void KICList::createItems()
{
QList<KBase*> ICList = ::g_ICMap.values();
KBase* pIC;
for(int i = 0; i < ICList.count(); ++i)
{
pIC = ICList[i];
QPixmap pix = createPixmap(pIC);
QListWidgetItem* item = new QListWidgetItem(
QIcon(pix), pIC->name());
item->setToolTip(pIC->description());
addItem(item);
}
}
// build the square matrix of position utilities.
// Row is actor i, Column is position of actor j,
// U(i,j) is utility to actor i of position of actor j.
// If there are duplicate positions, there will be duplicate columns.
void RP2State::setAllAUtil(ReportingLevel) {
const unsigned int na = eMod->numAct;
assert(Model::minNumActor <= na);
assert(na <= Model::maxNumActor);
aUtil = {};
aUtil.resize(na);
auto uMat = KMatrix(na, na); // they will all be the same in this demo
for (unsigned int j = 0; j < na; j++) {
unsigned int nj = posNdx(j);
auto utilJ = actorUtilVectFn(-1, nj); // all have objective perspective
for (unsigned int i = 0; i < na; i++) {
uMat(i, j) = utilJ[i];
}
}
for (unsigned int i = 0; i < na; i++) {
aUtil[i] = uMat;
}
return;
}
void initScen(uint64_t sd) {
using KBase::quadUfromV;
auto rng = new PRNG(sd);
const unsigned int numA = 40;
const unsigned int numItm = 7;
const auto govCost = KMatrix::uniform(rng, 1, numItm, 25, 100); //col-vec
const double govBudget = 0.625 * sum(govCost); // cannot afford everything
const double obFactor = 0.125;
// The 'riVals' matrix shows the value to the actor (row) of each reform item (clm),
// not the utility of entire positions (which are permutations of items)
const KMatrix riVal = KMatrix::uniform(rng, numA, numItm, 10, 100);
const double pDecline = 0.8750;
vector<double> aCap = {};
for (unsigned int i = 0; i < numA; i++)
{
double ac = rng->uniform(2.0, 4.0); // [4.0, 16.0] with median at 9.0
ac = rng->uniform(17.0, 18.0)*ac*ac;
aCap.push_back(ac);
}
vector<double> prob = {};
double pj = 1.0;
printf("pDecline factor: %.3f \n", pDecline);
printf("obFactor: %.3f \n", obFactor);
// rate of decline has little effect on the results.
for (unsigned int j = 0; j < numItm; j++) {
prob.push_back(pj);
pj = pj * pDecline;
}
cout << "Computing positions ... " << endl;
vector<VUI> positions; // list of all positions
VUI pstn;
// build the first permutation: 1,2,3,...
for (unsigned int i = 0; i < numItm; i++) {
pstn.push_back(i);
}
positions.push_back(pstn);
while (next_permutation(pstn.begin(), pstn.end())) {
positions.push_back(pstn);
}
const unsigned int numPos = positions.size();
cout << "For " << numItm << " reform items there are ";
cout << numPos << " positions" << endl;
cout << "Building position utility matrix ... " << flush;
auto rpValFn = [positions, riVal, aCap, govCost, govBudget, obFactor, prob](unsigned int ai, unsigned int pj) {
VUI pstn = positions[pj];
double rvp = rawValPos(ai, pstn, riVal, aCap, govCost, govBudget, obFactor, prob);
return rvp;
};
const auto rpRawVal = KMatrix::map(rpValFn, numA, numPos);
const auto rpNormVal = KBase::rescaleRows(rpRawVal, 0.0, 1.0);
const double bigR = 0.5;
auto curve = [bigR](double v) {
return quadUfromV(v, bigR);
};
const auto rpUtil = KMatrix::map(curve, rpNormVal);
cout << "done." << endl << flush;
// This is an attempt to define a domain-independent measure of similarity,
// by looking at the difference in outcomes to actors.
