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MBulli
2018-02-26 10:13:43 +01:00
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tex/chapters/experiments.tex
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@@ -52,8 +52,8 @@ However, both cases do not give a deeper insight of the error behavior of our me
\subsection{Performance}
In the following, we underpin the promising theoretical linear time complexity of our method with empirical time measurements compared to other methods.
All tests are performed on a Intel Core \mbox{i5-7600K} CPU with a frequency of \SI{4.2}{\giga\hertz}, and \SI{16}{\giga\byte} main memory.
We compare our C++ implementation of the extended box filter based KDE approximation to the KernSmooth R package and the \qq{FastKDE} Python implementation \cite{oBrien2016fast}.
The KernSmooth packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
We compare our C++ implementation of the box filter based KDE approximation to the \texttt{ks} R package and the fastKDE Python implementation \cite{oBrien2016fast}.
The \texttt{ks} packages provides a FFT-based BKDE implementation based on optimized C functions at its core.
% Vergleich zu weighted average (in c++) um unseren großen Geschwindigkeitsvorteil zu zeigen.
With state estimation problems in mind, we additionally provide a C++ implementation of a weighted average estimator.
\commentByToni{Vielleicht sollten wir hier noch paar Worte über die Implementierung verlieren. Ist das alles std c++? nehmen wir iwas mega kraßes? usw. vielleicht im camera ready sogar nen link zum coder oder sowas.}