antoinecarme
commented
3 years ago Added a OneClassSVM outlier detection model.
Closed antoinecarme closed 3 years ago
antoinecarme
commented
3 years ago Added a OneClassSVM outlier detection model.
antoinecarme
commented
3 years ago OneClassSVM C++ sample code:
namespace {
std::vector<std::any> lProblem_data_dual = {
1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 0.9345504106360158 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 0.06544958936398419 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 , 1.0 ,};
std::vector<std::vector<std::any> > lProblem_data_sv = {
{ 5.1, 3.5, 1.4, 0.2 },
{ 4.9, 3.0, 1.4, 0.2 },
{ 4.7, 3.2, 1.3, 0.2 },
{ 4.6, 3.1, 1.5, 0.2 },
{ 5.0, 3.6, 1.4, 0.2 },
{ 4.6, 3.4, 1.4, 0.3 },
{ 4.4, 2.9, 1.4, 0.2 },
{ 4.9, 3.1, 1.5, 0.1 },
{ 5.4, 3.7, 1.5, 0.2 },
{ 4.8, 3.4, 1.6, 0.2 },
{ 4.8, 3.0, 1.4, 0.1 },
{ 4.3, 3.0, 1.1, 0.1 },
{ 5.8, 4.0, 1.2, 0.2 },
{ 5.7, 4.4, 1.5, 0.4 },
{ 5.4, 3.9, 1.3, 0.4 },
{ 5.1, 3.5, 1.4, 0.3 },
{ 5.7, 3.8, 1.7, 0.3 },
{ 5.1, 3.8, 1.5, 0.3 },
{ 4.6, 3.6, 1.0, 0.2 },
{ 5.2, 3.4, 1.4, 0.2 },
{ 4.7, 3.2, 1.6, 0.2 },
{ 4.8, 3.1, 1.6, 0.2 },
{ 5.2, 4.1, 1.5, 0.1 },
{ 5.5, 4.2, 1.4, 0.2 },
{ 4.9, 3.1, 1.5, 0.2 },
{ 5.0, 3.2, 1.2, 0.2 },
{ 5.5, 3.5, 1.3, 0.2 },
{ 4.9, 3.6, 1.4, 0.1 },
{ 4.4, 3.0, 1.3, 0.2 },
{ 5.0, 3.5, 1.3, 0.3 },
{ 4.5, 2.3, 1.3, 0.3 },
{ 4.4, 3.2, 1.3, 0.2 },
{ 4.8, 3.0, 1.4, 0.3 },
{ 5.1, 3.8, 1.6, 0.2 },
{ 4.6, 3.2, 1.4, 0.2 },
{ 5.3, 3.7, 1.5, 0.2 },
{ 5.0, 3.3, 1.4, 0.2 },
{ 5.0, 2.0, 3.5, 1.0 },
{ 6.3, 3.3, 6.0, 2.5 },
{ 7.1, 3.0, 5.9, 2.1 },
{ 6.3, 2.9, 5.6, 1.8 },
{ 6.5, 3.0, 5.8, 2.2 },
{ 7.6, 3.0, 6.6, 2.1 },
{ 4.9, 2.5, 4.5, 1.7 },
{ 7.3, 2.9, 6.3, 1.8 },
{ 6.7, 2.5, 5.8, 1.8 },
{ 7.2, 3.6, 6.1, 2.5 },
{ 6.4, 2.7, 5.3, 1.9 },
{ 6.8, 3.0, 5.5, 2.1 },
{ 5.7, 2.5, 5.0, 2.0 },
{ 5.8, 2.8, 5.1, 2.4 },
{ 6.4, 3.2, 5.3, 2.3 },
{ 6.5, 3.0, 5.5, 1.8 },
{ 7.7, 3.8, 6.7, 2.2 },
{ 7.7, 2.6, 6.9, 2.3 },
{ 6.0, 2.2, 5.0, 1.5 },
{ 6.9, 3.2, 5.7, 2.3 },
{ 7.7, 2.8, 6.7, 2.0 },
{ 6.7, 3.3, 5.7, 2.1 },
{ 7.2, 3.2, 6.0, 1.8 },
{ 6.4, 2.8, 5.6, 2.1 },
{ 7.2, 3.0, 5.8, 1.6 },
{ 7.4, 2.8, 6.1, 1.9 },
{ 7.9, 3.8, 6.4, 2.0 },
{ 6.4, 2.8, 5.6, 2.2 },
{ 6.1, 2.6, 5.6, 1.4 },
{ 7.7, 3.0, 6.1, 2.3 },
{ 6.3, 3.4, 5.6, 2.4 },
{ 6.4, 3.1, 5.5, 1.8 },
{ 6.9, 3.1, 5.4, 2.1 },
{ 6.7, 3.1, 5.6, 2.4 },
{ 6.9, 3.1, 5.1, 2.3 },
{ 6.8, 3.2, 5.9, 2.3 },
{ 6.7, 3.3, 5.7, 2.5 },
{ 6.7, 3.0, 5.2, 2.3 },
{ 6.2, 3.4, 5.4, 2.3 },
};
std::vector<std::string> get_input_names(){
std::vector<std::string> lFeatures = { "Feature_0", "Feature_1", "Feature_2", "Feature_3" };
return lFeatures;
}
std::vector<std::string> get_output_names(){
std::vector<std::string> lOutputs = {
"AnomalyScore","OutlierIndicator" };
return lOutputs;
}
