Algorithm::LibSVM - A Raku 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);
# heart_scale is here: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/heart_scale
my Algorithm::LibSVM::Problem $problem = Algorithm::LibSVM::Problem.from-file('heart_scale');
my @r = $libsvm.cross-validation($problem, $parameter, 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;
my @x;
my @y;
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 {
-1
}
default { Nil }
}
if $label.defined {
@y.push: $label;
@x.push: [$x, $y];
}
}
(@x, @y)
}
my (@train-x, @train-y) := gen-train;
my @test-x = 1 => 0.5e0, 2 => 0.5e0;
my $libsvm = Algorithm::LibSVM.new;
my Algorithm::LibSVM::Parameter $parameter .= new(svm-type => C_SVC,
kernel-type => LINEAR);
my Algorithm::LibSVM::Problem $problem = Algorithm::LibSVM::Problem.from-matrix(@train-x, @train-y);
my $model = $libsvm.train($problem, $parameter);
say $model.predict(features => @test-x)<label> # 1
Algorithm::LibSVM is a Raku bindings for libsvm.
Defined as:
method cross-validation(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param, Int $nr-fold --> List)
Conducts $nr-fold-fold cross validation and returns predicted values.
Defined as:
method train(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param --> 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 --> Algorithm::LibSVM::Problem)
multi method load-problem(Str $filename --> Algorithm::LibSVM::Problem)
Loads libsvm-format data.
Defined as:
method load-model(Str $filename --> Algorithm::LibSVM::Model)
Loads libsvm model.
Defined as:
method evaluate(@true-values, @predicted-values --> 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(--> Int:D)
Returns the maximum index of all the features.
Defined as:
sub parse-libsvmformat(Str $text --> List) is export
Is a helper routine for handling libsvm-format text.
As a workaround for RT130187, I applied the patch programs (e.g. src/3.22/svm.cpp.patch) for the sake of disabling random access of the problematic array.
Sadly to say, those patches drastically increase the complexity of using PRECOMPUTED kernel.
titsuki [email protected]
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.