// Notice that if we sort the columns by difference from #i,
// those with small differences in outcome might do it by very different
// means, so that columns from all over the matrix are placed near i.
auto diffFn = [](unsigned int i, unsigned int j, const KMatrix& uMat) {
const auto colI = KBase::vSlice(uMat, i);
const auto colJ = KBase::vSlice(uMat, j);
for (unsigned int k=0;k<colI.numR(); k++){
}
double dij = KBase::norm(colI - colJ);
return dij;
};
auto diffVUI = [&rpUtil, numPos, diffFn](unsigned int n, unsigned int nSim) {
vector<TDI> vdk = {};
for (unsigned int k = 0; k < numPos; k++) {
double dkn = diffFn(k, n, rpUtil);
auto dk = TDI(dkn, k);
vdk.push_back(dk);
}
auto tupleLess = [](TDI t1, TDI t2) {
double d1 = get<0>(t1);
double d2 = get<0>(t2);
return (d1 < d2);
};
std::sort(vdk.begin(), vdk.end(), tupleLess);
vector<TDI> sdk = {};
for (unsigned int i = 0; ((i < nSim) && (i < numPos)); i++) {
sdk.push_back(vdk[i]);
}
return sdk;
};
// The permutations with outcomes most similar to the given
// one are those which make the most minor changes
// (somewhat similar to a vector hill climbing search where the
// points in a nearby neighborhood make small changes in objective).
// So looking at a few dozen (out of thousands) might be enough to
// get significant variation to keep the search progressing.
unsigned int numClose = 50;
unsigned int rand1 = rng->uniform(0, numPos);
auto rslt1 = diffVUI(rand1, numClose);
cout << "Permutations with outcomes most similar to " << rand1 << endl;
for (unsigned int i = 0; i < rslt1.size(); i++) {
auto dp = rslt1[i];
double d = get<0>(dp);
unsigned int p = get<1>(dp);
auto posP = positions[p];
printf("%2u: %4u %.4f ", i, p, d);
KBase::printVUI(posP);
cout << endl;
}
delete rng;
rng = nullptr;
return;
}
RPState* RPState::doSUSN(ReportingLevel rl) const {
RPState* s2 = nullptr;
const unsigned int numA = model->numAct;
assert(numA == rpMod->actrs.size());
const unsigned int numU = uIndices.size();
assert ((0 < numU) && (numU <= numA));
assert (numA == eIndices.size());
// TODO: filter out essentially-duplicate positions
//printf("RPState::doSUSN: numA %i \n", numA);
//printf("RPState::doSUSN: numP %i \n", numP);
//cout << endl << flush;
const KMatrix u = aUtil[0]; // all have same beliefs in this demo
auto vpm = VPModel::Linear;
const unsigned int numP = pstns.size();
// Given the utility matrix, uMat, calculate the expected utility to each actor,
// as a column-vector. Again, this is from the perspective of whoever developed uMat.
auto euMat = [rl, numA, numP, vpm, this](const KMatrix & uMat) {
// BTW, be sure to lambda-bind uMat *after* it is modified.
assert(uMat.numR() == numA); // must include all actors
assert(uMat.numC() <= numP); // might have dropped some duplicates
auto uRng = [uMat](unsigned int i, unsigned int j) {
if ((uMat(i, j) < 0.0) || (1.0 < uMat(i, j))) {
printf("%f %i %i \n", uMat(i, j), i, j);
cout << flush;
cout << flush;
}
assert(0.0 <= uMat(i, j));
assert(uMat(i, j) <= 1.0);
return;
};
KMatrix::mapV(uRng, uMat.numR(), uMat.numC());
// vote_k ( i : j )
auto vkij = [this, uMat](unsigned int k, unsigned int i, unsigned int j) {
auto ak = (RPActor*)(rpMod->actrs[k]);
auto v_kij = Model::vote(ak->vr, ak->sCap, uMat(k, i), uMat(k, j));
return v_kij;
};
// the following uses exactly the values in the given euMat,
// which may or may not be square
const KMatrix c = Model::coalitions(vkij, uMat.numR(), uMat.numC());
const KMatrix pv = Model::vProb(vpm, c); // square
const KMatrix p = Model::probCE(PCEModel::ConditionalPCM, pv); // column
const KMatrix eu = uMat*p; // column
assert(numA == eu.numR());
assert(1 == eu.numC());
auto euRng = [eu](unsigned int i, unsigned int j) {
// due to round-off error, we must have a tolerance factor
const double tol = 1E-10;
const double euij = eu(i, j);
assert(0.0 <= euij+tol);
assert(euij <= 1.0+tol);
return;
};
KMatrix::mapV(euRng, eu.numR(), eu.numC());
if (ReportingLevel::Low < rl) {
printf("Util matrix is %i x %i \n", uMat.numR(), uMat.numC());
cout << "Assessing EU from util matrix: " << endl;
uMat.mPrintf(" %.6f ");
cout << endl << flush;
cout << "Coalition strength matrix" << endl;
c.mPrintf(" %12.6f ");
cout << endl << flush;
cout << "Probability Opt_i > Opt_j" << endl;
pv.mPrintf(" %.6f ");
cout << endl << flush;
cout << "Probability Opt_i" << endl;
p.mPrintf(" %.6f ");
cout << endl << flush;
cout << "Expected utility to actors: " << endl;
eu.mPrintf(" %.6f ");
cout << endl << flush;
}
return eu;
};
// end of euMat
auto euState = euMat(u);
cout << "Actor expected utilities: ";
KBase::trans(euState).mPrintf("%6.4f, ");
cout << endl << flush;
if (ReportingLevel::Low < rl) {
printf("--------------------------------------- \n");
printf("Assessing utility of actual state to all actors \n");
for (unsigned int h = 0; h < numA; h++) {
cout << "not available" << endl;
}
cout << endl << flush;
printf("Out of %u positions, %u were unique: ", numA, numU);
cout << flush;
for (auto i : uIndices) {
printf("%2i ", i);
}
cout << endl;
cout << flush;
}
auto uufn = [u, this](unsigned int i, unsigned int j1) {
return u(i, uIndices[j1]);
};
auto uUnique = KMatrix::map(uufn, numA, numU);
// Get expected-utility vector, one entry for each actor, in the current state.
