int main(int argc, const char * argv[]) {
// remember the time in order to calculate processing time at the end
time_t startTime = time(NULL);
// clear screen of terminal window
clearScreen();
printf(" MNIST-3LNN: a simple 3-layer neural network processing the MNIST handwritten digit images\n\n");
// Create neural network using a manually allocated memory space
Network *nn = createNetwork(MNIST_IMG_HEIGHT*MNIST_IMG_WIDTH, 20, 10);
// displayNetworkWeightsForDebugging(nn);
// exit(1);
// Training the network by adjusting the weights based on error using the TRAINING dataset
trainNetwork(nn);
// Testing the during training derived network using the TESTING dataset
testNetwork(nn);
// Free the manually allocated memory for this network
free(nn);
locateCursor(36, 5);
// Calculate and print the program's total execution time
time_t endTime = time(NULL);
double executionTime = difftime(endTime, startTime);
printf("\n DONE! Total execution time: %.1f sec\n\n",executionTime);
return 0;
}
MarOscInWindow::MarOscInWindow(string fileName)
{
QWidget *w = new QWidget;
setCentralWidget(w);
QLabel *gainLabel1 = new QLabel("gain");
gainSlider_ = new QSlider(Qt::Horizontal);
gainSlider_->setValue(100);
createNetwork();
QGridLayout *gridLayout = new QGridLayout;
gridLayout->addWidget(gainLabel1, 2, 1);
gridLayout->addWidget(gainSlider_, 3, 1);
connect(gainSlider_, SIGNAL(valueChanged(int)), this, SLOT(gainChanged(int)));
w->setLayout(gridLayout);
startNetwork();
cout << "Playing file=(" << fileName << ")" << endl;
play(fileName);
}
//====================================================================
// Load network from file
//====================================================================
bool BPNet::load( ifstream &ist )
{
if ( !ist.good() )
return false;
int numLayers = 0;
int numNodes = 0;
int numLinks = 0;
ist >> numLayers; // num layers
vector<int> layers(numLayers);
for(int i = 0; i != numLayers; ++i)
ist >> layers[i]; // number of nodes in each layer
ist >> numNodes;
ist >> numLinks;
// create network structure (nodes and links)
createNetwork(0, 0, layers);
// load nodes data
for (i = 0; i != numNodes; ++i)
_nodes[i]->load(ist);
// load links data
for(i = 0; i != numLinks; ++i)
_links[i]->load(ist);
if (!ist.good())
return false;
return true;
}
void EmbedderOptimalFlexDraw::optimizeOverEmbeddings(
StaticPlanarSPQRTree &T,
node parent,
node mu,
int bends,
NodeArray<int> cost[],
NodeArray<long long> embedding[])
{
cost[bends][mu] = numeric_limits<int>::max();
long long embeddingsCount = T.numberOfNodeEmbeddings(mu);
for (long long currentEmbedding = 0; currentEmbedding < embeddingsCount; ++currentEmbedding) {
T.embed(mu, currentEmbedding);
Skeleton &skeleton = T.skeleton(mu);
Graph skeletonGraph = skeleton.getGraph();
ConstCombinatorialEmbedding skeletonEmbedding(skeletonGraph);
NodeArray<node> vertexNode(skeletonGraph);
EdgeArray<node> edgeNode(skeletonGraph);
FaceArray<node> faceNode(skeletonEmbedding);
Graph N;
EdgeArray<int> upper(N);
EdgeArray<int> perUnitCost(N);
NodeArray<int> supply(N);
createNetwork(
parent,
mu,
bends,
cost,
embedding,
skeleton,
edgeNode,
N,
upper,
perUnitCost,
supply);
EdgeArray<int> lower(N, 0);
EdgeArray<int> flow(N);
NodeArray<int> dual(N);
m_minCostFlowComputer.get().call(N, lower, upper, perUnitCost, supply, flow, dual);
int currentCost = 0;
for (edge e = N.firstEdge(); e != nullptr; e = e->succ())
currentCost += perUnitCost[e] * flow[e];
