NAME
    Algorithm::AM - Classify data with Analogical Modeling

VERSION
    version 3.02

SYNOPSIS
     use Algorithm::AM;
     my $dataset = dataset_from_file('finnverb');
     my $am = Algorithm::AM->new(training_set => $dataset);
     my $result = $am->classify($dataset->get_item(0));
     print @{ $result->winners };
     print ${ $result->statistical_summary };

DESCRIPTION
    Analogical Modeling is an exemplar-based way to model language usage.
    This module analyzes data sets using Analogical Modeling, an
    exemplar-based approach to modeling language usage or other sticky
    phenomena. This module logs information using Log::Any, so if you want
    automatic print-outs you need to set an adaptor. See the "classify"
    method for more information on logged data.

EXPORTS
    When this module is imported, it also imports the following:

    Algorithm::AM::Result
    Algorithm::AM::DataSet
        Also imports the "dataset_from_file" in Algorithm::AM::DataSet
        function.

    Algorithm::AM::DataSet::Item
        Also imports the "new_item" in Algorithm::AM::DataSet::Item
        function.

    Algorithm::AM::BigInt
        Also imports the "bigcmp" in Algorithm::AM::BigInt function.

METHODS
  "new"
    Creates a new instance of an analogical modeling classifier. This method
    takes named parameters which set set state described in the
    documentation for the relevant methods. The only required parameter is
    "training_set", which should be an instance of Algorithm::AM::DataSet,
    and which defines the set of items used for training during
    classification. All of the accepted parameters are listed below:

    "training_set"
    "exclude_nulls"
    "exclude_given"
    "linear"

  "training_set"
    Returns (but will not set) the dataset used for training. This is an
    instance of Algorithm::AM::DataSet.

  "exclude_nulls"
    Get/set a boolean value indicating whether features with null values in
    the test item should be ignored. If false, they will be treated as
    having a specific value representing null. Defaults to true.

  "exclude_given"
    Get/set a boolean value indicating whether the test item should be
    removed from the training set if it is found there during
    classification. Defaults to true.

  "linear"
    Get/set a boolean value indicating whether the analogical set should be
    computed using *occurrences* (linearly) or *pointers* (quadratically).
    To understand what this means, you should read the algorithm page. A
    false value indicates quadratic counting. Defaults to false.

  "classify"
      $am->classify(new_item(features => ['a','b','c']));

    Using the analogical modeling algorithm, this method classifies the
    input test item and returns a Result object.

    Log::Any is used for logging. The full classification configuration is
    logged at the info level. A notice is printed at the warning level if no
    training items can be compared with the test item, preventing any
    classification.

HISTORY
    Initially, Analogical Modeling was implemented as a Pascal program.
    Subsequently, it was ported to Perl, with substantial improvements made
    in 2000. In 2001, the core of the algorithm was rewritten in C, while
    the parsing, printing, and statistical routines remained in C; this was
    accomplished by embedding a Perl interpreter into the C code.

    In 2004, the algorithm was again rewritten, this time in order to handle
    more features and large data sets. The algorithm breaks the
    supracontextual lattice into the direct product of four smaller ones,
    which the algorithm manipulates individually before recombining. These
    lattices can be manipulated in parallel when using the right hardware,
    and so the module was named "AM::Parallel". This implementation was
    written with the core lattice-filling algorithm in XS, and hooks were
    provided to help the user create custom reports and control
    classification dynamically.

    The present version has been renamed to "Algorithm::AM", which seemed a
    better fit for CPAN. While the XS has largely remained intact, the Perl
    code has been completely reorganized and updated to be both more
    "modern" and modular. Most of the functionality of "AM::Parallel"
    remains.

SEE ALSO
    The <home page|http://humanities.byu.edu/am/> for Analogical Modeling
    includes information about current research and publications, as well as
    sample data sets.

    The Wikipedia article <http://en.wikipedia.org/wiki/Analogical_modeling>
    has details and even illustrations on analogical modeling.

AUTHOR
    Theron Stanford <shixilun@yahoo.com>, Nathan Glenn
    <garfieldnate@gmail.com>

COPYRIGHT AND LICENSE
    This software is copyright (c) 2013 by Royal Skousen.

    This is free software; you can redistribute it and/or modify it under
    the same terms as the Perl 5 programming language system itself.