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package Algorithm::MCL; |
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# ABSTRACT: perl module implementing Markov Cluster Algorithm using PDL |
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use PDL; |
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use Inline 'Pdlpp'; |
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use Mouse; |
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no PDL::NiceSlice; |
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BEGIN { |
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$PDL::BIGPDL = 1; |
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} |
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has '_allVerts' => ( is => 'rw', isa => 'HashRef', default => sub { {} } ); |
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has '_vectors' => ( is => 'rw', isa => 'HashRef', default => sub { {} } ); |
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has '_orderedAllVerts' => ( is => 'rw', isa => 'ArrayRef', default => sub { [] } ); |
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sub addEdge { |
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my $self = shift; |
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my ( $vertex1, $vertex2, $edgeWeight ) = @_; |
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$self->addDirectEdge($vertex1, $vertex2, $edgeWeight); |
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$self->addDirectEdge($vertex2, $vertex1, $edgeWeight); |
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} |
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sub addDirectEdge { |
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my $self = shift; |
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my ( $from, $to, $edgeWeight ) = @_; |
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$edgeWeight ||= 1; |
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unless ( $self->_allVerts->{$from} ) { |
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push @{$self->_orderedAllVerts}, "$from"; |
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$self->_allVerts->{$from} = $from; |
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} |
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unless ( $self->_allVerts->{$to} ) { |
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push @{$self->_orderedAllVerts}, "$to"; |
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$self->_allVerts->{$to} = $to; |
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} |
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my $vertexEdges = $self->_vectors->{ $from }; |
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unless ( $vertexEdges ) |
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{ |
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$vertexEdges = {}; |
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$self->_vectors->{ $from } = $vertexEdges; |
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} |
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$vertexEdges->{ $to } = $edgeWeight; |
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my $toVertexEdges = $self->_vectors->{ $to }; |
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unless ( $toVertexEdges ) |
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{ |
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$toVertexEdges = {}; |
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$self->_vectors->{ $to } = $toVertexEdges; |
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} |
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unless (defined $toVertexEdges->{$from}) |
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{ |
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$toVertexEdges->{$from} = 0; |
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} |
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} |
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sub run { |
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my $self = shift; |
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my @verts = @{$self->_orderedAllVerts}; |
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my $numOfVerts = scalar @verts; |
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my $vertsOffsets = {}; |
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for (my $kk=0; $kk < $numOfVerts; ++$kk) |
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{ |
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$vertsOffsets->{ $verts[ $kk ] } = $kk; |
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} |
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my $matrix = zeros($numOfVerts, $numOfVerts); |
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for (my $ii=0; $ii < $numOfVerts; ++$ii) |
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{ |
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my $vector = $self->_vectors->{ $verts[ $ii ] }; |
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for (my $jj=0; $jj < $numOfVerts; ++$jj) |
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{ |
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if (exists $vector->{ $verts[ $jj ] }) |
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{ |
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$matrix->set( $ii, $jj, $vector->{ $verts[ $jj ] } ); |
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} |
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} |
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} |
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$matrix->inplace->addLoops; |
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$matrix->inplace->makeStochastic; |
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my $resultMatrix = $self->mcl( $matrix ); |
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my $clusters = $self->extractClusters($resultMatrix, $numOfVerts); |
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return $clusters; |
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} |
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sub mcl { |
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my $self = shift; |
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my ( $matrix ) = @_; |
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my $chaos = 1; |
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while ($chaos > 0.0001) { |
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my $mx = $matrix x $matrix; |
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my $cList = $mx->inplace->inflate; |
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$chaos = $cList->max; |
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$matrix = $mx; |
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} |
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$matrix->inplace->cleanSmall; |
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return $matrix; |
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} |
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sub extractClusters { |
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my $self = shift; |
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my ( $resultMatrix, $numOfVerts ) = @_; |
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my $clIdxs = $resultMatrix->getClustersIndex; |
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my $clusters = []; |
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for ( my $ii=0; $ii < $numOfVerts; ++$ii ) |
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{ |
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if ($clIdxs->at( $ii )) |
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{ |
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my $cluster = []; |
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push @$cluster, $self->getIdxObj( $ii ); |
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for ( my $jj=0; $jj < $numOfVerts; ++$jj ) |
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{ |
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if ($resultMatrix->at( $ii, $jj )) |
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{ |
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if ( $ii != $jj ) |
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{ |
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push @$cluster, $self->getIdxObj( $jj ); |
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$clIdxs->set( $jj, 0 ); |
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} |
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} |
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} |
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push @$clusters, $cluster |
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} |
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} |
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return $clusters; |
