;$i<$totalHeaders;$i++) {
my $header = new AMF::Connection::MessageHeader();
AMQP
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
ANSI-Heatmap
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
API-Assembla
view release on metacpan or search on metacpan
END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
( run in 0.681 second using v1.01-cache-2.11-cpan-d6f9594c0a5 )