.. _library_knn:

`knn`

k-Nearest Neighbors classifier supporting multiple distance metrics, weighting schemes, and both categorical and continuous features.

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#knn <../../docs/library_index.html#knn>`__ link in a web browser.

Loading

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

| ?- logtalk_load(knn(loader)).

Testing

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

| ?- logtalk_load(knn(tester)).

Features

Multiple Distance Metrics: Euclidean, Manhattan, Chebyshev, Minkowski
Flexible Weighting: Uniform, distance-based, and Gaussian weighting
Mixed Features: Automatically handles categorical and continuous features
Configurable Options: k value, distance metric, and weighting scheme 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 (k=3, euclidean, uniform) | ?- knn::learn(my_dataset, Classifier). ...

% Learn with custom options | ?- knn::learn(my_dataset, Classifier, [k(5), distance_metric(manhattan)]). ...

Making Predictions ~~~~~~~~~~~~~~~~~~

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

% Predict with custom options | ?- knn::predict(Classifier, Instance, PredictedClass, [k(5), weight_scheme(distance)]). ...

% Get probability distribution | ?- knn::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 | ?- knn::learn(my_dataset, Classifier), knn::classifier_to_clauses(my_dataset, Classifier, my_classifier, Clauses). Clauses = [my_classifier(...)] ...

% Export to a file | ?- knn::learn(my_dataset, Classifier), knn::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(Classifier), Instance = [...], knn::predict(Classifier, Instance, Class). Class = ... ...

Options

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

k(K): Number of neighbors to consider (default: 3)
distance_metric(Metric): Distance metric to use. Options: euclidean (default), manhattan, chebyshev, minkowski
weight_scheme(Scheme): Weighting scheme for neighbor votes. Options: uniform (default), distance, gaussian

Classifier Representation

The learned classifier is represented as a compound term with the functor chosen by the user when exporting the classifier and arity 3. The default functor is knn_classifier/3:

knn_classifier(AttributeNames, FeatureTypes, Instances)

Where:

AttributeNames: List of attribute names in order
FeatureTypes: List of types (numeric or categorical)
Instances: List of Values-Class pairs (the training data in compact form)

References

Cover, T. & Hart, P. (1967). "Nearest neighbor pattern classification". IEEE Transactions on Information Theory.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). "The Elements of Statistical Learning". Chapter 13.
Mitchell, T. (1997). "Machine Learning". Chapter 8: Instance-Based Learning.