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Raw term frequency - equal to the number of times a feature occurs in
the document.

=item x

A synonym for 't'.

=item n

Normalized term frequency - 0.5 + 0.5 * t/max(t).  This is the same as
the 't' specification, but with term frequency normalized to lie
between 0.5 and 1.

=back

The second character specifies the "collection frequency" component, which
can take the following values:

=over 4

=item f

Inverse document frequency - multiply term C<t>'s value by C<log(N/n)>,
where C<N> is the total number of documents in the collection, and
C<n> is the number of documents in which term C<t> is found.

=item p

Probabilistic inverse document frequency - multiply term C<t>'s value
by C<log((N-n)/n)> (same variable meanings as above).

=item x

No change - multiply by 1.

=back


The third character specifies the "normalization" component, which
can take the following values:

=over 4

=item c

Apply cosine normalization - multiply by 1/length(document_vector).

=item x

No change - multiply by 1.

=back

The three components may alternatively be specified by the
C<term_weighting>, C<collection_weighting>, and C<normalize_weighting>
parameters respectively.

=item verbose

If set to a true value, some status/debugging information will be
output on C<STDOUT>.

=back


=item categories()

In a list context returns a list of all Category objects in this
KnowledgeSet.  In a scalar context returns the number of such objects.

=item documents()

In a list context returns a list of all Document objects in this
KnowledgeSet.  In a scalar context returns the number of such objects.

=item document()

Given a document name, returns the Document object with that name, or
C<undef> if no such Document object exists in this KnowledgeSet.

=item features()

Returns a FeatureSet object which represents the features of all the
documents in this KnowledgeSet.

=item verbose()

Returns the C<verbose> parameter of this KnowledgeSet, or sets it with
an optional argument.

=item scan_stats()

Scans all the documents of a Collection and returns a hash reference
containing several statistics about the Collection.  (XXX need to describe stats)

=item scan_features()

This method scans through a Collection object and determines the
"best" features (words) to use when loading the documents and training
the Learner.  This process is known as "feature selection", and it's a
very important part of categorization.

The Collection object should be specified as a C<collection> parameter,
or by giving the arguments to pass to the Collection's C<new()> method.

The process of feature selection is governed by the
C<feature_selection> and C<features_kept> parameters given to the
KnowledgeSet's C<new()> method.

This method returns the features as a FeatureVector whose values are
the "quality" of each feature, by whatever measure the
C<feature_selection> parameter specifies.  Normally you won't need to
use the return value, because this FeatureVector will become the
C<use_features> parameter of any Document objects created by this
KnowledgeSet.

=item save_features()

Given the name of a file, this method writes the features (as
determined by the C<scan_features> method) to the file.