Commit 2ebabba7 authored by Joe Grimes's avatar Joe Grimes
Browse files

Hotfixes to clustering

parent 6fd97c95

src/descriptor_identifier/model/ModelClassifier.cpp

+1 −1
Original line number Diff line number Diff line
@@ -100,6 +100,7 @@ ModelClassifier::ModelClassifier(std::string prop_label, 
void ModelClassifier::set_n_misclassified()

{

    // Objects needed to calculate the number of points in the overlapping regions of the convex hulls



    set_train_test_error();



    std::vector<bool> train_misclassified(_n_samp_train);
@@ -404,7 +405,6 @@ std::string ModelClassifier::error_summary_string(std::vector<double> prop, 
        }

        case LOSS_TYPE::SILHOUETTE_SCORE:

        {

            std::cout << "HERE HERE " << (*_loss)(_feats) << std::endl;

            error_stream << "Sil Score Loss : " << (*_loss)(_feats);

            break;

        }

src/loss_function/LossFunctionCalinskiHarabasz.cpp

+1 −1
Original line number Diff line number Diff line
@@ -101,7 +101,7 @@ double LossFunctionCalinskiHarabasz::operator()(const std::vector<int>& inds) 
double LossFunctionCalinskiHarabasz::operator()(const std::vector<model_node_ptr>& feats)

{

    populate_from_feats(feats);

    return compute_CH();

    return -1.0 * compute_CH();

}



double LossFunctionCalinskiHarabasz::compute_w()

src/loss_function/LossFunctionClustering.cpp

+6 −4
Original line number Diff line number Diff line
@@ -145,6 +145,7 @@ void LossFunctionClustering::populate_from_inds(const std::vector<int>& inds) 
}

void LossFunctionClustering::populate_from_feats(const std::vector<model_node_ptr>& feats)

{



    for (int f = 0; f < feats.size(); f++)

    {

        dcopy_(_n_samp, feats[f]->svm_value().data(), 1, &_a[f], _n_feat);
@@ -154,12 +155,13 @@ void LossFunctionClustering::populate_from_feats(const std::vector<model_node_pt 
    int test_start = 0;

    for (int i = 0; i < _n_task; i++)

    {

        std::vector<double*> train_ptrs(_n_feat);

        std::vector<double*> test_ptrs(_n_feat);

        for (int j = 0; j < _n_feat; j++)

        std::vector<double*> train_ptrs(feats.size());

        std::vector<double*> test_ptrs(feats.size());

        for (int j = 0; j < feats.size(); j++)

        {



            test_ptrs[j] = feats[j]->test_value_ptr() + test_start;

            train_ptrs[j] = feats[j]->value_ptr() + train_start;

            test_ptrs[j] = feats[j]->test_value_ptr() + train_start;

        }



        _svm[i]->train(train_ptrs);

tests/googletest/loss_function/test_silhouette_score_loss.cc

+1 −1
Original line number Diff line number Diff line
@@ -194,7 +194,7 @@ TEST_F(LossFunctionSilhouetteScoreTests, ManualDeterminationFeats) 


    double silhouette_score = loss(_model_phi);

    double sil_score = loss_copy({0,1});

    EXPECT_NEAR(sil_score, -0.187931, 0.0001);

    EXPECT_NEAR(sil_score, 0.187931, 0.0001);

    EXPECT_NEAR(silhouette_score,0.466666, 0.0001);

}