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Given a 2D matrix A as input, how to compute its covariance matrix? For example, in Matlab, I use cov(A) to do the computation.

Thanks

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you can do that by using Summary Statistical Functions. Vslsssnewtask(*&task*, *p*, *n*, *xstorage*, *x*, *w*, *indices*) where P -- Dimension of the task, number of variables

There are many examples show how to do that. You can find these examples into manual or into <mkl_root>\examples\vsls\source\"

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More specifically you should be interested in vslsbasicstats.c/vsldbasicstats.c for C or vslsbasicstats.f/vsldbasicstats.f for Fortran examples.

At the same time those examples show calculation for all basic stats including min/max, moments, skewness, etc.

In order to calculate covariance only you can skip several steps there, the rest will be: vslsSSNewTask() + vslsSSEditCovCor() + vslsSSCompute() + vslSSDeleteTask().

You can also look here:

- Summary Statistics Application Notes, and especially this part is about calculation cov/cor and meaning of each step.
- There is also an older blog post, which gives some insight for use case.

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