Here’s a description of some examples of using
lppl. You can find the source here.
Requirements and installation
To build and run the examples, you need CMake 3.20.0 or newer, a C++20 compiler, and
lppl v0.9.0 or newer. You can get
cd include && git clone -b master email@example.com:drdewhurst/lppl.git. To generate the plots, you need python 3.9 with pandas and matplotlib installed. Create a new conda environment and use the
requirements.txt if in doubt.
- Building the examples:
cd build && cmake .. && make
- Running the examples:
cd build && ./the_executable
- Plotting the examples:
cd src && my/python/install the_plot_script
dynamic.cpp: filtering algorithm comparison – rolling your own time series filtering using queryers and importance sampling vs. using builtin generic filtering algorithms
linear-regression.cpp: linear regression, fast and slow (with worse and better user-defined proposal distributions)
sts.cpp: WIP basic structural time series models in discrete time (for now – continuous time later)
symbolic-regression.cpp: symbolic regression over a pure-functional DSL, featuring the simplest interpreter ever
lppl-examples is licensed under GPL v3. If you would like a license exception please contact us at firstname.lastname@example.org. Copyright David Rushing Dewhurst, 2022 - present.