void terminal_t::schedule_solve_after(const netlist_time after)
{
// Nets may belong to railnets which do not have a solver attached
if (this->has_net())
if (net().solver() != nullptr)
net().solver()->update_after(after);
}
ATTR_COLD netlist_analog_output_t::netlist_analog_output_t()
: netlist_output_t(OUTPUT, ANALOG)
{
net().m_last.Analog = 0.97;
net().m_cur.Analog = 0.98;
net().m_new.Analog = 0.99;
}
void terminal_t::solve_now()
{
// Nets may belong to railnets which do not have a solver attached
if (this->has_net())
if (net().solver() != nullptr)
net().solver()->update_forced();
}
NetUdpSocket* Net::udpSocket() { //NetTcpSocket on default if
if ( net().m_defaultIf == NULL )
return NULL;
NetUdpSocket* pNetUdpSocket = net().m_defaultIf->udpSocket();
pNetUdpSocket->m_refs++;
return pNetUdpSocket;
}
/**
* @return true of the net is selected.
*/
bool CGSegment::selected()
{
if ( net() != NULL )
{
return net()->isSelected();
}
return false;
}
ATTR_COLD analog_output_t::analog_output_t(core_device_t &dev, const pstring &aname)
: analog_t(OUTPUT), m_proxied_net(nullptr)
{
this->set_net(m_my_net);
set_state(STATE_OUT);
net().m_cur_Analog = NL_FCONST(0.99);
analog_t::init_object(dev, aname);
net().init_object(dev.netlist(), aname + ".net");
net().register_railterminal(*this);
}
int main(void) {
/* Read network from stdin. */
std::auto_ptr<NetType> netPointer(readNet<EdgeData>());
NetType& net = *netPointer; // Create a reference for easier handling of net.
/* strength of each node and average strength */
float strengthSum=0;
for (size_t i=0; i<net.size(); ++i) {
std::cout << net(i).weight() << "\n";
strengthSum += net(i).weight();
}
std::cerr << "\nAverage strength in the network: " << strengthSum / net.size() << "\n\n";
}
//unsigned char buffer[STATIC_ALLOCATOR_SIZE];
//StaticAllocator myAlloc(buffer, STATIC_ALLOCATOR_SIZE);
int main() {
//Alloc::init(&myAlloc);
// DummyAgent agent;
QLearningEGreedyPolicy egreedy(0.1f);
NeuralNetwork net(DIM_OBSERVATIONS + DIM_ACTIONS, N_HIDDEN, 1, 0.1f);
QLearningAgent agent(&net, DIM_OBSERVATIONS, DIM_ACTIONS, N_ACTIONS,
1.0f, 0.1f, &egreedy, false); // lambda = 1.0 => no history
LibMapperEnvironment env;
RLQualia qualia(&agent, &env);
qualia.init();
qualia.start();
for (;;) {
// for (int i=0; i<10; i++) {
qualia.step();
#if is_computer()
printf("Current agent action: %d\n", agent.currentAction.conflated());
printf("Current environment observation: %f %f\n", (double)env.currentObservation.observations[0], (double)env.currentObservation.observations[1]);
#endif
}
// if (myAlloc.nLeaks)
// printf("WARNING: Static Allocator has leaks: %d\n", myAlloc.nLeaks);
return 0;
}
ATTR_COLD netlist_analog_output_t::netlist_analog_output_t()
: netlist_output_t(OUTPUT, ANALOG), m_proxied_net(NULL)
{
this->set_net(m_my_net);
net().as_analog().m_cur_Analog = 0.98;
}
QString process_socket_file(const QString &filename, QString *symlink, int sock, F fp) {
Q_ASSERT(symlink);
QFile net(filename);
net.open(QIODevice::ReadOnly | QIODevice::Text);
if(net.isOpen()) {
QTextStream in(&net);
QString line;
// ditch first line, it is just table headings
in.readLine();
// read in the first line we care about
line = in.readLine();
// a null string means end of file (but not an empty string!)
while(!line.isNull()) {
QString lline(line);
const QStringList lst = lline.replace(":", " ").split(" ", QString::SkipEmptyParts);
if(fp(symlink, sock, lst)) {
break;
}
line = in.readLine();
}
}
return *symlink;
}
int main(int argc, char* argv[]) {
struct MarsiliArgs args;
readMarsiliArgs(args, argc, argv);
RandNumGen<> generator(args.randseed);
_NetType net(args.netSize);
//time_t starttime = time(NULL);
// Generate the network
Marsili(net, args, generator);
//time_t endtime = time(NULL);
//std::cerr << "Time taken by generation: " << difftime(endtime, starttime) << " s\n";
std::auto_ptr<_NetType> netPointer2(findLargestComponent<_NetType>(net));
_NetType& net2 = *netPointer2; // Create a reference for easier handling of net.
