Statistics::Cluto - Perl binding for CLUTO
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NAME
Statistics::Cluto - Perl binding for CLUTO
INSTALLATION
Download CLUTO from http://glaros.dtc.umn.edu/gkhome/views/cluto.
Find libcluto.a which matches your environment and place it under
your library path (or specify its path with LIBS option as shown below).
Then do:
perl Makefile.PL [LIBS='-L/where/to/find/libcluto.a -lcluto']
make
make test
make install
Tested with cluto-2.1.2/Darwin-i386, cluto-2.1.2/Darwin-ppc and
cluto-2.1.1/Linux-i686.
SYNOPSIS
use Statistics::Cluto;
use Data::Dumper;
my $c = new Statistics::Cluto;
$c->set_dense_matrix(4, 5, [
[8, 8, 0, 3, 2],
[2, 9, 9, 1, 4],
[7, 6, 1, 2, 3],
[1, 7, 8, 2, 1]
]);
$c->set_options({
rowlabels => [ 'row0', 'row1', 'row2', 'row3' ],
collabels => [ 'col0', 'col1', 'col2', 'col3', 'col4' ],
nclusters => 2,
rowmodel => CLUTO_ROWMODEL_NONE,
colmodel => CLUTO_COLMODEL_NONE,
pretty_format => 1,
});
my $clusters = $c->VP_ClusterRB;
print Dumper $clusters;
my $cluster_features = $c->V_GetClusterFeatures;
print Dumper $cluster_features;
DESCRIPTION
This is a perl binding for CLUTO.
Please refer to the CLUTO's manual sections 5.6 - 5.8 for details
of each function. Basically, Statistics::Cluto has all
corresponding methods for functions described in the manual.
loading matrix
Initial matrix can be set either via set_dense_matrix or via
set_sparse_matrix method.
# loading 4x5 dense matrix
#
# 1 1 0 1 1
# 1 0 0 1 0
# 0 1 1 0 0
# 0 0 1 0 0
my $c = new Statistics::Cluto;
my $nrows = 4;
my $ncols = 5;
my $rowval = [
[1, 1, 0, 0, 1],
[1, 1, 0, 1, 1],
[1, 0, 1, 1, 0],
[1, 0, 1, 0, 0]
];
$c->set_dense_matrix($nrows, $ncols, $rowval);
# loading 4x5 sparse matrix
#
# 1 1 0 1 1
# 1 0 0 1 0
# 0 1 1 0 0
# 0 0 1 0 0
my $c = new Statistics::Cluto;
my $nrows = 4;
my $ncols = 5;
my $rowval = [
[1, 1, 2, 1, 4, 1, 5, 1],
[1, 1, 4, 1],
[2, 1, 3, 1],
[3, 1]
];
$c->set_sparse_matrix($nrows, $ncols, $rowval)
Sparse matrix can also be set with set_raw_sparse_matrix,
using the data format described in the manual section 3.3, Fig 16.
# loading sparse matrix via set_raw_sparse_matrix()
#
# 1 1 0 1 1
# 1 0 0 1 0
# 0 1 1 0 0
# 0 0 1 0 0
my $c = new Statistics::Cluto;
my $nrows = 4;
my $ncols = 5;
my $rowptr = [0, 4, 6, 8, 9];
my $rowind = [0, 1, 3, 4, 0, 3, 1, 2, 2];
my $rowval = [1, 1, 1, 1, 1, 1, 1, 1, 1];
$c->set_raw_sparse_matrix($nrows, $ncols, $rowptr, $rowind, $rowval);
Input parameters nrows, ncols, rowptr, rowind, rowval are
set automatically when initial matrix is loaded. All other input
parameters should be set before calling clustering functions via
set_options method. See sections 5.6 - 5.8 for necessary parameters.
$c->set_options({
rowlabels => ['row0', 'row1', 'row2', 'row3', 'row4'],
collabels => ['col0', 'col1', 'col2', 'col3', 'col4'],
nclusters => 2,
nfeatures => 2,
clfun => CLUTO_CLFUN_I2,
treetype => CLUTO_TREE_TOP,
});
calling functions
CLUTO's api functions described in the manual sections from 5.6 to 5.8 can be
called with methods of the same name, but without prefix "CLUTO_".
e.g. CLUTO_VP_ClusterDirect (in section 5.6.1) is named VP_ClusterDirect
in this package.
Routines with a single output parameter will return a single value / arrayref.
Routines with multiple output parameters will return an array, each member of
the array being the output parameters appearing in the same order as the manual.
