Result:
found more than 1270 distributions - search limited to the first 2001 files matching your query
( run in 1.089 )
Algorithm-HyperLogLog
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
Algorithm-IRCSRP2
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
Algorithm-IncludeExclude
view release on metacpan or search on metacpan
lib/Algorithm/IncludeExclude.pm view on Meta::CPAN
our $VERSION = '0.01';
=head1 SYNOPSIS
Algorithm::IncludeExclude lets you define a tree of include / exclude
rules and then allows you to determine the best rule for a given path.
For example, to include everything, then exclude everything under
C<bar> or C<baz> but then include everything under C<foo baz>, you
could write:
Algorithm-KMeans
view release on metacpan or search on metacpan
examples/cluster_and_visualize.pl view on Meta::CPAN
## other hand, if for the same data file, you want to carry out 2D
## clustering on the last two columns, your data mask will be N011.
##
## 3) Next, you need to decide how many clusters you want the program to return.
## If you want the program to figure out on its own how many clusters to
## partition the data into, see the script find_best_K_and_cluster.pl in this
## directory.
##
## 4) Next you need to decide whether you want to `random' seeding or `smart'
## seeding. Bear in mind that `smart' seeding may produce worse results
## than `random' seeding, depending on how the data clusters are actually
Algorithm-KNN-XS
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
Algorithm-Kelly
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
Algorithm-LBFGS
view release on metacpan or search on metacpan
/* Copy the value of x to the work area. */
veccpy(wa, x, n);
/*
The variables stx, fx, dgx contain the values of the step,
function, and directional derivative at the best step.
The variables sty, fy, dgy contain the value of the step,
function, and derivative at the other endpoint of
the interval of uncertainty.
The variables stp, f, dg contain the values of the step,
function, and derivative at the current step.
Algorithm-LUHN
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
Algorithm-LUHN_XS
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
Algorithm-LibLinear
view release on metacpan or search on metacpan
lib/Algorithm/LibLinear.pm view on Meta::CPAN
Shorthand alias for C<find_parameters> only works on C<cost> parameter.
Notice that C<loss_sensitivity> is affected too when C<update> is set.
=head2 find_parameters(data_set => $data_set, num_folds => $num_folds [, initial_cost => -1.0] [, initial_loss_sensitivity => -1.0] [, update => 0])
Finds the best parameters by N-fold cross validation. If C<initial_cost> or C<initial_loss_sensitivity> is a negative, the value is automatically calculated.
Works only for 3 solvers: C<'L2R_LR'>, C<'L2R_L2LOSS_SVC'> and C<'L2R_L2LOSS_SVR'>. Error will be thrown for otherwise.
When C<update> is set true, the instance is updated to use the found parameters. This behaviour is disabled by default.
Return value is an ArrayRef containing 3 values: found C<cost>, found C<loss_sensitivity> (only if solver is C<'L2R_L2LOSS_SVR'>) and mean accuracy of cross validation with the found parameters.
Algorithm-Line-Lerp
view release on metacpan or search on metacpan
#ifndef IVSIZE
# ifdef LONGSIZE
# define IVSIZE LONGSIZE
# else
# define IVSIZE 4 /* A bold guess, but the best we can make. */
# endif
#endif
#ifndef UVTYPE
# define UVTYPE unsigned IVTYPE
#endif
#undef SvGETMAGIC
#ifndef SvGETMAGIC
# define SvGETMAGIC(x) ((void)(UNLIKELY(SvGMAGICAL(x)) && mg_get(x)))
#endif
/* That's the best we can do... */
#ifndef sv_catpvn_nomg
# define sv_catpvn_nomg sv_catpvn
#endif
#ifndef sv_catsv_nomg
Algorithm-LinearManifoldDataClusterer
view release on metacpan or search on metacpan
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
}
push @trailing_eigenvec_matrices_for_all_subspaces,$trailing_eigenvec_matrix;
push @reference_vecs_for_all_subspaces, $mean;
}
$self->set_termination_reconstruction_error_threshold(\@reference_vecs_for_all_subspaces);
my %best_subspace_based_partition_of_data;
foreach my $i (0..$self->{_KM}-1) {
$best_subspace_based_partition_of_data{$i} = [];
}
foreach my $data_tag (@{$self->{_data_tags}}) {
my $data_vec = Math::GSL::Matrix->new($self->{_data_dimensions},1);
$data_vec->set_col(0, $self->{_data_hash}->{$data_tag});
my @errors = map {$self->reconstruction_error($data_vec,
$trailing_eigenvec_matrices_for_all_subspaces[$_],
$reference_vecs_for_all_subspaces[$_])}
0 .. $self->{_KM}-1;
my ($minval, $index_for_closest_subspace) = minimum(\@errors);
$total_reconstruction_error_this_iteration += $minval;
push @{$best_subspace_based_partition_of_data{$index_for_closest_subspace}},
$data_tag;
}
print "Finished calculating the eigenvectors for the clusters produced by the previous\n" .
