Hi all,
I hope this is the right place to ask questions. If not I am sorry, but
it would be nice to direct me to the right place then.
So my question is about the unit tests. Currently I am implementing a
simple gaussian classifier. Besides the class prior probabilities,
this implementation also outputs the respective mean values,
determinants, and covariance matrices, respectively their inverses.
Now I face the problem, that the values of my SystemDS implementation
and my R implementation are quite off for random generated test
matrices. I assume that this is due to floating point errors / floating
point precision. At first glance they look quite similar, but since it
outputs scientific notation,
one can clearly see that the magnitude by which they are off is quite a
lot. E.g. for my determinant comparison I got the following:
(1,1): 1.2390121975770675E14 <--> 1.2390101941279517E14
(3,1): 1.510440018532407E85 <--> 1.5104388050968705E85
(2,1): 1.6420264128994816E38 <--> 1.6420263615987703E38
(5,1): 8.881025000211518E70 <--> 8.881037540234089E70
(4,1): 1.7888589555748764E22 <--> 1.78885700537877E22
I face similar issues with the inverses of my covariance matrices.
Since I use the eigenvalues and eigenvectors for calculating the
determinant and the inverse in SystemDL, I already compared them to the
eigenvalues and vectors which R
computes, and already there, differences (due to floating point
differences) are observable.
My question would be now, how to test, respectively compare such
matrices and vectors?
It seems a bit odd to me, to set the tolerance to something like
"2.0E80" or so.
Would be great if someone could help me out!
Best,
Kevin
