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AI-FuzzyInference

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    $self->{IMPLICATION}     = $_implication{default};
    $self->{AGGREGATION}     = $_aggregation{default};
    $self->{DEFUZZIFICATION} = $_defuzzification{default};

    for my $op (qw/& | !/) {
	$self->{OPERATIONS}{$op} = $_operations{$op}{default};
    }
}

# sub implication() - public method.
#
# one optional argument: has to match one of the keys of the %_implication hash.
# used to query/set the implication method.

sub implication {
    my ($self,
	$new,
	) = @_;

    if (defined $new and exists $_implication{$new}) {
	$self->{IMPLICATION} = $new;
    }

    return $self->{IMPLICATION};
}

# sub aggregation() - public method.
#
# one optional argument: has to match one of the keys of the %_aggregation hash.
# used to query/set the aggregation method.

sub aggregation {
    my ($self,
	$new,
	) = @_;

    if (defined $new and exists $_aggregation{$new}) {
	$self->{AGGREGATION} = $new;
    }

    return $self->{AGGREGATION};
}

# sub defuzzification() - public method.
#
# one optional argument: has to match one of the keys of the %_defuzzification hash.
# used to query/set the defuzzification method.

sub defuzzification {
    my ($self,
	$new,
	) = @_;

    if (defined $new and exists $_defuzzification{$new}) {
	$self->{DEFUZZIFICATION} = $new;
    }

    return $self->{DEFUZZIFICATION};
}

# sub operation() - public method.
#
# two arguments: first one mandatory and specifies the logic operation
# in question. Second one is optional and has to match one of the keys
# of the %{$_operations{$first_arg}} hash.
# Used to query/set the logic operations method.

sub operation {
    my ($self,
	$op,
	$new,

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    return unless defined $op && exists $_operations{$op};

    if (defined $new and exists $_operations{$op}{$new}) {
	$self->{OPERATIONS}{$op} = $new;
    }

    return $self->{OPERATIONS}{$op};
}

# sub inVar() - public method.
#
# 4 arguments or more : First is a name of a new input variable.
# Second and third are the min and max values of that variable.
# These define the universe of discourse for that variable.
# Additional argumets constitute a hash. The keys of the hash
# are term set names defined for the given variable. The values
# are the coordinates of the vertices of the term sets.
#
# ex. $obj->inVar('height',
#                 5, 8,   # xmin, xmax (in feet, say)

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    $self->{INVARS}{$var} = [$xmin, $xmax];

    while (@sets) {
	my $s = shift @sets;
	my $c = shift @sets;

	$self->{SET}->add("$var:$s", $xmin, $xmax, @$c);
    }
}

# sub outVar() - public method.
#
# 4 arguments or more : First is a name of a new output variable.
# Second and third are the min and max values of that variable.
# These define the universe of discourse for that variable.
# Additional argumets constitute a hash. The keys of the hash
# are term set names defined for the given variable. The values
# are the coordinates of the vertices of the term sets.

sub outVar {
    my ($self,

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    $self->{OUTVARS}{$var} = [$xmin, $xmax];

    while (@sets) {
	my $s = shift @sets;
	my $c = shift @sets;

	$self->{SET}->add("$var:$s", $xmin, $xmax, @$c);
    }
}

# sub addRule() - public method.
#
# Adds fuzzy if-then inference rules.
#
# $obj->addRule('x=medium'         => 'z = slow',
#               'x=low  & y=small' => 'z = fast',
#               'x=high & y=tiny'  => 'z=veryfast');
# spaces are optional. The characters [&=|] are special.

sub addRule {
    my ($self, %rules) = @_;

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    for my $k (keys %rules) {
	my $v = $rules{$k};
	s/\s+//g for $v, $k;

	push @{$self->{RULES}} => [$k, $v];
    }

    return 1;
}

# sub show() - public method.
#
# This method displays the computed values of all
# output variables.
# It is ugly, and will be removed. Here for debugging.

sub show {
    my $self = shift;

    for my $var (keys %{$self->{RESULTS}}) {
	print "Var $var = $self->{RESULTS}{$var}.\n";
    }
}

# sub value() - public method.
#
# one argument: the name of an output variable.
# This method returns the computed value of a given output var.

sub value {
    my ($self,
	$var,
	) = @_;

    return undef unless exists $self->{RESULTS}{$var};
    return $self->{RESULTS}{$var};
}

# sub reset() - public method
# 
# cleans the data structures used.

sub reset {
    my $self = shift;

    my @list   =  $self->{SET}->listMatching(q|:implicated$|);
    push @list => $self->{SET}->listMatching(q|:aggregated$|);

    $self->{SET}->delete($_) for @list;

    $self->{RESULTS} = {};
}

# sub compute() - public method
#
# This method takes as input crisp values for each
# of the input vars, and produces a crisp output value
# based on the application of the fuzzy if-then rules.
# ex.
# $z = $obj->compute(x => 5,
#                    y => 24);

sub compute {
    my ($self,

FuzzyInference.pm view on Meta::CPAN

fuzzy operators to generate a single fuzzy set for each of the
output variables.

=head2 Defuzzification

Finally, a defuzzification operator is applied to the aggregated fuzzy
set to generate a single crisp value for each of the output variables.

For a more detailed explanation of fuzzy inference, you can check out
the tutorial by Jerry Mendel at
S<http://sipi.usc.edu/~mendel/publications/FLS_Engr_Tutorial_Errata.pdf>.

Note: The terminology used in this module might differ from that used
in the above tutorial.

=head1 PUBLIC METHODS

The module has the following public methods:

=over 4

=item new()

This is the constructor. It takes no arguments, and returns an
initialized AI::FuzzyInference object.

=item operation()

README view on Meta::CPAN

    fuzzy operators to generate a single fuzzy set for each of the output
    variables.

  Defuzzification

    Finally, a defuzzification operator is applied to the aggregated fuzzy
    set to generate a single crisp value for each of the output variables.

    For a more detailed explanation of fuzzy inference, you can check out
    the tutorial by Jerry Mendel at
    http://sipi.usc.edu/~mendel/publications/FLS_Engr_Tutorial_Errata.pdf.

    Note: The terminology used in this module might differ from that used in
    the above tutorial.

PUBLIC METHODS
    The module has the following public methods:

    new()
        This is the constructor. It takes no arguments, and returns an
        initialized AI::FuzzyInference object.

    operation()
        This method is used to set/query the fuzzy operations. It takes at
        least one argument, and at most 2. The first argument specifies the
        logic operation in question, and can be either "&" for logical
        *AND*, "|" for logical *OR*, or "!" for logical *NOT*. The second

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