NAME GraphViz - Interface to AT&T's GraphViz. Deprecated. See GraphViz2 SYNOPSIS use GraphViz; my $g = GraphViz->new(); $g->add_node('London'); $g->add_node('Paris', label => 'City of\nlurve'); $g->add_node('New York'); $g->add_edge('London' => 'Paris'); $g->add_edge('London' => 'New York', label => 'Far'); $g->add_edge('Paris' => 'London'); print $g->as_png; DESCRIPTION This module provides an in
NAME AI::DecisionTree - Automatically Learns Decision Trees VERSION version 0.11 SYNOPSIS use AI::DecisionTree; my $dtree = new AI::DecisionTree; # A set of training data for deciding whether to play tennis $dtree->add_instance (attributes => {outlook => 'sunny', temperature => 'hot', humidity => 'high'}, result => 'no'); $dtree->add_instance (attributes => {outlook => 'overcast', temperature => '
NAME String::Similarity - calculate the similarity of two strings SYNOPSIS use String::Similarity; $similarity = similarity $string1, $string2; $similarity = similarity $string1, $string2, $limit; DESCRIPTION $factor = similarity $string1, $string2, [$limit] The similarity-function calculates the similarity index of its two arguments. A value of 0 means that the strings are entirely different. A v
NAME String::Approx - Perl extension for approximate matching (fuzzy matching) SYNOPSIS use String::Approx 'amatch'; print if amatch("foobar"); my @matches = amatch("xyzzy", @inputs); my @catches = amatch("plugh", ['2'], @inputs); DESCRIPTION String::Approx lets you match and substitute strings approximately. With this you can emulate errors: typing errorrs, speling errors, closely related vocabul
NAME MaxEntropy - Perl5 module for Maximum Entropy Modeling and Feature Induction SYNOPSIS use Statistics::MaxEntropy; # debugging messages; default 0 $Statistics::MaxEntropy::debug = 0; # maximum number of iterations for IIS; default 100 $Statistics::MaxEntropy::NEWTON_max_it = 100; # minimal distance between new and old x for Newton's method; # default 0.001 $Statistics::MaxEntropy::NEWTON_min =
NAME Algorithm::Cluster - Perl interface to the C Clustering Library. DESCRIPTION This module is an interface to the C Clustering Library, a general purpose library implementing functions for hierarchical clustering (pairwise simple, complete, average, and centroid linkage), along with k-means and k-medians clustering, and 2D self-organizing maps. This library was developed at the Human Genome Cen
NAME Algorithm::SVM - Perl bindings for the libsvm Support Vector Machine library. SYNOPSIS use Algorithm::SVM; # Load the model stored in the file 'sample.model' $svm = new Algorithm::SVM(Model => 'sample.model'); # Classify a dataset. $ds1 = new Algorithm::SVM::DataSet(Label => 1, Data => [0.12, 0.25, 0.33, 0.98]); $res = $svm->predict($ds); # Train a new SVM on some new datasets. $svm->train(@t
NAME Algorithm::SVMLight - Perl interface to SVMLight Machine-Learning Package SYNOPSIS use Algorithm::SVMLight; my $s = new Algorithm::SVMLight; $s->add_instance (attributes => {foo => 1, bar => 1, baz => 3}, label => 1); $s->add_instance (attributes => {foo => 2, blurp => 1}, label => -1); ... repeat for several more instances, then: $s->train; # Find results for unseen instances my $result = $s
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