# suppose $c is initialized with 5x5 sparse matrix:
# col0 ... col4
# row0: 2 2 0 2 2
# row1: 2 1 0 1 4
# row2: 0 2 5 0 0
# row3: 0 1 6 0 0
# row4: 2 1 0 3 4
$c->set_options({
rowlabels => ['row0', 'row1', 'row2', 'row3', 'row4'],
collabels => ['col0', 'col1', 'col2', 'col3', 'col4'],
nclusters => 2,
nfeatures => 2,
});
my $part = $c->VP_ClusterDirect;
# $part = [
# '1',
# '1',
# '0',
# '0',
# '1'
# ];
my ($internalids, $internalwgts, $externalids, $externalwgts) = $c->V_GetClusterFeatures;
# $internalids =
# [
# '2',
# '0',
# '4',
# '0'
# ]
# $internalwgts =
# [
# '1',
# '0',
# '0.598181843757629',
# '0.209491595625877'
# ]
# $externalids =
# [
# '2',
# '4',
# '2',
# '4'
# ]
# $externalwgts =
# [
# '0.5',
# '0.299090921878815',
# '0.5',
# '0.299090921878815'
# ]
Please refer to the manual for the details of the returned data structure.
When pretty_format option is set to 1, results are returned in a single
hashref, and in a (hopefully) little bit more comprehensible way.
Meaning of the returned data should be pretty much self-explanatory.
# with the same matrix and options as above...
$c->set_options({ pretty_format => 1 });
my $result = $c->VP_ClusterDirect;
# $result =
# [
# [
# { 'row' => 2, 'rowlabel' => 'row2' },
# { 'row' => 3, 'rowlabel' => 'row3' }
# ],
# [
# { 'row' => 0, 'rowlabel' => 'row0' },
# { 'row' => 1, 'rowlabel' => 'row1' },
# { 'row' => 4, 'rowlabel' => 'row4' }
# ]
# ];
$result = $c->V_GetClusterFeatures;
# $result =
# [
# [
# {
# 'discriminating' => [
# {
# 'externalwgt' => '0.5',
# 'collabel' => 'col2',
# 'externalid' => 2
# },
# {
# 'externalwgt' => '0.299090921878815',
# 'collabel' => 'col4',
# 'externalid' => 4
# }
# ],
# 'descriptive' => [
# {
# 'internalid' => 2,
# 'internalwgt' => '1',
# 'collabel' => 'col2'
# },
# {
# 'internalid' => 0,
# 'internalwgt' => '0',
# 'collabel' => 'col0'
# }
# ]
# },
# {
# 'discriminating' => [
# {
# 'externalwgt' => '0.5',
# 'collabel' => 'col2',
# 'externalid' => 2
# },
# {
# 'externalwgt' => '0.299090921878815',
# 'collabel' => 'col4',
# 'externalid' => 4
# }
# ],
# 'descriptive' => [
# {
# 'internalid' => 4,
# 'internalwgt' => '0.598181843757629',
# 'collabel' => 'col4'
# },
# {
# 'internalid' => 0,
# 'internalwgt' => '0.209491595625877',
# 'collabel' => 'col0'
# }
# ]
# }
# ]
# ];
Exportable constants
use Statistics::Cluto qw(:all)
will export all constants defined in cluto.h. (Auto generated by
h2xs).
See section 5 of CLUTO's manual, or cluto.h for details.
