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[programming challenge, SSN] neural network's code seems to be more or less complete; not tested though

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This commit is contained in:
mateuszb 2012-05-21 20:11:02 +00:00
parent 2f7ba07050
commit cc9823bd73
1 changed files with 72 additions and 46 deletions
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118
Odpalarka/main.cpp
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@ -91,7 +91,7 @@ double cmpArtSets(DuelParameters dp, TArtSet setL, TArtSet setR)
return battleOutcome;
}
std::vector<CArtifactInstance*> genArts()
std::vector<CArtifactInstance*> genArts(const std::vector<Bonus> & bonusesToGive)
{
std::vector<CArtifactInstance*> ret;
@ -101,31 +101,41 @@ std::vector<CArtifactInstance*> genArts()
nowy->constituentOf = nowy->constituents = NULL;
nowy->possibleSlots.push_back(Arts::LEFT_HAND);
CArtifactInstance *artinst = new CArtifactInstance(nowy);
auto &arts = VLC->arth->artifacts;
CArtifactInstance *inny = new CArtifactInstance(VLC->arth->artifacts[15]);
artinst->addNewBonus(new Bonus(Bonus::PERMANENT, Bonus::PRIMARY_SKILL, Bonus::ARTIFACT_INSTANCE, +25, nowy->id, PrimarySkill::ATTACK));
artinst->addNewBonus(new Bonus(Bonus::PERMANENT, Bonus::PRIMARY_SKILL, Bonus::ARTIFACT_INSTANCE, +25, nowy->id, PrimarySkill::DEFENSE));
auto bonuses = artinst->getBonuses([](const Bonus *){ return true; });
BOOST_FOREACH(Bonus *b, *bonuses)
BOOST_FOREACH(auto b, bonusesToGive)
{
std::cout << format("%s (%d) value:%d, description: %s\n") % bonusTypeToString(b->type) % b->subtype % b->val % b->Description();
CArtifactInstance *artinst = new CArtifactInstance(nowy);
auto &arts = VLC->arth->artifacts;
artinst->addNewBonus(new Bonus(b));
ret.push_back(artinst);
}
// auto bonuses = artinst->getBonuses([](const Bonus *){ return true; });
// BOOST_FOREACH(Bonus *b, *bonuses)
// {
// std::cout << format("%s (%d) value:%d, description: %s\n") % bonusTypeToString(b->type) % b->subtype % b->val % b->Description();
// }
return ret;
}
//returns how good the artifact is for the neural network
double runSSN(FANN::neural_net & net, CArtifactInstance * inst)
double runSSN(FANN::neural_net & net, const DuelParameters dp, CArtifactInstance * inst)
{
TArtSet setL, setR;
setL[inst->artType->possibleSlots[0]] = inst;
return 0.0;
double resultLR = cmpArtSets(dp, setL, setR),
resultRL = cmpArtSets(dp, setR, setL),
resultsBase = cmpArtSets(dp, TArtSet(), TArtSet());
double LRgain = resultLR - resultsBase,
RLgain = resultRL - resultsBase;
return LRgain+RLgain;
}
const unsigned int num_input = 2;
const unsigned int num_input = 16;
double * genSSNinput(const DuelParameters & dp, CArtifactInstance * art)
{
@ -170,15 +180,31 @@ double * genSSNinput(const DuelParameters & dp, CArtifactInstance * art)
}
//bonus description
auto & blist = art->getBonusList();
*(cur++) = art->Attack();
*(cur++) = art->Defense();
*(cur++) = blist.valOfBonuses(Selector::type(Bonus::STACKS_SPEED));
*(cur++) = blist.valOfBonuses(Selector::type(Bonus::STACK_HEALTH));
return ret;
}
void learnSSN(FANN::neural_net & net, const DuelParameters & dp, CArtifactInstance * art, double desiredVal)
void learnSSN(FANN::neural_net & net, const std::vector<std::pair<DuelParameters, CArtifactInstance *> > & input)
{
double * input = genSSNinput(dp, art);
net.train(input, &desiredVal);
delete input;
FANN::training_data td;
double ** inputs = new double *[input.size()];
double ** outputs = new double *[input.size()];
for(int i=0; i<input.size(); ++i)
{
inputs[i] = genSSNinput(input[i].first, input[i].second);
outputs[i] = new double;
*(outputs[i]) = runSSN(net, input[i].first, input[i].second);
}
td.set_train_data(input.size(), num_input, inputs, 1, outputs);
net.train_epoch(td);
}
void initNet(FANN::neural_net & ret)
@ -206,7 +232,6 @@ void initNet(FANN::neural_net & ret)
void SSNRun()
{
auto availableArts = genArts();
std::vector<std::pair<CArtifactInstance *, double> > artNotes;
TArtSet setL, setR;
@ -214,27 +239,27 @@ void SSNRun()
FANN::neural_net network;
initNet(network);
for(int i=0; i<availableArts.size(); ++i)
{
artNotes.push_back(std::make_pair(availableArts[i], runSSN(network, availableArts[i])));
}
boost::range::sort(artNotes,
[](const std::pair<CArtifactInstance *, double> & a1, const std::pair<CArtifactInstance *, double> & a2)
{return a1.second > a2.second;});
//pick best arts into setL
BOOST_FOREACH(auto & ap, artNotes)
{
auto art = ap.first;
BOOST_FOREACH(auto slot, art->artType->possibleSlots)
{
if(setL.find(slot) != setL.end())
{
setL[slot] = art;
break;
}
}
}
// for(int i=0; i<availableArts.size(); ++i)
// {
// artNotes.push_back(std::make_pair(availableArts[i], runSSN(network, availableArts[i])));
// }
// boost::range::sort(artNotes,
// [](const std::pair<CArtifactInstance *, double> & a1, const std::pair<CArtifactInstance *, double> & a2)
// {return a1.second > a2.second;});
//
// //pick best arts into setL
// BOOST_FOREACH(auto & ap, artNotes)
// {
// auto art = ap.first;
// BOOST_FOREACH(auto slot, art->artType->possibleSlots)
// {
// if(setL.find(slot) != setL.end())
// {
// setL[slot] = art;
// break;
// }
// }
// }
//duels to test on
@ -274,17 +299,18 @@ void SSNRun()
}
auto arts = genArts(btt);
//evaluate
std::vector<std::pair<DuelParameters, CArtifactInstance *> > setups;
for(int i=0; i<dps.size(); ++i)
{
auto & dp = dps[i];
double resultLR = cmpArtSets(dp, setL, setR),
resultRL = cmpArtSets(dp, setR, setL),
resultsBase = cmpArtSets(dp, TArtSet(), TArtSet());
double LRgain = resultLR - resultsBase,
RLgain = resultRL - resultsBase;
for(int j=0; j<arts.size(); ++j)
{
setups.push_back(std::make_pair(dps[i], arts[j]));
}
}
learnSSN(network, setups);
}
int main(int argc, char **argv)