void AdaBoost::trainRound() {
calcWeightSum();
DecisionStump bestClassifier;
for (int featureIndex = 0; featureIndex < featureTotal_; ++featureIndex) {
DecisionStump optimalClassifier = learnOptimalClassifier(featureIndex);
if (optimalClassifier.featureIndex() < 0) continue;
if (bestClassifier.error() < 0 || optimalClassifier.error() < bestClassifier.error()) {
bestClassifier = optimalClassifier;
}
}
updateWeight(bestClassifier);
weakClassifiers_.push_back(bestClassifier);
}
vector<int> slashBurn(Graph &graph, int K)
{
//printGraph(graph);
int p = 0, q = (int)graph.size() - 1;
vector<int> weightsum(q + 1, 0);
vector<int> idX(q + 1, 0), xId(q + 1, 0);
for(int i = 0; i < q + 1; i++){
idX[i] = xId[i] = i;
}
calcWeightSum(weightsum, graph);
while(q - p + 1 > K){
if(TKDE){
/* TKDE: select topK in greedy way */
for(int i = 0; i < K; i++, p++){
int maxloc = select_biggest(weightsum, xId, p, q);
remove_vertex_from_graph(graph, xId[maxloc], weightsum);
iter_swap(idX.begin() + xId[p], idX.begin() + xId[maxloc]);
iter_swap(xId.begin() + maxloc, xId.begin() + p);
}
}
else{
/* ICDM: select topK directly */
vector<int> maxloc = select_topk(weightsum, xId, p, q, K);
for(vector<int>::iterator it = maxloc.begin(); it != maxloc.end(); ++it, p++){
remove_vertex_from_graph(graph, xId[*it], weightsum);
iter_swap(idX.begin() + xId[p], idX.begin() + xId[*it]);
iter_swap(xId.begin() + *it, xId.begin() + p);
}
}
//Map or vector, trade-off between space and time
vector<int> vertexCC(graph.size(), 0);
vector<pair<int, int> > ccMetric(graph.size());
find_cc(graph, p, q, xId, idX, vertexCC, ccMetric);
vector<int> newx2X;
int Q = sort_vertex(newx2X, vertexCC, ccMetric);
if(Q == -1){
break;
}
for(int i = Q; i < (int)newx2X.size(); i++){
remove_vertex_from_graph(graph, xId[newx2X[i]], weightsum);
}
shuffle_vertex(newx2X, xId, idX, p);
q = p + Q - 1;
}
return idX;
}