double avg_clust = 0;
for (size_t i = 0; i < net2.size(); i++)
avg_clust += clusteringCoefficient(net2, i);
size_t edges = numberOfEdges(net2);
std::cout << net2.size() << " " << (double)2*edges/net2.size() << " " << avg_clust/net2.size() << " " << pearsonCoeff2(net2) << "\n";
}
/* Run a regular ring network test, but with an additional variable-sized
* population of unconnected neurons of a different type.
*
* The additional Poisson source neurons should not have any effect on the
* simulation but should expose errors related to mixing local/global partition
* indices.
*/
void
testNeuronTypeMixture(backend_t backend, unsigned szOthers, bool izFirst)
{
const unsigned szRing = 1024;
boost::scoped_ptr<nemo::Network> net(new nemo::Network());
if(izFirst) {
createRing(net.get(), szRing);
}
unsigned poisson = net->addNeuronType("PoissonSource");
float p = 0.001f;
for(unsigned n=szRing; n<szRing+szOthers; ++n) {
net->addNeuron(poisson, n, 1, &p);
}
if(!izFirst) {
createRing(net.get(), szRing);
}
nemo::Configuration conf = configuration(false, 1024, backend);
boost::scoped_ptr<nemo::Simulation> sim;
BOOST_REQUIRE_NO_THROW(sim.reset(nemo::simulation(*net, conf)));
/* Stimulate a single neuron to get the ring going */
sim->step(std::vector<unsigned>(1, 0));
const unsigned duration = 1000;
for(unsigned ms=1; ms < duration; ++ms) {
std::vector<unsigned> fired = sim->step();
BOOST_REQUIRE(fired.size() > 0);
std::sort(fired.begin(), fired.end());
BOOST_REQUIRE_EQUAL(fired[0], ms % szRing);
if(fired.size() > 1) {
BOOST_REQUIRE(fired[1] >= szRing);
}
}
}
int main() {
// Construct Network
Network net("name", Network::Undirected);
net.populate(10000);
net.erdos_renyi(5);
cout << "days_ahead" << "," << "transmissibility1" << "," << "transmissibility2" << "," << "strain1" << "," << "strain2" << endl;
for (int days_ahead = 0; days_ahead < 20; days_ahead ++){
for (double transmissibility1 = 0.2; transmissibility1 <= 0.5; transmissibility1 += 0.05){
for (double transmissibility2 = 0.2; transmissibility2 <= 0.5; transmissibility2 += 0.05){
int i = 0;
while ( i < 100){
// Choose and run simulation
Two_Strain_Percolation_Sim sim(&net);
sim.set_transmissibility1(transmissibility1);
sim.set_transmissibility2(transmissibility2);
sim.rand_infect(5, 0);
sim.run_headstart_simulation(days_ahead, 5);
if(sim.epidemic_size1() > 50){
cout << days_ahead << "," << transmissibility1 << "," << transmissibility2 << "," << sim.epidemic_size1() << "," << sim.epidemic_size2() << endl;
i++;
}
sim.reset();
}
}
}
}
return 0;
}
void Daemon::join( InputMessage &message, Channel channel ) {
NOTE();
OutputMessage response( MessageType::Control );
int peerId;
std::string peerName;
message >> peerId >> peerName;
channel->receive( message );
if ( _state != State::FormingGroup ) {
response.tag( Code::Refuse );
channel->send( response );
return;
}
response.tag( Code::OK );
channel->send( response );
std::string address( net().peerAddress( *channel ) );
Line peer = std::make_shared< Peer >(
peerId,
std::move( peerName ),
address.c_str(),
std::move( channel )
);
connections().lockedInsert( peerId, std::move( peer ) );
}
network user_settings_io::read_network_from_yaml(const YAML::Node& node)
{
const YAML::Node& nodes = node[usc::nodes];
const std::size_t size = nodes.size();
network net(size);
node_positions_type node_positions;
for(std::size_t i = 0; i < size; ++i)
{
const point_type& position = nodes[i].as<point_type>();
net.set_node_position(i, position);
node_positions.push_back(position);
}
settings_.set_node_positions(node_positions);
if(node[usc::links])
{
