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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_neighbors_plot_regression.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_neighbors_plot_regression.py:


============================
Nearest Neighbors regression
============================

Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.

.. GENERATED FROM PYTHON SOURCE LINES 11-51



.. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_regression_001.png
   :alt: KNeighborsRegressor (k = 5, weights = 'uniform'), KNeighborsRegressor (k = 5, weights = 'distance')
   :srcset: /auto_examples/neighbors/images/sphx_glr_plot_regression_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

    # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
    #         Fabian Pedregosa <fabian.pedregosa@inria.fr>
    #
    # License: BSD 3 clause (C) INRIA


    # #############################################################################
    # Generate sample data
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import neighbors

    np.random.seed(0)
    X = np.sort(5 * np.random.rand(40, 1), axis=0)
    T = np.linspace(0, 5, 500)[:, np.newaxis]
    y = np.sin(X).ravel()

    # Add noise to targets
    y[::5] += 1 * (0.5 - np.random.rand(8))

    # #############################################################################
    # Fit regression model
    n_neighbors = 5

    for i, weights in enumerate(['uniform', 'distance']):
        knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
        y_ = knn.fit(X, y).predict(T)

        plt.subplot(2, 1, i + 1)
        plt.scatter(X, y, color='darkorange', label='data')
        plt.plot(T, y_, color='navy', label='prediction')
        plt.axis('tight')
        plt.legend()
        plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors,
                                                                    weights))

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.116 seconds)


.. _sphx_glr_download_auto_examples_neighbors_plot_regression.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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