.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` 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_beta_divergence.py:
==============================
Beta-divergence loss functions
==============================
A plot that compares the various Beta-divergence loss functions supported by
the Multiplicative-Update ('mu') solver in :class:`sklearn.decomposition.NMF`.
.. image:: /auto_examples/decomposition/images/sphx_glr_plot_beta_divergence_001.png
:alt: beta-divergence(1, x)
:class: sphx-glr-single-img
.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition._nmf import _beta_divergence
print(__doc__)
x = np.linspace(0.001, 4, 1000)
y = np.zeros(x.shape)
colors = 'mbgyr'
for j, beta in enumerate((0., 0.5, 1., 1.5, 2.)):
for i, xi in enumerate(x):
y[i] = _beta_divergence(1, xi, 1, beta)
name = "beta = %1.1f" % beta
plt.plot(x, y, label=name, color=colors[j])
plt.xlabel("x")
plt.title("beta-divergence(1, x)")
plt.legend(loc=0)
plt.axis([0, 4, 0, 3])
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.228 seconds)
.. _sphx_glr_download_auto_examples_decomposition_plot_beta_divergence.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_beta_divergence.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_beta_divergence.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_beta_divergence.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_