RuyTune

Home * Automated Tuning * RuyTune

RuyTune’s hyperbolic tangent based Sigmoid [1] RuyTune,

an open source framework for tuning evaluation function parameters, written by Álvaro Begué in C++, released on Bitbucket [2] as introduced in November 2016 [3]. RuyTune applies logistic regression using a limited-memory BFGS, a quasi-Newton method that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm with limited amount of memory. It uses the libLBFGS library [4] along with reverse-mode automatic differentiation and requires that the evaluation function is converted to a C++ template function where the score type is a template parameter, and a database of quiescent positions with associated results [5].

See also#

• Arasan’s Tuning
• Eval Tuning in Deep Thought
• RuyDos
• Stockfish’s Tuning Method
• Texel’s Tuning Method

Forum Posts#

• A database for learning evaluation functions by Álvaro Begué, CCC, October 28, 2016
• C++ code for tuning evaluation function parameters by Álvaro Begué, CCC, November 10, 2016
• Re: Texel tuning method question by Peter Österlund, CCC, June 07, 2017

Re: Texel tuning method question by Álvaro Begué, CCC, June 07, 2017

External Links#

• alonamaloh / ruy_tune — Bitbucket ( Wayback Machine)

References#

1. ↑ tanh(0.43s) , s=-10 to 10 pawnunit plot by Wolfram Alpha
2. ↑ alonamaloh / ruy_tune — Bitbucket ( Wayback Machine)
3. ↑ C++ code for tuning evaluation function parameters by Álvaro Begué, CCC, November 10, 2016
4. ↑ libLBFGS: L-BFGS library written in C
5. ↑ A database for learning evaluation functions by Álvaro Begué, CCC, October 28, 2016
6. ↑ Re: Texel tuning method question by Álvaro Begué, CCC, June 07, 2017

Up one Level