gfx_animated_object::gfx_animated_object(qtgl::batch const batch_)
: m_batch(batch_)
, m_animation_names()
, m_animation_index(0U)
, m_animation_time(0.0f)
, m_keyframes()
{
TMPROF_BLOCK();
ASSUMPTION(get_batch().ready());
ASSUMPTION(get_batch().get_available_resources().skeletal() != nullptr);
boost::filesystem::path anim_root_dir =
canonical_path(get_batch().get_available_resources().data_root_dir())
/ "animations"
/ "skeletal"
/ get_batch().get_available_resources().skeletal()->skeleton_name()
;
for (boost::filesystem::directory_entry& entry : boost::filesystem::directory_iterator(anim_root_dir))
if (boost::filesystem::is_directory(entry.path()))
m_animation_names.push_back(entry.path().string());
if (m_animation_names.empty())
return;
m_keyframes.insert_load_request(m_animation_names.at(m_animation_index));
ASSUMPTION(!m_keyframes.empty());
}
LL_Batch *VngoClear3D::get_batch(int num_batches)
{
if ((BatchCount + num_batches) < CLEAR3D_MAX_BATCH - 1)
{
BatchCount += num_batches;
LL_Batch *ret_val = pBatch;
pBatch += num_batches;
return ret_val;
}
else
{
run_dl();
return get_batch(num_batches);
}
}
void network::learning(const train_couple* training_data, int data_count, int batch_size, double eta, int epoch_number)
{
train_couple* data_copy=new train_couple[data_count]; //data to shuffle later in order to have no affect on original training_data
for (int i=0; i<data_count; i++)
data_copy[i]=training_data[i];
int nbatches=data_count/batch_size;
for (int i=0; i<epoch_number; i++) {
shuffle(data_copy,data_count);
for (int batch_number=0; batch_number<nbatches; batch_number++){
train_couple* mini_batch=get_batch(data_copy,batch_size,batch_number);
SGD(mini_batch,batch_size,eta,data_count);
delete [] mini_batch;
}
cout<<"epoch # "<<i<<" has been finished\n";
}
delete [] data_copy;
}
void get_batch_nwg () {
if (get_batch (0))
saved_batch = 1;
}
void get_batch_wg () {
(void) get_batch (1);
}