const KMatrix eu0 = euMat(uUnique); // 'u' with duplicates, 'uUnique' without duplicates
s2 = new RPState(model);
//s2->pstns = vector<KBase::Position*>();
for (unsigned int h = 0; h < numA; h++) {
s2->pstns.push_back(nullptr);
}
// TODO: clean up the nesting of lambda-functions.
// need to create a hypothetical state and run setOneAUtil(h,Silent) on it
//
// The newPosFn does a GA optimization to find the best next position for actor h,
// and stores it in s2. To do that, it defines three functions for evaluation, neighbors, and show:
// efn, nfn, and sfn.
auto newPosFn = [this, rl, euMat, u, eu0, s2](const unsigned int h) {
s2->pstns[h] = nullptr;
auto ph = ((const MtchPstn *)(pstns[h]));
// Evaluate h's estimate of the expected utility, to h, of
// advocating position mp. To do this, build a hypothetical utility matrix representing
// h's estimates of the direct utilities to all other actors of h adopting this
// Position. Do that by modifying the h-column of h's matrix.
// Then compute the expected probability distribution, over h's hypothetical position
// and everyone else's actual position. Finally, compute the expected utility to
// each actor, given that distribution, and pick out the value for h's expected utility.
// That is the expected value to h of adopting the position.
auto efn = [this, euMat, rl, u, h](const MtchPstn & mph) {
// This correctly handles duplicated/unique options
// We modify the given euMat so that the h-column
// corresponds to the given mph, but we need to prune duplicates as well.
// This entails some type-juggling.
const KMatrix uh0 = aUtil[h];
assert(KBase::maxAbs(u - uh0) < 1E-10); // all have same beliefs in this demo
if (mph.match.size() != rpMod->numItm) {
cout << mph.match.size() << endl << flush;
cout << rpMod->numItm << endl << flush;
cout << flush << flush;
}
assert(mph.match.size() == rpMod->numItm);
auto uh = uh0;
for (unsigned int i = 0; i < rpMod->numAct; i++) {
auto ai = (RPActor*)(rpMod->actrs[i]);
double uih = ai->posUtil(&mph);
uh(i, h) = uih; // utility to actor i of this hypothetical position by h
}
// 'uh' now has the correct h-column. Now we need to see how many options
// are unique in the hypothetical state, and keep only those columns.
// This entails juggling back and forth between the all current positions
// and the one hypothetical position (mph at h).
// Thus, the next call to euMat will consider only unique options.
auto equivHNdx = [this, h, mph](const unsigned int i, const unsigned int j) {
// this little function takes care of the different types needed to compare
// dynamic pointers to positions (all but h) with a constant position (h itself).