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} |
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sub getIdxObj { |
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my $self = shift; |
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my ( $idx ) = @_; |
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return$self->_allVerts->{$self->_orderedAllVerts->[$idx]}; |
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} |
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__PACKAGE__->meta->make_immutable(); |
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=pod |
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=head1 NAME |
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Algorithm::MCL - perl module implementing Markov Cluster Algorithm using PDL |
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=head1 VERSION |
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version 0.004 |
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184
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=head1 SYNOPSIS |
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186
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use Algorithm::MCL; |
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188
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my $obj1 = new MyClass; |
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my $ref2 = {}; |
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my $ref3 = \"abc"; |
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my $ref4 = \$val1; |
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my $ref5 = []; |
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my $mcl1 = Algorithm::MCL->new(); |
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# create graph by adding edges |
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$mcl1->addEdge($obj1, $ref2); |
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$mcl1->addEdge($obj1, $ref3); |
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$mcl1->addEdge($ref2, $ref3); |
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$mcl1->addEdge($ref3, $ref4); |
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$mcl1->addEdge($ref4, $ref5); |
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# run MCL algorithm on created graph |
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my $clusters1 = $mcl1->run(); |
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206
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# get clusters |
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foreach my $cluster ( @$clusters1 ) { |
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print "Cluster size: ". scalar @$cluster. "\n"; |
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} |
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211
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212
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#################################### |
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my $val1 = \"aaa"; |
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my $val2 = \"bbb"; |
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my $val3 = \"ccc"; |
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my $val4 = \"ddd"; |
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my $val5 = \"eee"; |
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220
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my $mcl2 = Algorithm::MCL->new(); |
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$mcl2->addEdge($val1, $val2); |
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$mcl2->addEdge($val1, $val3); |
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$mcl2->addEdge($val2, $val3); |
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$mcl2->addEdge($val3, $val4); |
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$mcl2->addEdge($val4, $val5); |
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my $clusters2 = $mcl2->run(); |
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229
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foreach my $cluster ( @$clusters2 ) { |
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print "Found Cluster\n"; |
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foreach my $vertex ( @$cluster ) { |
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print " Cluster element: $$vertex \n"; |
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} |
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} |
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236
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=head1 DESCRIPTION |
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238
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This module is perl implementation of Markov Cluster Algorithm (MCL) based on Perl Data Language (PDL). |
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240
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MCL is algorithm of finding clusters of vertices in graph. More information about MCL can be found at L. There is also perl script implementing MCL - minimcl L. |
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242
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This module try to solve two problems: |
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244
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=over 2 |
245
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246
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=item * |
247
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248
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easy integration MCL in perl scripts and modules. Algorithm::MCL accept references as input and every reference will be found later in some cluster. |
249
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250
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=item * |
251
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252
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performance and scale. Algorithm::MCL use Perl Data Language for most of its processing and should run very fast on very big clusters. Main Algorithm::MCL procedures are written with "pdlpp". |
253
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254
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=back |
255
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256
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=head1 METHODS |
257
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258
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=head2 new() |
259
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260
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create new Algorithm::MCL object that accumulate graph edges and process data. |
261
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262
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=head2 addEdge($ref1, $ref2, $distance) |
263
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264
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add new edge to graph. first two parameters are reference to vertex objects. third parameter is "connection strength measurement" between vetices. "connection strength measurement" should be number between 0 and 1, higher number means stronger connectivity. if "connection strength measurement" is not defined it set to 1. |
265
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266
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=head2 run() |
267
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268
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apply MCL algorithm on graph. return reference to array that every element is reference to cluser array. |
269
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270
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=head1 AUTHOR |
271
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272
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Pinkhas Nisanov |
273
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274
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=head1 COPYRIGHT AND LICENSE |
275
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276
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This software is copyright (c) 2012 by Pinkhas Nisanov. |
277
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278
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This is free software; you can redistribute it and/or modify it under |
279
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the same terms as the Perl 5 programming language system itself. |
280
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281
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=cut |
282
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283
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284
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__DATA__ |