std::vector<std::pair<std::size_t, std::size_t>> links;
const YAML::Node& links_node = node[usc::links];
for(std::size_t i = 0; i < links_node.size(); ++i)
{
std::pair<std::size_t, std::size_t> link = links_node[i].as<std::pair<std::size_t, std::size_t>>();
links.push_back(link);
net.add_link(link.first, link.second);
}
settings_.set_links(links);
}
return net;
}
const Error *Init( Amp &, uint16 slot, uint16 numAxes )
{
CML_ASSERT( numAxes <= MAX_AXIS );
if( numAxes > MAX_AXIS ) return &AmpError::BadAxis;
axisCt = numAxes;
RefObjLocker<Network> net( amp.GetNetworkRef() );
if( !net ) return &NodeError::NetworkUnavailable;
netRef = net->GrabRef();
const Error *err = SetType( 255 );
for( int i=0; i<numAxes; i++ )
{
uint16 val;
if( !err ) err = amp.Upld16( OBJID_CONTROL + 0x800*i, 0, val );
ctrl[i].Write( val );
if( !err ) err = ctrl[i].Init( OBJID_CONTROL+0x800*i, 0 );
if( !err ) err = AddVar( ctrl[i] );
}
if( !err ) err = amp.PdoSet( slot, *this );
return err;
}
int main(int argc, char* argv[]) {
if ( argc < 4 )
{
std::cerr << "\nPlease specify arguments: \n\tN, p, randseed\n\n"; exit(1);
}
size_t netSize = atoi(argv[1]);
float p = atof(argv[2]);
size_t randseed = atoi(argv[3]);
RandNumGen<> generator(randseed);
NetType net(netSize);
// Generate the network
ErdosRenyi2(net, p, generator);
std::auto_ptr<NetType> netPointer2(findLargestComponent<NetType>(net));
NetType& net2 = *netPointer2; // Create a reference for easier handling of net.
// Calculate average clustering coefficient
double avg_clust = 0;
for (size_t i = 0; i < net.size(); i++)
avg_clust += clusteringCoefficient(net, i);
size_t edges = numberOfEdges(net2);
std::cout << net2.size() << " " << (double)2*edges/net2.size() << " " << avg_clust/net2.size() << " " << pearsonCoeff(net2) << "\n";
}
int main() {
// Construct Network
Network net("name", false);
net.populate(10000);
// Let's connect the network using an arbitrary degree distribution
// Out of 10 nodes, none will have degrees 0 or 1, 1 will have degree 2
// 3 with degree 3, 2 with degree 4, 1 with degree 5, and 3 with degree 3
vector<double> degree_dist;
double tmp_array[] = {0, 0, 1, 3, 2, 1, 3};
degree_dist.assign(tmp_array,tmp_array+7); // 5 == the length of tmp_array
// Now make those probabilities that sum to 1
degree_dist = normalize_dist(degree_dist, sum(degree_dist));
// Finally, connect up the network using that user-defined degree distribution
// Note that poisson, exponential, and scale-free random network generators are built-in
net.rand_connect_user(degree_dist);
// Simulation parameters
int infectious_period = 10; // 10 days, hours, whatever
double T = 0.05; // per timestep, per outbound edge probability of transmission
for (int i = 0; i < 10; i++){
// Choose and run simulation
ChainBinomial_Sim sim(&net, infectious_period, T);
sim.rand_infect(1);
sim.run_simulation();
cout << sim.epidemic_size() << endl;
sim.reset();
}
return 0;
}
int main()
{
int inputs = 2;
int outputs = 1;
int depth = 3;
int hidden_layer_size = 2;
double learning_speed = 0.1;
double momentum = 0.9;
double error = 0.001;
neural_network net(inputs, depth, hidden_layer_size, outputs, learning_speed, momentum);
vector<pair<vector<double>, vector<double> > > tests;
vector<double> test(2);
vector<double> ans(1);
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 2; j++)
{
test[0] = i;
test[1] = j;
ans[0] = (i + j) % 2;
tests.push_back(make_pair(test, ans));
}
}
net.teach(tests, error);
for (int i = 0; i < tests.size(); i++)
{
vector<double> ans = net.calculate(tests[i].first);
printf("input: %d %d\n", (int)tests[i].first[0], (int)tests[i].first[1]);