// In other words, the comparisons for index 'h' use the hypothetical mph, not pstns[h]
bool rslt = false;
auto mpi = ((const MtchPstn *)(pstns[i]));
auto mpj = ((const MtchPstn *)(pstns[j]));
assert(mpi != nullptr);
assert(mpj != nullptr);
if (i == j) {
rslt = true; // Pi == Pj, always
}
else if (h == i) {
rslt = (mph == (*mpj));
}
else if (h == j) {
rslt = ((*mpi) == mph);
}
else {
rslt = ((*mpi) == (*mpj));
}
return rslt;
};
auto ns = KBase::uiSeq(0, model->numAct - 1);
const VUI uNdx = get<0>(KBase::ueIndices<unsigned int>(ns, equivHNdx));
const unsigned int numU = uNdx.size();
auto hypUtil = KMatrix(rpMod->numAct, numU);
// we need now to go through 'uh', copying column J the first time
// the J-th position is determined to be equivalent to something in the unique list
for (unsigned int i = 0; i < rpMod->numAct; i++) {
for (unsigned int j1 = 0; j1 < numU; j1++) {
unsigned int j2 = uNdx[j1];
hypUtil(i, j1) = uh(i, j2); // hypothetical utility in column h
}
}
if (false) {
cout << "constructed hypUtil matrix:" << endl << flush;
hypUtil.mPrintf(" %8.2f ");
cout << endl << flush;
}
if (ReportingLevel::Low < rl) {
printf("--------------------------------------- \n");
printf("Assessing utility to %2i of hypo-pos: ", h);
printPerm(mph.match);
cout << endl << flush;
printf("Hypo-util minus base util: \n");
(uh - uh0).mPrintf(" %+.4E ");
cout << endl << flush;
}
const KMatrix eu = euMat(hypUtil); // uh or hypUtil
// BUG: If we use 'uh' here, it passes the (0 <= delta-EU) test, because
// both hypothetical and actual are then calculated without dropping duplicates.
// If we use 'hypUtil' here, it sometimes gets (delta-EU < 0), because
// the hypothetical drops duplicates but the actual (computed elsewhere) does not.
// FIX: fix the 'elsewhere'
const double euh = eu(h, 0);
assert(0 < euh);
//cout << euh << endl << flush;
//printPerm(mp.match);
//cout << endl << flush;
//cout << flush;
return euh;
}; // end of efn
/*
// I do not actually use prevMP, but it is still an example for std::set
auto prevMP = [](const MtchPstn & mp1, const MtchPstn & mp2) {
bool r = std::lexicographical_compare(
mp1.match.begin(), mp1.match.end(),
mp2.match.begin(), mp2.match.end());
return r;
};
std::set<MtchPstn, bool(*)(const MtchPstn &, const MtchPstn &)> mpSet(prevMP);
*/
// return vector of neighboring 1-permutations
auto nfn = [](const MtchPstn & mp0) {
const unsigned int numI = mp0.match.size();
auto mpVec = vector <MtchPstn>();
mpVec.push_back(MtchPstn(mp0));
// one-permutations
for (unsigned int i = 0; i < numI; i++) {
for (unsigned int j = i + 1; j < numI; j++) {
unsigned int ei = mp0.match[i];
unsigned int ej = mp0.match[j];
auto mij = MtchPstn(mp0);
mij.match[i] = ej;
mij.match[j] = ei;
mpVec.push_back(mij);
}
}
// two-permutations
for (unsigned int i = 0; i < numI; i++) {
for (unsigned int j = i + 1; j < numI; j++) {
for (unsigned int k = j + 1; k < numI; k++) {
unsigned int ei = mp0.match[i];
unsigned int ej = mp0.match[j];
unsigned int ek = mp0.match[k];
auto mjki = MtchPstn(mp0);
mjki.match[i] = ej;
mjki.match[j] = ek;
mjki.match[k] = ei;
mpVec.push_back(mjki);
auto mkij = MtchPstn(mp0);
mkij.match[i] = ek;
mkij.match[j] = ei;
mkij.match[k] = ej;
mpVec.push_back(mkij);
}
}
}
//unsigned int mvs = mpVec.size() ;
//cout << mvs << endl << flush;
//cout << flush;
return mpVec;
}; // end of nfn
// show some representation of this position on cout
auto sfn = [](const MtchPstn & mp0) {
printPerm(mp0.match);
return;
};
auto ghc = new KBase::GHCSearch<MtchPstn>();
ghc->eval = efn;
ghc->nghbrs = nfn;
ghc->show = sfn;
auto rslt = ghc->run(*ph, // start from h's current positions
ReportingLevel::Silent,
100, // iter max
3, 0.001); // stable-max, stable-tol
if (ReportingLevel::Low < rl) {
printf("---------------------------------------- \n");
printf("Search for best next-position of actor %2i \n", h);
//printf("Search for best next-position of actor %2i starting from ", h);
//trans(*aPos).printf(" %+.6f ");
cout << flush;
}
double vBest = get<0>(rslt);
MtchPstn pBest = get<1>(rslt);
unsigned int iterN = get<2>(rslt);
unsigned int stblN = get<3>(rslt);
delete ghc;
ghc = nullptr;
if (ReportingLevel::Medium < rl) {
printf("Iter: %u Stable: %u \n", iterN, stblN);
printf("Best value for %2i: %+.6f \n", h, vBest);
cout << "Best position: " << endl;
cout << "numCat: " << pBest.numCat << endl;
cout << "numItm: " << pBest.numItm << endl;
cout << "perm: ";
printPerm(pBest.match);
cout << endl << flush;
}
MtchPstn * posBest = new MtchPstn(pBest);
s2->pstns[h] = posBest;
// no need for mutex, as s2->pstns is the only shared var,
// and each h is different.
