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use strict;
########################################################################
# This script is to test the ability of the optimizer to tune:
#
# f(x) = sum_i (x_i - i) where i from 1 to N.
#
########################################################################
use Test::More (tests => 11);
sub BEGIN
{
use_ok('Math::Amoeba', 0.02, 'MinimiseND');
}
sub testPoly
{
my $tol = 1e-5;
my $N = 9;
my $y = [1..$N];
my $func = sub {
my $x = [@_];
my $cost = 0;
foreach my $i (0..$N-1) {
$cost += ($x->[$i] - $y->[$i])**2;
}
return $cost;
};
my $guess = [ map { rand()*2 - 1 } (1..$N) ];
my $scale = [ map { 1 } (1..$N) ];
my ($opt, $cost) = MinimiseND($guess, $scale, \&$func, $tol, 10000);
ok(abs($cost) < $tol, "Optimization success ($cost vs 0)");
foreach my $i (0..$N-1) {
ok(
abs($opt->[$i] - $y->[$i]) < 1e-2,
sprintf(
qq{The %d th parameter error okay (%.5f vs %.5f).},
$i, $opt->[$i], $y->[$i]
)
);
}
}
&testPoly();
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