for (adjEntry adj = mu->firstAdj(); adj != nullptr; adj = adj->succ())
currentCost += cost[0][adj->twinNode()];
if (currentCost < cost[bends][mu]) {
cost[bends][mu] = currentCost;
embedding[bends][mu] = currentEmbedding;
}
}
}
int main()
{
int width = 51 * 6; // lengths of strips
int height = 4;
uint8_t* buffer = (uint8_t*)malloc(width * height * 3);
createNetwork(buffer, width, height);
initEffects(width, height);
while (1)
{
clearEffect(buffer);
//runTestEffect(buffer);
//runFlameEffect(buffer);
runWaterfallEffect(buffer);
applyFixups(buffer, width, height);
#if OSC_OUTPUT_ENABLED
for (int ii = 0; ii < width; ii++)
{
for (int jj = 0; jj < height; jj++)
{
oscStream << osc::BeginBundleImmediate;
oscStream << osc::BeginMessage("led");
oscStream << ii * 3 << (height - jj - 1) * 10 << (ii + 1) * 3 << (height - jj) * 10;
int s = (jj * width + ii) * 3;
for (int c = 0; c < 3; c++)
{
oscStream << buffer[s + c];
}
oscStream << osc::EndMessage;
oscStream << osc::EndBundle;
oscSocket.Send(oscStream.Data(), oscStream.Size());
oscStream.Clear();
}
}
#endif
for (int ii = 0; ii < supplies.size(); ii++)
{
supplies[ii]->go();
}
usleep(70);
}
free(buffer);
return 0;
}
bool model::Network::emitNetwork(XMASNetwork &network) {
auto &componentMap = network.getComponentMap();
std::clock_t c_start = std::clock();
QVariantList compList;
for(auto &it : componentMap) {
XMASComponent *comp = it.second;
if (comp) {
QVariantMap map;
convertToQml(map, comp);
compList.append(map);
}
}
QVariantList channelList;
for (auto &it : componentMap) {
XMASComponent *comp = it.second;
if (comp) {
QVariantList list;
connectInQml(list, comp);
channelList.append(list);
}
}
QVariantMap qmlNetwork;
qmlNetwork["complist"] = compList;
qmlNetwork["channellist"] = channelList; emit packetChanged();
emit packetChanged();
// Var is not implemented in qml yet. No known semantics for var
//qmlNetwork["var"] = QString(network.getVar().stl().c_str());
auto cne = network.getNetworkExtension<CompositeNetworkExtension>(false);
if(cne){
this->m_alias = cne->alias.c_str();
this->m_boxedImage = cne->boxedImage;
this->m_imageName = cne->imageName.c_str();
}
auto cve = network.getNetworkExtension<CanvasNetworkExtension>(false);
if(cve){
this->m_size = QSize(cve->width,cve->height);
}
emit createNetwork(qmlNetwork);
std::clock_t c_end = std::clock();
qDebug() << "CPU time used: " << 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC << " ms"
<< " for " << compList.length() << " components and " << channelList.length() << " channels";
return true;
}
std::shared_ptr<consensus::yac::Yac> YacInit::createYac(std::string network_address,
std::shared_ptr<
uvw::Loop> loop,
ClusterOrdering initial_order) {
uint64_t delay_seconds = 5;
return Yac::create(YacVoteStorage(),
createNetwork(std::move(network_address),
initial_order.getPeers()),
createCryptoProvider(),
createTimer(std::move(loop)),
initial_order,
delay_seconds * 1000);
}
BPNet::BPNet( double lr, double mt, int layers, ... )
{
va_list vl;
// construct layers vector according to argument list
va_start(vl, layers );
vector<int> layersVec(layers);
for (int i = 0; i != layers; ++i)
layersVec[i] = va_arg(vl, int);
va_end( vl );
// create network
createNetwork( lr, mt, layersVec);
}
/*! @brief Constructor for the nubot
The parameters are for command line arguements. Webots gives the binary arguements which tell us the
robot's number. Consequently, these args are passed down to the webots platform.
