Algorithm::LibSVM - A Perl 6 bindings for libsvm
use Algorithm::LibSVM;
use Algorithm::LibSVM::Parameter;
use Algorithm::LibSVM::Problem;
use Algorithm::LibSVM::Model;
my $libsvm = Algorithm::LibSVM.new;
my Algorithm::LibSVM::Parameter $parameter .= new(svm-type => C_SVC,
kernel-type => RBF);
my Algorithm::LibSVM::Problem $problem = $libsvm.load-problem('heart_scale');
my @r = $libsvm.cross-validation($problem, $param, 10);
$libsvm.evaluate($problem.y, @r).say; # {acc => 81.1111111111111, mse => 0.755555555555556, scc => 1.01157627463546}
use Algorithm::LibSVM;
use Algorithm::LibSVM::Parameter;
use Algorithm::LibSVM::Problem;
use Algorithm::LibSVM::Model;
sub gen-train {
my $max-x = 1;
my $min-x = -1;
my $max-y = 1;
my $min-y = -1;
do for ^300 {
my $x = $min-x + rand * ($max-x - $min-x);
my $y = $min-y + rand * ($max-y - $min-y);
my $label = do given $x, $y {
when ($x - 0.5) ** 2 + ($y - 0.5) ** 2 <= 0.2 {
1
}
when ($x - -0.5) ** 2 + ($y - -0.5) ** 2 <= 0.2 {
2
}
default { Nil }
}
($label,"1:$x","2:$y") if $label.defined;
}.sort({ $^a.[0] cmp $^b.[0] })>>.join(" ")
}
my Str @train = gen-train;
my Pair @test = (q:to/END/).split(" ", 2)[1].split(" ")>>.split(":").map: { .[0].Int => .[1].Num };
1 1:0.5 2:0.5
END
my $libsvm = Algorithm::LibSVM.new;
my Algorithm::LibSVM::Parameter $parameter .= new(svm-type => C_SVC,
kernel-type => LINEAR);
my Algorithm::LibSVM::Problem $problem = $libsvm.load-problem(@train);
my $model = $libsvm.train($problem, $parameter);
say $model.predict(features => @test)<label> # 1
Algorithm::LibSVM is a Perl 6 bindings for libsvm.
Defined as:
method cross-validation(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param, Int $nr-fold) returns Array
Conducts $nr-fold-fold cross validation and returns predicted values.
Defined as:
method train(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param) returns Algorithm::LibSVM::Model
Trains a SVM model.
-
$problemThe instance of Algorithm::LibSVM::Problem. -
$paramThe instance of Algorithm::LibSVM::Parameter.
Defined as:
multi method load-problem(\lines) returns Algorithm::LibSVM::Problem
multi method load-problem(Str $filename) returns Algorithm::LibSVM::Problem
Loads libsvm-format data.
Defined as:
method load-model(Str $filename) returns Algorithm::LibSVM::Model
Loads libsvm model.
Defined as:
method evaluate(@true-values, @predicted-values) returns Hash
Evaluates the performance of the three metrics (i.e. accuracy, mean squared error and squared correlation coefficient)
-
@true-valuesThe array that contains ground-truth values. -
@predicted-valuesThe array that contains predicted values.
Defined as:
method nr-feature returns Int:D
Returns the number of the features.
titsuki titsuki@cpan.org
Copyright 2016 titsuki
This library is free software; you can redistribute it and/or modify it under the terms of the MIT License.
libsvm ( https://github.com/cjlin1/libsvm ) by Chih-Chung Chang and Chih-Jen Lin is licensed under the BSD 3-Clause License.