tTable compute_outlier_scores(std::any Feature_0, std::any Feature_1, std::any Feature_2, std::any Feature_3) {
std::vector<std::any> inputs = { Feature_0, Feature_1, Feature_2, Feature_3 };
std::any lDotProduct = rbf_kernel( lProblem_data_dual, lProblem_data_sv, inputs, 0.06416744863614975 ) + -44.517533261328936;
tTable lTable;
lTable["AnomalyScore"] = { lDotProduct } ;
lTable["OutlierIndicator"] = { ( lDotProduct >= 0.0) ? 1 : -1 } ;
return lTable;
}
tTable compute_model_outputs_from_table( tTable const & iTable) {
tTable lTable = compute_outlier_scores(iTable.at("Feature_0")[0], iTable.at("Feature_1")[0], iTable.at("Feature_2")[0], iTable.at("Feature_3")[0]);
return lTable;
}
} // eof namespace Added sklearn.covariance.EllipticEnvelope models
antoinecarme
commented
3 years ago EllipticEnvelope model C++ sample code:
namespace {
std::vector<std::string> get_input_names(){
std::vector<std::string> lFeatures = { "Feature_0", "Feature_1", "Feature_2", "Feature_3" };
return lFeatures;
}
std::vector<std::string> get_output_names(){
std::vector<std::string> lOutputs = {
"AnomalyScore","OutlierIndicator" };
return lOutputs;
}
tTable compute_outlier_scores(std::any Feature_0, std::any Feature_1, std::any Feature_2, std::any Feature_3) {
std::any Feature_0_c = Feature_0 - 5.754411764705883;
std::any Feature_1_c = Feature_1 - 3.054411764705883;
std::any Feature_2_c = Feature_2 - 3.6080882352941193;
std::any Feature_3_c = Feature_3 - 1.1500000000000004;
std::any lMahalanobis = 10.874533961614407 * Feature_0_c * Feature_0_c + -7.523594252028072 * Feature_0_c * Feature_1_c + -8.670095822562605 * Feature_0_c * Feature_2_c + 8.302222841507668 * Feature_0_c * Feature_3_c + -7.523594252028072 * Feature_1_c * Feature_0_c + 13.824125187366795 * Feature_1_c * Feature_1_c + 10.307810357345911 * Feature_1_c * Feature_2_c + -13.196091595841748 * Feature_1_c * Feature_3_c + -8.670095822562605 * Feature_2_c * Feature_0_c + 10.307810357345911 * Feature_2_c * Feature_1_c + 15.996574228771326 * Feature_2_c * Feature_2_c + -25.652281932055583 * Feature_2_c * Feature_3_c + 8.302222841507668 * Feature_3_c * Feature_0_c + -13.196091595841748 * Feature_3_c * Feature_1_c + -25.652281932055583 * Feature_3_c * Feature_2_c + 48.37648645607273 * Feature_3_c * Feature_3_c;
std::any lScore = -lMahalanobis -(-10.226154869292477);
tTable lTable;
lTable["AnomalyScore"] = { lScore } ;
lTable["OutlierIndicator"] = { ( lScore >= 0.0 ) ? 1 : -1 } ;
return lTable;
}
tTable compute_model_outputs_from_table( tTable const & iTable) {
tTable lTable = compute_outlier_scores(iTable.at("Feature_0")[0], iTable.at("Feature_1")[0], iTable.at("Feature_2")[0], iTable.at("Feature_3")[0]);
return lTable;
}
} // eof namespaceDone.
sklearn.svm.OneClassSVM sklearn.covariance.EllipticEnvelope
Follow the six steps described in #1