int
label_compare(const uint8_t *left, const uint8_t *right)
{
int left_length;
int right_length;
size_t size;
int result;
assert(left);
assert(right);
assert(label_is_normal(left));
assert(label_is_normal(right));
left_length = label_length(left);
right_length = label_length(right);
size = left_length < right_length ? left_length : right_length;
result = memcmp(label_data(left), label_data(right), size);
if (result) {
return result;
} else {
return (int) left_length - (int) right_length;
}
}
double Svm::classify()
{
if (data->train_projections.empty()) {
return -1.;
}
int train_size = int(data->train_projections.size());
int components = data->eigenvalues.rows;
Mat train_data(train_size, components, CV_32FC1);
Mat label_data(train_size, 1, CV_32SC1);
for (int i = 0; i < train_size; i++) {
data->train_projections[i].row(0).copyTo(train_data.row(i));
label_data.at<int>(i, 0) = data->labels[i];
}
Ptr<ml::SVM> svm = ml::SVM::create();
svm->setType(ml::SVM::C_SVC);
svm->setGamma(0.0001);
svm->setC(2000);
svm->setKernel(ml::SVM::RBF);
svm->train(train_data, ml::ROW_SAMPLE, label_data);
int predictions = 0;
int total = 0;
for (int i = 0; i < data->test_projections.size(); i++) {
for (const auto &projection : data->test_projections[i]) {
total++;
int prediction = svm->predict(projection);
if (prediction == i)
predictions++;
}
}
return double(predictions * 100) / total;
}