printf("output: %f\n\n", ans[0]);
}
return 0;
}
void CttView::ADTFill(const COLORREF color){
if (m_point.size() <= 2)return;
CDC* pDC = GetWindowDC();
CPen pen1(0, 0, color);
pDC->SelectObject(&pen1);
int start, end, msz=m_point.size();
start = 10000;
end = 0;
for (int i = 0; i < msz; i++){
start = min(start, m_point[i].y);
end = max(end, m_point[i].y);
}
vector<EdgeTable> net(end+1);
net.assign(end + 1, EdgeTable());
Edge te;
int miny;
for (int i = 0; i < msz-1; i++){
te = Edge(m_point[i], m_point[i + 1], miny);
net[miny].Add(te);
}
te = Edge(m_point[msz-1], m_point[0], miny);
net[miny].Add(te);
EdgeTable adt = EdgeTable(start);
for (int i = start; i <= end; i++){
for (int j = 0; j < net[i].e.size(); j++)
adt.Add(net[i].e[j]);
for (int j = 1; j < adt.e.size(); j += 2){
pDC->MoveTo((int)adt.e[j-1].x0, i);
pDC->LineTo((int)adt.e[j].x0, i);
}
adt.Update();
}
}
double lodDistanceBetweenPos(Vec2i a, Vec2i b, int blockSize)
{
int baseBlockSize = 8;
Vec2i net(a.x-b.x, a.y-b.y);
return std::sqrt(net.x*net.x + net.y*net.y) * blockSize / baseBlockSize;
}
void
testCurrentStimulus(backend_t backend)
{
unsigned ncount = 1500;
unsigned duration = ncount * 2;
nemo::Configuration conf = configuration(false, 1024, backend);
boost::scoped_ptr<nemo::Network> net(createRing(ncount));
boost::scoped_ptr<nemo::Simulation> sim(nemo::simulation(*net, conf));
nemo::Simulation::current_stimulus istim;
// add some noise before and after
istim.push_back(std::make_pair(5U, 0.001f));
istim.push_back(std::make_pair(8U, 0.001f));
istim.push_back(std::make_pair(0U, 1000.0f));
istim.push_back(std::make_pair(100U, 0.001f));
istim.push_back(std::make_pair(1U, 0.001f));
/* Simulate a single neuron to get the ring going */
sim->step(istim);
for(unsigned ms=1; ms < duration; ++ms) {
const std::vector<unsigned>& fired = sim->step();
BOOST_CHECK_EQUAL(fired.size(), 1U);
BOOST_REQUIRE_EQUAL(fired.front(), ms % ncount);
}
}
NetworkType Node::GetNetworkType( void )
{
RefObjLocker<Network> net( GetNetworkRef() );
if( !net ) return NET_TYPE_INVALID;
return net->GetNetworkType();
}
BaseBulkRound::BaseBulkRound(const Group &group,
const PrivateIdentity &ident,
const Id &round_id,
const QSharedPointer<Network> &network,
GetDataCallback &get_data,
const QSharedPointer<BuddyMonitor> &bm,
CreateRound create_shuffle) :
Round(group, ident, round_id, network, get_data, bm),
_get_shuffle_data(this, &BaseBulkRound::GetShuffleData)
{
QVariantHash headers = GetNetwork()->GetHeaders();
headers["bulk"] = true;
GetNetwork()->SetHeaders(headers);
QSharedPointer<Network> net(GetNetwork()->Clone());
headers["bulk"] = false;
net->SetHeaders(headers);
Id sr_id(Hash().ComputeHash(GetRoundId().GetByteArray()));
_shuffle_round = create_shuffle(GetGroup(), GetPrivateIdentity(), sr_id, net,
_get_shuffle_data, bm);
_shuffle_round->SetSink(&_shuffle_sink);
QObject::connect(_shuffle_round.data(), SIGNAL(Finished()),
this, SLOT(SlotShuffleFinished()));
}
int main() {
MTRand mtrand = MTRand();
for (int i = 0; i < 20; i++ ) {
cout << i << endl;
Network net("test", false );
net.populate(N_NODES);
//cout << net.size() << " ";
//net.erdos_renyi(4);
net.rand_connect_poisson(10);
// sleep(5);
//cout << net.mean_deg() << endl;
//cout << net.transitivity(net.get_nodes()) << endl;
//vector<Node*> comp = net.get_major_component();
//cout << comp.size() << endl;
//net.graphviz("");
/*
vector<Node*> my_nodes = net.get_nodes();
my_nodes[1] = my_nodes[2];
vector<Node*> your_nodes = net.get_nodes();
if (my_nodes[1] == your_nodes[2]) cerr << "Yikes!!\n";
for (int i = 0; i < N_NODES; i++) {
net.get_node(i)->mean_min_path();