"iteration and re-assigning the data samples to the new subspaces on the basis of\n".
"the least reconstruction error.\n\n" .
"Total reconstruction error in this iteration: $total_reconstruction_error_this_iteration\n"
if $self->{_terminal_output};
foreach my $i (0..$self->{_KM}-1) {
$clusters[$i] = $best_subspace_based_partition_of_data{$i};
}
display_clusters(\@clusters) if $self->{_terminal_output};
# Check if any cluster has lost all its elements. If so, fragment the worst
# existing cluster to create the additional clusters needed:
if (any {@$_ == 0} @clusters) {
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
$trailing_eigenvec_matrix->set_col($j, $trailing_eigenvecs[$j]);
}
push @trailing_eigenvec_matrices_for_all_subspaces,$trailing_eigenvec_matrix;
push @reference_vecs_for_all_subspaces, $mean;
}
my %best_subspace_based_partition_of_data;
foreach my $i (0..$self->{_KM}-1) {
$best_subspace_based_partition_of_data{$i} = [];
}
foreach my $data_tag (@{$self->{_data_tags}}) {
my $data_vec = Math::GSL::Matrix->new($self->{_data_dimensions},1);
$data_vec->set_col(0, $self->{_data_hash}->{$data_tag});
my @errors = map {reconstruction_error($data_vec,
$trailing_eigenvec_matrices_for_all_subspaces[$_],
$reference_vecs_for_all_subspaces[$_])}
0 .. $self->{_KM}-1;
my ($minval, $index_for_closest_subspace) = minimum(\@errors);
$total_reconstruction_error_this_iteration += $minval;
push @{$best_subspace_based_partition_of_data{$index_for_closest_subspace}},
$data_tag;
}
print "empty-cluster jag: total reconstruction error in this iteration: \n" .
"$total_reconstruction_error_this_iteration\n"
if $self->{_debug};
foreach my $i (0..$self->{_KM}-1) {
$clusters[$i] = $best_subspace_based_partition_of_data{$i};
}
display_clusters(\@newclusters) if $self->{_terminal_output};
@clusters = grep {@$_ != 0} @newclusters;
die "linear manifold based algorithm does not appear to work in this case $!"
