.. 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_gmlvq.py: ========================================================= Generalized Relevance Matrix Learning Vector Quantization ========================================================= This example shows how GRMLVQ 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_gmlvq_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none GMLVQ: ('variance coverd by projection:', 100.0) ('classification accuracy:', 0.995) | .. code-block:: python import matplotlib.pyplot as plt import numpy as np from sklearn_lvq import GmlvqModel 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('GMLVQ:') toy_data = np.append( np.random.multivariate_normal([0, 0], np.array([[5, 4], [4, 6]]), size=nb_ppc), np.random.multivariate_normal([9, 0], np.array([[5, 4], [4, 6]]), size=nb_ppc), axis=0) gmlvq = GmlvqModel() gmlvq.fit(toy_data, toy_label) plot2d(gmlvq, toy_data, toy_label, 1, 'gmlvq') print('classification accuracy:', gmlvq.score(toy_data, toy_label)) plt.show() **Total running time of the script:** ( 0 minutes 0.107 seconds) .. _sphx_glr_download_auto_examples_plot_gmlvq.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_gmlvq.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_gmlvq.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_