AI-SimulatedAnnealing
view release on metacpan or search on metacpan
lib/AI/SimulatedAnnealing.pm view on Meta::CPAN
# number representing a cost to be minimized). The method tests every
# possible combination of numbers matching the specifications and returns a
# reference to an array containing the optimal numbers, where "optimal"
# means producing the lowest cost.
sub use_brute_force {
my $number_specs = validate_number_specs($_[0]);
my $cost_function = $_[1];
my @optimized_list;
my @lists;
my @cursors;
# Populate the list of lists of numbers:
for my $number_spec (@{ $number_specs }) {
my @list;
my $num = $number_spec->{"LowerBound"};
while ($num <= $number_spec->{"UpperBound"}) {
push @list, $num;
$num += 1 / (10 ** $number_spec->{"Precision"});
} # end while
push @lists, \@list;
} # next $number_spec
# Populate @cursors with the starting position for each list of numbers:
for (0..$#lists) {
push @cursors, 0;
} # next
# Perform the tests:
my $lowest_cost = undef;
my $finished = $FALSE;
do {
# Perform a test using the current cursors:
my @candidate_list;
my $cost;
for my $dex (0..$#lists) {
push @candidate_list, $lists[$dex]->[$cursors[$dex]];
} # next $dex
$cost = $cost_function->(\@candidate_list);
unless (defined($lowest_cost) && $cost >= $lowest_cost) {
$lowest_cost = $cost;
@optimized_list = @candidate_list;
} # end unless
# Adjust the cursors for the next test if not finished:
for my $dex (reverse(0..$#lists)) {
my $cursor = $cursors[$dex];
if ($cursor < $#{ $lists[$dex] }) {
$cursor++;
$cursors[$dex] = $cursor;
last;
}
elsif ($dex == 0) {
$finished = $TRUE;
last;
}
else {
$cursors[$dex] = 0;
} # end if
} # next $dex
} until ($finished);
# Return the result:
return \@optimized_list;
} # end sub
# The validate_number_specs() function takes a reference to an array of
# number specifications (which are references to hashes with "LowerBound",
( run in 0.237 second using v1.01-cache-2.11-cpan-4d50c553e7e )