CLUTO_CLFUN_CLINK
CLUTO_CLFUN_CLINK_W
CLUTO_CLFUN_CUT
CLUTO_CLFUN_E1
CLUTO_CLFUN_G1
CLUTO_CLFUN_G1P
CLUTO_CLFUN_H1
CLUTO_CLFUN_H2
CLUTO_CLFUN_I1
CLUTO_CLFUN_I2
CLUTO_CLFUN_MMCUT
CLUTO_CLFUN_NCUT
CLUTO_CLFUN_RCUT
CLUTO_CLFUN_SLINK
CLUTO_CLFUN_SLINK_W
CLUTO_CLFUN_UPGMA
CLUTO_CLFUN_UPGMA_W
CLUTO_COLMODEL_IDF
CLUTO_COLMODEL_NONE
CLUTO_CSTYPE_BESTFIRST
CLUTO_CSTYPE_LARGEFIRST
CLUTO_CSTYPE_LARGESUBSPACEFIRST
CLUTO_DBG_APROGRESS
CLUTO_DBG_CCMPSTAT
CLUTO_DBG_CPROGRESS
CLUTO_DBG_MPROGRESS
CLUTO_DBG_PROGRESS
CLUTO_DBG_RPROGRESS
CLUTO_GRMODEL_ASYMETRIC_DIRECT
CLUTO_GRMODEL_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_NONE
CLUTO_GRMODEL_SYMETRIC_DIRECT
CLUTO_GRMODEL_SYMETRIC_LINKS
CLUTO_MEM_NOREUSE
CLUTO_MEM_REUSE
CLUTO_MTYPE_HEDGE
CLUTO_MTYPE_HSTAR
CLUTO_MTYPE_HSTAR2
CLUTO_OPTIMIZER_MULTILEVEL
CLUTO_OPTIMIZER_SINGLELEVEL
CLUTO_ROWMODEL_LOG
CLUTO_ROWMODEL_MAXTF
CLUTO_ROWMODEL_NONE
CLUTO_ROWMODEL_SQRT
CLUTO_SIM_CORRCOEF
CLUTO_SIM_COSINE
CLUTO_SIM_EDISTANCE
CLUTO_SIM_EJACCARD
CLUTO_SUMMTYPE_MAXCLIQUES
CLUTO_SUMMTYPE_MAXITEMSETS
CLUTO_TREE_FULL
CLUTO_TREE_TOP
CLUTO_VER_MAJOR
CLUTO_VER_MINOR
CLUTO_VER_SUBMINOR
SEE ALSO
http://glaros.dtc.umn.edu/gkhome/views/cluto
AUTHOR
Ikuhiro IHARA <tsukue@gmail.com>
COPYRIGHT AND LICENSE
Copyright (C) 2007 by Ikuhiro IHARA
This library is free software; you can redistribute it and/or modify
it under the same terms as Perl itself, either Perl version 5.8.5 or,
at your option, any later version of Perl 5 you may have available.
package Statistics::Cluto;
use 5.008005;
use strict;
use warnings;
use Carp;
require Exporter;
use AutoLoader;
our @ISA = qw(Exporter);
# Items to export into callers namespace by default. Note: do not export
# names by default without a very good reason. Use EXPORT_OK instead.
# Do not simply export all your public functions/methods/constants.
# This allows declaration use Statistics::Cluto ':all';
# If you do not need this, moving things directly into @EXPORT or @EXPORT_OK
# will save memory.
our %EXPORT_TAGS = ( 'all' => [ qw(
CLUTO_CLFUN_CLINK
CLUTO_CLFUN_CLINK_W
CLUTO_CLFUN_CUT
CLUTO_CLFUN_E1
CLUTO_CLFUN_G1
CLUTO_CLFUN_G1P
CLUTO_CLFUN_H1
CLUTO_CLFUN_H2
CLUTO_CLFUN_I1
CLUTO_CLFUN_I2
CLUTO_CLFUN_MMCUT
CLUTO_CLFUN_NCUT
CLUTO_CLFUN_RCUT
CLUTO_CLFUN_SLINK
CLUTO_CLFUN_SLINK_W
CLUTO_CLFUN_UPGMA
CLUTO_CLFUN_UPGMA_W
CLUTO_COLMODEL_IDF
CLUTO_COLMODEL_NONE
CLUTO_CSTYPE_BESTFIRST
CLUTO_CSTYPE_LARGEFIRST
CLUTO_CSTYPE_LARGESUBSPACEFIRST
CLUTO_DBG_APROGRESS
CLUTO_DBG_CCMPSTAT
CLUTO_DBG_CPROGRESS
CLUTO_DBG_MPROGRESS
CLUTO_DBG_PROGRESS
CLUTO_DBG_RPROGRESS
CLUTO_GRMODEL_ASYMETRIC_DIRECT
CLUTO_GRMODEL_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_NONE
