.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/decomposition/plot_pca_vs_lda.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_decomposition_plot_pca_vs_lda.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py:


=======================================================
LDA和PCA在鸢尾花数据集上的二维投影比较
=======================================================

鸢尾花数据集代表了3种鸢尾花（山鸢尾、变色鸢尾和维吉尼亚鸢尾），包含4个属性：花萼长度、花萼宽度、花瓣长度和花瓣宽度。

主成分分析（PCA）应用于该数据集，识别出能够解释数据中最大方差的属性组合（主成分，或特征空间中的方向）。在这里，我们将不同的样本绘制在前两个主成分上。

线性判别分析（LDA）试图识别能够解释*类间*最大方差的属性。特别地，LDA与PCA不同，是一种有监督的方法，使用已知的类别标签。

.. GENERATED FROM PYTHON SOURCE LINES 13-58



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_001.png
         :alt: PCA of IRIS dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_002.png
         :alt: LDA of IRIS dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_pca_vs_lda_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    explained variance ratio (first two components): [0.92461872 0.05306648]






|

.. code-block:: Python


    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.decomposition import PCA
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    iris = datasets.load_iris()

    X = iris.data
    y = iris.target
    target_names = iris.target_names

    pca = PCA(n_components=2)
    X_r = pca.fit(X).transform(X)

    lda = LinearDiscriminantAnalysis(n_components=2)
    X_r2 = lda.fit(X, y).transform(X)

    # 每个成分解释的方差百分比
    print(
        "explained variance ratio (first two components): %s"
        % str(pca.explained_variance_ratio_)
    )

    plt.figure()
    colors = ["navy", "turquoise", "darkorange"]
    lw = 2

    for color, i, target_name in zip(colors, [0, 1, 2], target_names):
        plt.scatter(
            X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=0.8, lw=lw, label=target_name
        )
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.title("PCA of IRIS dataset")

    plt.figure()
    for color, i, target_name in zip(colors, [0, 1, 2], target_names):
        plt.scatter(
            X_r2[y == i, 0], X_r2[y == i, 1], alpha=0.8, color=color, label=target_name
        )
    plt.legend(loc="best", shadow=False, scatterpoints=1)
    plt.title("LDA of IRIS dataset")

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.086 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_pca_vs_lda.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_pca_vs_lda.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_pca_vs_lda.recommendations


.. only:: html

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

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