If you do need to stabilise a test with Monte Carlo simulations, it
can be broken into two. The first test checks the operation up to the
MC sims (if required). Then the second test loads a saved state
whereby all operations up to the simulations have been completed.
I.e. the randomness of the Monte Carlo simulations is removed by
storing and using the same random result each time.
Regards,
Edward
On 30 July 2014 12:01, Edward d'Auvergne <edward@xxxxxxxxxxxxx> wrote:
Hi,
I'd have to look at that. This sometimes happens if the base data are
exponential curves, as then the R2eff errors are determined by Monte
Carlo simulations which are random. And because it is a test the
number of simulations are low so the randomness is quite large. This
randomness will then be propagated into the higher models. Apart from
the unique case of Monte Carlo simulations (or the unit tests for a
few other functions which use the 'random' Python module), a test
should return the same result to machine precision every single time.
It must be identical.
Regards,
Edward
On 30 July 2014 11:47, Troels Emtekær Linnet <tlinnet@xxxxxxxxx> wrote:
Hi Ed.
I am using system test:
Relax_disp.test_r1rho_kjaergaard_auto
to check the outcome of the graphs i am aiming for.
But the shape of the graphs are changing every time I run the test.
The test is running fast by:
OPT_FUNC_TOL = 1e-1
OPT_MAX_ITERATIONS = 1000
But I don't understand why it does not come to the same result everytime?
Is there some "random" involved?
best
Troels
Troels Emtekær Linnet
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Re: How can minimisation give random results?