CLUTO_GRMODEL_SYMETRIC_DIRECT
CLUTO_GRMODEL_SYMETRIC_LINKS
CLUTO_MEM_NOREUSE
CLUTO_MEM_REUSE
CLUTO_MTYPE_HEDGE
CLUTO_MTYPE_HSTAR
CLUTO_MTYPE_HSTAR2
CLUTO_OPTIMIZER_MULTILEVEL
CLUTO_OPTIMIZER_SINGLELEVEL
CLUTO_ROWMODEL_LOG
CLUTO_ROWMODEL_MAXTF
CLUTO_ROWMODEL_NONE
CLUTO_ROWMODEL_SQRT
CLUTO_SIM_CORRCOEF
CLUTO_SIM_COSINE
CLUTO_SIM_EDISTANCE
CLUTO_SIM_EJACCARD
CLUTO_SUMMTYPE_MAXCLIQUES
CLUTO_SUMMTYPE_MAXITEMSETS
CLUTO_TREE_FULL
CLUTO_TREE_TOP
CLUTO_VER_MAJOR
CLUTO_VER_MINOR
CLUTO_VER_SUBMINOR
) ] );
our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );
our @EXPORT = qw(
CLUTO_CLFUN_CLINK
CLUTO_CLFUN_CLINK_W
CLUTO_CLFUN_CUT
CLUTO_CLFUN_E1
CLUTO_CLFUN_G1
CLUTO_CLFUN_G1P
CLUTO_CLFUN_H1
CLUTO_CLFUN_H2
CLUTO_CLFUN_I1
CLUTO_CLFUN_I2
CLUTO_CLFUN_MMCUT
CLUTO_CLFUN_NCUT
CLUTO_CLFUN_RCUT
CLUTO_CLFUN_SLINK
CLUTO_CLFUN_SLINK_W
CLUTO_CLFUN_UPGMA
CLUTO_CLFUN_UPGMA_W
CLUTO_COLMODEL_IDF
CLUTO_COLMODEL_NONE
CLUTO_CSTYPE_BESTFIRST
CLUTO_CSTYPE_LARGEFIRST
CLUTO_CSTYPE_LARGESUBSPACEFIRST
CLUTO_DBG_APROGRESS
CLUTO_DBG_CCMPSTAT
CLUTO_DBG_CPROGRESS
CLUTO_DBG_MPROGRESS
CLUTO_DBG_PROGRESS
CLUTO_DBG_RPROGRESS
CLUTO_GRMODEL_ASYMETRIC_DIRECT
CLUTO_GRMODEL_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_EXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_EXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_ASYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_ASYMETRIC_LINKS
CLUTO_GRMODEL_INEXACT_SYMETRIC_DIRECT
CLUTO_GRMODEL_INEXACT_SYMETRIC_LINKS
CLUTO_GRMODEL_NONE
CLUTO_GRMODEL_SYMETRIC_DIRECT
CLUTO_GRMODEL_SYMETRIC_LINKS
CLUTO_MEM_NOREUSE
CLUTO_MEM_REUSE
CLUTO_MTYPE_HEDGE
CLUTO_MTYPE_HSTAR
CLUTO_MTYPE_HSTAR2
CLUTO_OPTIMIZER_MULTILEVEL
CLUTO_OPTIMIZER_SINGLELEVEL
CLUTO_ROWMODEL_LOG
CLUTO_ROWMODEL_MAXTF
CLUTO_ROWMODEL_NONE
CLUTO_ROWMODEL_SQRT
CLUTO_SIM_CORRCOEF
CLUTO_SIM_COSINE
CLUTO_SIM_EDISTANCE
CLUTO_SIM_EJACCARD
CLUTO_SUMMTYPE_MAXCLIQUES
CLUTO_SUMMTYPE_MAXITEMSETS
CLUTO_TREE_FULL
CLUTO_TREE_TOP
CLUTO_VER_MAJOR
CLUTO_VER_MINOR
CLUTO_VER_SUBMINOR
);
our $VERSION = '0.01';
sub AUTOLOAD {
# This AUTOLOAD is used to 'autoload' constants from the constant()
# XS function.
my $constname;
our $AUTOLOAD;
($constname = $AUTOLOAD) =~ s/.*:://;
croak "&Statistics::Cluto::constant not defined" if $constname eq 'constant';
my ($error, $val) = constant($constname);
if ($error) { croak $error; }
{
no strict 'refs';
# Fixed between 5.005_53 and 5.005_61
#XXX if ($] >= 5.00561) {
#XXX *$AUTOLOAD = sub () { $val };
#XXX }
#XXX else {
*$AUTOLOAD = sub { $val };
#XXX }
}
goto &$AUTOLOAD;
}
sub DESTROY {}
require XSLoader;
XSLoader::load('Statistics::Cluto', $VERSION);