unless @clusters == $self->{_KM};
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
print "\n\nNot all data clustered. The most reliable clusters found by graph partitioning:\n";
display_clusters(\@final_clusters);
print "\n\nData not yet clustered:\n\n@data_tags_not_clustered\n";
}
if ($self->{_data_dimensions} == 3) {
$visualization_msg = "$self->{_K}_best_clusters_produced_by_graph_partitioning";
$self->visualize_clusters_on_sphere($visualization_msg, \@final_clusters)
if $self->{_visualize_each_iteration};
$self->visualize_clusters_on_sphere($visualization_msg, \@final_clusters, "png")
if $self->{_make_png_for_each_iteration};
}
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
print "\nHere come sorted eigenvectors for A --- from the largest to the smallest:\n";
foreach my $i (0..@sorted_eigenvecs-1) {
print "eigenvec: @{$sorted_eigenvecs[$i]} eigenvalue: $sorted_eigenvals[$i]\n";
}
}
my $best_partitioning_eigenvec = $sorted_eigenvecs[@sorted_eigenvec_indexes-2];
print "\nBest graph partitioning eigenvector: @$best_partitioning_eigenvec\n" if $self->{_terminal_output};
my $how_many_positive = reduce {$a + $b} map {$_ > 0 ? 1 : 0 } @$best_partitioning_eigenvec;
my $how_many_negative = scalar(@$best_partitioning_eigenvec) - $how_many_positive;
print "Have $how_many_positive positive and $how_many_negative negative elements in the partitioning vec\n"
if $self->{_terminal_output};
if ($how_many_clusters_looking_for <= 3) {
my @merged_cluster;
my $final_cluster;
my @newclusters;
if ($how_many_positive == 1) {
foreach my $i (0..@$clusters-1) {
if ($best_partitioning_eigenvec->[$i] > 0) {
$final_cluster = $clusters->[$i];
} else {
push @newclusters, $clusters->[$i];
}
}
return ([$final_cluster], \@newclusters);
} elsif ($how_many_negative == 1) {
foreach my $i (0..@$clusters-1) {
if ($best_partitioning_eigenvec->[$i] < 0) {
$final_cluster = $clusters->[$i];
} else {
push @newclusters, $clusters->[$i];
}
}
return ([$final_cluster], \@newclusters);
} elsif ($how_many_positive <= $self->{_cluster_search_multiplier}) {
foreach my $i (0..@$clusters-1) {
if ($best_partitioning_eigenvec->[$i] > 0) {
push @merged_cluster, @{$clusters->[$i]};
} else {
push @newclusters, $clusters->[$i];
}
}
return ([\@merged_cluster], \@newclusters);
} elsif ($how_many_negative <= $self->{_cluster_search_multiplier}) {
foreach my $i (0..@$clusters-1) {
if ($best_partitioning_eigenvec->[$i] < 0) {
push @merged_cluster, @{$clusters->[$i]};
} else {
push @newclusters, $clusters->[$i];
}
}
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
}
} else {
my @positive_clusters;
my @negative_clusters;
foreach my $i (0..@$clusters-1) {
if ($best_partitioning_eigenvec->[$i] > 0) {
push @positive_clusters, $clusters->[$i];
} else {
push @negative_clusters, $clusters->[$i];
}
}
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
my @intersection_set = keys %intersection_hash;
my $cluster_unimodality_index = scalar(@intersection_set) / scalar(@$cluster);
return $cluster_unimodality_index;
}
sub find_best_ref_vector {
my $self = shift;
my $cluster = shift;
my $trailing_eigenvec_matrix = shift;
my $mean = shift; # a GSL marix ref
my @min_bounds;
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
return @cluster_center_indices;
}
# The purpose of this routine is to form initial clusters by assigning the data
# samples to the initial clusters formed by the previous routine on the basis of the
# best proximity of the data samples to the different cluster centers.
sub assign_data_to_clusters_initial {
my $self = shift;
my @cluster_centers = @{ shift @_ };
my @clusters;
foreach my $ele (@{$self->{_data_tags}}) {
my $best_cluster;
my @dist_from_clust_centers;
foreach my $center (@cluster_centers) {
push @dist_from_clust_centers, $self->distance($ele, $center);
}
my ($min, $best_center_index) = minimum( \@dist_from_clust_centers );
push @{$clusters[$best_center_index]}, $ele;
}
return \@clusters;
}
# The following routine is for computing the distance between a data point specified
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
That brings us to the purpose of this module, which is to cluster data that resides
on a nonlinear manifold. Since a nonlinear manifold is locally linear, we can think
of each data cluster on a nonlinear manifold as falling on a locally linear portion
of the manifold, meaning on a hyperplane. The logic of the module is based on
finding a set of hyperplanes that best describes the data, with each hyperplane
derived from a local data cluster. This is like constructing a piecewise linear
approximation to data that falls on a curve as opposed to constructing a single
straight line approximation to all of the data. So whereas the frequently used PCA
algorithm gives you a single hyperplane approximation to all your data, what this
module returns is a set of hyperplane approximations, with each hyperplane derived by
applying the PCA algorithm locally to a data cluster.
