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

.. only:: html

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

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

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

.. _sphx_glr_examples_classifiers_nn.py:


=================================
Multiclass classification with NN
=================================

The figures contain the training instances within a section of the selected feature space.
The training instances are coloured according to their true labels,
while the feature space is coloured according to predictions on the basis of the training instances,
making the decision boundaries visible.

Two subfigures are displayed: the first represents strict NN (`k == 1`),
while the second represents distance-weighted NN with `k == 5`.

.. GENERATED FROM PYTHON SOURCE LINES 14-77



.. image-sg:: /examples/classifiers/images/sphx_glr_nn_001.png
   :alt: NN applied to iris dataset, Strict, Distance-weighted with k = 5
   :srcset: /examples/classifiers/images/sphx_glr_nn_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap
    from sklearn import datasets

    from frlearn.base import select_class
    from frlearn.classifiers import NN

    # Import example data and reduce to 2 dimensions.
    iris = datasets.load_iris()
    X = iris.data[:, :2]
    y = iris.target

    # Define color maps.
    cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
    cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

    # Initialise figure with wide aspect for two side-by-side subfigures.
    plt.figure(figsize=(8, 4))

    for i, distance_weighted, k, title in [
        (1, False, 1, 'Strict'),
        (2, True, 20, 'Distance-weighted with k = 5')
    ]:
        axes = plt.subplot(1, 2, i)

        # Create an instance of the NN classifier and construct the model.
        clf = NN(distance_weighted=distance_weighted, k=k, )
        model = clf(X, y)

        # Create a mesh of points in the attribute space.
        step_size = .02
        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
        xx, yy = np.meshgrid(np.arange(x_min, x_max, step_size), np.arange(y_min, y_max, step_size))

        # Query mesh points to obtain class values and select highest valued class.
        Z = model(np.c_[xx.ravel(), yy.ravel()])
        Z = select_class(Z, labels=model.classes)

        # Plot mesh.
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

        # Plot training instances.
        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                    edgecolor='k', s=20)

        # Set subplot aspect to standard aspect ratio.
        axes.set_aspect(1.0 / axes.get_data_ratio() * .75)

        # Set plot dimensions.
        plt.xlim(xx.min(), xx.max())
        plt.ylim(yy.min(), yy.max())

        # Describe the subfigures.
        plt.title(title)

    plt.suptitle('NN applied to iris dataset', fontsize=14)
    plt.show()



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

   **Total running time of the script:** (0 minutes 0.330 seconds)


.. _sphx_glr_download_examples_classifiers_nn.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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