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How to Find Outliers in a dataset?
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2D (and higher dimensions) linear interpolation
Probably embarrassing simple question, how to perform linear interpolation on a 2D dataset (a table) given x and y inputs?
(And higher dimensions for that matter, but 2D is my current need)
5 posts - 2 participants
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Upgrade MathNet Native MKL provider for Linux
Hi,
At my firm, we use MathNet Numeric with Native MKL provider (version 2.3.0) on window. We are planning to migrate to Linux, but most recent version for MathNet.Numerics.MKL.Linux-x64 available at Nuget website is 2.0.0 (which is 6 years old). We do not want to downgrade MKL version, newer version should be fine.
is there any plan to distribute newer version of Native MKL provider for Linux by this project ?
I read some licensing restriction on this project page that says.
"The Math NET Numerics project does own an Intel MKL license (for Windows, no longer for Linux) and thus does have the right to distribute it along Math NET Numerics. "
Not sure how latest that information is, as I was able to install Intel MKL for free on my system. and Intel licensing contract Intel® Math Kernel Library License FAQ says that it can re-distributed. Obviously, I am missing something.
Anyway, even if license is required, my firm might able to buy one for this project depending on the cost.
Please suggest what are the available options for me.
Thanks.
Dilip
2 posts - 2 participants
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Global fit of multiple data sets with shared parameters
When fitting mathematical models to data, it is often not enough to simply fit one dataset; it is necessary to globally fit many sets simultaneously, for which some of the model parameters are shared, and others are per set.
Does Numerics have capability for solving this problem? If not (at least I don’t see it), is this planned? Or maybe someone knows an example of a working framework (classes, methods …) to implement such a global fit.
I am currently using the following algorithm. Data from different datasets are combined into one contiguous dataset with a shift in the independent variable so that each subsequent dataset starts where the previous one ends. To fit such a combined set, a fitting function is used that takes parameters combined of a set of the shared parameters (common for the entire data group), and sets of individual parameters for each section, plus fixed parameters corresponding to the shifts of individual sections in this common set. So far, I have not brought this algorithm to a universal form, but I am implementing it as needed, in special cases. Maybe this could serve as an idea for someone who can find the time to develop the framework.
1 post - 1 participant
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