That brings us to the problem of how to actually discover the best set of hyperplane
approximations to the data. What is probably the most popular algorithm today for
that purpose is based on the following key idea: Given a set of subspaces to which a
data element can be assigned, you assign it to that subspace for which the
B<reconstruction error> is the least. But what do we mean by a B<subspace> and what
is B<reconstruction error>?
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
least.> On the face of it, this sounds like a chicken-and-egg sort of a problem. You
need to have already clustered the data in order to construct the subspaces at
different places on the manifold so that you can figure out which cluster to place a
data element in.
Such problems, when they do possess a solution, are best tackled through iterative
algorithms in which you start with a guess for the final solution, you rearrange the
measured data on the basis of the guess, and you then use the new arrangement of the
data to refine the guess. Subsequently, you iterate through the second and the third
steps until you do not see any discernible changes in the new arrangements of the
data. This forms the basis of the clustering algorithm that is described under
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
Phase 1 of the algorithm. When the difference in the total reconstruction error from
one iteration to the next is less than the value given to this parameter, the
iterations come to an end. B<IMPORTANT: I have noticed that the larger the number of
data samples that need to be clustered, the larger must be the value give to this
parameter. That makes intuitive sense since the total reconstruction error is the
sum of all such errors for all of the data elements.> Unfortunately, the best value
for this parameter does NOT appear to depend linearly on the total number of data
records to be clustered.
=item C<terminal_output>:
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
You might wish to change the value given to the parameter
C<cluster_search_multiplier> to see how it affects the number of attempts needed to
achieve success.
The C<examples> directory also includes PNG image files that show some intermediate
and the best final results that can be achieved by the first three examples, that
is, for the scripts C<example1.pl>, C<example2.pl>, and C<example3.pl>. If you are on
a Linux machine and if you have the C<ImageMagick> library installed, you can use the
C<display> command to see the results in the PNG images. The results you get with
your own runs of the three example scripts may or may not look like what you see in
the outputs shown in the PNG files depending on how the module seeds the clusters.
Your best results should look like what you see in the PNG images.
The C<examples> directory also contains the following utility scripts:
For generating the data for experiments with clustering:
lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN
None by design.
=head1 CAVEATS
The performance of the algorithm depends much on the values you choose for the
constructor parameters. And, even for the best choices for the parameters, the
algorithm is not theoretically guaranteed to return the best results.
Even after you have discovered the best choices for the constructor parameters, the
best way to use this module is to try it multiple times on any given data and accept
only those results that make the best intuitive sense.
The module was designed with the hope that it would rather fail than give you wrong
results. So if you see it failing a few times before it returns a good answer, that's
a good thing.
Algorithm-Loops
view release on metacpan or search on metacpan
lib/Algorithm/Loops.pm view on Meta::CPAN
which can be fixed by dropping the parentheses:
... Filter {y/aeiou/UAEIO/} @list;
So if you need or want to use parentheses when calling Filter, it is best
to also include the C<sub> keyword and the comma:
# v <--------- These ---------> v
... Filter( sub {y/aeiou/UAEIO/}, @list );
# require ^^^ <--- these ---> ^ (sometimes)
Algorithm-MCL
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
Algorithm-MOS
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
Algorithm-MasterMind
view release on metacpan or search on metacpan
lib/Algorithm/MasterMind/Canonical_GA.pm view on Meta::CPAN
if ( $ranked_pop[0]->{'_matches'} == $rules ) { #Already found!
