STC
LLR: 2.96 (-2.94,2.94) [-3.00,1.00]
Total: 58462 W: 10615 L: 10558 D: 37289
LTC
LLR: 2.95 (-2.94,2.94) [-3.00,1.00]
Total: 65061 W: 8539 L: 8477 D: 48045
It is worth noting that an attempt to only increase the bonus passed STC but failed LTC, and
an attempt to remove the cap without increasing the bonus is still running at STC, but will probably fail after more than 100k.
Bench: 6188591
Closes#1063
Minor non-functional simplifications in computing the scale factor.
In my opinion, the code is now slightly more readable:
- remove one condition which can never be satisfied.
- immediately return instead of assigning the sf variable.
Tested for non-regression:
LLR: 2.95 (-2.94,2.94) [-3.00,1.00]
Total: 62162 W: 11166 L: 11115 D: 39881
No functional change
Closes#992
After we have taken into account all cheap evaluation
terms, we check whether the score exceeds a given threshold.
If this is the case, we return a scaled down evaluation.
STC:
LLR: 3.35 (-2.94,2.94) [0.00,5.00]
Total: 12575 W: 2316 L: 2122 D: 8137
LTC:
LLR: 2.95 (-2.94,2.94) [0.00,5.00]
Total: 67480 W: 9016 L: 8677 D: 49787
Current version is the one rewritten by ceebo
further edited by me.
Bench: 5367704
In evaluate, we start by initializing the pos.psq_score
and adding the material imbalance. After that, we check
whether a specialized eval exists and if yes we return
that value and discard whatever we have computed until now.
It sounds more logical to first probe material entry and
return if we have a specialized eval, and only if it is
not the case initialize eval with some values. There is
no measurable speed-difference on my computer.
Non functional change.
Collect and give a second try to some almost passed tuning attempts and
one-line tweaks from the last month.
Passed STC
LLR: 3.07 (-2.94,2.94) [0.00,4.00]
Total: 15124 W: 2974 L: 2756 D: 9394
And LTC
LLR: 2.95 (-2.94,2.94) [0.00,4.00]
Total: 21577 W: 3507 L: 3289 D: 14781
Bench: 8855226
Resolves#464
