https://github.com/cran/GPGame
Revision becef560c88451a1d5de0ef4209f74e7d9114b50 authored by Victor Picheny on 10 June 2017, 05:17:19 UTC, committed by cran-robot on 10 June 2017, 05:17:19 UTC
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Tip revision: becef560c88451a1d5de0ef4209f74e7d9114b50 authored by Victor Picheny on 10 June 2017, 05:17:19 UTC
version 1.0.0
version 1.0.0
Tip revision: becef56
PSNE_sparseMat_matrix.cpp
#include <Rcpp.h>
using namespace Rcpp;
// Computes the pure strategy Nash Equilibria in a N-player finite game.
// ---------------------------------------------------------------------
// input:
//
// NS(i) : total number of pure strategies of player (i)
//
// Poff : sparse matrix representation of Payoffs
// Poff(i,_) (i^th row): columns 1:N are payoffs for the N objectives for strategies in columns N+1:2N
//
// output : isNash, vector giving for each line of Poffs if it is a Nash equilibrium (0) or not (1)
//
// ---------------------------------------------------------------------
// A. Habbal Inria 2016-05
// V. Picheny INRA 2016-06
// M. Binois Chicago Booth 2016-07
// @details
// WARNING: Poffs are supposed to correspond to [Z11, ..., Z1p, Z21, ..., Z2p, ..., Znp] (simulations par objectives)
// [[Rcpp::export]]
LogicalMatrix PSNE_sparseMat(NumericVector NS, NumericMatrix Poffs, IntegerMatrix expindices){
int nplay = NS.length();
int nalt = Poffs.nrow();
int nsim = Poffs.ncol()/nplay;
bool isStrat_i;
double bestPoffi_j; // Store best payoff for player i and strategies -k
LogicalMatrix isNash(nalt, nsim); // 0 if possible Nash equilibrium
//IntegerVector strat_i(nplay); // store the strategy -i considered
std::vector<int> ind_old; // Store addresses of isNash of current best(s) payoffs
// Initialization
for(int i = 0; i < nalt; i++)
for(int j = 0; j < nsim; j++)
isNash(i, j) = true;
for(int s = 0; s < nsim; s++){
for(int i = 0; i < nplay; i++){
for(int j = 0; j < (nalt - 1); j++){
// Continue if not previously screened out
if(isNash(j, s)){
bestPoffi_j = Poffs(j, i * nsim + s); // Store best payoff for player i and strategies -k
ind_old.push_back(j);
// for(int p = 0; p < nplay; p++){
// strat_i(p) = Poffs(j, nplay + p);
// }
for(int k = 0; k < nalt; k++){
if(k == j)
continue;
isStrat_i = true;
for(int p = 0; p < nplay; p++){
// if(s != i && Poffs(k, nplay + s) != strat_i(s)){
if(p != i && expindices(k, p) != expindices(j, p)){
isStrat_i = false;
break;
}
}
if(isStrat_i){
if(Poffs(k, i * nsim + s) < bestPoffi_j){
for(int p = 0; p < ind_old.size(); p++){
isNash(ind_old[p], s) = false;
}
ind_old.clear();
ind_old.push_back(k);
bestPoffi_j = Poffs(k, i * nsim + s);
}else{
if(Poffs(k, i * nsim + s) == bestPoffi_j){
ind_old.push_back(k);
}else{
isNash(k, s) = false;
}
}
}
}
ind_old.clear();
}
}
}
}
return isNash;
}
// Computes the pure strategy Nash Equilibria in a N-player finite game.
