From 21e640b40ed969dccb2564afe12400c6e4b9ab4c Mon Sep 17 00:00:00 2001 From: leiizko Date: Mon, 7 Aug 2017 15:10:38 +0200 Subject: [PATCH] pub init --- LICENSE | 661 ++++++++++++++++++++++++++++++++++++++++++ LICENSE_ORIGINAL | 53 ++++ README.md | 47 ++- src/basemath.cpp | 47 +++ src/factorgraph.cpp | 265 +++++++++++++++++ src/factorgraph.h | 95 ++++++ src/gaussian.cpp | 60 ++++ src/gaussian.h | 23 ++ src/main_plugin.cpp | 221 ++++++++++++++ src/makedll.sh | 9 + src/makedll_win32.cmd | 11 + src/matrix.cpp | 177 +++++++++++ src/matrix.h | 11 + src/trueskill.cpp | 461 +++++++++++++++++++++++++++++ src/trueskill.h | 28 ++ 15 files changed, 2168 insertions(+), 1 deletion(-) create mode 100644 LICENSE create mode 100644 LICENSE_ORIGINAL create mode 100644 src/basemath.cpp create mode 100644 src/factorgraph.cpp create mode 100644 src/factorgraph.h create mode 100644 src/gaussian.cpp create mode 100644 src/gaussian.h create mode 100644 src/main_plugin.cpp create mode 100644 src/makedll.sh create mode 100644 src/makedll_win32.cmd create mode 100644 src/matrix.cpp create mode 100644 src/matrix.h create mode 100644 src/trueskill.cpp create mode 100644 src/trueskill.h diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..80a61fa --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/LICENSE_ORIGINAL b/LICENSE_ORIGINAL new file mode 100644 index 0000000..5da8727 --- /dev/null +++ b/LICENSE_ORIGINAL @@ -0,0 +1,53 @@ +Caution +======= + +This TrueSkill project is opened under the BSD license but the +TrueSkill(TM) brand is not. Microsoft permits only Xbox Live games or +non-commercial projects to use TrueSkill(TM). If your project is +commercial, you should find another rating system. + +References +========== + +The core ideas used in this project were described in +"TrueSkill (TM): A Bayesian Skill Rating System" available at +http://research.microsoft.com/apps/pubs/default.aspx?id=67956 + +Some concepts were based on Jeff Moser's code and documents, available +at http://www.moserware.com/2010/03/computing-your-skill.html + +License +======= + +All the Python code and the documentation in this TrueSkill project is +Copyright (c) 2012-2015 by Heungsub Lee. All rights reserved. + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +* Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + +* The names of the contributors may not be used to endorse or + promote products derived from this software without specific + prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md index 8932642..2217a14 100644 --- a/README.md +++ b/README.md @@ -1 +1,46 @@ -# cod4_trueskill_plugin \ No newline at end of file +# CoD4X Trueskill plugin +A game rating system made available to use as CoD4X plugin + +## Copyright notice +Trueskill algorithm code in this plugin is a port of [sublee/trueskill](https://github.com/sublee/trueskill) from python to C++. For more information consult [LICENSE_ORIGINAL](https://github.com/leiizko/cod4x_trueskill/blob/master/LICENSE_ORIGINAL). + +All C++ code is released under AGPL-v3. + +## Added functions +``` +* TS_Rate( , ) + - : Integer, number of teams in game, each player counts as a team in FFA mode + - : String, team placements separated by space. Lower number is better, same numbers is a tie. + Needs to be in a specfic order, the string gets tokenized into an array. Array index+1 represents a team. Example: + TS_Rate( 2, "0 1" ) - Number of teams is 2, Axis is 1, Allies is 2: Axis wins the round, Allies lose. + TS_Rate( 5, "2 0 0 1 3" ) - FFA mode, 5 players. Player 1 is third, players 2 and 3 are tied at first place, + player 4 is second and player 5 is fourth. + Returns array of updated MEAN(mu) and VARIANCE(sigma) values in the same order as added by TS_AddPlayer. + Arr[ i ][ 0 ] = MEAN(mu) + Arr[ i ][ 1 ] = VARIANCE(sigma) + Clears all players added by TS_AddPlayer function. + +* TS_Quality( ) + - : Integer, number of teams in game, each player counts as a team in FFA mode + Returns a float of game quality ( 0 - 1 ). Multiply by 100 to get percentage of draw chance + Clears all players added by TS_AddPlayer function. + +* TS_AddPlayer( , , , , [] ) + - - Integer, slot number + - - Float, mean rating of a player + - - Float, variance rating of a player + - - Integer, team of a player, start at 1, in FFA each player is a team, example: + TDM mode, Allies = 1, Axis = 2 + FFA mode, player 1 = 1, player 2 = 2, player 3 = 3, etc.. + - [], OPTIONAL, Float, weight for the player, defaults to 1. + +* TS_ClearAllPlayers() + Clears all players added by TS_AddPlayer function. + +* TS_UpdateMu( ) + - - Float, updates the default rating values. New players must start at this mean value, sigma is MU/3 + +* TS_UpdateDraw( ) + - - Float, 0 - 1, updates the default draw chance (0.1) +``` + diff --git a/src/basemath.cpp b/src/basemath.cpp new file mode 100644 index 0000000..8369d52 --- /dev/null +++ b/src/basemath.cpp @@ -0,0 +1,47 @@ +#include +#ifndef M_PI + static const double M_PI = 3.14159265358979323846; +#endif + +double erfcinv( double y ); + +// Probability density function +double pdf( double x, double mu, double sigma ) +{ + return ( 1 / sqrt( 2 * M_PI ) * std::abs( sigma ) * exp( -pow( ( x - mu ) / std::abs( sigma ), 2 ) / 2 ) ); +} + +// Cumulative