static
void dump_dot_8(const NFA *nfa, FILE *f) {
auto m = (const mcsheng *)getImplNfa(nfa);
dumpDotPreambleDfa(f);
for (u16 i = 1; i < m->state_count; i++) {
describeNode(nfa, m, i, f);
u16 t[ALPHABET_SIZE];
next_states(nfa, i, t);
describeEdge(f, m, t, i);
}
fprintf(f, "}\n");
}
boost::optional<std::vector<pos>> random_walk_impl(
const State &st, set_t &memo, std::mt19937 &mt, uint64_t &count) {
if (st.bd.is_goal()) return std::vector<pos>();
if (count % 10000 == 0) std::cerr << count << std::endl;
++count;
memo.insert(st.bd.units);
auto nexts = next_states(st);
std::vector<std::tuple<State, pos>> coodinates;
for (auto &next : nexts)
if (memo.count(std::get<0>(next).bd.units) == 0)
coodinates.push_back(next);
if (coodinates.empty()) return boost::none;
std::shuffle(std::begin(coodinates), std::end(coodinates), mt);
for (auto next : coodinates) {
if (auto opt_res = random_walk_impl(std::get<0>(next), memo, mt, count)) {
opt_res->push_back(std::get<1>(next));
return opt_res;
}
}
return boost::none;
}
void legion_network::calculate_states(const legion_stimulus & stimulus, const solve_type solver, const double t, const double step, const double int_step) {
std::vector<void *> argv(2, NULL);
std::vector<differ_result<double> > next_states(size());
argv[0] = (void *) this;
unsigned int number_int_steps = (unsigned int) (step / int_step);
for (unsigned int index = 0; index < size(); index++) {
argv[1] = (void *) &index;
differ_state<double> inputs { m_oscillators[index].m_excitatory, m_oscillators[index].m_inhibitory };
if (m_params.ENABLE_POTENTIAL) {
inputs.push_back(m_oscillators[index].m_potential);
}
switch(solver) {
case solve_type::FAST: {
throw std::runtime_error("Forward Euler first-order method is not supported due to low accuracy.");
}
case solve_type::RK4: {
if (m_params.ENABLE_POTENTIAL) {
runge_kutta_4(&legion_network::adapter_neuron_states, inputs, t, t + step, number_int_steps, false /* only last states */, argv, next_states[index]);
}
else {
runge_kutta_4(&legion_network::adapter_neuron_simplify_states, inputs, t, t + step, number_int_steps, false /* only last states */, argv, next_states[index]);
}
break;
}
case solve_type::RKF45: {
if (m_params.ENABLE_POTENTIAL) {
runge_kutta_fehlberg_45(&legion_network::adapter_neuron_states, inputs, t, t + step, 0.00001, false /* only last states */, argv, next_states[index]);
}
else {
runge_kutta_fehlberg_45(&legion_network::adapter_neuron_simplify_states, inputs, t, t + step, 0.00001, false /* only last states */, argv, next_states[index]);
}
break;
}
default: {
throw std::runtime_error("Unknown type of solver");
}
}
std::vector<unsigned int> * neighbors = get_neighbors(index);
double coupling = 0.0;
for (std::vector<unsigned int>::const_iterator index_neighbor_iterator = neighbors->begin(); index_neighbor_iterator != neighbors->end(); index_neighbor_iterator++) {
coupling += m_dynamic_connections[index][*index_neighbor_iterator] * heaviside(m_oscillators[*index_neighbor_iterator].m_excitatory - m_params.teta_x);
}
delete neighbors;
m_oscillators[index].m_buffer_coupling_term = coupling - m_params.Wz * heaviside(m_global_inhibitor - m_params.teta_xz);
}
differ_result<double> inhibitor_next_state;
differ_state<double> inhibitor_input { m_global_inhibitor };
switch (solver) {
case solve_type::RK4: {
runge_kutta_4(&legion_network::adapter_inhibitor_state, inhibitor_input, t, t + step, number_int_steps, false /* only last states */, argv, inhibitor_next_state);
break;
}
case solve_type::RKF45: {
runge_kutta_fehlberg_45(&legion_network::adapter_inhibitor_state, inhibitor_input, t, t + step, 0.00001, false /* only last states */, argv, inhibitor_next_state);
break;
}
}
m_global_inhibitor = inhibitor_next_state[0].state[0];
for (unsigned int i = 0; i < size(); i++) {
m_oscillators[i].m_excitatory = next_states[i][0].state[0];
m_oscillators[i].m_inhibitory = next_states[i][0].state[1];
if (m_params.ENABLE_POTENTIAL) {
m_oscillators[i].m_potential = next_states[i][0].state[2];
}
m_oscillators[i].m_coupling_term = m_oscillators[i].m_buffer_coupling_term;
m_oscillators[i].m_noise = m_noise_distribution(m_generator);
}
}
