Joseph Heled wrote on 18 July 2006 13:27
> Ian Shaw wrote:
> >
> > If we could split the nets, I would try using fuzzy logic to smooth
> > out the gaps at the edges. A position could be evaluated as
> > being 60%
> > prime, 35% holding, 5% race. The evaluations from those nets would
> > then be combined proportionally to give an overall equity. Maybe
> > Snowie already does this.
>
> Well, you first have to find a way to assess the percentages
> (using fixed one does not buy you anything in my book. IMBW
> (i may be wrong).
I totally agree. You can't use a fixed percentage. Accurately choosing a
split is a tricky proposition in its own right. I don't intend to pursue
it.
> > Does anyone have any idea why those particular inputs were chosen,
> > what they are intended to model, and how effective they
> are? Some of
> > them are obvious well commented, but others at the bottom
> of the enum
> > are undocumented.
>
> I spent considerable amount of time on this issue with only
> limited success. some of them I inherited from Gary's 0.1
> version of gnubg.
> Some I added. Some I eliminated when I decided they don't contribute.
Are the deleted ones still in the code - and can I tell which are in
use? I don't want to be raking over old ground.
> The issue of inputs to a neural net is as close to the pit of
> hell as you can get. There is no science I know of, just
> black magic. Nothing I tried really worked. For example
> - train net only on raw inputs.
> - for each of the extra inputs, correlate it with the net error.
> (here is one place where a benchmark comes handy)
> - take the input with highest score, add it and re-train.
> - repeat the steps above
He he! I suspected as much.
Do you have any stuff I can use? I know you didn't use gnubg itself, so
I guess you've got something more suitable for a NN development test
bed. I'd also need the training and benchmark databases.
Of course, if you or somebody else with more programming competency than
I would like to take the lead role, then I'd be happy to tack a back
seat and contribute ideas and processing power. That's more what I had
in mind when I started this discussion, because it would be much faster.
I'm interested in the coding, but in all honesty I'm nowhere near up to
speed on neural network design or C (nor any other conventional
programming language, for that matter, and I can't see myself
implementing neural networks in PLC ladder logic!)
> > What is going on? For example, BREAK_CONTACT always seems
> to be zero,
> > even when there is contact.
>
> Then something is horribly wrong.
>
Perhaps it's just the output to the window. dE/dI values do exist.
Here is the starting position. BREAK_CONTACT should be 1, because it is
normalized to the start position.
Position ID: 4HPwATDgc/ABMA
Match ID: cAngAAAAAAAA
Evaluator: CONTACT
Input Value dE/dI
OFF1 0.000 0.672
OFF2 0.000 0.757
OFF3 0.000 0.374
BREAK_CONTACT 0.000 0.574
BACK_CHEQUER 0.000 0.624
BACK_ANCHOR 1.000 0.693
FORWARD_ANCHOR 0.000 0.388
PIPLOSS 0.000 (0.000 avg) 0.570
P1 0.000 (0.000/36) 0.613
P2 0.000 (0.000/36) 0.688
BACKESCAPES 0.000 (0.000/36) 0.396
ACONTAIN 0.000 (0.000/36) 0.559
CONTAIN 0.000 (0.000/36) 0.663
MOBILITY 0.000 0.549
MOMENT2 0.000 0.577
ENTER 0.000 (0.000/12) 0.617
ENTER2 0.000 0.601
TIMING 0.000 0.520
BACKBONE 0.000 0.536
BACKGAME 0.000 0.582
BACKGAME1 0.000 0.355
FREEPIP 0.000 0.494
Win W(g) W(bg) L(g) L(bg) Equity Cubeful
static: 0.519 0.145 0.006 0.130 0.006 +0.054 +0.076
1 ply: 0.535 0.149 0.008 0.123 0.004 +0.102 +0.142
2 ply: 0.519 0.145 0.006 0.126 0.005 +0.060 +0.084
Cube analysis
2-ply cubeless equity +0.060 (Money: +0.059)
0.519 0.145 0.006 - 0.481 0.126 0.005
Cubeful equities:
1. No double +0.084
2. Double, pass +1.000 ( +0.916)
3. Double, take -0.224 ( -0.308)
Proper cube action: No double, take (25.2%)
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