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t/01-fuzzyEngine.t  view on Meta::CPAN

    $degree = $v->low(0.2, 0.1);
    is( $degree, 0.1, 'and / or for degree of low work' );

    $v->reset;
    is( $v->low, 0, 'reset works' );

    # Throw errors!
    throws_ok { $v->fuzzify(0)
              } qr/internal/, 'Checks illegal fuzzify call';
    throws_ok { $v->defuzzify
              } qr/internal/, 'Checks illegal defuzzify call';
    throws_ok { $v->change_set( low => [[]=>[]] )
              } qr/internal/i, 'Blocks change_set';
};

$fe = $class->new();

subtest "$class as factory" => sub {

    my $v = $fe->new_variable( 0 => 10,
                               'low'  => [0, 1, 10, 0],
                               'high' => [0, 0, 10, 1],
                             );
    isa_ok( $v, $var_class, 'What $fe->new_variable returns' );
    is_deeply( [$v->from, $v->to, [ sort keys %{ $v->sets } ] ],
               [       0,     10, [ sort qw(low high)       ] ],
               'Variable attributes and set names by new_variable',
             );

    my $w = $fe->new_variable( 0 => 1,
                               'low'  => [0, 1],
                               'high' => [1, 0],
                             );

    is_deeply( [ $fe->variables() ],
               [$v, $w],
               'Engine stores variables (should be weakened)',
             );

    $v->low( 0.1 );
    $w->low( 0.2 );

    my $v_resetted = $v->reset;
    isa_ok( $v_resetted,
            $var_class,
            'What variable->reset returns',
          ) or exit;
    is( $v->low, 0.0, 'Variable can be resetted'       );
    is( $w->low, 0.2, 'Other variables stay unchanged' );

    my $fe_resetted = $fe->reset();
    isa_ok( $fe_resetted,
            $class,
            'What fuzzyEngine->reset returns',
          );
    is( $w->low, 0.0, 'FuzzyEngine resets all variables' );
};

subtest 'synopsis' => sub {

    # Engine (or factory) provides fuzzy logical arithmetic
    my $fe = $class->new();

    # Disjunction:
    my $a = $fe->or ( 0.2, 0.5, 0.8, 0.7 ); # 0.8
    # Conjunction:
    my $b = $fe->and( 0.2, 0.5, 0.8, 0.7 ); # 0.2
    # Negation:
    my $c = $fe->not( 0.4 );                # 0.6
    # Always true:
    my $t = $fe->true();                    # 1.0
    # Always false:
    my $f = $fe->false();                   # 0.0

    # These functions are constitutive for the operations
    # on the fuzzy sets of the fuzzy variables:

    # VARIABLES (AI::FuzzyEngine::Variable)

    # input variables need definition of membership functions of their sets
    my $flow = $fe->new_variable( 0 => 2000,
                        small => [0, 1,  500, 1, 1000, 0                  ],
                        med   => [       400, 0, 1000, 1, 1500, 0         ],
                        huge  => [               1000, 0, 1500, 1, 2000, 1],
                   );
    my $cap  = $fe->new_variable( 0 => 1800,
                        avg   => [0, 1, 1500, 1, 1700, 0         ],
                        high  => [      1500, 0, 1700, 1, 1800, 1],
                   );
    # internal variables need sets, but no membership functions
    my $saturation = $fe->new_variable( # from => to may be ommitted
                        low   => [],
                        crit  => [],
                        over  => [],
                   );
    # But output variables need membership functions for their sets:
    my $green = $fe->new_variable( -5 => 5,
                        decrease => [-5, 1, -2, 1, 0, 0            ],
                        ok       => [       -2, 0, 0, 1, 2, 0      ],
                        increase => [              0, 0, 2, 1, 5, 1],
                   );

    # Reset FuzzyEngine (resets all variables)
    $fe->reset();

    # Reset a fuzzy variable directly
    $flow->reset;

    # Membership functions can be changed via the set's variable.
    # This might be useful during parameter identification algorithms
    # Changing a function resets the respective variable.
    $flow->change_set( med => [500, 0, 1000, 1, 1500, 0] );

    # Fuzzification of input variables
    $flow->fuzzify( 600 );
    $cap->fuzzify( 1000 );

    # Membership degrees of the respective sets are now available:
    my $flow_is_small = $flow->small(); # 0.8
    my $flow_is_med   = $flow->med();   # 0.2
    my $flow_is_huge  = $flow->huge();  # 0.0