@param argc the number of command line arguements
@param *argv[] the array of command line arguements
*/
NUbot::NUbot(int argc, const char *argv[])
{
#if DEBUG_NUBOT_VERBOSITY > 0
debug << "NUbot::NUbot()." << endl;
#endif
NUbot::m_this = this;
createErrorHandling();
createPlatform(argc, argv);
createBlackboard();
createNetwork();
createModules();
createThreads();
#if DEBUG_NUBOT_VERBOSITY > 0
debug << "NUbot::NUbot(). Finished." << endl;
#endif
}
MarOscMultiGainWindow::MarOscMultiGainWindow(string fileName1, string fileName2, string fileName3)
{
QWidget *w = new QWidget;
setCentralWidget(w);
QLabel *gain1Label = new QLabel(QString(fileName1.c_str()));
gain1Slider_ = new QSlider(Qt::Horizontal);
gain1Slider_->setValue(0);
QLabel *gain2Label = new QLabel(QString(fileName2.c_str()));
gain2Slider_ = new QSlider(Qt::Horizontal);
gain2Slider_->setValue(0);
QLabel *gain3Label = new QLabel(QString(fileName3.c_str()));
gain3Slider_ = new QSlider(Qt::Horizontal);
gain3Slider_->setValue(0);
createNetwork(fileName1,fileName2,fileName3);
QGridLayout *gridLayout = new QGridLayout;
gridLayout->addWidget(gain1Label, 1, 1);
gridLayout->addWidget(gain1Slider_, 2, 1);
gridLayout->addWidget(gain2Label, 3, 1);
gridLayout->addWidget(gain2Slider_, 4, 1);
gridLayout->addWidget(gain3Label, 5, 1);
gridLayout->addWidget(gain3Slider_, 6, 1);
connect(gain1Slider_, SIGNAL(valueChanged(int)), this, SLOT(gain1Changed(int)));
connect(gain2Slider_, SIGNAL(valueChanged(int)), this, SLOT(gain2Changed(int)));
connect(gain3Slider_, SIGNAL(valueChanged(int)), this, SLOT(gain3Changed(int)));
w->setLayout(gridLayout);
// startNetwork();
cout << "Playing files=(" << fileName1 << ")" << "(" << fileName2 << ")" << "(" << fileName3 << ")" << endl;
play(fileName1,fileName2,fileName3);
}
void Simulator::initializeNetwork(std::map<int,MechanicalNode*>* inMap)
{
if (!timerID)
killTimer(timerID);
nodeMap.clear();
edgeVec.clear();
graphicalObjects.clear();
network = inMap;
xRot = yRot = zRot = 0;
zHomeView = -40;
timerID = startTimer(20);
createNetwork();
if (!timerID)
updateGL();
}
/* private slots */
void NetControlThread::run()
{
Result result;
switch (task.action) {
case GET_ALL_ENTITY_STATE :
result = getAllNetworkList();
break;
case CREATE_ENTITY :
result = createNetwork();
break;
case DEFINE_ENTITY :
result = defineNetwork();
break;
case START_ENTITY :
result = startNetwork();
break;
case DESTROY_ENTITY :
result = destroyNetwork();
break;
case UNDEFINE_ENTITY :
result = undefineNetwork();
break;
case CHANGE_ENTITY_AUTOSTART :
result = changeAutoStartNetwork();
break;
case GET_XML_DESCRIPTION :
result = getVirtNetXMLDesc();
break;
default:
break;
};
// task.srcConnPtr reference will closed in destructor as ptr_ConnPtr
//virConnectClose(*task.srcConnPtr);
result.type = "network";
result.number = number;
result.action = task.action;
emit resultData(result);
}
void Network::createMultiLayer(int M, int N, double weight, double parameter)
{
int nbNeurons = (M+N)*(M-N+1)/2;
createNetwork(nbNeurons);
// init neurons
_neuronParameters.fill(parameter);
// init synapses
// for all layer
for (int i=M,k=0; i>N; i--)
