## Piece weights with regression analysis (in Russian)

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### Re: Piece weights with regression analysis (in Russian)

Can You give similar output for Stockfish? It's interesting to see if it will differ from human values.

Pawel Koziol

http://www.pkoziol.cal24.pl/rodent/rodent.htm

http://www.pkoziol.cal24.pl/rodent/rodent.htm

### Re: Piece weights with regression analysis (in Russian)

The logistic regression methods for tuning are really nice and I enjoyed reading Buro's paper. Obviously from a practical viewpoint such tuning methods work quite well.

Question: is there any research into why such statistically correct evaluation functions are expected to work?

The problem I have is that "statistical correctness" does not propagate through the game tree. In other words: if the leaf evaluation is statistically correct then there is a priori no reason why the result of a 1-ply search would be statistically correct.

The propagation through the game tree is via min/max functions and these have bad statistical properties...

Question: is there any research into why such statistically correct evaluation functions are expected to work?

The problem I have is that "statistical correctness" does not propagate through the game tree. In other words: if the leaf evaluation is statistically correct then there is a priori no reason why the result of a 1-ply search would be statistically correct.

The propagation through the game tree is via min/max functions and these have bad statistical properties...

Ideas=science. Simplification=engineering.

Without ideas there is nothing to simplify.

Without ideas there is nothing to simplify.

### Re: Piece weights with regression analysis (in Russian)

Hi Michel,

I haven't seen search/eval interaction modelled anywhere, but don't follow papers closely anymore. It seems very difficult, but could lead to biased error functions that give better results when combined with deep searches.

One element as to why it works at all for me is that regression can compensate its drawbacks by handling a large number of weights, even in the millions.

Fabien.

I fully agree with you, and propagating a random eval will quickly convince the sceptic. Machine learning in general expects you to use its results directly. Sometimes a bias such as a cost matrix (http://cse-wiki.unl.edu/wiki/index.php/ ... e_Learning) is introduced.Michel wrote:The logistic regression methods for tuning are really nice and I enjoyed reading Buro's paper. Obviously from a practical viewpoint such tuning methods work quite well.

Question: is there any research into why such statistically correct evaluation functions are expected to work?

The problem I have is that "statistical correctness" does not propagate through the game tree. In other words: if the leaf evaluation is statistically correct then there is a priori no reason why the result of a 1-ply search would be statistically correct.

The propagation through the game tree is via min/max functions and these have bad statistical properties...

I haven't seen search/eval interaction modelled anywhere, but don't follow papers closely anymore. It seems very difficult, but could lead to biased error functions that give better results when combined with deep searches.

One element as to why it works at all for me is that regression can compensate its drawbacks by handling a large number of weights, even in the millions.

Fabien.

### Re: Piece weights with regression analysis (in Russian)

I must be doing something wrong. For 7442 games at 40/2+0.05 (two seconds for 40 moves plus 0.05 increment) between zurichess and itself I get very strange values. Using Texel's Tuning method I get good values.WinPooh wrote:My article on a polular Russian IT portal:

http://habrahabr.ru/post/254753/

It is in Russian, but I believe the text is quite understandable even without translation.

Code: Select all

```
./pgnlearn.exe ../../pgn/games.pgn
Reading file: ../../pgn/games.pgn
Games: 7442
Created file: .mat
Loading dataset...
[ 12807 x 5 ]
Solving (gradient method)...
Iter 0: [ 0 0 0 0 0 ] -> 0.693147
Iter 1000: [ 0.173482 0.210955 0.288834 0.391243 0.17849 ] -> 0.690838
Iter 2000: [ 0.174092 0.212807 0.2914 0.472017 0.187411 ] -> 0.69083
Iter 3000: [ 0.174207 0.213172 0.29191 0.487982 0.188865 ] -> 0.69083
Iter 4000: [ 0.17423 0.213245 0.292011 0.49115 0.18915 ] -> 0.69083
Iter 5000: [ 0.174234 0.213259 0.292031 0.491779 0.189206 ] -> 0.69083
Iter 6000: [ 0.174235 0.213262 0.292035 0.491904 0.189218 ] -> 0.69083
Iter 7000: [ 0.174236 0.213263 0.292036 0.491928 0.18922 ] -> 0.69083
Iter 8000: [ 0.174236 0.213263 0.292036 0.491933 0.18922 ] -> 0.69083
Iter 9000: [ 0.174236 0.213263 0.292036 0.491934 0.18922 ] -> 0.69083
Iter 10000: [ 0.174236 0.213263 0.292036 0.491934 0.18922 ] -> 0.69083
PIECE VALUES:
Pawn: 100
Knight: 122.399
Bishop: 167.61
Rook: 282.339
Queen: 108.6
Press ENTER to finish
```

- WinPooh
**Posts:**250**Joined:**Fri Mar 17, 2006 7:01 am**Location:**Russia**Full name:**Vladimir Medvedev-
**Contact:**

### Re: Piece weights with regression analysis (in Russian)

Probably it is due to my gradient solver - it is very simple and straightforward, looks like some local minimum was caught. Another reason can be PGN file - the parser can't handle comments or folded lines of moves.brtzsnr wrote:I must be doing something wrong. For 7442 games at 40/2+0.05 (two seconds for 40 moves plus 0.05 increment) between zurichess and itself I get very strange values. Using Texel's Tuning method I get good values.WinPooh wrote:My article on a polular Russian IT portal:

http://habrahabr.ru/post/254753/

It is in Russian, but I believe the text is quite understandable even without translation.

Code: Select all

`./pgnlearn.exe ../../pgn/games.pgn Reading file: ../../pgn/games.pgn Games: 7442 Created file: .mat Loading dataset... [ 12807 x 5 ] Solving (gradient method)... Iter 0: [ 0 0 0 0 0 ] -> 0.693147 Iter 1000: [ 0.173482 0.210955 0.288834 0.391243 0.17849 ] -> 0.690838 Iter 2000: [ 0.174092 0.212807 0.2914 0.472017 0.187411 ] -> 0.69083 Iter 3000: [ 0.174207 0.213172 0.29191 0.487982 0.188865 ] -> 0.69083 Iter 4000: [ 0.17423 0.213245 0.292011 0.49115 0.18915 ] -> 0.69083 Iter 5000: [ 0.174234 0.213259 0.292031 0.491779 0.189206 ] -> 0.69083 Iter 6000: [ 0.174235 0.213262 0.292035 0.491904 0.189218 ] -> 0.69083 Iter 7000: [ 0.174236 0.213263 0.292036 0.491928 0.18922 ] -> 0.69083 Iter 8000: [ 0.174236 0.213263 0.292036 0.491933 0.18922 ] -> 0.69083 Iter 9000: [ 0.174236 0.213263 0.292036 0.491934 0.18922 ] -> 0.69083 Iter 10000: [ 0.174236 0.213263 0.292036 0.491934 0.18922 ] -> 0.69083 PIECE VALUES: Pawn: 100 Knight: 122.399 Bishop: 167.61 Rook: 282.339 Queen: 108.6 Press ENTER to finish`