double du = vBest - eu0(h, 0); // (hypothetical, future) - (actual, current)
if (ReportingLevel::Low < rl) {
printf("EU improvement for %2i of %+.4E \n", h, du);
}
//printf(" vBest = %+.6f \n", vBest);
//printf(" eu0(%i, 0) for %i = %+.6f \n", h, h, eu0(h,0));
//cout << endl << flush;
// Logically, du should always be non-negative, as GHC never returns a worse value than the starting point.
// However, actors plan on the assumption that all others do not change - yet they do.
const double eps = 0.05; // 0.025; // enough to avoid problems with round-off error
assert(-eps <= du);
return;
}; // end of newPosFn
const bool par = true;
auto ts = vector<thread>();
// Each actor, h, finds the position which maximizes their EU in this situation.
for (unsigned int h = 0; h < numA; h++) {
if (par) { // launch all, concurrent
ts.push_back(thread([newPosFn, h]() {
newPosFn(h);
return;
}));
}
else { // do each, sequential
newPosFn(h);
}
}
if (par) { // now join them all before continuing
for (auto& t : ts) {
t.join();
}
}
assert(nullptr != s2);
assert(numP == s2->pstns.size());
assert(numA == s2->model->numAct);
for (auto p : s2->pstns) {
assert(nullptr != p);
}
s2->setUENdx();
return s2;
}
void RPModel::readXML(string fileName) {
using tinyxml2::XMLDocument;
using tinyxml2::XMLElement;
using KBase::KException;
// read XML file with tinyXML2 then do equivalent of
// initScen and configScen
XMLDocument d1;
try {
d1.LoadFile(fileName.c_str());
auto eid = d1.ErrorID();
if (0 != eid) {
cout << "ErrorID: " << eid << endl;
throw KException(d1.GetErrorStr1());
}
else {
// missing data causes the missing XMLElement* to come back as nullptr,
// so we get a segmentation violation, which is not catchable.
XMLElement* scenEl = d1.FirstChildElement( "Scenario" );
XMLElement* scenNameEl = scenEl->FirstChildElement( "name" );
assert (nullptr != scenNameEl);
try {
const char * sName = scenNameEl->GetText();
printf( "Name of scenario: %s\n", sName );
}
catch (...) {
throw (KException("Error reading file header"));
}
XMLElement* scenDescEl = scenEl->FirstChildElement( "desc" );
assert (nullptr != scenDescEl);
const char* sn2 = scenDescEl->GetText();
assert (nullptr != sn2);
// get some top level values from the corresponding Elements (no Attributes)
XMLElement* gbEl = scenEl->FirstChildElement( "govBudget" );
assert (nullptr != gbEl);
double gb = 1000.0; // impossible value
gbEl->QueryDoubleText(&gb);
assert (0.0 < gb);
assert (gb <= 100.0);
govBudget = ((unsigned int) (0.5 + gb));
XMLElement* obEl = scenEl->FirstChildElement( "outOfBudgetFactor" );
assert (nullptr != obEl);
double obf = 1000.0; // impossible value
obEl->QueryDoubleText(&obf);
assert (0.0 < obf);
assert (obf < 1.0);
obFactor = obf;
XMLElement* pdEl = scenEl->FirstChildElement( "orderFactor" );
assert (nullptr != pdEl);
double pdf = 1000.0; // impossible value
pdEl->QueryDoubleText(&pdf);
assert (0.0 < pdf);
assert (pdf < 1.0);
pDecline = pdf;
// TODO: read these two parameters from XML
KBase::VotingRule vrScen = KBase::VotingRule::Proportional;
RfrmPri::RPActor::PropModel pmScen = RfrmPri::RPActor::PropModel::ExpUtil;
// read all the categories
unsigned int nc = 0;
try {
XMLElement* catsEl = scenEl->FirstChildElement( "Categories" );
assert (nullptr != catsEl);
XMLElement* cEl = catsEl->FirstChildElement( "category" );
assert (nullptr != cEl); // has to be at least one
while (nullptr != cEl) {
nc++;
cEl = cEl->NextSiblingElement( "category" );
}
printf("Found %i categories \n", nc);
numCat = nc;
}
catch (...) {
throw (KException("Error reading Categories data"));
}
// In this case, the number of items should equal to the number of categories,
// so we can setup the matrix with the following size.