# Preloaded methods go here.
our $MATRIX_TYPE_DENSE = 0;
our $MATRIX_TYPE_SPARSE = 1;
our $NO_OPTIMIZE_SOLUTION = 0;
our $OPTIMIZE_SOLUTION = 1;
sub new {
my $class = shift;
my $self = {
nrows => 0,
ncols => 0,
nnz => 0,
rowptr => [],
rowind => [],
rowval => [],
# global (not method-specific) defaults
simfun => CLUTO_SIM_COSINE(),
cstype => CLUTO_CSTYPE_BESTFIRST(),
rowmodel => CLUTO_ROWMODEL_NONE(),
colprune => 1.0,
nnbrs => 40,
grmodel => CLUTO_GRMODEL_EXACT_SYMETRIC_DIRECT(),
edgeprune => -1,
vtxprune => -1,
mincomponent => 5,
kwayrefine => $NO_OPTIMIZE_SOLUTION,
ntrials => 10,
niter => 10,
seed => time,
dbglvl => 0,
nclusters => 1,
nfeatures => 5,
pretty_format => 0,
@_,
};
bless $self, $class;
return $self;
}
sub set_options {
my ($self, $opts) = @_;
while (my ($key, $val) = each(%$opts)) {
$self->{$key} = $val;
}
}
#
# matrix loading functions
#
sub set_sparse_matrix {
my ($self, $nrows, $ncols, $rowval) = @_;
die ('number of rows does not match') if ($nrows != $#$rowval + 1);
$self->{matrix_type} = $MATRIX_TYPE_SPARSE;
$self->{nrows} = $nrows;
$self->{ncols} = $ncols;
$self->{nnz} = 0;
my @rowptr = ();
my @rowind = ();
my @rowval = ();
for (my $rowptr = 0; $rowptr < $nrows; $rowptr++) {
my $row = $$rowval[$rowptr];
push @rowptr, $#rowind + 1;
for (my $j = 0; $j <= $#$row; $j+=2) {
my $col = $$row[$j];
die ("inappropriate col#$col in row#".($rowptr+1)) if ($col > $ncols);
push @rowind, $col - 1;
push @rowval, $$row[$j + 1];
$self->{nnz} ++;
}
}
push @rowptr, $#rowind + 1;
$self->{rowptr} = \@rowptr;
$self->{rowind} = \@rowind;
$self->{rowval} = \@rowval;
}
sub set_raw_sparse_matrix {
my ($self, $nrows, $ncols, $rowptr, $rowind, $rowval) = @_;
# $$rowptr[$#$rowptr + 1] = $#$rowind + 1 if ($$rowptr[-1] != $#$rowind + 1);
if ($$rowptr[-1] != $#$rowind + 1 or $#$rowptr != $nrows) {
die('rowptr not appropriate');
}
$self->{matrix_type} = $MATRIX_TYPE_SPARSE;
$self->{nrows} = $nrows;
$self->{ncols} = $ncols;
$self->{nnz} = $#$rowval + 1;
$self->{rowptr} = $rowptr;
$self->{rowind} = $rowind;
$self->{rowval} = $rowval;
}
sub set_dense_matrix {
my ($self, $nrows, $ncols, $rowval) = @_;
die ('number of rows does not match') if ($nrows != $#$rowval + 1);
$self->{matrix_type} = $MATRIX_TYPE_DENSE;
$self->{nrows} = $nrows;
$self->{ncols} = $ncols;
$self->{nnz} = -1;
my @rowval = ();
for (my $i = 0; $i <= $#$rowval; $i++) {
my $row = $$rowval[$i];
die ('number of cols does not match: row #'.($i+1)) if ($#$row+1 != $self->{ncols});
push @rowval, @$row;
}
$self->{rowval} = \@rowval;
}
sub set_dense_matrix_as_sparse {
my ($self, $nrows, $ncols, $matrix) = @_;
my $rowval = [];
die ('number of rows does not match') if ($nrows != $#$matrix + 1);
for my $row_n (0..$nrows-1) {
die ('number of cols does not match: row #'.($row_n+1)) if ($#{$matrix->[$row_n]} + 1 != $ncols);
$rowval->[$row_n] = [];
for my $col_n (0..$ncols-1) {
my $val = $matrix->[$row_n][$col_n];
if ($val) {
push @{$rowval->[$row_n]}, $col_n + 1;
push @{$rowval->[$row_n]}, $val;
}
}
}
$self->set_sparse_matrix($nrows, $ncols, $rowval);
}
#
# API wrappers
#
sub VP_ClusterDirect {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{part} = [];
# call xs
&_VP_ClusterDirect($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{ntrials}, $self->{niter}, $self->{seed}, $self->{dbglvl}, $self->{nclusters}, $self->{part});
return $self->{pretty_format} && $self->format_cluster
|| $self->{part};
}
sub VP_ClusterRB {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{part} = [];
# call xs
&_VP_ClusterRB($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{ntrials}, $self->{niter}, $self->{seed}, $self->{cstype}, $self->{kwayrefine}, $self->{dbglvl}, $self->{nclusters}, $self->{part});
return $self->{pretty_format} && $self->format_cluster
|| $self->{part};
}
sub VA_Cluster {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_UPGMA();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{part} = [];
$self->{ptree} = [];
$self->{tsims} = [];
$self->{gains} = [];
# call xs
&_VA_Cluster($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{tsims}, $self->{gains});
return $self->{pretty_format} && {
clusters => $self->format_cluster,
tree => $self->format_tree
}
|| map $self->{$_}, qw(part ptree tsims gains);
}
