Author: Dann Corbit
Date: 11:30:05 04/28/04
Go up one level in this thread
On April 27, 2004 at 23:19:53, Dann Corbit wrote:
>On April 27, 2004 at 22:10:56, Joel wrote:
>
>>Hey Dann,
>>
>>When you say tune, do you mean the evaluation function only?
>>
>>If this is the case, don't even think you could say you improved your tactical
>>strength if you just tuned it for a few (or maybe many testsuites). What I think
>>would be likely is that you would be mucking your eval terms up so they solved
>>the positions the search struggled with, with the dominating woodcount along
>>with the search doing well in the rest of the positions.
>>
>>Is there someplace you suggest (your ftp perhaps?) where I can find a bit more
>>information about this parabolic fit technique you use? I would be interested in
>>trying some experiments of my own with it.
>
>ftp://cap.connx.com/pub/chess-engines/new-approach/beocurve.zip
>
>Take a look at curve.c, pers.c, and parser.c
>
>The test positions are defined in testdat.h
I added 10,000 quiet positions, and did some curves for some pruning constants.
It seems that prune_soften is not a parabolic fit (or the data varies wildly to
the degree that a parabola cannot be fitted with any confidence). When it says
"at 4" that means at a depth of 4 plies.
prune_ratio=51 at 4; stddev=3.415650
prune_factor=373 at 4; stddev=0.000000
prune_constant=599 at 4; stddev=0.000000
prune_soften=4999 at 4; {badfit}
prune_ratio=52 at 5; stddev=6.750661
prune_factor=305 at 5; stddev=0.000000
prune_constant=1 at 5; stddev=0.000000
prune_soften=1667 at 5; {badfit}
prune_ratio=51 at 6; stddev=108.080933
prune_factor=456 at 6; stddev=0.243975
prune_constant=512 at 6; stddev=0.000000
prune_soften=4999 at 6; {badfit}
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