A pure C++ implementation of the lowess algorithm. For more information, see the following publication:
Cleveland, W. S. (1981) LOWESS: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician 35, 54.
In short, the algorithm performs locally-weighted polynomial regression in two dimensions, which leads to a smoothing of the data. This is useful in the presence of noise in the data.
The algorithm uses a weighted regression of the closest points around x to make a prediction of the y-value at the position x. To speed up the calculation, the regression is only calculated at a subset of the points.
The algorithm is contained in a single header file in
include/CppLowess/Lowess.h and can be included directly in your C++ project.
You can then instantiate an object of type TemplatedLowess to call the lowess
function. The template parameter are the container and the value type used:
CppLowess::TemplatedLowess< std::vector<double>, double > dlowess;
CppLowess::TemplatedLowess< std::vector<float>, float > flowess;
CppLowess::TemplatedLowess< TestContainer, double > testlowess;
here, STL containers as well as compatible classes can be used as template arguments.
To build and run the tests, you can do the following after download
cmake .
make
./testLowess
The folder src/python contains Python bindings to the C++ code. To build and test them, please go to this folder and run
python setup.py build_ext --inplace
python nosetests testLowess.py
this has been tested with Python version 2.7 and 3.2. It requires the Python development headers as well as Cython.
The code is available under the 3 clause BSD licence, see the LICENCE file