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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_mini_batch_kmeans.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_cluster_plot_mini_batch_kmeans.py:


====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================

We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but gives slightly different results (see
:ref:`mini_batch_kmeans`).

We will cluster a set of data, first with KMeans and then with
MiniBatchKMeans, and plot the results.
We will also plot the points that are labelled differently between the two
algorithms.

.. GENERATED FROM PYTHON SOURCE LINES 15-117



.. image:: /auto_examples/cluster/images/sphx_glr_plot_mini_batch_kmeans_001.png
    :alt: KMeans, MiniBatchKMeans, Difference
    :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

    import time

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.cluster import MiniBatchKMeans, KMeans
    from sklearn.metrics.pairwise import pairwise_distances_argmin
    from sklearn.datasets import make_blobs

    # #############################################################################
    # Generate sample data
    np.random.seed(0)

    batch_size = 45
    centers = [[1, 1], [-1, -1], [1, -1]]
    n_clusters = len(centers)
    X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)

    # #############################################################################
    # Compute clustering with Means

    k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
    t0 = time.time()
    k_means.fit(X)
    t_batch = time.time() - t0

    # #############################################################################
    # Compute clustering with MiniBatchKMeans

    mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size,
                          n_init=10, max_no_improvement=10, verbose=0)
    t0 = time.time()
    mbk.fit(X)
    t_mini_batch = time.time() - t0

    # #############################################################################
    # Plot result

    fig = plt.figure(figsize=(8, 3))
    fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
    colors = ['#4EACC5', '#FF9C34', '#4E9A06']

    # We want to have the same colors for the same cluster from the
    # MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per
    # closest one.
    k_means_cluster_centers = k_means.cluster_centers_
    order = pairwise_distances_argmin(k_means.cluster_centers_,
                                      mbk.cluster_centers_)
    mbk_means_cluster_centers = mbk.cluster_centers_[order]

    k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
    mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)

    # KMeans
    ax = fig.add_subplot(1, 3, 1)
    for k, col in zip(range(n_clusters), colors):
        my_members = k_means_labels == k
        cluster_center = k_means_cluster_centers[k]
        ax.plot(X[my_members, 0], X[my_members, 1], 'w',
                markerfacecolor=col, marker='.')
        ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=6)
    ax.set_title('KMeans')
    ax.set_xticks(())
    ax.set_yticks(())
    plt.text(-3.5, 1.8,  'train time: %.2fs\ninertia: %f' % (
        t_batch, k_means.inertia_))

    # MiniBatchKMeans
    ax = fig.add_subplot(1, 3, 2)
    for k, col in zip(range(n_clusters), colors):
        my_members = mbk_means_labels == k
        cluster_center = mbk_means_cluster_centers[k]
        ax.plot(X[my_members, 0], X[my_members, 1], 'w',
                markerfacecolor=col, marker='.')
        ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
                markeredgecolor='k', markersize=6)
    ax.set_title('MiniBatchKMeans')
    ax.set_xticks(())
    ax.set_yticks(())
    plt.text(-3.5, 1.8, 'train time: %.2fs\ninertia: %f' %
             (t_mini_batch, mbk.inertia_))

    # Initialise the different array to all False
    different = (mbk_means_labels == 4)
    ax = fig.add_subplot(1, 3, 3)

    for k in range(n_clusters):
        different += ((k_means_labels == k) != (mbk_means_labels == k))

    identic = np.logical_not(different)
    ax.plot(X[identic, 0], X[identic, 1], 'w',
            markerfacecolor='#bbbbbb', marker='.')
    ax.plot(X[different, 0], X[different, 1], 'w',
            markerfacecolor='m', marker='.')
    ax.set_title('Difference')
    ax.set_xticks(())
    ax.set_yticks(())

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.267 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_mini_batch_kmeans.py:


.. only :: html

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


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

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