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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

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nearest_centroid

Nearest Centroid classifier.

The library implements the classifier_protocol defined in the classifier_protocols library. It provides predicates for learning a classifier from a dataset, using it to make predictions, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the dataset_protocol protocol from the classifier_protocols library. See test_files directory for examples.

API documentation

Open the [../../docs/library_index.html#nearest_centroid](../../docs/library_index.html#nearest_centroid) link in a web browser.

Loading

To load this library, load the loader.lgt file:

| ?- logtalk_load(nearest_centroid(loader)).

Testing

To test this library predicates, load the tester.lgt file:

| ?- logtalk_load(nearest_centroid(tester)).

Features

• Multiple Distance Metrics: Euclidean, Manhattan, cosine
• Mixed Features: Automatically handles categorical and continuous features
• Configurable Option: distance metric via predicate options
• Probability Estimation: Provides confidence scores for predictions
• Classifier Export: Learned classifiers can be exported as predicate clauses

Usage

Learning a Classifier

% Learn from a dataset object with default options (euclidean distance)
| ?- nearest_centroid::learn(my_dataset, Classifier).
...

% Learn with custom options
| ?- nearest_centroid::learn(my_dataset, Classifier, [distance_metric(manhattan)]).
...

Making Predictions

% Predict class for a new instance
| ?- Instance = [attr1-value1, attr2-value2, ...],
     nearest_centroid::learn(my_dataset, Classifier),
     nearest_centroid::predict(Classifier, Instance, PredictedClass).
PredictedClass = ...
...

% Predict with custom options
| ?- nearest_centroid::predict(Classifier, Instance, PredictedClass, [distance_metric(cosine)]).
...

% Get probability distribution
| ?- nearest_centroid::predict_probabilities(Classifier, Instance, Probabilities).
Probabilities = [class1-0.67, class2-0.33]
...

Exporting the Classifier

Learned classifiers can be exported as a list of clauses or to a file for later use.

% Export as predicate clauses
| ?- nearest_centroid::learn(my_dataset, Classifier),
     nearest_centroid::classifier_to_clauses(my_dataset, Classifier, my_classifier, Clauses).
Clauses = [my_classifier(...)]
...

% Export to a file
| ?- nearest_centroid::learn(my_dataset, Classifier),
     nearest_centroid::classifier_to_file(my_dataset, Classifier, my_classifier, 'classifier.pl').
...

Using a learned classifier

Learned and saved classifiers can later be used for predictions without needing to access the original training dataset.

% Later, load the file and use the classifier
| ?- consult('classifier.pl'),
     my_classifier(AttributeNames, FeatureTypes, Centroids),
     Instance = [...],
     nearest_centroid::predict(my_classifier(AttributeNames, FeatureTypes, Centroids), Instance, Class).
Class = ...
...

Options

The following options can be passed to the predict/4 and predict_probabilities/4 predicates:

• distance_metric(Metric): Distance metric to use. Options: euclidean (default), manhattan, cosine

Classifier Representation

The learned classifier is represented as a compound term with the functor chosen by the user when exporting the classifier and arity 4. For example, assuming the my_classifier/1 functor:

nc_classifier(AttributeNames, FeatureTypes, Centroids)

Where:

• AttributeNames: List of attribute names in order
• FeatureTypes: List of types (numeric or categorical)
• Centroids: List of computed Class-Centroid pairs

References

1. Manning, Raghavan & Schütze (2008) - "Introduction to Information Retrieval". Cambridge University Press.
2. Tibshirani, Hastie, Narasimhan & Chu (2002) - "Diagnosis of multiple cancer types by shrunken centroids of gene expression". Proceedings of the National Academy of Sciences, 99(10), 6567-6572.
3. Hastie, Tibshirani & Friedman (2009) - "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (2nd Edition). Springer.