public void encodePercept(symbol_list_t symlist, UInt64 observation, UInt64 reward)
{
symlist.clear();
encode(symlist, (int)observation, m_obs_bits);
encode(symlist, (int)reward, m_rew_bits);
}
// generate a specified number of random symbols distributed according to
// the context tree statistics and update the context tree with the newly
// generated bits
public void genRandomSymbolsAndUpdate(Random rng, symbol_list_t symbols, UInt64 bits)
{
// TODONE: implement
symbols.clear();
//Random rng = new Random();
for (UInt64 i = 0; i < bits; i++)
{
// flip a biased coin for each bit
double prediction = predict(false);
bool rand_sym = rng.NextDouble() < prediction ? false : true;
symbols.push_back(rand_sym);
update(rand_sym); // TODO: optimise this loop
}
}
void getContext(symbol_list_t context)
{
// if (!m_context_functor.empty())
// {
// m_context_functor(context);
// return;
// }
context.clear();
// history_t::const_reverse_iterator ri = m_history.rbegin();
int ri = m_history.mem.Count - 1;
for (UInt64 c = 0; ri >= 0 && c < m_depth; --ri, c++)
{
context.push_back((bool)m_history.mem[(int)ri]);
}
}
// encoding/decoding actions and percepts to/from symbol lists
void encodeAction(symbol_list_t symlist, action_t action)
{
symlist.clear();
encode(symlist, (int)action, m_actions_bits);
}