{
// select a neuron of this main layer
for (int j=0; j<i; j++)
{
// add synapses from neurons of the main layer to all neurons of the sub-layer
for (int l=0; l<i-1; l++)
{
initSynapse(k+j, k+i+l, weight);
}
}
k += i;
}
// input neurons
for (int i=0; i<M; i++)
{
addInput(i);
}
// output neurons
for (int i=0; i<N; i++)
{
addOutput(nbNeurons - N + i);
}
}
int main (int argc, char* argv[])
{
if (argc == 1)
{
srand(time(NULL));
struct neural_network *network = createNetwork(3, 3, 2);
int loopCount = 0, count = 1, proceed = 0;
double out00, out01, out10, out11;
do
{
do
{
loopCount++;
setInput(1,1, network);
out11 = getOutput(network);
learn(network, 0);
setInput(0,0, network);
out00 = getOutput(network);
learn(network, 0);
setInput(0,1,network);
out01 = getOutput(network);
learn(network, 1);
setInput(1,0, network);
out10 = getOutput(network);
learn(network, 1);
printf("0xor0: %f 0xor1: %f 1xor0: %f 1xor1: %f\n",out00, out01,
out10, out11);
}while ((out00 > 0.05 || out01 < 0.95 || out10 < 0.95 ||
out11 > 0.05) && loopCount < 200000);
proceed = loopCount == 200000;
if(proceed)
{
printf("creating new network\n");
resetWeights(network);
printf("weights reset");
count++;
loopCount = 0;
}
}while(proceed);
freeNetwork(network);
printf("Network freed.\n");
printf("Loop count : %i\n",(count - 1)*200000 + loopCount);
printf("Everything went fine. Exiting...\n");
_Exit(0);
}
else if (argc == 2)
{
IplImage *img = load(argv[1]);
if(img != NULL)
{
const char* window_title = "Perfect Image";
cvNamedWindow (window_title, CV_WINDOW_AUTOSIZE);
cvShowImage (window_title, img);
cvWaitKey(0);
char path[] = "result.bmp";
cvSaveImage(&path[0], img, NULL);
cvDestroyAllWindows();
cvReleaseImage(&img);
printf("Everything went fine. Exiting...\n");
return 0;
}
else
{
printf("Error loading image.\n");
return -1;
}
}
else
{
printf("Argument issue. Exiting...\n");
return -1;
}
}
MarLpcWindow::MarLpcWindow()
{
frequencyPole_ = 0;
amplitudePole_ = .85;
QWidget *w = new QWidget;
setCentralWidget(w);
createActions();
createMenus();
QLabel *breathinessLabel = new QLabel("breathiness");
breathinessSlider_ = new QSlider(Qt::Horizontal);
QLabel *cutOffLabel = new QLabel("cutOff");
QSlider *cutOffSlider = new QSlider(Qt::Horizontal);
QLabel *frequencyPoleLabel1 = new QLabel("frequencyPole");
QLabel *frequencyPoleLabel2 = new QLabel("frequencyPole");
frequencyPoleSlider_ = new QSlider(Qt::Horizontal);
QLabel *amplitudePoleLabel1 = new QLabel("amplitudePole");
QLabel *amplitudePoleLabel2 = new QLabel("amplitudePole");
amplitudePoleSlider_ = new QSlider(Qt::Horizontal);
QLabel *tiltLabel = new QLabel("Tilt");
tiltSlider_ = new QSlider(Qt::Horizontal);
QLabel *posLabel = new QLabel("Pos");
posSlider_ = new QSlider(Qt::Horizontal);
breathinessLabel->setMinimumWidth(150);
cutOffLabel->setMinimumWidth(150);
frequencyPoleSlider_->setValue(50);
amplitudePoleSlider_->setValue(50);
tiltSlider_->setValue(50);
createNetwork();
QGridLayout *gridLayout = new QGridLayout;
gridLayout->addWidget(breathinessLabel, 0, 0);
gridLayout->addWidget(breathinessSlider_, 1, 0);
gridLayout->addWidget(tiltLabel, 0, 1);
