Author: Dann Corbit
Date: 11:33:37 04/28/04
Go up one level in this thread
On April 28, 2004 at 14:30:05, Dann Corbit wrote:
>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}
An example bad fit curve:
Bad data this pass -- no fit. (prune_soften):
(x=1667.000000, y=4081.000001),
(x=3333.000000, y=4081.000001),
(x=4999.000000, y=4081.000001),
(x=6665.000000, y=4081.000001),
(x=8331.000000, y=4081.000001),
(x=9997.000000, y=4081.000001),
(x=11663.000000, y=4081.000001),
prune_soften=4999 at 6; {badfit}
As you can see, prune_soften has no effect on solution rate.
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