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This file is part of Logtalk https://logtalk.org/ SPDX-FileCopyrightText: 1998-2026 Paulo Moura <pmoura@logtalk.org> SPDX-License-Identifier: Apache-2.0

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

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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

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