double CAdaBoost::Deviance(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate) {
double loss = 0.0;
double weight = 0.0;
// Switch to validation set if necessary
unsigned long num_of_rows_in_set = kData.get_size_of_set();
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : loss, weight) num_threads(get_num_threads())
for (unsigned long i = 0; i < num_of_rows_in_set; i++) {
loss += kData.weight_ptr()[i] *
std::exp(-(2 * kData.y_ptr()[i] - 1) *
(kData.offset_ptr()[i] + kFuncEstimate[i]));
weight += kData.weight_ptr()[i];
}
// TODO: Check if weights are all zero for validation set
if ((weight == 0.0) && (loss == 0.0)) {
return nan("");
} else if (weight == 0.0) {
return HUGE_VAL;
}
return loss / weight;
}
double CGaussian::Deviance(const CDataset& kData, const Bag& kBag,
const double* kFuncEstimate) {
double loss = 0.0;
double weight = 0.0;
unsigned long num_rows_in_set = kData.get_size_of_set();
#pragma omp parallel for schedule(static, get_array_chunk_size()) \
reduction(+ : loss, weight) num_threads(get_num_threads())
for (unsigned long i = 0; i < num_rows_in_set; i++) {
const double tmp =
(kData.y_ptr()[i] - kData.offset_ptr()[i] - kFuncEstimate[i]);
loss += kData.weight_ptr()[i] * tmp * tmp;
weight += kData.weight_ptr()[i];
}
// TODO: Check if weights are all zero for validation set
if ((weight == 0.0) && (loss == 0.0)) {
return nan("");
} else if (weight == 0.0) {
return copysign(HUGE_VAL, loss);
}
return loss / weight;
}