distribution function +double cdf( double x, double mu, double sigma ) +{ + return 0.5 * erfc( -( x - mu ) / ( sigma * sqrt( 2 ) ) ); +} + +// Inverse cdf +double ppf( double x, double mu, double sigma ) +{ + return mu - sigma * sqrt( 2 ) * erfcinv( 2 * x ); +} + +// Inverse ercf ( error complementary function ) +double erfcinv( double y ) +{ + if( y >= 2 ) + return -100; + else if( y <= 0 ) + return 100; + + bool zero_point = y < 1; + if( !zero_point ) + y = 2 - y; + + double t = sqrt( -2 * log( y / 2 ) ); + double x = -0.70711 * ( ( 2.30753 + t * 0.27061 ) / ( 1 + t * ( 0.99229 + t * 0.04481 ) ) - t ); + for( int i = 0; i < 2; i++ ) + { + double err = erfc( x ) - y; + x += err / ( 1.12837916709551257 * exp( -( pow( x, 2 ) ) ) - x * err ); + } + + return zero_point ? x : -x; +} \ No newline at end of file diff --git a/src/factorgraph.cpp b/src/factorgraph.cpp new file mode 100644 index 0000000..bb6a85b --- /dev/null +++ b/src/factorgraph.cpp @@ -0,0 +1,265 @@ +#include "factorgraph.h" + +static const double INF = std::numeric_limits::infinity(); + +double Variable::update_value( Factor *factor, double pi, double tau ) +{ + Gaussian value = Gaussian( pi, tau ); + Gaussian old_message = this->messages[ *factor ]; + Gaussian multi = value * &old_message; + Gaussian div = multi / &this->value; + attach_factor( factor, &div ); + + return set( &value ); +} + +double Variable::update_value( Factor *factor, Gaussian *value ) +{ + Gaussian old_message = this->messages[ *factor ]; + Gaussian multi = *value * &old_message; + Gaussian div = multi / &this->value; + attach_factor( factor, &div ); + + return set( value ); +} + +double Variable::update_message( Factor *factor, double pi, double tau ) +{ + Gaussian message = Gaussian( pi, tau ); + Gaussian old_message = this->messages[ *factor ]; + attach_factor( factor, &message ); + Gaussian div = this->value / &old_message; + Gaussian multi = div * &message; + return set( &multi ); +} + +double Variable::update_message( Factor *factor, Gaussian *message ) +{ + Gaussian old_message = this->messages[ *factor ]; + attach_factor( factor, message ); + Gaussian div = this->value / &old_message; + Gaussian multi = div * message; + + return set( &multi ); +} + +Gaussian Variable::operator[]( Factor *factor ) +{ + return this->messages[ *factor ]; +} + +void Variable::attach_factor( Factor *factor, Gaussian *message ) +{ + this->messages[ *factor ] = *message; +} + +double Variable::delta( Gaussian *other ) +{ + double pi_delta = std::abs( this->value._pi() - other->_pi() ); + + if ( pi_delta == INF ) + return 0; + + return std::max( std::abs( this->value._tau() - other->_tau() ), std::sqrt( pi_delta ) ); +} + +double Variable::set( Gaussian *other ) +{ + double delta = this->delta( other ); + this->value = Gaussian( other->_pi(), other->_tau() ); + + return delta; +} + +Gaussian Variable::getVal() +{ + return this->value; +} + +int Factor::s_id; + +void Factor::initF( std::vector< Variable* > &vars ) +{ + this->vars = vars; + for ( unsigned int i = 0; i < vars.size(); i++ ) + { + Gaussian msg = Gaussian(); + vars[ i ]->attach_factor( this, &msg ); + } +} + +std::vector< Variable* > Factor::getVar() +{ + return this->vars; +} + +PriorFactor::PriorFactor( Variable *var, Gaussian *val, double dynamic ) +{ + std::vector< Variable* > vars; + vars.push_back( var ); + initF( vars ); + this->val = *val; + this->dynamic = dynamic; +} + +double PriorFactor::down() +{ + double sigma = std::sqrt( std::pow( this->val._sigma(), 2 ) + std::pow( this->dynamic, 2 ) ); + Gaussian value = Gaussian( this->val._mu(), sigma, 0, 0 ); + return ( this->vars )[ 0 ]->update_value( this, &value ); +} + +LikelihoodFactor::LikelihoodFactor( Variable *mean_var, Variable *value_var, double variance ) +{ + std::vector< Variable* > vars; + vars.push_back( mean_var ); + vars.push_back( value_var ); + initF( vars ); + + this->mean = mean_var; + this->value = value_var; + this->variance = variance; +} + +double LikelihoodFactor::calc_a( Gaussian *val ) +{ + return 1.0 / ( 1.0 + this->variance * val->_pi() ); +} + +double LikelihoodFactor::down() +{ + Gaussian by = (*this->mean )[this]; + Gaussian msg = this->mean->getVal() / &by; + double a = calc_a( &msg ); + return this->value->update_message( this, a * msg._pi(), a * msg._tau() ); +} + +double LikelihoodFactor::up() +{ + Gaussian by = ( *this->value )[ this ]; + Gaussian msg = this->value->getVal() / &by; + double a = calc_a( &msg ); + return this->mean->update_message( this, a * msg._pi(), a * msg._tau() ); +} + +SumFactor::SumFactor( Variable *sum, std::vector< Variable* > &terms, std::vector< double > &coeffs ) +{ + std::vector< Variable* > vars; + vars.push_back( sum ); + for ( unsigned int i = 0; i < terms.size(); i++ ) + { + vars.push_back( terms[ i ] ); + } + + initF( vars ); + this->sum = sum; + this->terms = terms; + this->coeffs = coeffs; +} + +double SumFactor::down() +{ + std::vector< Gaussian > msgs; + for ( unsigned int i = 0; i < this->terms.size(); i++ ) + { + msgs.push_back( ( *this->terms[ i ] )[ this ] ); + } + + return update( this->sum, this->terms, msgs, this->coeffs ); +} + +double SumFactor::up( unsigned int index ) +{ + double coeff = this->coeffs[ index ]; + std::vector< double > coeffs; + for ( unsigned int i = 0; i < this->coeffs.size(); i++ ) + { + if ( coeff == 0.0 ) + { + coeffs.push_back( 0 ); + continue; + } + if ( i == index ) + { + coeffs.push_back( 1.0 / coeff ); + } + else + { + coeffs.push_back( ( this->coeffs[ i ] / coeff ) * -1 ); + } + } + + std::vector< Variable* > vals; + for ( unsigned int i = 0; i < this->terms.size(); i++ ) + { + if ( i == index ) + { + vals.push_back( this->sum ); + } + else + { + vals.push_back( this->terms[ i ] ); + } + } + std::vector< Gaussian > msgs; + for ( unsigned int i = 0; i < vals.size(); i++ ) + { + msgs.push_back( ( *vals[ i ] )[ this ] ); + } + + return update( this->terms[ index ], vals, msgs, coeffs ); +} + +double SumFactor::update( Variable *var, std::vector< Variable* > &vals, std::vector< Gaussian > &msgs, std::vector< double > coeffs ) +{ + double pi_inv = 0, mu = 0; + + for ( unsigned int i = 0; i < vals.size(); i++ ) + { + Gaussian div; + div = vals[ i ]->getVal() / &msgs[ i ]; + mu += coeffs[ i ] * div._mu(); + if ( pi_inv == INF ) + { + continue; + } + if ( !div._pi() ) + { + pi_inv = INF; + } + else + { + pi_inv += std::pow( coeffs[ i ], 2 ) / div._pi(); + } + } + + double pi = 1.0 / pi_inv; + double tau = pi * mu; + return var->update_message( this, pi, tau ); +} + +TruncateFactor::TruncateFactor( Variable *var, std::function< double( double, double ) > v_func, std::function< double( double, double ) > w_func, double draw_margin ) +{ + std::vector< Variable* > vars; + vars.push_back( var ); + initF( vars ); + + this->v_func = v_func; + this->w_func = w_func; + this->draw_margin = draw_margin; +} + +double TruncateFactor::up() +{ + Variable *val = this->vars[ 0 ]; + Gaussian msg = (*this->vars[ 0 ])[ this ]; + Gaussian div = val->getVal() / &msg; + double sqrt_pi = std::sqrt( div._pi() ); + double v = this->v_func( div._tau() / sqrt_pi, this->draw_margin * sqrt_pi ); + double w = this->w_func( div._tau() / sqrt_pi, this->draw_margin * sqrt_pi ); + double denom = ( 1.0 - w ); + double pi = div._pi() / denom; + double tau = ( div._tau() + sqrt_pi * v ) / denom; + + return val->update_value( this, pi, tau ); +} \ No newline at end of file diff --git a/src/factorgraph.h b/src/factorgraph.h new file mode 100644 index 0000000..934f5dd --- /dev/null +++ b/src/factorgraph.h @@ -0,0 +1,95 @@ +#pragma once + +#include +#include +#include +#include +#include +#include "gaussian.h" + +class Variable; + +class Factor +{ + static int s_id; +protected: + std::vector< Variable* > vars; +public: + int id; + Factor() : id( s_id++ ) {}; + void initF( std::vector< Variable* > &vars ); + std::vector< Variable* > getVar(); +}; + +struct FactorKeyMapper +{ + bool operator()( const Factor& lhs, const Factor& rhs ) const + { + return lhs.id < rhs.id; + } +}; + +class Variable +{ + Gaussian value; + std::map< Factor, Gaussian, FactorKeyMapper > messages; + +public: + Variable() : value(Gaussian()) {}; + double delta( Gaussian *other ); + double set( Gaussian *other ); + double update_message( Factor *factor, double pi, double tau ); + double update_message( Factor *factor, Gaussian *message ); + double update_value( Factor *factor, double pi, double tau ); + double update_value( Factor *factor, Gaussian *value ); + Gaussian operator[]( Factor *factor ); + void attach_factor( Factor *factor, Gaussian *message ); + Gaussian getVal(); +}; + +class PriorFactor : public Factor +{ + double dynamic; + Gaussian val; + +public: + PriorFactor( Variable *var, Gaussian *val, double dynamic ); + double down(); +}; + +class LikelihoodFactor : public Factor +{ + Variable *value; + Variable *mean; + double variance; + +public: + LikelihoodFactor( Variable *mean_var, Variable *value_var, double variance ); + double calc_a( Gaussian *val ); + double down(); + double up(); +}; + +class SumFactor : public Factor +{ + Variable *sum; + std::vector< Variable* > terms; + std::vector< double > coeffs; + +public: + SumFactor( Variable *sum, std::vector< Variable* > &terms, std::vector< double > &coeffs ); + double down(); + double up( unsigned int index ); + double update( Variable *var, std::vector< Variable* > &vals, std::vector< Gaussian > &msgs, std::vector< double > coeffs ); +}; + +class TruncateFactor : public Factor +{ + std::function< double( double, double ) > v_func; + std::function< double( double, double ) > w_func; + double draw_margin; + +public: + TruncateFactor( Variable *var, std::function< double( double, double ) > v_func, std::function< double( double, double ) > w_func, double draw_margin ); + double up(); +}; \ No newline at end of file diff --git a/src/gaussian.cpp b/src/gaussian.cpp new file mode 100644 index 0000000..cfb910a --- /dev/null +++ b/src/gaussian.cpp @@ -0,0 +1,60 @@ +#include "gaussian.h" + +static const double INF = std::numeric_limits::infinity(); + +Gaussian::Gaussian() +{ + this->mu = 0; + this->sigma = INF; + this->pi = 0; + this->tau = 0; +} + +Gaussian::Gaussian( double pi, double tau ) +{ + setMu( pi, tau ); + setSigma( pi ); + this->pi = pi; + this->tau = tau; +} + +Gaussian::Gaussian( double mu, double sigma, double pi, double tau ) +{ + this->mu = mu; + this->sigma = sigma; + this->pi = pow( this->sigma, -2 ); + this->tau = this->pi * this->mu; +} + +Gaussian Gaussian::operator*( Gaussian *other ) +{ + double pi, tau; + pi = this->pi + other->pi; + tau = this->tau + other->tau; + Gaussian result = Gaussian( pi, tau ); + return result; +} + +Gaussian Gaussian::operator/( Gaussian *other ) +{ + double pi, tau; + pi = this->pi - other->pi; + tau = this->tau - other->tau; + Gaussian result = Gaussian( pi, tau ); + return result; +} + +void Gaussian::setMu( double pi, double tau ) +{ + this->mu = pi ? tau / pi : pi; +} + +void Gaussian::setSigma( double pi ) +{ + this->sigma = pi ? sqrt( 1 / pi ) : INF; +} + +double Gaussian::_pi() { return this->pi; }; +double Gaussian::_tau() { return this->tau; }; +double Gaussian::_mu() { return this->mu; }; +double Gaussian::_sigma() { return this->sigma; }; \ No newline at end of file diff --git a/src/gaussian.h b/src/gaussian.h new file mode 100644 index 0000000..e8a9dee --- /dev/null +++ b/src/gaussian.h @@ -0,0 +1,23 @@ +#pragma once + +#include +#include + +class Gaussian +{ + double pi, tau, mu, sigma; + +public: + Gaussian(); + Gaussian( double pi, double tau ); + Gaussian( double mu, double sigma, double pi, double tau ); + double _pi(); + double _tau(); + double _mu(); + double _sigma(); + void setSigma( double pi ); + void setMu( double pi, double tau ); + + Gaussian operator*( Gaussian *other ); + Gaussian operator/( Gaussian *other ); +}; \ No newline at end of file diff --git a/src/main_plugin.cpp b/src/main_plugin.cpp new file mode 100644 index 0000000..3e68bf1 --- /dev/null +++ b/src/main_plugin.cpp @@ -0,0 +1,221 @@ +#include "../pinc.h" +#include "trueskill.h" + +static std::vector< std::vector< double > > players; + +void addPlayer( double mu, double sigma, double team, double weight, double identifier ); +bool validateTeams( std::vector< std::vector< double > > &T, int t ); + +/* + Needed: + +// INPUT: T = Vector matrix of teams and trueskill properties +// T[ i ][ 0 ] = mu +// T[ i ][ 1 ] = sigma +// T[ i ][ 2 ] = team ( team 1 = 1 | team 2 = 2 | team 3 = 3 |..... ) +// T[ i ][ 3 ] = Weight +// T[ i ][ 4 ] = Identifier +// R = Vector array of team ranks = index = team number - 1 +// Lower value is better (win), same value is draw +// Example: Team 2 and team 3 draw, team 1 loses +// R[0] = 1 ; R[1] = 0 ; R[2] = 0 +// Example: Team 2 wins, team 1 loses +// R[0] = 1 ; R[1] = 0 +// t = total number of teams +// Optional param min_delta = Loop will stop once delta is lower than this param. default: 0.0001 +// OUTPUT: Recalculated player ratings in the same order as inputed +*/ +void SV_TSRate() +{ + if( Plugin_Scr_GetNumParam() < 2 ) + { + Plugin_Scr_Error( "Usage: TS_Rate( (INT) ), (STRING) \n" ); + return; + } + + if( players.size() < 2 ) + { + Plugin_Scr_Error( "Usage: Add atleast 2 players with TS_AddPlayer function.\n" ); + return; + } + + int teams = Plugin_Scr_GetInt( 0 ); + + if( !validateTeams( players, teams ) ) + { + return; + } + + char *placements = Plugin_Scr_GetString( 1 ); + char *p = _strdup( placements ); + char *tok; + std::vector< int > ranks; + + tok = strtok( p, " " ); + while( tok != NULL ) + { + ranks.push_back( atoi( tok ) ); + tok = strtok( NULL, " " ); + } + + free( p ); + + rate( players, ranks, teams ); + + Plugin_Scr_MakeArray(); + for( unsigned int i = 0; i < players.size(); i++ ) + { + Plugin_Scr_MakeArray(); + Plugin_Scr_AddFloat( ( float )players[ i ][ 0 ] ); + Plugin_Scr_AddArray(); + Plugin_Scr_AddFloat( ( float )players[ i ][ 1 ] ); + Plugin_Scr_AddArray(); + Plugin_Scr_AddArray(); + } + + players.clear(); +} + +void SV_TSQuality() +{ + if( Plugin_Scr_GetNumParam() < 1 ) + { + Plugin_Scr_Error( "Usage: TS_Quality( (INT) ) \n" ); + return; + } + + if( players.size() < 2 ) + { + Plugin_Scr_Error( "Add atleast 2 players with TS_AddPlayer function \n" ); + return; + } + + int teams = Plugin_Scr_GetInt( 0 ); + + if( !validateTeams( players, teams ) ) + { + return; + } + + double result = quality( players, teams ); + + players.clear(); + Plugin_Scr_AddFloat( ( float )result ); +} + +bool validateTeams( std::vector< std::vector< double > > &T, int t ) +{ + if( t < 2 ) + { + Plugin_Scr_Error( "There must be atleast 2 teams. in FFA each player counts as a team \n" ); + return false; + } + + for( unsigned int i = 0; i < T.size(); i++ ) + { + if( ( int )T[ i ][ 2 ] > t ) + { + Plugin_Scr_Error( "Amount of teams missmatch. Check if number of teams is correct \n" ); + return false; + } + } + + return true; +} + +void SV_TSAddPlayer() +{ + if( Plugin_Scr_GetNumParam() < 4 ) + { + Plugin_Scr_Error( "Usage: TS_AddPlayer( (INT), (FLOAT), (FLOAT), (INT), (FLOAT)[] )\n" ); + return; + } + + float mu, sigma, weight; + + int identifier = Plugin_Scr_GetInt( 0 ); + mu = Plugin_Scr_GetFloat( 1 ); + sigma = Plugin_Scr_GetFloat( 2 ); + int team = Plugin_Scr_GetInt( 3 ); + + if( Plugin_Scr_GetNumParam() == 5 ) + weight = Plugin_Scr_GetFloat( 4 ); + else + weight = 1; + + addPlayer( ( double )mu, ( double )sigma, ( double )team, ( double )weight, ( double )identifier ); +} + +void SV_TSClearAllPlayers() +{ + players.clear(); +} + +void addPlayer( double mu, double sigma, double team, double weight, double identifier ) +{ + unsigned int n = players.size(); + + players.resize( n + 1 ); + players[ n ].resize( 5 ); + players[ n ][ 0 ] = mu; + players[ n ][ 1 ] = sigma; + players[ n ][ 2 ] = team; + players[ n ][ 3 ] = weight; + players[ n ][ 4 ] = identifier; +} + +void SV_TS_UpdateMu() +{ + if( Plugin_Scr_GetNumParam() < 1 ) + { + Plugin_Scr_Error( "Usage: TS_UpdateMu( (FLOAT) )\n" ); + return; + } + + float mu = Plugin_Scr_GetFloat( 0 ); + + update_mu( ( double )mu ); +} + +void SV_TS_UpdateDraw() +{ + if( Plugin_Scr_GetNumParam() < 1 ) + { + Plugin_Scr_Error( "Usage: TS_UpdateDraw( (FLOAT) ) \n" ); + return; + } + + float draw = Plugin_Scr_GetFloat( 0 ); + + if( draw < 0 || draw > 1 ) + { + Plugin_Scr_Error( " must be between 0 and 1 \n" ); + return; + } + + update_draw( ( double )draw ); +} + +PCL int OnInit() +{ + Plugin_ScrAddFunction( (char*)"TS_Quality", &SV_TSQuality ); + Plugin_ScrAddFunction( (char*)"TS_Rate", &SV_TSRate ); + Plugin_ScrAddFunction( (char*)"TS_AddPlayer", &SV_TSAddPlayer ); + Plugin_ScrAddFunction( (char*)"TS_ClearAllPlayers", &SV_TSClearAllPlayers ); + Plugin_ScrAddFunction( (char*)"TS_UpdateMu", &SV_TS_UpdateMu ); + Plugin_ScrAddFunction( (char*)"TS_UpdateDraw", &SV_TS_UpdateDraw ); + + return 0; +} + +PCL void OnInfoRequest(pluginInfo_t *info) +{ + info->handlerVersion.major = PLUGIN_HANDLER_VERSION_MAJOR; + info->handlerVersion.minor = PLUGIN_HANDLER_VERSION_MINOR; + + info->pluginVersion.major = 0; + info->pluginVersion.minor = 1; + strncpy(info->fullName, "Trueskill plugin by Leiizko", sizeof(info->fullName)); + strncpy(info->shortDescription, "Game rating system.", sizeof(info->shortDescription)); + strncpy(info->longDescription, "This plugin is used to rank players according to their individual skill.", sizeof(info->longDescription)); +} \ No newline at end of file diff --git a/src/makedll.sh b/src/makedll.sh new file mode 100644 index 0000000..b6e4d97 --- /dev/null +++ b/src/makedll.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +echo Compiling: release +g++ -std=gnu++0x -m32 -Wall -O1 -s -g -fvisibility=hidden -mtune=core2 -c *.cpp + +echo Linking +g++ -std=gnu++0x -m32 -s -g -shared -o trueskillplugin.so *.o +echo Cleaning up +rm *.o \ No newline at end of file diff --git a/src/makedll_win32.cmd b/src/makedll_win32.cmd new file mode 100644 index 0000000..cfca909 --- /dev/null +++ b/src/makedll_win32.cmd @@ -0,0 +1,11 @@ +@echo off + +echo Compiling: release +g++ -std=gnu++0x -m32 -Wall -O1 -s -g -mtune=core2 -c *.cpp + +echo Linking +g++ -std=gnu++0x -m32 -s -g -shared -static-libgcc -static-libstdc++ -o trueskillplugin.dll *.o -L../ -lcom_plugin +echo Cleaning up +del *.o + +pause \ No newline at end of file diff --git a/src/matrix.cpp b/src/matrix.cpp new file mode 100644 index 0000000..f5fac5e --- /dev/null +++ b/src/matrix.cpp @@ -0,0 +1,177 @@ +#include +#include +#include "matrix.h" + +// INPUT: Vector matrix ( MUST BE SQUARE MATRIX ) +// OUTPUT: Determinant as a double value +double determinant( std::vector< std::vector< double > > &A, int n ) +{ + if ( n == 1 ) + return A[ 0 ][ 0 ]; + + int i, sign = 1; + double M = 0; + std::vector< std::vector< double > > temp = initMatrix( n, n ); + + for ( i = 0; i < n; i++ ) + { + getCofactor( A, temp, 0, i, n ); + M += sign * A[ 0 ][ i ] * determinant( temp, n - 1 ); + + sign = -sign; + } + + return M; +} + +void getCofactor( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &temp, int p, int q, int n ) +{ + int i = 0, j = 0; + for ( int row = 0; row < n; row++ ) + { + for ( int col = 0; col < n; col++ ) + { + if ( row != p && col != q ) + { + temp[ i ][ j++ ] = A[ row ][ col ]; + + if ( j == n - 1 ) + { + j = 0; + i++; + } + } + } + } +} + +// Returns 1 if adjoint of matrix is actually 1 +int adjugate( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &M ) +{ + int n = A.size(); + if ( n == 1 ) + { + return 1; + } + + int i, j, sign = 1; + std::vector< std::vector< double > > temp = initMatrix( n, n ); + + for ( i = 0; i < n; i++ ) + { + for ( j = 0; j < n; j++ ) + { + getCofactor( A, temp, i, j, n ); + + sign = ( ( i + j ) % 2 == 0 ) ? 1 : -1; + + M[ j ][ i ] = ( sign ) * ( determinant( temp, n - 1 ) ); + } + } + + return 0; +} + +// Returns 1 on faliure +int inverse( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &M ) +{ + int n = A.size(); + double det = determinant( A, n ); + if ( det == 0 ) + { + return 1; + } + + if( n == 1 ) + { + M[ 0 ][ 0 ] = pow( A[ 0 ][ 0 ], -1 ); + return 0; + } + + int i, j; + std::vector< std::vector< double > > adj = initMatrix( n, n ); + + adjugate( A, adj ); + + for ( i = 0; i < n; i++ ) + for ( j = 0; j < n; j++ ) + M[ i ][ j ] = adj[ i ][ j ] / det; + + return 0; +} + +// INPUT: Vector matrix to transpose, number of rows and colums +// OUTPUT: Transposed vector matrix +std::vector< std::vector< double > > transpose( std::vector< std::vector< double > > &A ) +{ + unsigned int i, j; + + std::vector< std::vector< double > > M = initMatrix( A[ 0 ].size(), A.size() ); + + for ( i = 0; i < A[ 0 ].size(); i++ ) + { + for ( j = 0; j < A.size(); j++ ) + { + M[ i ][ j ] = A[ j ][ i ]; + } + } + + return M; +} + +// INPUT: Number of rows and columns +// OUTPUT: Vector matrix with initiated 0 value +std::vector< std::vector< double > > initMatrix( int r, int c ) +{ + std::vector< std::vector< double > > A( r, std::vector< double >( c, 0 ) ); + + return A; +} + +std::vector< std::vector< double > > matrixMultiply( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &B ) +{ + std::vector< std::vector< double > > M = initMatrix( A.size(), B[ 0 ].size() ); + + unsigned int i, j, k; + for( i = 0; i < A.size(); i++ ) + { + for( j = 0; j < B[ 0 ].size(); j++ ) + { + for( k = 0; k < A[ 0 ].size(); k++ ) + { + M[ i ][ j ] += A[ i ][ k ] * B[ k ][ j ]; + } + } + } + return M; +} + +std::vector< std::vector< double > > matrixMultiplyScalar( std::vector< std::vector< double > > &A, double S ) +{ + std::vector< std::vector< double > > M = initMatrix( A.size(), A[ 0 ].size() ); + + unsigned int i, j; + for( i = 0; i < A.size(); i++ ) + { + for( j = 0; j < A[ 0 ].size(); j++ ) + { + M[ i ][ j ] += A[ i ][ j ] * S; + } + } + return M; +} + +std::vector< std::vector< double > > matrixAddition( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &B ) +{ + std::vector< std::vector< double > > M = initMatrix( A.size(), A[ 0 ].size() ); + + unsigned int i, j; + for( i = 0; i < A.size(); i++ ) + { + for( j = 0; j < A[ 0 ].size(); j++ ) + { + M[ i ][ j ] += A[ i ][ j ] + B[ i ][ j ]; + } + } + return M; +} \ No newline at end of file diff --git a/src/matrix.h b/src/matrix.h new file mode 100644 index 0000000..c5a2d26 --- /dev/null +++ b/src/matrix.h @@ -0,0 +1,11 @@ +#pragma once + +double determinant( std::vector< std::vector< double > > &A, int n ); +void getCofactor( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &temp, int p, int q, int n ); +int adjugate( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &M ); +int inverse( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &M ); +std::vector< std::vector< double > > transpose( std::vector< std::vector< double > > &A ); +std::vector< std::vector< double > > initMatrix( int r, int c ); +std::vector< std::vector< double > > matrixAddition( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &B ); +std::vector< std::vector< double > > matrixMultiplyScalar( std::vector< std::vector< double > > &A, double S ); +std::vector< std::vector< double > > matrixMultiply( std::vector< std::vector< double > > &A, std::vector< std::vector< double > > &B ); \ No newline at end of file diff --git a/src/trueskill.cpp b/src/trueskill.cpp new file mode 100644 index 0000000..00414b9 --- /dev/null +++ b/src/trueskill.cpp @@ -0,0 +1,461 @@ +#include "trueskill.h" + +// TODO: +// Dynamic draw probability + +// updates the TAU and BETA params +// update_mu( double new_mu ) +static double MU = 25; +static double TAU = MU / 3 / 100; +static double BETA = MU / 3 / 2; +// updates DRAW_PROBABILITY param +// update_draw( double new_draw ) +static double DRAW_PROBABILITY = 0.1; + +// INPUT: T = Vector matrix of teams and trueskill properties +// T[ i ][ 0 ] = mu +// T[ i ][ 1 ] = sigma +// T[ i ][ 2 ] = team ( team 1 = 1 | team 2 = 2 | team 3 = 3 |..... ) +// T[ i ][ 3 ] = Weight +// T[ i ][ 4 ] = Identifier +// t = total number of teams +// OUTPUT: Quality of the game +double quality( std::vector< std::vector< double > > &T, int t ) +{ + validateWeights( T ); + sortTeams( T, t ); + + t--; + if( !t ) + return 0; + + int i, j, n = T.size(); + + std::vector< std::vector< double > > mean_matrix = initMatrix( n, 1 ); + std::vector< std::vector< double > > variance_matrix = initMatrix( n, n ); + std::vector< std::vector< double > > rotated_a_matrix = initMatrix( t, n ); + std::vector< std::vector< double > > a_matrix = initMatrix( t, n ); + std::vector< std::vector< double > > _ata = initMatrix( t, t ); + std::vector< std::vector< double > > _atsa = initMatrix( t, t ); + std::vector< std::vector< double > > start = initMatrix( 1, t ); + std::vector< std::vector< double > > middle = initMatrix( t, t ); + std::vector< std::vector< double > > middleinv = initMatrix( t, t ); + std::vector< std::vector< double > > end = initMatrix( t, 1 ); + std::vector< std::vector< double > > ra_matrix = initMatrix( t, n ); + std::vector< std::vector< double > > rv_matrix = initMatrix( t, n ); + std::vector< std::vector< double > > mean_matrix_transposed = initMatrix( n, 1 ); + std::vector< std::vector< double > > start_middleinv = initMatrix( t, t ); + std::vector< std::vector< double > > start_middleinv_end = initMatrix( t, 1 ); + std::vector< std::vector< double > > start_middleinv_end_scalar = initMatrix( t, 1 ); + + // Mu + for ( i = 0; i < n; i++ ) + { + mean_matrix[ i ][ 0 ] = T[ i ][ 0 ]; + } + + // Sigma ^ 2 + for ( i = 0; i < n; i++ ) + { + variance_matrix[ i ][ i ] = pow( T[ i ][ 1 ], 2 ); + } + + // 1 2 2 3 4 + // | 0.00 0.00 0.00 1.00 -1.00 | + // | 0.00 1.00 1.00 -1.00 0.00 | + // | 1.00 -1.00 -1.00 0.00 0.00 | + // Teams + for( i = 0; i < t; i++ ) + { + for( j = 0; j < n; j++ ) + { + if ( T[ j ][ 2 ] < t - i ) + { + continue; + } + else if ( T[ j ][ 2 ] > t - i + 1 ) + { + break; + } + + if ( T[ j ][ 2 ] == t - i ) + { + rotated_a_matrix[ i ][ j ] = 1 * T[ j ][ 3 ]; + } + else + { + rotated_a_matrix[ i ][ j ] = -1 * T[ j ][ 3 ]; + } + } + } + + a_matrix = transpose( rotated_a_matrix ); + ra_matrix = matrixMultiply( rotated_a_matrix, a_matrix ); + rv_matrix = matrixMultiply( rotated_a_matrix, variance_matrix ); + mean_matrix_transposed = transpose( mean_matrix ); + _ata = matrixMultiplyScalar( ra_matrix, pow( BETA, 2 ) ); + _atsa = matrixMultiply( rv_matrix, a_matrix ); + start = matrixMultiply( mean_matrix_transposed, a_matrix ); + middle = matrixAddition( _ata, _atsa ); + end = matrixMultiply( rotated_a_matrix, mean_matrix ); + int check = inverse( middle, middleinv ); + + // Matrix inversion failed, cannot compute quality + if( check ) + return 0; + + start_middleinv = matrixMultiply( start, middleinv ); + start_middleinv_end = matrixMultiply( start_middleinv, end ); + start_middleinv_end_scalar = matrixMultiplyScalar( start_middleinv_end, -0.5 ); + + double e_arg = determinant( start_middleinv_end_scalar, start_middleinv_end_scalar.size() ); + double s_arg = determinant( _ata, _ata.size() ) / determinant( middle, middle.size() ); + + return exp( e_arg ) * sqrt( s_arg ); +} + +// INPUT: T = Vector matrix of teams and trueskill properties +// T[ i ][ 0 ] = mu +// T[ i ][ 1 ] = sigma +// T[ i ][ 2 ] = team ( team 1 = 1 | team 2 = 2 | team 3 = 3 |..... ) +// T[ i ][ 3 ] = Weight +// T[ i ][ 4 ] = Identifier +// R = Vector array of team ranks = index = team number - 1 +// Lower value is better (win), same value is draw +// Example: Team 2 and team 3 draw, team 1 loses +// R[0] = 1 ; R[1] = 0 ; R[2] = 0 +// Example: Team 2 wins, team 1 loses +// R[0] = 1 ; R[1] = 0 +// t = total number of teams +// Optional param min_delta = Loop will stop once delta is lower than this param. default: 0.0001 +// OUTPUT: Recalculated player ratings in the same order as inputed +void rate( std::vector< std::vector< double > > &T, std::vector< int > &R, int t, double min_delta ) +{ + validateWeights( T ); + std::vector< std::vector< double > > org; + org = T; + sortTeamsByRank( T, R ); + unsigned int size = T.size(), index = 0; + double ident = T[ 0 ][ 2 ]; + std::vector< std::vector< Gaussian > > ratings( t ); + + for ( unsigned int i = 0; i < size; i++ ) + { + if ( T[ i ][ 2 ] != ident ) + { + ident = T[ i ][ 2 ]; + index++; + } + + ratings[ index ].push_back( Gaussian( T[ i ][ 0 ], T[ i ][ 1 ], 0, 0 ) ); + } + + size = T.size(); + std::vector< Variable > rating_vars( size, Variable() ); + std::vector< Variable > perf_vars( size, Variable() ); + std::vector< Variable > team_perf_vars( t, Variable() ); + std::vector< Variable > team_diff_vars( t - 1, Variable() ); + std::vector< int > team_sizes = getTeamSize( T, t ); + + std::vector< PriorFactor > build_rating_layer; + std::vector< LikelihoodFactor > build_perf_layer; + for ( unsigned int i = 0; i < size; i++ ) + { + Gaussian rating = Gaussian( T[ i ][ 0 ], T[ i ][ 1 ], 0, 0 ); + build_rating_layer.push_back( PriorFactor( &rating_vars[ i ], &rating, TAU ) ); + build_perf_layer.push_back( LikelihoodFactor( &rating_vars[ i ], &perf_vars[ i ], std::pow( BETA, 2 ) ) ); + } + + std::vector< SumFactor > build_team_perf_layer; + std::vector< Variable* > child_perf_vars; + std::vector< double > coeffs; + int start, end; + for ( int i = 0; i < t; i++ ) + { + i > 0 ? start = team_sizes[ i - 1 ] : start = 0; + end = team_sizes[ i ]; + for ( int n = start; n < end; n++ ) + { + child_perf_vars.push_back( &perf_vars[ n ] ); + coeffs.push_back( T[ n ][ 3 ] ); + } + + build_team_perf_layer.push_back( SumFactor( &team_perf_vars[ i ], child_perf_vars, coeffs ) ); + child_perf_vars.clear(); + coeffs.clear(); + } + + for ( unsigned int i = 0; i < build_rating_layer.size(); i++ ) + { + build_rating_layer[ i ].down(); + } + + for ( unsigned int i = 0; i < build_perf_layer.size(); i++ ) + { + build_perf_layer[ i ].down(); + } + + for ( unsigned int i = 0; i < build_team_perf_layer.size(); i++ ) + { + build_team_perf_layer[ i ].down(); + } + + std::vector< SumFactor > build_team_diff_layer; + std::function< double( double, double ) > v_func, w_func; + int calcsize; + double draw_margin; + coeffs.push_back( 1 ); + coeffs.push_back( -1 ); + for ( int i = 0; i < t - 1; i++ ) + { + for ( int n = i; n < i + 2; n++ ) + { + child_perf_vars.push_back( &team_perf_vars[ n ] ); + } + build_team_diff_layer.push_back( SumFactor( &team_diff_vars[ i ], child_perf_vars, coeffs ) ); + child_perf_vars.clear(); + } + coeffs.clear(); + + std::vector< TruncateFactor > build_trunc_layer; + for ( int i = 0; i < t - 1; i++ ) + { + calcsize = 0; + for ( int n = i; n < i + 2; n++ ) + { + calcsize += ratings[ n ].size(); + } + draw_margin = calc_draw_margin( DRAW_PROBABILITY, calcsize ); + if ( R[ i ] == R[ i + 1 ] ) + { + v_func = v_draw; + w_func = w_draw; + } + else + { + v_func = v_win; + w_func = w_win; + } + + build_trunc_layer.push_back( TruncateFactor( &team_diff_vars[ i ], v_func, w_func, draw_margin ) ); + } + + unsigned int team_diff_len = build_team_diff_layer.size(); + double delta; + for ( int i = 0; i < 10; i++ ) + { + if ( team_diff_len == 1 ) + { + build_team_diff_layer[ 0 ].down(); + delta = build_trunc_layer[ 0 ].up(); + } + else + { + delta = 0; + for ( unsigned int n = 0; n < team_diff_len - 1; n++ ) + { + build_team_diff_layer[ n ].down(); + delta = std::max( delta, build_trunc_layer[ n ].up() ); + build_team_diff_layer[ n ].up( 1 ); + } + for ( unsigned int n = team_diff_len - 1; n > 0; n-- ) + { + build_team_diff_layer[ n ].down(); + delta = std::max( delta, build_trunc_layer[ n ].up() ); + build_team_diff_layer[ n ].up( 0 ); + } + } + if ( delta <= min_delta ) + { + break; + } + } + + build_team_diff_layer[ 0 ].up( 0 ); + build_team_diff_layer[ team_diff_len - 1 ].up( 1 ); + for ( unsigned int i = 0; i < build_team_perf_layer.size(); i++ ) + { + for ( unsigned int n = 0; n < build_team_perf_layer[ i ].getVar().size() - 1; n++ ) + { + build_team_perf_layer[ i ].up( n ); + } + } + + for ( unsigned int i = 0; i < build_perf_layer.size(); i++ ) + { + build_perf_layer[ i ].up(); + } + + for ( unsigned int i = 0; i < T.size(); i++ ) + { + T[ i ][ 0 ] = rating_vars[ i ].getVal()._mu(); + T[ i ][ 1 ] = rating_vars[ i ].getVal()._sigma(); + } + + for ( unsigned int i = 0; i < T.size(); i++ ) + { + for ( unsigned int n = 0; n < T.size(); n++ ) + { + if ( org[ i ][ 4 ] == T[ n ][ 4 ] ) + { + org[ i ][ 0 ] = T[ n ][ 0 ]; + org[ i ][ 1 ] = T[ n ][ 1 ]; + break; + } + } + } + T = org; +} + +void sortTeamsByRank( std::vector< std::vector< double > > &T, std::vector< int > &R ) +{ + unsigned int i, n, idx; + std::vector< std::vector< double > > sorted; + std::vector< int > ranks_temp = R; + std::vector< int > ranks_sorted( R.size(), 999 ); + + + for ( i = 0; i < R.size(); i++ ) + { + idx = -1; + for ( n = 0; n < ranks_temp.size(); n++ ) + { + if ( ranks_temp[ n ] < ranks_sorted[ i ] ) + { + ranks_sorted[ i ] = ranks_temp[ n ]; + idx = n; + } + } + ranks_sorted[ i ] = idx + 1; + ranks_temp[ idx ] = 999; + } + + for ( i = 0; i < R.size(); i++ ) + { + for ( n = 0; n < T.size(); n++ ) + { + if ( ( int )T[ n ][ 2 ] == ranks_sorted[ i ] ) + { + sorted.push_back( T[ n ] ); + } + } + } + + T = sorted; +} + +void validateWeights( std::vector< std::vector< double > > &T ) +{ + for ( unsigned int i = 0; i < T.size(); i++ ) + { + if ( T[ i ][ 3 ] <= 0 ) + { + T[ i ][ 3 ] = 0.001; + } + } +} + +std::vector< int > getTeamSize( std::vector< std::vector< double > > &T, int t ) +{ + int count = 0; + unsigned int n, i = 0, size = T.size(); + double ident = T[ 0 ][ 2 ]; + std::vector< int > team_size( t ); + for ( n = 0; n < size; n++ ) + { + if ( T[ n ][ 2 ] != ident ) + { + ident = T[ n ][ 2 ]; + team_size[ i ] = count; + i++; + } + count++; + } + + team_size[ i ] = count; + + return team_size; +} + +void sortTeams( std::vector< std::vector< double > > &T, int t ) +{ + std::vector< std::vector< double > > A = initMatrix( T.size(), 5 ); + unsigned int n, j = 0; + + for ( int i = 1; i <= t; i++ ) + { + for ( n = 0; n < T.size(); n++ ) + { + if ( ( int )T[ n ][ 2 ] == i ) + { + A[ j ] = T[ n ]; + j++; + } + } + } + + T = A; +} + +double v_win( double diff, double draw_margin ) +{ + double x = diff - draw_margin; + double denom = cdf( x ); + + return denom ? ( pdf( x ) / denom ) : -x; +} + +double v_draw( double diff, double draw_margin ) +{ + double abs_diff = std::abs( diff ); + double a = draw_margin - abs_diff; + double b = -draw_margin - abs_diff; + double denom = cdf( a ) - cdf( b ); + double numer = pdf( b ) - pdf( a ); + return (denom ? ( numer / denom ) : a) * (diff < 0 ? -1 : 1); +} + +double w_win( double diff, double draw_margin ) +{ + double x = diff - draw_margin; + double v = v_win( diff, draw_margin ); + double w = v * ( v + x ); + + if ( 0 < w && w < 1 ) + { + return w; + } + return -1; +} + +double w_draw( double diff, double draw_margin ) +{ + double abs_diff = std::abs( diff ); + double a = draw_margin - abs_diff; + double b = -draw_margin - abs_diff; + double denom = cdf( a ) - cdf( b ); + if ( !denom ) + { + return -1; + } + + double v = v_draw( abs_diff, draw_margin ); + return std::pow( v, 2 ) + ( a * pdf( a ) - b * pdf( b ) ) / denom;; +} + +double calc_draw_margin( double draw_probability, int size ) +{ + return ppf( ( draw_probability + 1 ) / 2 ) * std::sqrt( size ) * BETA; +} + +void update_mu( double new_mu ) +{ + MU = new_mu; + TAU = MU / 3 / 100; + BETA = MU / 3 / 2; +} + +void update_draw( double new_draw ) +{ + DRAW_PROBABILITY = new_draw; +} \ No newline at end of file diff --git a/src/trueskill.h b/src/trueskill.h new file mode 100644 index 0000000..22109b0 --- /dev/null +++ b/src/trueskill.h @@ -0,0 +1,28 @@ +#pragma once + +#include +#include +#include +#include "matrix.h" +#include "gaussian.h" +#include "factorgraph.h" + +// Trueskill +double quality( std::vector< std::vector< double > > &T, int t ); +void sortTeams( std::vector< std::vector< double > > &T, int t ); +void rate( std::vector< std::vector< double > > &T, std::vector< int > &R, int t, double min_delta = 0.0001 ); +void sortTeamsByRank( std::vector< std::vector< double > > &T, std::vector< int > &R ); +std::vector< int > getTeamSize( std::vector< std::vector< double > > &T, int t ); +double v_win( double diff, double draw_margin ); +double v_draw( double diff, double draw_margin ); +double w_win( double diff, double draw_margin ); +double w_draw( double diff, double draw_margin ); +double calc_draw_margin( double draw_probability, int size ); +void validateWeights( std::vector< std::vector< double > > &T ); +void update_mu( double new_mu ); +void update_draw( double new_draw ); + +// BaseMath +double pdf( double x, double mu = 0, double sigma = 1 ); +double cdf( double x, double mu = 0, double sigma = 1 ); +double ppf( double x, double mu = 0, double sigma = 1 ); \ No newline at end of file