.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\UnsupervisedLearning\DiscriminantAnalysisClassification.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_UnsupervisedLearning_DiscriminantAnalysisClassification.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_UnsupervisedLearning_DiscriminantAnalysisClassification.py:


Classification with discriminant analysis
===============================================================

This script classifies synthetic data using linear and quadratic disrciminant analysis. 
In the visualisations, one is clearly able to see the difference  between the algorithms 
using the decision boundaries.

.. GENERATED FROM PYTHON SOURCE LINES 9-78



.. image-sg:: /auto_examples/UnsupervisedLearning/images/sphx_glr_DiscriminantAnalysisClassification_001.png
   :alt: LDA DLL, LDA sklearn, QDA DLL, QDA sklearn
   :srcset: /auto_examples/UnsupervisedLearning/images/sphx_glr_DiscriminantAnalysisClassification_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    DLL LDA accuracy: 0.955
    SKlearn LDA accuracy: 0.955
    DLL QDA accuracy: 0.955
    SKlearn LDA accuracy: 0.955






|

.. code-block:: Python

    import torch
    import matplotlib.pyplot as plt
    from sklearn import datasets
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis

    from DLL.MachineLearning.UnsupervisedLearning.DimensionalityReduction import LDA, QDA
    from DLL.Data.Preprocessing import data_split
    from DLL.Data.Metrics import accuracy


    X, y = datasets.make_blobs(n_samples=1000, n_features=2, cluster_std=2, centers=4, random_state=3)

    X = torch.from_numpy(X)
    y = torch.from_numpy(y)
    X_train, y_train, X_test, y_test, _, _ = data_split(X, y)

    model1 = LDA()
    model1.fit(X_train, y_train)
    predictions1 = model1.predict(X_test)
    print(f"DLL LDA accuracy: {round(accuracy(predictions1, y_test), 3)}")
    n = 100
    x_min, X_max = X[:, 0].min(), X[:, 0].max()
    y_min, y_max = X[:, 1].min(), X[:, 1].max()
    x_grid = torch.linspace(x_min, X_max, n)
    y_grid = torch.linspace(y_min, y_max, n)
    Xv, Yv = torch.meshgrid(x_grid, y_grid, indexing="ij")
    X_grid = torch.stack((Xv.flatten(), Yv.flatten()), dim=1).to(X.dtype)
    grid_predictions1 = model1.predict(X_grid).reshape((n, n))

    model2 = LinearDiscriminantAnalysis()
    model2.fit(X_train, y_train)
    predictions2 = model2.predict(X_test)
    print(f"SKlearn LDA accuracy: {round(accuracy(torch.from_numpy(predictions2), y_test), 3)}")
    grid_predictions2 = model2.predict(X_grid).reshape((n, n))

    model3 = QDA()
    model3.fit(X_train, y_train)
    predictions3 = model3.predict(X_test)
    print(f"DLL QDA accuracy: {round(accuracy(predictions3, y_test), 3)}")
    grid_predictions3 = model3.predict(X_grid).reshape((n, n))

    model4 = QuadraticDiscriminantAnalysis()
    model4.fit(X_train, y_train)
    predictions4 = model4.predict(X_test)
    print(f"SKlearn LDA accuracy: {round(accuracy(torch.from_numpy(predictions4), y_test), 3)}")
    grid_predictions4 = model4.predict(X_grid).reshape((n, n))

    fig, axes = plt.subplots(2, 2, figsize=(8, 8))
    axes = axes.ravel()
    axes[0].contourf(Xv.numpy(), Yv.numpy(), grid_predictions1.numpy(), alpha=0.5)
    axes[0].scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=5)
    axes[0].scatter([x[0] for x in model1.class_means], [x[1] for x in model1.class_means], c="red")
    axes[0].set_title("LDA DLL")

    axes[1].contourf(Xv.numpy(), Yv.numpy(), grid_predictions2, alpha=0.5)
    axes[1].scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=5)
    axes[1].scatter([x[0] for x in model2.means_], [x[1] for x in model2.means_], c="red")
    axes[1].set_title("LDA sklearn")

    axes[2].contourf(Xv.numpy(), Yv.numpy(), grid_predictions3.numpy(), alpha=0.5)
    axes[2].scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=5)
    axes[2].scatter([x[0] for x in model3.class_means], [x[1] for x in model3.class_means], c="red")
    axes[2].set_title("QDA DLL")

    axes[3].contourf(Xv.numpy(), Yv.numpy(), grid_predictions4, alpha=0.5)
    axes[3].scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=5)
    axes[3].scatter([x[0] for x in model4.means_], [x[1] for x in model4.means_], c="red")
    axes[3].set_title("QDA sklearn")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.461 seconds)


.. _sphx_glr_download_auto_examples_UnsupervisedLearning_DiscriminantAnalysisClassification.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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