return $self->{'_last'} = $ranked_pop[0]->{'_str'};
} else {
my @pop_by_matches;
my $best;
do {
$ga->apply( $pop );
print "Población ", scalar @$pop, "\n";
map( $_->{'_matches'} = $_->{'_matches'}?$_->{'_matches'}:-1, @$pop ); #To avoid warnings
@pop_by_matches = sort { $b->{'_matches'} <=> $a->{'_matches'} } @$pop;
$best = $pop_by_matches[0];
} while ( $best->{'_matches'} < $rules );
return $self->{'_last'} = $best->{'_str'};
}
}
"some blacks, 0 white"; # Magic true value required at end of module
Algorithm-MedianSelect-XS
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
Algorithm-MinPerfHashTwoLevel
view release on metacpan or search on metacpan
mph_hv_macro.h view on Meta::CPAN
#define U64 uint64_t
#endif
#endif
#ifndef ROTL32
/* Find best way to ROTL32/ROTL64 */
#if defined(_MSC_VER)
#include <stdlib.h> /* Microsoft put _rotl declaration in here */
#define ROTL32(x,r) _rotl(x,r)
#define ROTR32(x,r) _rotr(x,r)
#define ROTL64(x,r) _rotl64(x,r)
Algorithm-NCS
view release on metacpan or search on metacpan
Algorithm-NCS/lib/Algorithm/NCS.pm view on Meta::CPAN
Number of Common Substrings (NCS) - A model and algorithm
for sequence alignment.
The change detection problem is aimed at identifying common and
different strings and usually has non-unique solutions. The identification of the
best alignment is canonically based on finding a
longest common subsequence
(LCS) and is widely used for various purposes. However, many recent version
control systems prefer alternative heuristic algorithms which not only are faster
but also usually produce better alignment than finding an
LCS.
Algorithm-NGram
view release on metacpan or search on metacpan
t/Algorithm-NGram.t view on Meta::CPAN
use Test::More qw/no_plan/;
BEGIN { use_ok('Algorithm::NGram') };
my $ng = Algorithm::NGram->new(ngram_width => 3);
$ng->add_text('yesterday my best dog went to the deli');
$ng->add_text('yesterday my best friend went to the market');
like($ng->generate_text, qr/yesterday my best (friend|dog) went to the (deli|market)/, "Text trigram");
my $ser = $ng->serialize;
my $ng2 = Algorithm::NGram->deserialize($ser);
is_deeply($ng2->token_table, $ng->token_table, 'Serialize/deserialize');
Algorithm-Networksort-Chooser
view release on metacpan or search on metacpan
Makefile.PL view on Meta::CPAN
PREREQ_PM => {
'common::sense' => 0,
'Getopt::Long' => 0,
'Math::Combinatorics' => 0,
## Assumes default when there is no best to be batcher, changed to this in 1.30
'Algorithm::Networksort' => 1.30,
},
LIBS => [],
DEFINE => '',
LICENSE => 'perl',
Algorithm-Networksort
view release on metacpan or search on metacpan
my $inputs;
my $name;
if (@ARGV == 0)
{
warn "best.pl <input size>\nbest.pl <key name>\n\nPlease give an input size or a key name.";
exit(0);
}
if ($ARGV[0] =~ /^[0-9]+/)
{
$inputs = $ARGV[0];
my(@names) = nw_best_names($inputs);
if (@names == 0)
{
print "No 'best' networks available for size $inputs.\n";
exit(0);
}
print "Available names for size $inputs: ", join(", ", @names), "\n";
exit(0);
}
$name = $ARGV[0] || die "Please use a keyname or a number.";
my $nw = nwsrt_best(name => $name);
print $nw;
exit(0);
Algorithm-Nhash
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
Algorithm-PageRank-XS
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
Algorithm-Pair-Best2
view release on metacpan or search on metacpan
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
# initial items as passed to us
for (my $idx = 0; $idx < @w_idxs; $idx += 2) {
$score += $self->get_score($w_idxs[$idx], $w_idxs[$idx + 1]);
}
# pair this window
($score, @w_idxs) = $self->_r_best(0, $score, @w_idxs);
### my $combs = 1;
### map { $combs *= (2 * $_ - 1) } (1 .. @w_idxs / 2);
### print scalar keys %all, ' combinations';
### print " (should be $combs)" if ($combs != scalar keys %all);
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
}
return wantarray ? @results : \@results;
