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lib/AI/Pathfinding/OptimizeMultiple/App/CmdLine.pm  view on Meta::CPAN

package AI::Pathfinding::OptimizeMultiple::App::CmdLine;
$AI::Pathfinding::OptimizeMultiple::App::CmdLine::VERSION = '0.0.17';
use strict;
use warnings;

use MooX qw/late/;

use Getopt::Long qw(GetOptionsFromArray);
use IO::File     ();

use AI::Pathfinding::OptimizeMultiple                ();
use AI::Pathfinding::OptimizeMultiple::PostProcessor ();

# TODO : restore later.
# use MyInput;

use Carp ();

has argv             => ( isa => 'ArrayRef[Str]', is => 'ro', required => 1, );
has _arbitrator      => ( is  => 'rw' );
has _add_horne_prune => ( isa => 'Bool',     is => 'rw' );
has _chosen_scans    => ( isa => 'ArrayRef', is => 'rw' );
has _should_exit_immediately =>
    ( isa => 'Bool', is => 'rw', default => sub { 0; }, );
has input_obj_class  => ( isa => 'Str', is => 'rw' );
has _input_obj       => ( is  => 'rw' );
has _is_flares       => ( is  => 'rw',  isa => 'Bool', default => sub { 0; }, );
has _num_boards      => ( isa => 'Int', is  => 'rw' );
has _offset_quotas   => ( isa => 'Int', is  => 'rw' );
has _optimize_for    => ( isa => 'Str', is  => 'rw' );
has _output_filename => ( isa => 'Str', is  => 'rw' );
has _post_processor => (
    isa => 'Maybe[AI::Pathfinding::OptimizeMultiple::PostProcessor]',
    is  => 'rw'
);
has _quotas_are_cb        => ( isa => 'Bool',       is => 'rw' );
has _quotas_expr          => ( isa => 'Maybe[Str]', is => 'rw' );
has _should_rle_be_done   => ( isa => 'Bool',       is => 'rw' );
has _should_trace_be_done => ( isa => 'Bool',       is => 'rw' );
has _simulate_to          => ( isa => 'Maybe[Str]', is => 'rw' );
has _start_board          => ( isa => 'Int',        is => 'rw' );
has _stats_factors =>
    ( isa => 'HashRef', is => 'rw', default => sub { return +{}; }, );

my $_component_re = qr/[A-Za-z][A-Za-z0-9_]*/;
my $_module_re    = qr/$_component_re(?:::$_component_re)*/;

sub BUILD
{
    my $self = shift;

    # Command line parameters
    my $_start_board         = 1;
    my $num_boards           = 32000;
    my $output_filename      = "-";
    my $should_trace_be_done = 0;
    my $should_rle_be_done   = 1;
    my $_quotas_expr         = undef;
    my $quotas_are_cb        = 0;
    my $optimize_for         = "speed";
    my $offset_quotas        = 0;
    my $simulate_to          = undef;
    my $_add_horne_prune     = 0;
    my $input_obj_class = 'AI::Pathfinding::OptimizeMultiple::DataInputObj';
    my %stats_factors;

    my $help = 0;
    my $man  = 0;
    GetOptionsFromArray(
        $self->argv(),
        'help|h'          => \$help,
        man               => \$man,
        "o|output=s"      => \$output_filename,
        "num-boards=i"    => \$num_boards,
        "trace"           => \$should_trace_be_done,
        "rle!"            => \$should_rle_be_done,
        "start-board=i"   => \$_start_board,
        "quotas-expr=s"   => \$_quotas_expr,
        "quotas-are-cb"   => \$quotas_are_cb,
        "offset-quotas"   => \$offset_quotas,
        "opt-for=s"       => \$optimize_for,
        "simulate-to=s"   => \$simulate_to,
        "sprtf"           => \$_add_horne_prune,
        "input-class=s"   => \$input_obj_class,
        "stats-factors=f" => \%stats_factors,
    ) or die "Extracting options from ARGV array failed - $!";

    if ($help)
    {
        $self->_should_exit_immediately(1);
        print <<"EOF";
$0 - optimize a game AI multi-tasking configuration

