int main() {
typedef KNearestNeighbor< ValueNominal<string> > KNN;
typedef KNN::AttrDomain AttrDomain;
typedef KNN::Domains Domains;
typedef KNN::Beliefs Beliefs;
typedef KNN::ExampleTest ExampleTest;
typedef KNN::ExamplesTrain ExamplesTrain;
Domains attribs;
string A1[] = {"sunny", "overcast", "rain" }; attribs.push_back( createDomain("outlook", A1, A1 + 3) );
string A2[] = {"hot", "mild", "cold"}; attribs.push_back( createDomain("temperature", A2, A2 + 3) );
string A3[] = {"normal", "high"}; attribs.push_back( createDomain("humidity", A3, A3 + 2) );
string A4[] = {"strong", "weak"}; attribs.push_back( createDomain("wind", A4, A4 + 2) );
string C[] = {"good","bad"}; AttrDomain cat = createDomain("", C, C+2);
KNN knn( attribs, cat ); //create the 'empty' classifier
ExamplesTrain ex;
string E01[] = { "sunny", "hot", "high", "weak"}; ex.push_back( createExample( E01, E01 + 4, "bad", knn ) );
string E02[] = { "sunny", "hot", "high", "strong"}; ex.push_back( createExample( E02, E02 + 4, "bad" , knn) );
string E03[] = { "overcast", "hot", "high", "weak"}; ex.push_back( createExample( E03, E03 + 4, "good" , knn) );
string E04[] = { "rain", "mild", "high", "weak"}; ex.push_back( createExample( E04, E04 + 4, "good" , knn) );
string E05[] = { "rain", "cold", "normal", "weak"}; ex.push_back( createExample( E05, E05 + 4, "good" , knn) );
string E06[] = { "rain", "cold", "normal", "strong"}; ex.push_back( createExample( E06, E06 + 4, "bad" , knn) );
string E07[] = { "overcast", "cold", "normal", "strong"}; ex.push_back( createExample( E07, E07 + 4, "good" , knn) );
string E08[] = { "sunny", "mild", "high", "weak"}; ex.push_back( createExample( E08, E08 + 4, "bad" , knn) );
string E09[] = { "sunny", "cold", "normal", "weak"}; ex.push_back( createExample( E09, E09 + 4, "good" , knn) );
string E10[] = { "rain", "mild", "normal", "weak"}; ex.push_back( createExample( E10, E10 + 4, "good" , knn) );
string E11[] = { "sunny", "mild", "normal", "strong"}; ex.push_back( createExample( E11, E11 + 4, "good" , knn) );
string E12[] = { "overcast", "mild", "high", "strong"}; ex.push_back( createExample( E12, E12 + 4, "good" , knn) );
string E13[] = { "overcast", "hot", "normal", "weak"}; ex.push_back( createExample( E13, E13 + 4, "good" , knn) );
string E14[] = { "rain", "mild", "high", "strong"}; ex.push_back( createExample( E14, E14 + 4, "bad" , knn) );
knn.train( ex ); //train
string ET[] = { "overcast", "hot", "high", "weak"}; ExampleTest et = createExample( ET, ET + 4, knn);
//the classifier shoulde return the 'good' category
std::cout << ( knn.getCategory(et) == knn.getCategoryIdd("good") ) << std::endl; //true
//cross-validation on given set of examples
std::cout << checkCross(ex, 14, knn) << std::endl;
return 0;
}
int main() {
typedef DecisionTree< ValueNominal<string> > DTC;
typedef DTC::AttrDomain AttrDomain;
typedef DTC::Domains Domains;
typedef DTC::Beliefs Beliefs;
typedef DTC::ExampleTest ExampleTest;
typedef DTC::ExamplesTrain ExamplesTrain;
Domains attribs;
string A1[] = {"sunny", "overcast", "rain" }; attribs.push_back( createDomain("outlook", A1, A1 + 3) );
string A2[] = {"hot", "mild", "cold"}; attribs.push_back( createDomain("temperature", A2, A2 + 3) );
string A3[] = {"normal", "high"}; attribs.push_back( createDomain("humidity", A3, A3 + 2) );
string A4[] = {"strong", "weak"}; attribs.push_back( createDomain("wind", A4, A4 + 2) );
string C[] = {"good","bad"}; AttrDomain cat = createDomain("", C, C+2);
DTC dt( attribs, cat ); //create the 'empty' classifier
ExamplesTrain ex;
string E01[] = { "sunny", "hot", "high", "weak"}; ex.push_back( createExample( E01, E01 + 4, "bad", dt ) );
string E02[] = { "sunny", "hot", "high", "strong"}; ex.push_back( createExample( E02, E02 + 4, "bad" , dt) );
string E03[] = { "overcast", "hot", "high", "weak"}; ex.push_back( createExample( E03, E03 + 4, "good" , dt) );
string E04[] = { "rain", "mild", "high", "weak"}; ex.push_back( createExample( E04, E04 + 4, "good" , dt) );
string E05[] = { "rain", "cold", "normal", "weak"}; ex.push_back( createExample( E05, E05 + 4, "good" , dt) );
string E06[] = { "rain", "cold", "normal", "strong"}; ex.push_back( createExample( E06, E06 + 4, "bad" , dt) );
string E07[] = { "overcast", "cold", "normal", "strong"}; ex.push_back( createExample( E07, E07 + 4, "good" , dt) );
string E08[] = { "sunny", "mild", "high", "weak"}; ex.push_back( createExample( E08, E08 + 4, "bad" , dt) );
string E09[] = { "sunny", "cold", "normal", "weak"}; ex.push_back( createExample( E09, E09 + 4, "good" , dt) );
string E10[] = { "rain", "mild", "normal", "weak"}; ex.push_back( createExample( E10, E10 + 4, "good" , dt) );
string E11[] = { "sunny", "mild", "normal", "strong"}; ex.push_back( createExample( E11, E11 + 4, "good" , dt) );
string E12[] = { "overcast", "mild", "high", "strong"}; ex.push_back( createExample( E12, E12 + 4, "good" , dt) );
string E13[] = { "overcast", "hot", "normal", "weak"}; ex.push_back( createExample( E13, E13 + 4, "good" , dt) );
string E14[] = { "rain", "mild", "high", "strong"}; ex.push_back( createExample( E14, E14 + 4, "bad" , dt) );
dt.train( ex ); //train
ostringstream oss;
boost::archive::xml_oarchive oa(oss);
oa << boost::serialization::make_nvp("DTC", dt );
//the serialized DTC is stored in oss.str() string
//uncomment if You want to see the xml
//cout << oss.str();
DTC empty(attribs, cat); //crete the 'empty' classifier
string ET[] = { "overcast", "hot", "high", "weak"};
ExampleTest et1 = createExample( ET, ET + 4, empty);
std::cout << empty.getCategory(et1) << std::endl; // should be null (for empty classifier)
std::istringstream iss(oss.str());
boost::archive::xml_iarchive ia(iss);
ia >> boost::serialization::make_nvp("DTC", empty ); //de-serialize the stored object
ExampleTest et2 = createExample( ET, ET + 4, empty );
std::cout << *empty.getCategory(et2) << std::endl; // should be "good"
std::cout << empty.getCategories(et2) << std::endl; // all categories with probabilities
return 0;
}