sub VA_ClusterBiased {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_UPGMA();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
$self->{npclusters} = int($self->{nrows}**.5);
# init return values
$self->{part} = [];
$self->{ptree} = [];
$self->{tsims} = [];
$self->{gains} = [];
# call xs
&_VA_ClusterBiased($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{dbglvl}, $self->{npclusters}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{tsims}, $self->{gains});
return $self->{pretty_format} && {
clusters => $self->format_cluster,
tree => $self->format_tree
}
|| map $self->{$_}, qw(part ptree tsims gains);
}
sub SP_ClusterDirect {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
warn ("number of rows not equal to number of cols") if ($self->{nrows} != $self->{ncols});
# init return values
$self->{part} = [];
# call xs
&_SP_ClusterDirect($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{crfun}, $self->{ntrials}, $self->{niter}, $self->{seed}, $self->{dbglvl}, $self->{nclusters}, $self->{part});
return $self->{pretty_format} && $self->format_cluster
|| $self->{part};
}
sub SP_ClusterRB {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
warn ("number of rows not equal to number of cols") if ($self->{nrows} != $self->{ncols});
# init return values
$self->{part} = [];
# call xs
&_SP_ClusterRB($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{crfun}, $self->{ntrials}, $self->{niter}, $self->{seed}, $self->{cstype}, $self->{kwayrefine}, $self->{dbglvl}, $self->{nclusters}, $self->{part});
return $self->{pretty_format} && $self->format_cluster
|| $self->{part};
}
sub VP_GraphClusterRB {
my $self = shift;
# method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_CUT();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{part} = [];
$self->{crvalue} = 0;
# call xs
my $rtn = &_VP_GraphClusterRB($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{grmodel}, $self->{nnbrs}, $self->{edgeprune}, $self->{vtxprune}, $self->{mincomponent}, $self->{ntrials}, $self->{seed}, $self->{cstype}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{crvalue});
return $self->{pretty_format} && $self->format_cluster
|| [ $rtn, $self->{part}, $self->{crvalue} ];
}
sub SP_GraphClusterRB {
my $self = shift;
# method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_CUT();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{part} = [];
$self->{crvalue} = 0;
# call xs
my $rtn = &_SP_GraphClusterRB($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{nnbrs}, $self->{edgeprune}, $self->{vtxprune}, $self->{mincomponent}, $self->{ntrials}, $self->{seed}, $self->{cstype}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{crvalue});
return $self->{pretty_format} && $self->format_cluster
|| [ $rtn, $self->{part}, $self->{crvalue} ];
}
sub SA_Cluster {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_UPGMA();
# init return values
$self->{part} = [];
$self->{ptree} = [];
$self->{tsims} = [];
$self->{gains} = [];
# call xs
&_SA_Cluster($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{crfun}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{tsims}, $self->{gains});
return $self->{pretty_format} && {
clusters => $self->format_cluster,
tree => $self->format_tree
}
|| map $self->{$_}, qw(part ptree tsims gains);
}
sub V_BuildTree {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
$self->{treetype} ||= CLUTO_TREE_TOP();
# init return values
$self->{ptree} = [];
$self->{tsims} = [];
$self->{gains} = [];
# call xs
&_V_BuildTree($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{treetype}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{tsims}, $self->{gains});
return $self->{pretty_format} && $self->format_tree
|| map $self->{$_}, qw(ptree tsims gains);
}
sub S_BuildTree {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
warn ("number of rows not equal to number of cols") if ($self->{nrows} != $self->{ncols});
$self->{treetype} ||= CLUTO_TREE_TOP();
# init return values
$self->{ptree} = [];
$self->{tsims} = [];
$self->{gains} = [];
# call xs
&_S_BuildTree($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{crfun}, $self->{treetype}, $self->{dbglvl}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{tsims}, $self->{gains});