cout << i << " mean dist: " << setprecision(10) << net.get_node(i)->mean_min_path() << endl;
vector<int> distances = net.get_node(i)->min_paths();
cout << i << " to 1 " << distances[1] << endl;
}*/
}
return 0;
}
const Error *Amp::RequestStatUpdt( void )
{
// For secondary axis objects, do this through the primary axis
if( primaryAmpRef )
{
RefObjLocker<Amp> pri( primaryAmpRef );
if( !pri ) return &AmpError::NotInit;
return pri->RequestStatUpdt();
}
// Note that 8367 based products don't support remote
// requests well, so we use an SDO to request the PDO
if( (hwType & 0xFF00) != 0x0300 )
{
RefObjLocker<Network> net( GetNetworkRef() );
if( !net ) return &NodeError::NetworkUnavailable;
if( !statPDO )
return &AmpError::NotInit;
return statPDO->Request(*net);
}
else
return Dnld8( OBJID_PDOREQUEST, 0, (uint8)0 );
}
void
runRing(backend_t backend, unsigned ncount, unsigned delay)
{
/* Make sure we go around the ring at least a couple of times */
const unsigned duration = ncount * 5 / 2;
nemo::Configuration conf = configuration(false, 1024);
setBackend(backend, conf);
boost::scoped_ptr<nemo::Network> net(createRing(ncount, 0, false, 1, delay));
boost::scoped_ptr<nemo::Simulation> sim(nemo::simulation(*net, conf));
/* Stimulate a single neuron to get the ring going */
sim->step(std::vector<unsigned>(1, 0));
for(unsigned ms=1; ms < duration; ++ms) {
const std::vector<unsigned>& fired = sim->step();
if(delay == 1) {
BOOST_CHECK_EQUAL(fired.size(), 1U);
BOOST_REQUIRE_EQUAL(fired.front(), ms % ncount);
} else if(ms % delay == 0) {
BOOST_CHECK_EQUAL(fired.size(), 1U);
BOOST_REQUIRE_EQUAL(fired.front(), (ms / delay) % ncount);
} else {
BOOST_CHECK_EQUAL(fired.size(), 0U);
}
}
}
void MainWindow::fileNew() {
if (_sdata)
if (!_sdata->isSaved())
maybeSave();
fileClose();
// if project exist,
// if not saved,
//maybe save?
// close
// run wizard
QMdiSubWindow * world = _mdiArea->addSubWindow(new WorldWidget());
world->setWindowTitle("*untitled");
world->setMinimumSize(300, 200);
world->show();
_sdata = new SimulationData();
_sdata->setSaved(false);
connect(_sdata, SIGNAL(fileNameChanged(QString)), world, SLOT(setWindowTitle(QString)));
SensorNetwork net(40);
net.push();
net.start();
_fileSaveAsAct->setEnabled(true);
_fileCloseAct->setEnabled(true);
}
int main(int argc, char** argv)
{
QCoreApplication app(argc, argv);
QHostAddress dest_addr;
unsigned int fft_send_interval = 1024;
if (argc > 1) {
QHostInfo host_info = QHostInfo::fromName(argv[1]);
if (host_info.error() != QHostInfo::NoError
|| host_info.addresses().empty())
{
qFatal("Could not resolve host: %s: %s", argv[1],
host_info.errorString().toUtf8().constData());
exit(1);
}
dest_addr = host_info.addresses().first();
// Increase the FFT send interval when sending data over the network
fft_send_interval *= 4;
} else {
dest_addr = QHostAddress::LocalHost;
}
qDebug() << "Sending FFT data to" << dest_addr << "port" << TRANSMIT_PORT
<< "interval" << fft_send_interval;
JackClient jc(fft_send_interval);
Networking net(dest_addr, TRANSMIT_PORT);
QObject::connect(&jc, SIGNAL(onset_detected()), &net, SLOT(transmit_onset()));
QObject::connect(&jc, SIGNAL(fft_data(int, float*)), &net, SLOT(transmit_fft_data(int, float*)));
QObject::connect(&jc, SIGNAL(pitch_data(float,float)), &net, SLOT(transmit_pitch_data(float,float)));
return app.exec();
}
/* Make sure that calling applyStdp gives an error */
void
testInvalidStdpUsage(backend_t backend)
{
boost::scoped_ptr<nemo::Network> net(createRing(10, 0, true));
nemo::Configuration conf;
setBackend(backend, conf);
BOOST_REQUIRE_THROW(simpleStdpRun(*net, conf), nemo::exception);
}