tree::tree(data_set& train_set, int max_leafs, int max_depth) : max_leafs(max_leafs)
{
std::set<int> features;
for (size_t i = 0; i < train_set[0].features.size(); i++)
{
features.insert(i);
}
leafs = 1;
int depth = 0;
root = new node(0);
root->data_begin = train_set.begin();
root->data_end = train_set.end();
root->calc_avg();
root->node_mse = calc_mse(root->data_begin, root->data_end, root->output_value, root->size);
std::vector<node*> layer;
layer.push_back(root);
layers.push_back(layer);
while (leafs < max_leafs && depth < max_depth && !features.empty())
{
float min_error = INF;
int best_feature = -1;
make_layer(depth);
for (std::set<int>::iterator cur_split_feature = features.begin(); cur_split_feature != features.end(); cur_split_feature++)
//choose best split feature at current depth
{
float cur_error = 0;
for (size_t i = 0; i < layers[depth].size(); i++)
{
cur_error += layers[depth][i]->split(*cur_split_feature);
}
if (cur_error < min_error)
{
min_error = cur_error;
best_feature = *cur_split_feature;
}
}
for (size_t i = 0; i < layers[depth].size(); i++)
{
layers[depth][i]->split(best_feature);
}
feature_id_at_depth.push_back(best_feature);
features.erase(best_feature);
depth++;
//std::cout << "level " << depth << " created. training error: " << min_error << " best feat: " << best_feature << " split_val: "
//<< root->split_value << std::endl;
}
for (size_t i = 0; i < layers.back().size(); i++)
{
layers.back()[i]->is_leaf = true;
}
//std::cout << "leafs before pruning: " << leafs << std::endl;
//prune(root);
//std::cout << "new tree! leafs after pruning: " << leafs << std::endl;
while (layers.back().empty())
{
layers.pop_back();
}
}
float tree::calculate_error(data_set& test_set)
{
float error = 0;
for (data_set::iterator cur_test = test_set.begin(); cur_test != test_set.end(); cur_test++)
{
float ans = calculate_anwser(*cur_test);
error += ((ans - cur_test->anwser) * (ans - cur_test->anwser));
}
error /= (1.0 * test_set.size());
return error;
}