--help | -h - displays this help screen
--output=[filename] | -o [filename] - output to this file instead of STDOUT.
EOF
        return;
    }

    $self->_start_board($_start_board);
    $self->_num_boards($num_boards);
    $self->_output_filename($output_filename);
    $self->_should_trace_be_done($should_trace_be_done);
    $self->_should_rle_be_done($should_rle_be_done);
    $self->_quotas_expr($_quotas_expr);
    $self->_quotas_are_cb($quotas_are_cb);
    $self->_optimize_for($optimize_for);
    $self->_offset_quotas($offset_quotas);
    $self->_simulate_to($simulate_to);
    $self->_add_horne_prune($_add_horne_prune);
    $self->_stats_factors( \%stats_factors );
    $self->input_obj_class($input_obj_class);

    {
        my $class = $self->input_obj_class();
        if ( $class !~ m{\A$_module_re\z} )
        {
            Carp::confess(
                "Input object class does not seem like a good class:"
                    . $self->input_obj_class() );
        }
        eval "require $class;";
        if ($@)
        {
            die "Could not load '$class' - <<$@>>";
        }

        # TODO : Restore later.
        $self->_input_obj(
            $class->new(
                {
                    start_board => $self->_start_board(),
                    num_boards  => $self->_num_boards(),
                }
            )
        );
    }

    $self->_post_processor(
        AI::Pathfinding::OptimizeMultiple::PostProcessor->new(
            {
                do_rle        => $self->_should_rle_be_done(),
                offset_quotas => $self->_offset_quotas(),
            }
        )
    );

    return;
}

sub _selected_scans
{
    my $self = shift;

    return $self->_input_obj->selected_scans();
}

sub _map_all_but_last
{
    my $self = shift;

    my ( $cb, $arr_ref ) = (@_);

    return [
        ( map { $cb->($_) } @$arr_ref[ 0 .. $#$arr_ref - 1 ] ),
        $arr_ref->[-1]
    ];
}

sub _get_quotas
{
    my $self = shift;
    if ( $self->_quotas_are_cb() )
    {
        return scalar( eval( $self->_quotas_expr() ) );
    }
    elsif ( defined( $self->_quotas_expr() ) )
    {
        return [ eval $self->_quotas_expr() ];
    }
    else
    {
        return $self->_get_default_quotas();
    }
}

sub _get_default_quotas
{
    return [ (350) x 5000 ];
}

sub _get_script_fh
{
    my $self = shift;
    return IO::File->new(
        ( $self->_output_filename() eq "-" )
        ? ">&STDOUT"
        : ( $self->_output_filename(), "w" )
    );

lib/AI/Pathfinding/OptimizeMultiple/App/CmdLine.pm  view on Meta::CPAN

sub _write_script
{
    my $self = shift;

    $self->_out_script( $self->_calc_script_text() );
}

sub _calc_scans_iters_pdls
{
    my $self = shift;

    my $method = (
        ( $self->_optimize_for() =~ m{len} )
        ? "get_scans_lens_iters_pdls"
        : "get_scans_iters_pdls"
    );

    return $self->_input_obj->$method();
}

sub _arbitrator_trace_cb
{
    my $args = shift;
    printf( "%s \@ %s (%s solved)\n",
        @$args{qw(iters_quota selected_scan_idx total_boards_solved)} );
}

sub _init_arbitrator
{
    my $self = shift;

    return $self->_arbitrator(
        AI::Pathfinding::OptimizeMultiple->new(
            {
                'scans' => [
                    map { +{ name => $_->id() } }
                        @{ $self->_input_obj->_suitable_scans_list() },
                ],
                'quotas'           => $self->_get_quotas(),
                'selected_scans'   => $self->_selected_scans(),
                'num_boards'       => $self->_num_boards(),
                'scans_iters_pdls' => $self->_calc_scans_iters_pdls(),
                'trace_cb'         => \&_arbitrator_trace_cb,
                'optimize_for'     => $self->_optimize_for(),
                'stats_factors'    => $self->_stats_factors(),
            }
        )
    );
}

sub _report_total_iters
{
    my $self = shift;
    if ( $self->_arbitrator()->get_final_status() eq "solved_all" )
    {
        print "Solved all!\n";
    }
    printf( "total_iters = %s\n", $self->_arbitrator()->get_total_iters() );
}

sub _arbitrator_process
{
    my $self = shift;

