.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/decomposition/plot_incremental_pca.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_incremental_pca.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_incremental_pca.py:


===============
增量PCA
===============

增量主成分分析（IPCA）通常在数据集过大无法全部加载到内存时，作为主成分分析（PCA）的替代方法使用。IPCA使用与输入数据样本数量无关的内存量，为输入数据构建低秩近似。它仍然依赖于输入数据的特征，但通过改变批量大小可以控制内存使用。

这个例子作为一个视觉检查，展示了IPCA能够找到与PCA相似的数据投影（符号翻转除外），同时每次只处理少量样本。这可以被认为是一个“玩具示例”，因为IPCA是为那些无法全部加载到主内存的大型数据集设计的，需要增量方法。

.. GENERATED FROM PYTHON SOURCE LINES 12-55



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


    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_001.png
         :alt: Incremental PCA of iris dataset Mean absolute unsigned error 0.002201
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_002.png
         :alt: PCA of iris dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_iris
    from sklearn.decomposition import PCA, IncrementalPCA

    iris = load_iris()
    X = iris.data
    y = iris.target

    n_components = 2
    ipca = IncrementalPCA(n_components=n_components, batch_size=10)
    X_ipca = ipca.fit_transform(X)

    pca = PCA(n_components=n_components)
    X_pca = pca.fit_transform(X)

    colors = ["navy", "turquoise", "darkorange"]

    for X_transformed, title in [(X_ipca, "Incremental PCA"), (X_pca, "PCA")]:
        plt.figure(figsize=(8, 8))
        for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
            plt.scatter(
                X_transformed[y == i, 0],
                X_transformed[y == i, 1],
                color=color,
                lw=2,
                label=target_name,
            )

        if "Incremental" in title:
            err = np.abs(np.abs(X_pca) - np.abs(X_ipca)).mean()
            plt.title(title + " of iris dataset\nMean absolute unsigned error %.6f" % err)
        else:
            plt.title(title + " of iris dataset")
        plt.legend(loc="best", shadow=False, scatterpoints=1)
        plt.axis([-4, 4, -1.5, 1.5])

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.105 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_incremental_pca.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_incremental_pca.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_incremental_pca.recommendations


.. only:: html

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

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