govCost = KMatrix(1, numCat);
// read all the items
unsigned int ni = 0;
try {
XMLElement* itemsEl = scenEl->FirstChildElement( "Items" );
assert (nullptr != itemsEl);
XMLElement* iEl = itemsEl->FirstChildElement( "Item" );
assert (nullptr != iEl); // has to be at least one
while (nullptr != iEl) {
double gci=0.0;
XMLElement* gcEl = iEl->FirstChildElement("cost");
gcEl->QueryDoubleText(&gci);
assert (0.0 < gci);
govCost(0, ni) = gci;
ni++;
iEl = iEl->NextSiblingElement( "Item" );
}
printf("Found %i items \n", ni);
assert (ni == nc); // for this problem, number of items and categories are equal
numItm = ni;
}
catch (...) {
throw (KException("Error reading Items data"));
}
// We now know numItm and obFactor, so we can fill in prob vector.
prob = vector<double>();
double pj = 1.0;
printf("pDecline factor: %.3f \n", pDecline);
printf("obFactor: %.3f \n", obFactor);
// rate of decline has little effect on the results.
for (unsigned int j = 0; j < numItm; j++) {
prob.push_back(pj);
pj = pj * pDecline;
}
// read all the actors
unsigned int na = 0;
try {
XMLElement* actorsEl = scenEl->FirstChildElement( "Actors" );
assert (nullptr != actorsEl);
XMLElement* aEl = actorsEl->FirstChildElement( "Actor" );
assert (nullptr != aEl); // has to be at least one
while (nullptr != aEl) {
const char* aName = aEl->FirstChildElement( "name" )->GetText();
const char* aDesc = aEl->FirstChildElement( "description" )->GetText();
double cap = 0.0; // another impossible value
aEl->FirstChildElement( "capability" )->QueryDoubleText(&cap);
assert(0.0 < cap);
auto ri = new RPActor(aName, aDesc, this);
ri->sCap = cap;
ri->riVals = vector<double>();
ri->idNum = na;
ri->vr = vrScen;
ri->pMod = pmScen;
XMLElement* ivsEl = aEl->FirstChildElement("ItemValues");
unsigned int numIVS = 0;
XMLElement* ivEl = ivsEl->FirstChildElement("iVal");
assert (nullptr != ivEl);
while (nullptr != ivEl) {
numIVS++;
double iv = -1.0; // impossible value
ivEl->QueryDoubleText(&iv);
assert (0 <= iv);
ri->riVals.push_back(iv);
ivEl = ivEl->NextSiblingElement("iVal");
}
assert(ni == numIVS); // must have a value for each item
addActor(ri);
// move to the next, if any
na++;
aEl = aEl->NextSiblingElement( "Actor" );
}
printf("Found %i actors \n", na);
assert (minNumActor <= na);
assert (na <= maxNumActor);
}
catch (...) {
throw (KException("Error reading Actors data"));
}
}
}
catch (const KException& ke) {
cout << "Caught KException in readXML: "<< ke.msg <<endl<<flush;
}
catch (...) {
cout << "Caught unidentified exception in readXML"<<endl<<flush;
}
return;
}
// return the list of the most self-interested position of each actor,
// with the CP last.
// As a side-affect, set each actor's min/max permutation values so as to
// compute normalized utilities later.
vector<VUI> scanAllPossiblePositions(const RPModel * rpm) {
unsigned int numA = rpm->numAct;
unsigned int numRefItem = rpm->numItm;
assert(numRefItem == rpm->numCat);
LOG(INFO) << "There are" << numA << "actors and" << numRefItem << "reform items";
KMatrix aCap = KMatrix(1, numA);
for (unsigned int i = 0; i < numA; i++) {
auto ri = ((const RPActor *)(rpm->actrs[i]));
aCap(0, i) = ri->sCap;
}
LOG(INFO) << "Actor capabilities: ";
aCap.mPrintf(" %.2f ");
LOG(INFO) << "Effective gov cost of items:";
(rpm->govCost).mPrintf("%.3f ");
LOG(INFO) << "Government budget: " << rpm->govBudget;
assert(0 < rpm->govBudget);
string log("Value to actors (rows) of individual reform items (columns):");
for (unsigned int i = 0; i < rpm->actrs.size(); i++) {
auto rai = ((const RPActor*)(rpm->actrs[i]));
for (unsigned int j = 0; j < numRefItem; j++) {
double vij = rai->riVals[j];
log += KBase::getFormattedString(" %6.2f", vij);
}
}
LOG(INFO) << log;
LOG(INFO) << "Computing positions ... ";
vector<VUI> allPositions; // list of all possiblepositions
VUI pstn;
// build the first permutation: 0,1,2,3,...
