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Henk wrote:I don't know how to get an estimate of the value of a position without using other engines.

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

sandermvdb wrote:

Henk wrote:I don't know how to get an estimate of the value of a position without using other engines.

Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

quiet-labeled.epd contains the outcome of every position

Henk wrote:

sandermvdb wrote:

Henk wrote: Maybe use your own engine and re-search it on shallow depth. But who will say that that gives a right estimate if your current evaluation is bad.

You can not evaluate every test position manually for that is too much work.

quiet-labeled.epd contains the outcome of every position

Don't understand. What is quiet-labeled.epd?

sandermvdb wrote:The basic idea is pretty simple: calculate the error of the evaluation when it is compared to the actual outcome of the positions.

flok wrote:

sandermvdb wrote:The basic idea is pretty simple: calculate the error of the evaluation when it is compared to the actual outcome of the positions.

What do you mean by that?
Do you mean the following:
- fen as input
- calc move with an eval val
- calc eval of the move that should've been moved
- compare these two (how? percentual difference? or what?)

public double calculateTotalError() {
	double totalError = 0;
	for &#40;Entry<String, Double> entry &#58; fens.entrySet&#40;)) &#123; // fens contains all positions, including the outcome
		ChessBoard cb = new ChessBoard&#40;entry.getKey&#40;));
		totalError += Math.pow&#40;entry.getValue&#40;) - calculateSigmoid&#40;Eval.calculateScore&#40;cb&#41;), 2&#41;;
	&#125;
	totalError /= fens.size&#40;);
	return totalError;
&#125;

public double calculateSigmoid&#40;int score&#41; &#123;
	return 1 / &#40;1 + Math.pow&#40;10, -1.3 * score / 400&#41;);
&#125;

sandermvdb wrote:

flok wrote:If anyone is willing to explain the Texel tuning method tht would be great!

Sofar I understand I have to let it play (well, run QS + eval on FENs) millions of games and then do something with the evaluation-value. But what? I don't understand the wiki explanation.

The basic idea is pretty simple: calculate the error of the evaluation when it is compared to the actual outcome of the positions. Lower a particular evaluation parameter and check if the error has improved, if not, higher the parameter, if again not improved, keep the original value. Do this for all parameters until you have reached the lowest error.

Henk wrote:All does not work if search space has a great many local optima and only very few global optima that you are interested in. But simulated annealing taking too long.

CheckersGuy wrote:

Henk wrote:All does not work if search space has a great many local optima and only very few global optima that you are interested in. But simulated annealing taking too long.

Local search and any other practical algorithm to minimize the error will end up in a local optimum.