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File for tracking changes in SysIdentPy
--------------------------------------------------------------------------------------------------------------

Changes in SysIdentPy
=====================

v0.1.9
------

CONTRIBUTORS
~~~~~~~~~~~~

- wilsonrljr
- samirmartins

CHANGES
~~~~~~~
- The update **v0.1.9** has been released with additional feature, minor API changes and fixes of the new features added in v0.1.7.

- MAJOR: Entropic Regression Algorithm
- Added the new class ER to build NARX models using the Entropic Regression algorithm.
- Only the Mutual Information KNN is implemented in this version and it may take too long to run on a high number of regressor, so the user should be careful regarding the number of candidates to put in the model.

- API: save_load
- Added a function to save and load models from file.

- API: Added tests for python 3.9

- Fix : Change condition for n_info_values in FROLS. Now the value defined by the user is compared against X matrix shape instead of regressor space shape. This fix the Fourier basis function usage with more the 15 regressors in FROLS.

- DOC: Save and Load models
- Added a notebook showing how to use the save_load method.

- DOC: Entropic Regression example
- Added notebook with a simple example of how to use AOLS

- DOC: Fourier Basis Function Example
- Added notebook with a simple example of how to use Fourier Basis Function

- DOC: PV forecasting benchmark
- FIX AOLS prediction. The example was using the meta_mss model in prediction, so the results for AOLS were wrong.

- DOC: Fixed minor grammatical and spelling mistakes.

- DOC: Fix issues related to html on Jupyter notebooks examples on documentation.


v0.1.8
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.aols.AOLS at 0x17af1262a30&gt;
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;sysidentpy.polynomial_basis.aols.AOLS at 0x204ca8223a0&gt;
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0018936074444791853
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0016822480366271283
Regressors Parameters ERR
0 y(k-1) 0.2001 0.00000000
0 y(k-1) 0.2000 0.00000000
1 x1(k-2) 0.9000 0.00000000
2 x1(k-1)y(k-1) 0.0999 0.00000000
3 x1(k-2)y(k-3) 0.0002 0.00000000
2 y(k-2)y(k-1) 0.0003 0.00000000
3 x1(k-1)y(k-1) 0.1000 0.00000000
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