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

method cross-validation(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param, Int $nr-fold --> List)

method train(Algorithm::LibSVM::Problem $problem, Algorithm::LibSVM::Parameter $param --> Algorithm::LibSVM::Model)

multi method load-problem(\lines --> Algorithm::LibSVM::Problem)
multi method load-problem(Str $filename --> Algorithm::LibSVM::Problem)

method load-model(Str $filename --> Algorithm::LibSVM::Model)

method evaluate(@true-values, @predicted-values --> Hash)

method nr-feature(--> Int:D)

sub parse-libsvmformat(Str $text --> List) is export