.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\Neighbours\KNN.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_Neighbours_KNN.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Neighbours_KNN.py:


K-Nearest Neighbors (KNN) Classification and Regression
============================================================

This script demonstrates the use of K-Nearest Neighbors (KNN) for both classification and regression 
tasks using the `KNNClassifier` and `KNNRegressor` models. It also showcases model serialization 
with `save_model`.

.. GENERATED FROM PYTHON SOURCE LINES 9-51



.. image-sg:: /auto_examples/Neighbours/images/sphx_glr_KNN_001.png
   :alt: KNN
   :srcset: /auto_examples/Neighbours/images/sphx_glr_KNN_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    tensor([[0.8000, 0.2000, 0.0000],
            [0.0000, 0.6800, 0.3200],
            [0.0000, 0.4800, 0.5200],
            [0.8000, 0.2000, 0.0000],
            [0.2800, 0.6400, 0.0800],
            [0.0000, 0.3600, 0.6400],
            [0.0000, 0.5400, 0.4600],
            [0.0000, 0.2200, 0.7800],
            [0.8000, 0.2000, 0.0000],
            [0.8000, 0.2000, 0.0000],
            [0.0000, 0.7400, 0.2600],
            [0.0000, 0.7600, 0.2400],
            [0.0000, 0.2200, 0.7800],
            [0.0000, 0.5200, 0.4800],
            [0.0000, 0.4600, 0.5400],
            [0.1600, 0.6800, 0.1600],
            [0.8000, 0.2000, 0.0000],
            [0.0000, 0.2800, 0.7200],
            [0.0000, 0.2400, 0.7600],
            [0.8000, 0.2000, 0.0000],
            [0.8000, 0.2000, 0.0000],
            [0.0000, 0.2800, 0.7200],
            [0.0000, 0.7400, 0.2600],
            [0.0000, 0.5800, 0.4200],
            [0.8000, 0.2000, 0.0000],
            [0.0000, 0.7400, 0.2600],
            [0.8000, 0.2000, 0.0000],
            [0.8000, 0.2000, 0.0000],
            [0.0000, 0.7000, 0.3000],
            [0.0000, 0.3200, 0.6800]])
    1.0






|

.. code-block:: Python

    import torch
    import matplotlib.pyplot as plt
    from sklearn import datasets

    from DLL.Data.Metrics import accuracy
    from DLL.Data.Preprocessing import data_split
    from DLL.MachineLearning.SupervisedLearning.Neighbors import KNNClassifier, KNNRegressor
    from DLL.DeepLearning.Model import save_model, load_model


    torch.manual_seed(0)

    iris = datasets.load_iris()
    X = torch.tensor(iris.data, dtype=torch.float32)
    y = torch.tensor(iris.target, dtype=torch.float32)
    # X = X[y != 2]
    # y = y[y != 2]
    X_train, y_train, X_test, y_test, _, _ = data_split(X, y)

    model = KNNClassifier(k=50, metric="manhattan")
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    print(model.predict_proba(X_test))
    print(accuracy(predictions, y_test))
    # save_model(model, filepath="./Tests/Neighbours/classifier.pkl")


    X = torch.linspace(0, 1, 100)
    y = X ** 2 + torch.randn_like(X) * 0.05
    X = X.unsqueeze(1)
    X_train, y_train, X_test, y_test, _, _ = data_split(X, y)

    model = KNNRegressor(k=5, metric="manhattan", weight="gaussian")
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
    # save_model(model, filepath="./Tests/Neighbours/regressor.pkl")

    plt.scatter(X_test, predictions, label="predictions")
    plt.scatter(X_test, y_test, label="true")
    plt.scatter(X_train, y_train, label="train")
    plt.legend()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.847 seconds)


.. _sphx_glr_download_auto_examples_Neighbours_KNN.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: KNN.ipynb <KNN.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: KNN.py <KNN.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: KNN.zip <KNN.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_