.. 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_rslvq.py: ======================================== Robust Soft Learning Vector Quantization ======================================== This example shows the different glvq algorithms and how they project different data sets. The data sets are chosen to show the strength of each algorithm. Each plot shows for each data point which class it belongs to (big circle) and which class it was classified to (smaller circle). It also shows the prototypes (light blue circle). The projected data is shown in the right plot. .. image:: /auto_examples/images/sphx_glr_plot_rslvq_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none RSLVQ: not implemented! ('classification accuracy:', 1.0) | .. code-block:: python import numpy as np import matplotlib.pyplot as plt from sklearn_lvq import RslvqModel from sklearn_lvq.utils import plot2d print(__doc__) nb_ppc = 100 print('RSLVQ:') x = np.append( np.random.multivariate_normal([0, 0], np.eye(2) / 2, size=nb_ppc), np.random.multivariate_normal([5, 0], np.eye(2) / 2, size=nb_ppc), axis=0) y = np.append(np.zeros(nb_ppc), np.ones(nb_ppc), axis=0) rslvq = RslvqModel(initial_prototypes=[[5,0,0],[0,0,1]]) rslvq.fit(x, y) plot2d(rslvq, x, y, 1, 'rslvq') print('classification accuracy:', rslvq.score(x, y)) plt.show() **Total running time of the script:** ( 0 minutes 0.866 seconds) .. _sphx_glr_download_auto_examples_plot_rslvq.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_rslvq.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_rslvq.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_