Nathan Gary Glenn > Algorithm-AM-3.05 > Algorithm::AM
Module Version: 3.05

NAME

Algorithm::AM - Classify data with Analogical Modeling

version 3.05

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

This module provides an object-oriented interface for classifying single items using the analogical modeling algorithm. To work with sets of items needing to be classified, see Algorithm::AM::Batch.

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.

BACKGROUND AND TERMINOLOGY

Analogical Modeling (or AM) was developed as an exemplar-based approach to modeling language usage, and has also been found useful in modeling other "sticky" phenomena. AM is especially suited to this because it predicts probabilistic occurrences instead of assigning static labels to instances.

The AM algorithm can be called a probabilistic, instance-based classifier. However, the probabilities given for each classification are not degrees of certainty, but actual probabilities of occurring in real usage. Thus in AM literature the classification is supposed to produce dynamic "outcomes", not static "labels". In AM proper, the last step of classification is to produce an outcome at random based on the calculated probability distribution. AM therefore predicts that "sticky" phenomena are "sticky" because they vary probabilistically, defying absolute prediction.

In this software, an outcome can be chosen probabilistically using "random_outcome" in Algorithm::AM::Result. However, in practice, usually only the highest-probability prediction(s) are used for classification tasks. These can be retrieved via "winners" in Algorithm::AM::Result, or "result" in Algorithm::AM::Result if you're just interested in classification accuracy on a test set. The entire outcome probability distribution can also be retrieved via "scores_normalized" in Algorithm::AM::Result. See Algorithm::AM::Result for other types of information available after classification. See Algorithm::AM::algorithm for details on the actual mechanism of classification.

Outside of the `random_outcome` method mentioned above, the rest of the software uses more general machine learning terminology. What would properly be called an "exemplar" is referred to simply as an "item", and, as is customary, "training" and "test" sets are used, even though AM never does any actual "training". Training items are assigned "class labels" (not "outcomes"), and classification results in a set of scores (or probabilities) for different "class labels", even though they would properly be called "outcomes". Finally, items contain vectors of "features", which were called "variables" in previous versions of this software.

EXPORTS

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

Algorithm::AM::Result
Algorithm::AM::DataSet

Also imports "dataset_from_file" in Algorithm::AM::DataSet.

Algorithm::AM::DataSet::Item

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

Algorithm::AM::BigInt

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

METHODS

`new`

Creates a new instance of an analogical modeling classifier. This method takes named parameters which 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.

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 has details and even illustrations on analogical modeling.

SUPPORT

Bugs / Feature Requests

Please report any bugs or feature requests through the issue tracker at https://github.com/garfieldnate/Algorithm-AM/issues. You will be notified automatically of any progress on your issue.

Source Code

This is open source software. The code repository is available for public review and contribution under the terms of the license.

https://github.com/garfieldnate/Algorithm-AM

`  git clone https://github.com/garfieldnate/Algorithm-AM.git`

AUTHOR

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

CONTRIBUTORS

• garfieldnate <garfieldnate@gmail.com>
• Nathan Glenn <garfieldnate@gmail.com>
• Nick <nlogan@gmail.com>