void
find_data_ranges(teaching_vector_type &teaching, SOM_element &max_range, SOM_element &min_range ) {
if(teaching.size() == 0) return;
max_range = min_range = teaching[0];
for(int i = 1; i < (int)teaching.size(); ++i) {
max_range.elementwise_max(teaching[i]);
min_range.elementwise_min(teaching[i]);
}
}
void SOMap::teach(teaching_vector_type &in) {
for(int i = 0; i < nPasses; ++i ) {
int j = (int)(randval(0, (double)in.size())); // this won't be reproducible.
if(j == in.size()) --j;
int min_x = -1;
int min_y = -1;
subsquare_type br2(0, (int)my_map.size(), 1, 0, (int)my_map[0].size(), 1);
(void) BMU_range(in[j],min_x, min_y, br2); // just need min_x, min_y
// radius of interest
double radius = max_radius * exp(-(double)i*radius_decay_rate);
// update circle is min_xiter to max_xiter inclusive.
double learning_rate = max_learning_rate * exp( -(double)i * learning_decay_rate);
epoch_update(in[j], i, min_x, min_y, radius, learning_rate);
}
}
void graph_teach(SOMap &map1, teaching_vector_type &in) {
build_BMU_graph(map1);
// normally the training would pick random exemplars to teach the SOM. We need
// the process to be reproducible, so we will pick the exemplars in order, [0, in.size())
int next_j = 0;
for(int epoch = 0; epoch < nPasses; ++epoch) {
global_i = epoch;
bool canceled_submaps = false;
int j = next_j; // try to make reproducible
next_j = (epoch+1) % in.size();
search_result_type min_sr;
if(epoch < speculation_start) {
(send_to[epoch%SPECULATION_CNT])->try_put(in[j]);
}
else if(epoch == speculation_start) {
(send_to[epoch%SPECULATION_CNT])->try_put(in[j]);
if(epoch < nPasses-1) {
(send_to[(epoch+1)%SPECULATION_CNT])->try_put(in[next_j]);
}
}
else if(epoch < nPasses - 1) {
(send_to[(epoch+1)%SPECULATION_CNT])->try_put(in[next_j]);
}
min_sr = graph_BMU(epoch % SPECULATION_CNT); //calls wait_for_all()
double min_distance = get<0>(min_sr);
double radius = max_radius * exp(-(double)epoch*radius_decay_rate);
double learning_rate = max_learning_rate * exp(-(double)epoch * learning_decay_rate);
if(epoch >= speculation_start && epoch < (nPasses - 1)) {
// have to cancel the affected submaps
cancel_submaps(get<XV>(min_sr), get<YV>(min_sr), radius, (epoch+1)%SPECULATION_CNT);
canceled_submaps = true;
}
map1.epoch_update(in[j], epoch, get<1>(min_sr), get<2>(min_sr), radius, learning_rate);
++global_i;
if(canceled_submaps) {
// do I have to wait for all the non-canceled speculative graph to complete first?
// yes, in case a canceled task was already executing.
wait_for_all_graphs((epoch+1) % SPECULATION_CNT); // wait for the array of subgraphs
restart_submaps(get<1>(min_sr), get<2>(min_sr), radius, (epoch+1)%SPECULATION_CNT, in[next_j]);
}
last_update = min_sr;
get<RADIUS>(last_update) = radius; // not smallest value, but range of effect
}
destroy_BMU_graph();
}
