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instances.

=item C<{smoothing}{$attribute}>

Attribute smoothing.  No smoothing if does not exist.  Implemented smoothing:

      - /^unseen count=/ followed by number, e.g., 0.5

=back

=head2 Attribute Smoothing

For an attribute A one can specify:

    $nb->{smoothing}{A} = 'unseen count=0.5';

to provide a count for unseen data.  The count is taken into
consideration in training and prediction, when any unseen attribute
values are observed.  Zero probabilities can be prevented in this way.
A count other than 0.5 can be provided, but if it is <=0 it will be
set to 0.5.  The method is similar to add-one smoothing.  A special
attribute value '*' is used for all unseen data. 

=head1 METHODS

=head2 Constructor Methods

=over 4

=item new()

Constructor. Creates a new C<AI::NaiveBayes1> object and returns it.

=item import_from_YAML($string)

Constructor. Creates a new C<AI::NaiveBayes1> object from a string where it is
represented in C<YAML>.  Requires YAML module.

=item import_from_YAML_file($file_name)

Constructor. Creates a new C<AI::NaiveBayes1> object from a file where it is
represented in C<YAML>.  Requires YAML module.

=back

=head2 Non-Constructor Methods

=over 4

=item add_table()

Add instances from a table.  The first row are attributes, followed by
values.  If the name of the last attribute is `count', it is
interpreted as a repetition count and used appropriatelly.  The last
attribute (after optionally removing `count') is the class attribute.
The attributes and values are separated by white space.

=item add_csv_file($filename)

Add instances from a CSV file.  Primitive format implementation (e.g.,
no commas allowed in attribute names or values).

=item drop_attributes(@attributes)

Delete attributes after adding instances.

=item set_real(list_of_attributes)

Delares a list of attributes to be real-valued.  During training,
their conditional probabilities will be modeled with Gaussian (normal)
distributions. 

=item C<add_instance(attributes=E<gt>HASH,label=E<gt>STRING|ARRAY)>

Adds a training instance to the categorizer.

=item C<add_instances(attributes=E<gt>HASH,label=E<gt>STRING|ARRAY,cases=E<gt>NUMBER)>

Adds a number of identical instances to the categorizer.

=item export_to_YAML()

Returns a C<YAML> string representation of an C<AI::NaiveBayes1>
object.  Requires YAML module.

=item C<export_to_YAML_file( $file_name )>

Writes a C<YAML> string representation of an C<AI::NaiveBayes1>
object to a file.  Requires YAML module.

=item C<print_model( OPTIONAL 'with counts' )>

Returns a string, human-friendly representation of the model.
The model is supposed to be trained before calling this method.
One argument 'with counts' can be supplied, in which case explanatory
expressions with counts are printed as well.

=item train()

Calculates the probabilities that will be necessary for categorization
using the C<predict()> method.

=item C<predict( attributes =E<gt> HASH )>

Use this method to predict the label of an unknown instance.  The
attributes should be of the same format as you passed to
C<add_instance()>.  C<predict()> returns a hash reference whose keys
are the names of labels, and whose values are corresponding
probabilities.

=item C<labels>

Returns a list of all the labels the object knows about (in no
particular order), or the number of labels if called in a scalar
context.

=back

=head1 THEORY

Bayes' Theorem is a way of inverting a conditional probability. It