    $self->_arbitrator()->calc_meta_scan();

    my $scans =
        $self->_post_processor->process( $self->_arbitrator->chosen_scans() );

    $self->_chosen_scans($scans);
}

sub _do_trace_for_board
{
    my $self  = shift;
    my $board = shift;

    my $results = $self->_arbitrator()->calc_board_iters($board);
    print "\@info=" . join( ",", @{ $results->{per_scan_iters} } ) . "\n";
    print +( $board + $self->_start_board() ) . ": "
        . $results->{board_iters} . "\n";
}

sub _real_do_trace
{
    my $self = shift;
    foreach my $board ( 0 .. $self->_num_boards() - 1 )
    {
        $self->_do_trace_for_board($board);
    }
}

sub _do_trace
{
    my $self = shift;

    # Analyze the results

    if ( $self->_should_trace_be_done() )
    {
        $self->_real_do_trace();
    }
}

sub _get_run_string
{
    my $self    = shift;
    my $results = shift;

    return join(
        "",
        map {
            sprintf( '%i@%i,',
                $_->iters(), $self->_map_scan_idx_to_id( $_->scan_idx() ) )
        } @{ $self->_post_processor->process( $results->scan_runs() ) },
    );
}

sub _do_simulation_for_board
{
    my ( $self, $board ) = @_;

    my $results = $self->_arbitrator()->simulate_board($board);

    my $scan_mapper = sub {
        my $index = shift;

        return $self->_map_scan_idx_to_id($index);
    };

    return sprintf( "%i:%s:%s:%i",
        $board + 1,
        $results->get_status(),
        $self->_get_run_string($results),
        $results->get_total_iters(),
    );
}

sub _real_do_simulation
{
    my $self = shift;

    open my $simulate_out_fh, ">", $self->_simulate_to()
        or Carp::confess( "Could not open " . $self->_simulate_to() . " - $!" );

    foreach my $board ( 0 .. $self->_num_boards() - 1 )
    {
        print {$simulate_out_fh} $self->_do_simulation_for_board($board), "\n";
    }

    close($simulate_out_fh);

    return;
}

sub _do_simulation
{
    my $self = shift;

    # Analyze the results

    if ( defined( $self->_simulate_to() ) )
    {
        $self->_real_do_simulation();
    }

    return;
}

sub run
{
    my $self = shift;

    if ( $self->_should_exit_immediately() )
    {
        return 0;
    }

    $self->_init_arbitrator();
    $self->_arbitrator_process();
    $self->_report_total_iters();
    $self->_write_script();
    $self->_do_trace();
    $self->_do_simulation();

    return 0;
}

sub run_flares
{
    my $self = shift;

    $self->_optimize_for("len");
    $self->_is_flares(1);

    $self->_init_arbitrator();

    $self->_arbitrator()->calc_flares_meta_scan();

    my $scans =
        $self->_post_processor->process( $self->_arbitrator->chosen_scans() );

    $self->_chosen_scans($scans);
    $self->_report_total_iters();
    $self->_write_script();
    $self->_do_trace();
    $self->_do_simulation();

    return 0;
}

1;

__END__

=pod

=encoding UTF-8

=head1 NAME

AI::Pathfinding::OptimizeMultiple::App::CmdLine - the command line application class.

=head1 VERSION

version 0.0.17

=head1 SUBROUTINES/METHODS

=head2 $self->run()

For internal use.

=head2 $self->run_flares()

For internal use.

=head2 $self->argv()

An array ref of command line arguments.

=head2 $self->input_obj_class()

The class to handle the input data - by default -
L<AI::Pathfinding::OptimizeMultiple::DataInputObj>.

=head2 BUILD()

Moo leftover. B<INTERNAL USE>.

=for :stopwords cpan testmatrix url bugtracker rt cpants kwalitee diff irc mailto metadata placeholders metacpan

=head1 SUPPORT

=head2 Websites

The following websites have more information about this module, and may be of help to you. As always,
in addition to those websites please use your favorite search engine to discover more resources.

=over 4