.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_grlvq.py: ================================================== Generalized Relevance Learning Vector Quantization ================================================== This example shows how GRLVQ projects and classifies. The plot shows the target class of each data point (big circle) and which class was predicted (smaller circle). It also shows the prototypes (black diamond) and their labels (small point inside the diamond). The projected data is shown in the right plot. .. image:: /auto_examples/images/sphx_glr_plot_grlvq_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none GRLVQ: ('variance coverd by projection:', 100.0) ('classification accuracy:', 1.0) | .. code-block:: python import numpy as np import matplotlib.pyplot as plt from sklearn_lvq import GrlvqModel from sklearn_lvq.utils import plot2d print(__doc__) nb_ppc = 100 toy_label = np.append(np.zeros(nb_ppc), np.ones(nb_ppc), axis=0) print('GRLVQ:') toy_data = np.append( np.random.multivariate_normal([0, 0], np.array([[0.3, 0], [0, 4]]), size=nb_ppc), np.random.multivariate_normal([4, 4], np.array([[0.3, 0], [0, 4]]), size=nb_ppc), axis=0) grlvq = GrlvqModel() grlvq.fit(toy_data, toy_label) plot2d(grlvq, toy_data, toy_label, 1, 'grlvq') print('classification accuracy:', grlvq.score(toy_data, toy_label)) plt.show() **Total running time of the script:** ( 0 minutes 5.710 seconds) .. _sphx_glr_download_auto_examples_plot_grlvq.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_grlvq.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_grlvq.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_