for (unsigned int i = 0; i < numRefItem; i++) {
pstn.push_back(i);
}
allPositions.push_back(pstn);
while (next_permutation(pstn.begin(), pstn.end())) {
allPositions.push_back(pstn);
}
const unsigned int numPos = allPositions.size();
LOG(INFO) << "For" << numRefItem << "reform items there are"
<< numPos << "positions";
// -------------------------------------------------
// The next section sets up actor utilities.
// First, we compute the unnormalized, raw utilities. The 'utilActorPos' checks
// to see if pvMin/pvMax have been set, and returns the raw scores if not.
// Then we scan across rows to find that actor's pvMin/pvMax, and record that
// so utilActorPos can use it in the future. Finally, we normalize the rows and
// display the normalized utility matrix.
auto ruFn = [allPositions, rpm](unsigned int ai, unsigned int pj) {
auto pstn = allPositions[pj];
double uip = rpm->utilActorPos(ai, pstn);
return uip;
};
LOG(INFO) << "Computing utilities of positions ... ";
// rows are actors, columns are all possible positions
auto rawUij = KMatrix::map(ruFn, numA, numPos);
// set the min/max for each actor
for (unsigned int i = 0; i < numA; i++) {
double pvMin = rawUij(i, 0);
double pvMax = rawUij(i, 0);
for (unsigned int j = 0; j < numPos; j++) {
double rij = rawUij(i, j);
if (rij < pvMin) {
pvMin = rij;
}
if (rij > pvMax) {
pvMax = rij;
}
}
assert(0 <= pvMin);
assert(pvMin < pvMax);
auto ai = ((RPActor*)(rpm->actrs[i]));
ai->posValMin = pvMin;
ai->posValMax = pvMax;
}
LOG(INFO) << "Normalizing utilities of positions ... ";
KMatrix uij = KBase::rescaleRows(rawUij, 0.0, 1.0); // von Neumann utility scale
string utilMtx("Complete (normalized) utility matrix of all possible positions (rows) versus actors (columns) \n");
for (unsigned int pj = 0; pj < numPos; pj++) {
utilMtx += KBase::getFormattedString("%3u ", pj);
auto pstn = allPositions[pj];
//printVUI(pstn);
utilMtx += KBase::stringVUI(pstn);
utilMtx += " ";
for (unsigned int ai = 0; ai < numA; ai++) {
double uap = uij(ai, pj);
utilMtx += KBase::getFormattedString("%6.4f, ", uap);
}
utilMtx += KBase::getFormattedString("\n");
}
LOG(INFO) << utilMtx;
// -------------------------------------------------
// The next section determines the most self-interested positions for each actor,
// as well as the 'central position' over all possible reform priorities
// (which 'office seeking politicans' would adopt IF proportional voting).
LOG(INFO) << "Computing best position for each actor";
vector<VUI> bestAP; // list of each actor's best position (followed by CP)
for (unsigned int ai = 0; ai < numA; ai++) {
unsigned int bestJ = 0;
double bestV = 0;
for (unsigned int pj = 0; pj < numPos; pj++) {
if (bestV < uij(ai, pj)) {
bestJ = pj;
bestV = uij(ai, pj);
}
}
string bestMtx("Best position for ");
string ais = std::to_string(ai);
//string bjs = std::to_string(bestJ);
string ps = KBase::stringVUI(allPositions[bestJ]);
bestMtx += ais + " is " + ps;
LOG(INFO) << bestMtx;
//LOG(INFO) << "Best for" << ai << "is ";
//printVUI(positions[bestJ]);
bestAP.push_back(allPositions[bestJ]);
}
LOG(INFO) << "Computing zeta ... ";
KMatrix zeta = aCap * uij;
assert((1 == zeta.numR()) && (numPos == zeta.numC()));
LOG(INFO) << "Sorting positions from most to least net support ...";
auto betterPR = [](tuple<unsigned int, double, VUI> pr1,
tuple<unsigned int, double, VUI> pr2) {
double v1 = get<1>(pr1);