// ---------------------------------------------------------------------
// input:
//
// NS(i) : total number of pure strategies of player (i)
//
// Poff : sparse matrix representation of Payoffs
// Poff(i,_) (i^th row): columns 1:N are payoffs for the N objectives for strategies in columns N+1:2N
//
// output : isNash, vector giving for each line of Poffs if it is a Nash equilibrium (0) or not (1)
//
// ---------------------------------------------------------------------
// A. Habbal Inria 2016-05
// V. Picheny INRA 2016-06
// M. Binois Chicago Booth 2016-07
// @details WARNING It is assumed that the strategies of the last player are sorted in increasing order
// [[Rcpp::export]]
LogicalMatrix PSNE_sparseMat_sorted(NumericVector NS, NumericMatrix Poffs, IntegerMatrix expindices){
int nplay = NS.length();
int nalt = Poffs.nrow();
int nsim = Poffs.ncol()/nplay;
bool isStrat_i;
double bestPoffi_j; // Store best payoff for player i and strategies -k
LogicalMatrix isNash(nalt, nsim); // true if possible Nash equilibrium
// IntegerVector strat_i(nplay); // store the strategy -i considered
std::vector<int> ind_old; // Store indices of isNash of current best(s) payoffs
// Initialization
for(int i = 0; i < nalt; i++)
for(int j = 0; j < nsim; j++)
isNash(i, j) = true;
IntegerVector start_index(NS(nplay - 1));
int tmp = 0;
// Compute index of first element for strategy of the last player
start_index(tmp) = expindices(tmp, nplay - 1);
for(int i = 1; i < nalt; i++){
if(expindices(i, nplay - 1) > expindices(i - 1, nplay - 1)){
tmp++;
start_index(tmp) = i;
}
}
for(int s = 0; s < nsim; s++){
for(int i = 0; i < nplay; i++){
for(int j = 0; j < (nalt - 1); j++){
// Continue if not previously screened out
if(isNash(j, s)){
bestPoffi_j = Poffs(j, i * nsim + s); // Store best payoff for player i and strategies -k
ind_old.push_back(j);
// for(int p = 0; p < nplay; p++){
// strat_i(p) = expindices(j, p);
// }
if(i < (nplay - 1)){
tmp = start_index(expindices(j, nplay - 1));
}else{
tmp = 0;
}
for(int k = tmp; k < nalt; k++){
if(k == j)
continue;
// Since the strategies of the last player are sorted, no need to check them all
if(expindices(j, nplay - 1) < expindices(k, nplay - 1) && i < nplay - 1)
break;
isStrat_i = true;
for(int p = 0; p < nplay; p++){
if(p != i && expindices(k, p) != expindices(j, p)){
isStrat_i = false;
break;
}
}
if(isStrat_i){
if(Poffs(k, i * nsim + s) < bestPoffi_j){
for(int p = 0; p < ind_old.size(); p++){
isNash(ind_old[p], s) = false;
}
ind_old.clear();
ind_old.push_back(k);
bestPoffi_j = Poffs(k, i * nsim + s);
}else{
if(Poffs(k, i * nsim + s) == bestPoffi_j){
ind_old.push_back(k);
}else{
isNash(k, s) = false;
}
}
}
}
ind_old.clear();
}
}
}
}
return isNash;
}
// Get Nash Poffs in the crossed case
// [[Rcpp::export]]
NumericMatrix getPoffs(LogicalMatrix isNash, NumericMatrix Poffs, int nsim, int nobj){
// Count number of Nash equilibriums
int tmp = 0;
for(int i = 0; i < isNash.nrow(); i++){
for(int j = 0; j < isNash.ncol(); j++){
if(isNash(i,j))
tmp++;
}
}
NumericMatrix NashPoffs(std::max(tmp, 1), nobj);
if(tmp > 0){
tmp = 0;
for(int j = 0; j < isNash.ncol(); j++){
for(int i = 0; i < isNash.nrow(); i++){
if(isNash(i, j)){
for(int k = 0; k < nobj; k++){
NashPoffs(tmp, k) = Poffs(i, k * nsim + j);
}
tmp++;
}
}
}
}else{
for(int i = 0; i < isNash.ncol(); i++){
NashPoffs(0, i) = NA_REAL;
}
}
return(NashPoffs);
}
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