rmontanana/BayesNet
rmontanana/BayesNet
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refactor main

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This commit is contained in:
Ricardo Montañana Gómez 2023-07-15 20:23:43 +02:00
parent e3863387bb
commit 29aca0b35f
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 24 additions and 26 deletions
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48
sample/main.cc
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@ -259,30 +259,30 @@ int main(int argc, char** argv)
}
states[className] = vector<int>(
maxes[className]);
cout << "****************** KDB ******************" << endl;
auto kdb = bayesnet::KDB(2);
kdb.fit(Xd, y, features, className, states);
for (auto line : kdb.show()) {
cout << line << endl;
}
cout << "Score: " << kdb.score(Xd, y) << endl;
cout << "****************** KDB ******************" << endl;
cout << "****************** SPODE ******************" << endl;
auto spode = bayesnet::SPODE(2);
spode.fit(Xd, y, features, className, states);
for (auto line : spode.show()) {
cout << line << endl;
}
cout << "Score: " << spode.score(Xd, y) << endl;
cout << "****************** SPODE ******************" << endl;
cout << "****************** AODE ******************" << endl;
auto aode = bayesnet::AODE();
aode.fit(Xd, y, features, className, states);
for (auto line : aode.show()) {
cout << line << endl;
}
cout << "Score: " << aode.score(Xd, y) << endl;
cout << "****************** AODE ******************" << endl;
// cout << "****************** KDB ******************" << endl;
// auto kdb = bayesnet::KDB(2);
// kdb.fit(Xd, y, features, className, states);
// for (auto line : kdb.show()) {
// cout << line << endl;
// }
// cout << "Score: " << kdb.score(Xd, y) << endl;
// cout << "****************** KDB ******************" << endl;
// cout << "****************** SPODE ******************" << endl;
// auto spode = bayesnet::SPODE(2);
// spode.fit(Xd, y, features, className, states);
// for (auto line : spode.show()) {
// cout << line << endl;
// }
// cout << "Score: " << spode.score(Xd, y) << endl;
// cout << "****************** SPODE ******************" << endl;
// cout << "****************** AODE ******************" << endl;
// auto aode = bayesnet::AODE();
// aode.fit(Xd, y, features, className, states);
// for (auto line : aode.show()) {
// cout << line << endl;
// }
// cout << "Score: " << aode.score(Xd, y) << endl;
// cout << "****************** AODE ******************" << endl;
cout << "****************** TAN ******************" << endl;
auto tan = bayesnet::TAN();
tan.fit(Xd, y, features, className, states);

1
src/Metrics.cc
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@ -127,6 +127,5 @@ namespace bayesnet {
auto result = vector<pair<int, int>>();
auto mst = MST(features, weights, root);
return mst.maximumSpanningTree();
}
}

1
src/Mst.cc
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@ -108,7 +108,6 @@ namespace bayesnet {
}
}
g.kruskal_algorithm();
//g.display_mst();
auto mst = g.get_mst();
return reorder(mst, root);
}