.. _library_nearest_centroid:

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.