features

Takes a raw matrix of data and converts into polynomial features

misc

This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
identification algorithm for finding ordinary differential equations for a measured
system from raw data (SINDy). The package includes a set of additional tools for
working with raw data, with an emphasis on cognitive science applications (Dale
and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>). See
<https://github.com/racdale/sindyr> for examples and updates.

Rick Dale

sindyr

Sparse Identification of Nonlinear Dynamics

features function

<dl> <dt>x</dt>
<dd>Raw data to be converted into features</dd> <dt>polyorder</dt>
<dd>Order of polynomials (including k-th self products)</dd>
 
 <dt>intercept</dt>
<dd>Include column of 1s in features to represent intercept (default = TRUE)</dd></dl>

Arguments

Author

Build a matrix of features for SINDy — features

<dl>

 <dt>x</dt>
<dd>Raw data to be converted into features</dd>

 <dt>polyorder</dt>
<dd>Order of polynomials (including k-th self products)</dd>
 
 <dt>intercept</dt>
<dd>Include column of 1s in features to represent intercept (default = TRUE)</dd>

</dl>

features: Build a matrix of features for SINDy

Description

Value

Arguments

Author

Details