gridLayout->addWidget(tiltSlider_, 1, 1);
gridLayout->addWidget(frequencyPoleLabel1, 2, 0);
gridLayout->addWidget(frequencyPoleSlider_, 3, 0);
gridLayout->addWidget(amplitudePoleLabel1, 2, 1);
gridLayout->addWidget(amplitudePoleSlider_, 3, 1);
gridLayout->addWidget(posLabel, 5, 0);
gridLayout->addWidget(posSlider_, 6, 0);
gridLayout->addWidget(posControl_, 6, 1);
gridLayout->addWidget(frequencyPoleControl_, 4, 0);
gridLayout->addWidget(amplitudePoleControl_, 4, 1);
connect(breathinessSlider_, SIGNAL(valueChanged(int)), this, SLOT(breathinessChanged(int)));
connect(cutOffSlider, SIGNAL(valueChanged(int)), this, SLOT(cutOffChanged(int)));
connect(frequencyPoleSlider_, SIGNAL(valueChanged(int)), this, SLOT(frequencyPoleChanged(int)));
connect(amplitudePoleSlider_, SIGNAL(valueChanged(int)), this, SLOT(amplitudePoleChanged(int)));
connect(tiltSlider_, SIGNAL(valueChanged(int)), this, SLOT(tiltChanged(int)));
connect(posSlider_, SIGNAL(sliderReleased()), this, SLOT(posChanged()));
connect(mwr_, SIGNAL(ctrlChanged(MarControlPtr)), this, SLOT(ctrlChanged(MarControlPtr)));
w->setLayout(gridLayout);
startNetwork();
}
void EmbedderOptimalFlexDraw::call(Graph &G, adjEntry &adjExternal)
{
StaticPlanarSPQRTree T(G);
NodeArray<int> cost[4];
NodeArray<long long> embedding[4];
for (int bends = 0; bends < 4; ++bends) {
cost[bends].init(T.tree());
embedding[bends].init(T.tree());
}
int minCost = numeric_limits<int>::max();
node minCostRoot;
long long minCostEmbedding;
for (node root = T.tree().firstNode(); root != nullptr; root = root->succ()) {
T.rootTreeAt(root);
for (adjEntry adj = root->firstAdj(); adj != nullptr; adj = adj->succ())
computePrincipalSplitComponentCost(T, cost, embedding, root, adj->twinNode());
optimizeOverEmbeddings(T, nullptr, root, 0, cost, embedding);
if (cost[0][root] < minCost) {
minCost = cost[0][root];
minCostEmbedding = embedding[0][root];
minCostRoot = root;
}
}
T.rootTreeAt(minCostRoot);
T.embed(minCostRoot, minCostEmbedding);
for (adjEntry adj = minCostRoot->firstAdj(); adj != nullptr; adj = adj->succ())
computePrincipalSplitComponentCost(T, cost, embedding, minCostRoot, adj->twinNode());
Skeleton &skeleton = T.skeleton(minCostRoot);
Graph skeletonGraph = skeleton.getGraph();
ConstCombinatorialEmbedding skeletonEmbedding(skeletonGraph);
EdgeArray<node> edgeNode(skeletonGraph);
Graph N;
EdgeArray<int> upper(N);
EdgeArray<int> perUnitCost(N);
NodeArray<int> supply(N);
createNetwork(
nullptr,
minCostRoot,
0,
cost,
embedding,
skeleton,
edgeNode,
N,
upper,
perUnitCost,
supply);
EdgeArray<int> lower(N, 0);
EdgeArray<int> flow(N);
NodeArray<int> dual(N);
m_minCostFlowComputer.get().call(N, lower, upper, perUnitCost, supply, flow, dual);
for (node mu = T.tree().firstNode(); mu != nullptr; mu = mu->succ()) {
if (mu == minCostRoot)
continue;
int bends = 0;
for (adjEntry adj = edgeNode[T.skeleton(mu).referenceEdge()]->firstAdj(); adj != nullptr; adj = adj->succ())
bends += abs(flow[adj->theEdge()]);
T.embed(mu, embedding[bends][mu]);
}
T.embed(G);
ConstCombinatorialEmbedding graphEmbedding(G);
adjExternal = graphEmbedding.externalFace()->firstAdj();
}
