void predictAll(bool train){
loadData(data,train);
loadUserItemData(users, items, data);
for(int i = 0; i < data.size(); ++i){
int user = data[i].user;
int item = data[i].item;
ui[user].push_back(item);
nu[user]++;
ni[item]++;
}
int u;
loadSim(train);
map<int,int> test;
if(train) getTestSet2(test);
else getTestSet(test);
string file = "../ret2/results-knni-all.txt";
if(train) file += ".0";
ofstream out(file.c_str());
for(map<int,int>::iterator k = test.begin(); k != test.end(); ++k){
int u = k->first;
vector< pair<int,float> > ret;
predict2(u, ret);
out << u << "\t";
for(int i = 0; i < ret.size() && i < 500; ++i)
out << ret[i].first << "\t" << ret[i].second / ret[0].second << "\t";
out << endl;
}
out.close();
}
int main(int argc, char ** argv){
loadData(data,true);
loadTestData();
loadUserItemData(users, items, data);
for(int i = 0; i < data.size(); ++i) nu[data[i].user]++;
#define K 7
vector< vector< map<int,float> > > tests(K);
vector< float > weight(K);
int k = 0;
loadResults("../ret2/results-knni-iuf.txt.0", tests[k]); weight[k] = 1; ++k;
loadResults("../ret2/results-knnu-iif.txt.0", tests[k]); weight[k] = 1; ++k;
loadResults("../ret2/results-knnui.txt.0", tests[k]); weight[k] = 1; ++k;
loadResults("../ret2/results-language.txt.0", tests[k]); weight[k] = 0.4; ++k;
loadResults("../ret2/results-repos.txt.0", tests[k]); weight[k] = 0.4; ++k;
loadResults("../ret2/results-reponame.txt.0", tests[k]); weight[k] = 0.2; ++k;
loadResults("../ret2/results-pop-nic.txt.0", tests[k]); weight[k] = 0.01; ++k;
srand(time(0));
int j = 0;
for(int step = 0; step < 20; ++step){
int i = j % K;
++j;
float w0 = weight[i];
int r0 = bag(tests, weight);
weight[i] *= (1 + 0.5 * (rand01() - 0.5));
int r1 = bag(tests, weight);
if(r1 <= r0)
weight[i] = w0;
cout << step << "\t" << r0 << "\t" << r1 << endl;
}
for(int i = 0; i < weight.size(); ++i) cout << i << "\t" << weight[i] << endl;
return 0;
}