}
# find best pairing of @idxs. try the first item in @idxs against every
# other item in the array. after picking the first and the current second
# item, recursively find the best arrangement of all the remaining items.
# the return values are the score followed by the new arrangment.
sub _r_best {
my ($self, $depth, $best_score, @idxs) = @_;
if (@idxs <= 2) {
croak sprintf("%d items left", scalar @idxs) if (@idxs <= 1);
return ($self->get_score(@idxs), @idxs);
}
my @best_trial = @idxs; # copy in case there is no improvement
my ($trial_0, $trial_1, @tail) = @idxs; # working copy
### push @head, $trial_0;
for my $idx (0 .. @idxs - 2) {
### push @head, $trial_1;
### $all{$self->make_key(@head, @tail)} = 0 if (@tail == 2); # collect every combination
# recursively get best pairing for tail
my ($trial_score, @trial_tail) = $self->_r_best($depth + 1, $best_score, @tail);
# add score for top pair
$trial_score += $self->get_score($trial_0, $trial_1); # first pair
### print join(', ', $trial_0, $trial_1, @trial_tail, $self->make_key($trial_0, $trial_1, @trial_tail)), "\n" if ($depth == 0);
### $self->dbg_hash($self->make_key($trial_0, $trial_1, @trial_tail), $trial_score);
if ($trial_score < $best_score) {
# aha! a potential candidate. save it
$best_score = $trial_score;
@best_trial = ($trial_0, $trial_1, @trial_tail);
## printf "%2d %2d Best %8.5f idxs %s\n",
## $depth,
## $idx,
## $best_score,
## $self->print_items(@best_trial) if ($depth < 2);
}
else {
## printf "%2d %2d Not best %8.5f idxs %s\n",
## $depth,
## $idx,
## $trial_score,
## $self->print_items($trial_0, $trial_1, @trial_tail) if ($depth < 2);
}
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
push @tail, $trial_1; # add second item to end of tail
$trial_1 = shift @tail; # move third item into second slot
### pop @head;
}
### pop @head;
### my $key = $self->make_key(@best_trial);
### print "best: $key = $best_score\n" if ($depth == 0);
return ($best_score, @best_trial);
}
1;
__END__
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
items to the list of items (i.e: players) to be paired. The final list
must contain an even number of items or B<pick>ing the pairs will throw an
exception.
Algorithm::Pair::Best2-E<gt>B<pick> explores all combinations of items and
returns the pairing list with the best (lowest) score. This can be an
expensive proposition - the number of combinations goes up very fast with
respect to the number of items:
items combinations
2 1 (1)
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
$pair->pick($window);
The list should be at least partially sorted so that reasonable
pairing candidates are within the 'sliding window' of each other.
Otherwise the final results may not be globally 'best', but only
locally good. For (e.g.) a tournament, sorting by rank is sufficient.
Here's how a window value of 5 works: the best list for items 1
through 10 (5 pairs) is found. Save the pairing for the top two items
and then slide the window down to pair items 2 through 12. Save the
top pairing from this result and slide down again to items 4 through
14. Keep sliding the window down until we reach the last 10 items
(which are completed in one iteration). In this way, a large number
lib/Algorithm/Pair/Best2.pm view on Meta::CPAN
or reference. They will be passed (a pair at a time) to the B<scoreSub>
callback.
=item @new_pairs = $pair-E<gt>B<pick> ( ?$window? )
Returns the best pairing found using the sliding window technique as
discussed in DESCRIPTION above. B<window> is the number of pairs in the
sliding window. If no B<window> argument is passed, the B<window> selected
in the B<new>, or the default value is used.
B<pick> returns the list (or a reference to the list in scalar context) of
Algorithm-Pair-Swiss
view release on metacpan or search on metacpan
lib/Algorithm/Pair/Swiss.pm view on Meta::CPAN
}
}
=item @pairs = $pairer-E<gt>B<pairs>
Returns the best pairings found as a list of arrayref's, each containing
one pair of parties.
=cut
sub pairs {
Algorithm-Paxos
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
Algorithm-Permute
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
Algorithm-Prefixspan
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
Algorithm-QuadTree
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 1.089 second using v1.01-cache-2.11-cpan-4e96b696675 )