.. index:: single: knn
.. _knn/0:

.. rst-class:: right

**object**

``knn``
=======

k-Nearest Neighbors classifier with multiple distance metrics and weighting options. Learns from a dataset object implementing the ``dataset_protocol`` protocol and returns a classifier term that can be used for prediction and exported as predicate clauses.

| **Availability:** 
|    ``logtalk_load(knn(loader))``

| **Author:** Paulo Moura
| **Version:** 1:0:0
| **Date:** 2026-02-20

| **Compilation flags:**
|    ``static, context_switching_calls``


| **Implements:**
|    ``public`` :ref:`classifier_protocol <classifier_protocol/0>`
| **Imports:**
|    ``public`` :ref:`options <options/0>`
| **Uses:**
|    :ref:`format <format/0>`
|    :ref:`list <list/0>`
|    :ref:`pairs <pairs/0>`
|    :ref:`type <type/0>`

| **Remarks:**

   - Algorithm: k-NN is a lazy learning algorithm that classifies instances based on the majority class among the k nearest training instances.
   - Distance metrics: Supports Euclidean, Manhattan, Chebyshev, and Minkowski distance metrics.
   - Weighting schemes: Supports uniform, distance-based, and Gaussian weighting of neighbors.
   - Feature types: Automatically handles numeric and categorical features.
   - Classifier representation: The learned classifier is represented (by default) as a ``knn_classifier(AttributeNames, FeatureTypes, Instances)`` where ``Instances`` contains the training data.

| **Inherited public predicates:**
|     :ref:`options_protocol/0::check_option/1`  :ref:`options_protocol/0::check_options/1`  :ref:`classifier_protocol/0::classifier_to_clauses/4`  :ref:`classifier_protocol/0::classifier_to_file/4`  :ref:`options_protocol/0::default_option/1`  :ref:`options_protocol/0::default_options/1`  :ref:`classifier_protocol/0::learn/2`  :ref:`options_protocol/0::option/2`  :ref:`options_protocol/0::option/3`  :ref:`classifier_protocol/0::predict/3`  :ref:`classifier_protocol/0::print_classifier/1`  :ref:`options_protocol/0::valid_option/1`  :ref:`options_protocol/0::valid_options/1`  

.. contents::
   :local:
   :backlinks: top

Public predicates
-----------------

.. index:: predict/4
.. _knn/0::predict/4:

``predict/4``
^^^^^^^^^^^^^

Predicts the class label for a new instance using the learned classifier and the given options. The instance is a list of ``Attribute-Value`` pairs.

| **Compilation flags:**
|    ``static``

| **Template:**
|    ``predict(Classifier,Instance,Class,Options)``
| **Mode and number of proofs:**
|    ``predict(+compound,+list,-atom,+list(compound))`` - ``one``


------------

.. index:: predict_probabilities/3
.. _knn/0::predict_probabilities/3:

``predict_probabilities/3``
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Predicts class probabilities for a new instance using the learned classifier and default options. Returns a list of ``Class-Probability`` pairs. The instance is a list of ``Attribute-Value`` pairs.

| **Compilation flags:**
|    ``static``

| **Template:**
|    ``predict_probabilities(Classifier,Instance,Probabilities)``
| **Mode and number of proofs:**
|    ``predict_probabilities(+compound,+list,-list)`` - ``one``


------------

.. index:: predict_probabilities/4
.. _knn/0::predict_probabilities/4:

``predict_probabilities/4``
^^^^^^^^^^^^^^^^^^^^^^^^^^^

Predicts class probabilities for a new instance using the learned classifier and the given options. Returns a list of ``Class-Probability`` pairs. The instance is a list of ``Attribute-Value`` pairs.

| **Compilation flags:**
|    ``static``

| **Template:**
|    ``predict_probabilities(Classifier,Instance,Probabilities,Options)``
| **Mode and number of proofs:**
|    ``predict_probabilities(+compound,+list,-list,+list(compound))`` - ``one``


------------

Protected predicates
--------------------

(no local declarations; see entity ancestors if any)

Private predicates
------------------

(no local declarations; see entity ancestors if any)

Operators
---------

(none)

.. seealso::

   :ref:`dataset_protocol <dataset_protocol/0>`, :ref:`c45 <c45/0>`, :ref:`isolation_forest <isolation_forest/0>`, :ref:`naive_bayes <naive_bayes/0>`, :ref:`nearest_centroid <nearest_centroid/0>`, :ref:`random_forest <random_forest/0>`, :ref:`ada_boost <ada_boost/0>`