double v2 = get<1>(pr2);
bool better = (v1 > v2);
return better;
};
auto pairs = vector<tuple<unsigned int, double, VUI>>();
for (unsigned int i = 0; i < numPos; i++) {
auto pri = tuple<unsigned int, double, VUI>(i, zeta(0, i), allPositions[i]);
pairs.push_back(pri);
}
sort(pairs.begin(), pairs.end(), betterPR);
const unsigned int maxDisplayed = 720; // factorial(6)
unsigned int numPr = (pairs.size() < maxDisplayed) ? pairs.size() : maxDisplayed;
LOG(INFO) << "Displaying highest" << numPr;
for (unsigned int i = 0; i < numPr; i++) {
auto pri = pairs[i];
unsigned int ni = get<0>(pri);
double zi = get<1>(pri);
VUI pi = get<2>(pri);
string ps = KBase::stringVUI(pi);
LOG(INFO) << KBase::getFormattedString(" %3u: %4u %.2f %s", i, ni, zi, ps.c_str());
//printVUI(pi);
}
VUI bestPerm = get<2>(pairs[0]);
bestAP.push_back(bestPerm);
return bestAP;
}
// --------------------------------------------
// JAH 20160728 modified to return a KTable object instead of the SQL string
KTable * SMPModel::createSQL(unsigned int n) {
string sql = "";
string name = "";
unsigned int grpID = 0;
// check total number of table exceeds
assert(n < Model::NumTables + NumTables);
if (n < Model::NumTables) {
return Model::createSQL(n);
}
else
{
switch (n-Model::NumTables) {
case 0: // coordinates of each actor's position
sql = "create table if not exists VectorPosition (" \
"ScenarioId VARCHAR(32) NOT NULL DEFAULT 'None', "\
"Turn_t INTEGER NOT NULL DEFAULT 0, "\
"Act_i INTEGER NOT NULL DEFAULT 0, "\
"Dim_k INTEGER NOT NULL DEFAULT 0, "\
"Pos_Coord FLOAT NOT NULL DEFAULT 0,"\
"Idl_Coord FLOAT NOT NULL DEFAULT 0, " \
"Mover_BargnId INTEGER NULL DEFAULT 0" \
");";
name = "VectorPosition";
grpID = 4;// JAH 20161010 put in group 4 all by itself
break;
case 1: // salience to each actor of each dimension
sql = "create table if not exists SpatialSalience (" \
"ScenarioId VARCHAR(32) NOT NULL DEFAULT 'None', "\
"Turn_t INTEGER NOT NULL DEFAULT 0, "\
"Act_i INTEGER NOT NULL DEFAULT 0, "\
"Dim_k INTEGER NOT NULL DEFAULT 0, "\
"Sal FLOAT NOT NULL DEFAULT 0.0"\
");";
name = "SpatialSalience";
grpID = 0;
break;
case 2: // scalar capability of each actor
sql = "create table if not exists SpatialCapability (" \
"ScenarioId VARCHAR(32) NOT NULL DEFAULT 'None', "\
"Turn_t INTEGER NOT NULL DEFAULT 0, "\
"Act_i INTEGER NOT NULL DEFAULT 0, "\
"Cap FLOAT NOT NULL DEFAULT 0.0"\
");";
name = "SpatialCapability";
grpID = 0;
break;
case 3: // names of dimensions, so the GUI can use it JAH 20160727
{
char *sqlBuff = newChars(500);
sprintf(sqlBuff, "create table if not exists DimensionDescription (" \
"ScenarioId VARCHAR(32) NOT NULL DEFAULT 'None', "\
"Dim_k INTEGER NOT NULL DEFAULT 0, "\
"\"Desc\" VARCHAR(%u) NOT NULL DEFAULT 'NoDesc' "\
");", maxDimDescLen);
sql = std::string(sqlBuff);
delete sqlBuff;
sqlBuff = nullptr;
name = "DimensionDescription";
grpID = 0;
}
break;
case 4: // affinities of actors, so the GUI can use it
{
sql = "create table if not exists Accommodation (" \
"ScenarioId VARCHAR(32) NOT NULL DEFAULT 'None', "\
"Act_i INTEGER NOT NULL DEFAULT 0, "\
"Act_j INTEGER NOT NULL DEFAULT 0, "\
"Affinity FLOAT NOT NULL DEFAULT 0.0"\
");";
name = "Accommodation";
grpID = 0;
break;
}
default:
throw(KException("SMPModel::createSQL unrecognized table number"));
}
}
assert(grpID<(Model::NumSQLLogGrps+NumSQLLogGrps));
auto tab = new KTable(n,name,sql,grpID);
return tab;
}