return $self->{pretty_format} && $self->format_tree
|| map $self->{$_}, qw(ptree tsims gains);
}
sub V_GetGraph {
my $self = shift;
# set method-specific defaults
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
$self->{growptr} = [];
$self->{growind} = [];
$self->{growval} = [];
# call xs
&_V_GetGraph($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{grmodel}, $self->{nnbrs}, $self->{dbglvl}, $self->{growptr}, $self->{growind}, $self->{growval});
return map $self->{$_}, qw(growptr growind growval);
}
sub S_GetGraph {
my $self = shift;
# set method-specific defaults
# init return values
$self->{growptr} = [];
$self->{growind} = [];
$self->{growval} = [];
# call xs
&_S_GetGraph($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{grmodel}, $self->{nnbrs}, $self->{dbglvl}, $self->{growptr}, $self->{growind}, $self->{growval});
return map $self->{$_}, qw(growptr growind growval);
}
sub V_GetSolutionQuality {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
# init return values
# call xs
return &_V_GetSolutionQuality($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{crfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part});
}
sub S_GetSolutionQuality {
my $self = shift;
# set method-specific defaults
$self->{crfun} ||= CLUTO_CLFUN_I2();
warn ("number of rows not equal to number of cols") if ($self->{nrows} != $self->{ncols});
# init return values
# call xs
return &_S_GetSolutionQuality($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{crfun}, $self->{nclusters}, $self->{part});
}
sub V_GetClusterStats {
my $self = shift;
# set method-specific defaults
# init return values
$self->{pwgts} = [];
$self->{cintsim} = [];
$self->{cintsdev} = [];
$self->{izscores} = [];
$self->{cextsim} = [];
$self->{cextsdev} = [];
$self->{ezscores} = [];
# call xs
&_V_GetClusterStats($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part}, $self->{pwgts}, $self->{cintsim}, $self->{cintsdev}, $self->{izscores}, $self->{cextsim}, $self->{cextsdev}, $self->{ezscores});
return $self->{pretty_format} && $self->format_cluster_stats
|| map $self->{$_}, qw(pwgts cintsim cintsdev izscores cextsim cextsdev ezscores);
}
sub S_GetClusterStats {
my $self = shift;
# set method-specific defaults
warn ("number of rows not equal to number of cols") if ($self->{nrows} != $self->{ncols});
# init return values
$self->{pwgts} = [];
$self->{cintsim} = [];
$self->{cintsdev} = [];
$self->{izscores} = [];
$self->{cextsim} = [];
$self->{cextsdev} = [];
$self->{ezscores} = [];
# call xs
&_S_GetClusterStats($self->{matrix_type}, $self->{nrows}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{nclusters}, $self->{part}, $self->{pwgts}, $self->{cintsim}, $self->{cintsdev}, $self->{izscores}, $self->{cextsim}, $self->{cextsdev}, $self->{ezscores});
return $self->{pretty_format} && $self->format_cluster_stats
|| map $self->{$_}, qw(pwgts cintsim cintsdev izscores cextsim cextsdev ezscores);
}
sub V_GetClusterFeatures {
my $self = shift;
# init return values
$self->{internalids} = [];
$self->{internalwgts} = [];
$self->{externalids} = [];
$self->{externalwgts} = [];
# call xs
&_V_GetClusterFeatures($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part}, $self->{nfeatures}, $self->{internalids}, $self->{internalwgts}, $self->{externalids}, $self->{externalwgts});
return $self->{pretty_format} && $self->format_cluster_features
|| map $self->{$_}, qw(internalids internalwgts externalids externalwgts);
}
sub V_GetClusterSummaries {
my $self = shift;
# set method-specific defaults
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
$self->{sumtype} ||= CLUTO_SUMMTYPE_MAXCLIQUES();
# init return values
$self->{r_nsum} = undef;
$self->{r_spid} = [];
$self->{r_swgt} = [];
$self->{r_sumptr} = [];
$self->{r_sumind} = [];
# call xs
&_V_GetClusterSummaries($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part}, $self->{sumtype}, $self->{nfeatures}, $self->{r_nsum}, $self->{r_spid}, $self->{r_swgt}, $self->{r_sumptr}, $self->{r_sumind});
return $self->{pretty_format} && $self->format_cluster_summaries
|| map $self->{$_}, qw(r_nsum r_spid r_swgt r_sumptr r_sumind);
}
sub V_GetTreeStats {
my $self = shift;
# set method-specific defaults
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
