.. _example_decomposition_plot_sparse_coding.py:
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Sparse coding with a precomputed dictionary
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Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` estimator. The Ricker (also known
as Mexican hat or the second derivative of a Gaussian) is not a particularly
good kernel to represent piecewise constant signals like this one. It can
therefore be seen how much adding different widths of atoms matters and it
therefore motivates learning the dictionary to best fit your type of signals.
The richer dictionary on the right is not larger in size, heavier subsampling
is performed in order to stay on the same order of magnitude.
.. image:: images/plot_sparse_coding_001.png
:align: center
**Python source code:** :download:`plot_sparse_coding.py `
.. literalinclude:: plot_sparse_coding.py
:lines: 17-
**Total running time of the example:** 1.04 seconds
( 0 minutes 1.04 seconds)