warn ("ptree not set or size does not equal to 2*nclusters") if ($#{$self->{ptree}}+1 != $self->{nclusters}*2);
# init return values
$self->{pwgts} = [];
$self->{cintsim} = [];
$self->{cextsim} = [];
# call xs
&_V_GetTreeStats($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{pwgts}, $self->{cintsim}, $self->{cextsim});
return $self->{pretty_format} && $self->format_tree_stats
|| map $self->{$_}, qw(pwgts cintsim cextsim);
}
sub V_GetTreeFeatures {
my $self = shift;
# set method-specific defaults
$self->{colmodel} ||=
($self->{simfun} == CLUTO_SIM_CORRCOEF() ? CLUTO_COLMODEL_NONE() : CLUTO_COLMODEL_IDF());
warn ("ptree not set or size does not equal to 2*nclusters")
if ($#{$self->{ptree}}+1 != $self->{nclusters}*2);
# init return values
$self->{internalids} = [];
$self->{internalwgts} = [];
$self->{externalids} = [];
$self->{externalwgts} = [];
# call xs
&_V_GetTreeFeatures($self->{matrix_type}, $self->{nrows}, $self->{ncols}, $self->{nnz}, $self->{rowptr}, $self->{rowind}, $self->{rowval}, $self->{simfun}, $self->{rowmodel}, $self->{colmodel}, $self->{colprune}, $self->{nclusters}, $self->{part}, $self->{ptree}, $self->{nfeatures}, $self->{internalids}, $self->{internalwgts}, $self->{externalids}, $self->{externalwgts});
return $self->{pretty_format} && $self->format_tree_features
|| map $self->{$_}, qw(internalids internalwgts externalids externalwgts);
}
#
# for prertty_format option
#
sub format_cluster {
my $self = shift;
my $clusters = [];
for my $i (0..$self->{nrows}-1) {
push @{$clusters->[$self->{part}->[$i]]}, {
row => $i,
rowlabel => $self->{rowlabels}->[$i]
} if ($self->{part}->[$i] >= 0);
}
return $clusters;
# return [ map {
# rowlabel => $self->{rowlabels}->[$_],
# cluster => $self->{part}->[$_]
# }, (0..$self->{nrows}-1)]
}
sub format_cluster_stats {
my $self = shift;
return {
clusters => [ map {
pwgt => $self->{pwgts}->[$_],
cintsim => $self->{cintsim}->[$_],
cintsdev => $self->{cintsdev}->[$_],
cextsim => $self->{cextsim}->[$_],
cextsdev => $self->{cextsdev}->[$_],
}, (0..$self->{nclusters}-1) ],
rows => [ map {
rowlabel => $self->{rowlabels}->[$_],
izscore => $self->{izscores}->[$_],
exscore => $self->{ezscores}->[$_]
}, (0..$self->{nrows}-1) ]
}
}
sub format_cluster_features {
my $self = shift;
return [ map {
descriptive =>
[ map {
internalid => $self->{internalids}->[$_],
collabel => $self->{collabels}->[$self->{internalids}->[$_]],
internalwgt => $self->{internalwgts}->[$_]
}, (($_*$self->{nfeatures})..(($_ + 1)*$self->{nfeatures} - 1)) ],
discriminating =>
[ map {
externalid => $self->{externalids}->[$_],
collabel => $self->{collabels}->[$self->{externalids}->[$_]],
externalwgt => $self->{externalwgts}->[$_]
}, (($_*$self->{nfeatures})..(($_ + 1)*$self->{nfeatures} - 1)) ],
}, (0..$self->{nclusters}-1)];
}
sub format_cluster_summaries {
my $self = shift;
return [ map {
cluster => $self->{r_spid}->[$_],
swgt => $self->{r_swgt}->[$_],
features => [ map $self->{r_sumind}->[$_], ($self->{r_sumptr}->[$_]..($self->{r_sumptr}->[$_+1]-1)) ],
}, (0..($self->{r_nsum}-1)) ];
}
sub format_tree {
my $self = shift;
return [ map {
parent => $self->{ptree}->[$_],
tsims => $self->{tsims}->[$_],
gains => $self->{gains}->[$_]
}, (0..$#{$self->{ptree}}-1) ];
}
sub format_tree_stats {
my $self = shift;
return [ map {
cintsim => $self->{cintsim}->[$_],
cextsim => $self->{cextsim}->[$_]
}, (0..$self->{nclusters}*2-1) ];
}
sub format_tree_features {
my $self = shift;
return [ map
[ map {
descriptive =>
[ map {
internalid => $self->{internalids}->[$_],
collabel => $self->{collabels}->[$self->{internalids}->[$_]],
internalwgt => $self->{internalwgts}->[$_]
},
grep defined($self->{internalids}->[$_]),
(($_*$self->{nfeatures})..(($_ + 1)*$self->{nfeatures} - 1)) ],
discriminating =>
[ map {
externalid => $self->{externalids}->[$_],
collabel => $self->{collabels}->[$self->{externalids}->[$_]],
externalwgt => $self->{externalwgts}->[$_]
},
grep defined($self->{externalids}->[$_]),
(($_*$self->{nfeatures})..(($_ + 1)*$self->{nfeatures} - 1)) ],
}, ($_..$_+$self->{nclusters}-1)]
, (0..$self->{nclusters}*2-1) ];
}
# Autoload methods go after =cut, and are processed by the autosplit program.
1;
__END__
# Below is stub documentation for your module. You'd better edit it!