C++ Meter::AttachPopulation方法代码示例

void NetworkProjectionTests::NetworkSetupMeters()
{
	Meter* layerMeterOut = new Meter("TestProjectionsLayer1.csv", Storage::CSV);
	layerMeterOut->AttachPopulation(this->GetLayer(0));
	Meter* layerMeterOut2 = new Meter("TestProjectionsLayer2.csv", Storage::CSV);
	layerMeterOut2->AttachPopulation(this->GetLayer(1));

	this->AddMeter(layerMeterOut);
	this->AddMeter(layerMeterOut2);
}

void NetworkTests::NetworkTestTrieschAndFoldiak(int mpiRank, int mpiSize)
{
	DataSources dataSources;

	int sizeX = 5;//10;
	int sizeY = 5;//10;
	int nrItems = 2500;

	bool isTriesch = true;

	Network* network = new Network();
	network->SetMPIParameters(mpiRank,mpiSize);

	int nrInputHypercolumns = 1;
	int nrInputRateUnits = sizeX*sizeY;
	int nrOutputHypercolumns = 2;
	int nrOutputRateUnits = 5;//sizeX+sizeY;

	PopulationColumns* layer1 = new PopulationColumns(network,nrInputHypercolumns,nrInputRateUnits,PopulationColumns::GradedThresholded);
	PopulationColumns* layer2 = new PopulationColumns(network,nrOutputHypercolumns,nrOutputRateUnits,PopulationColumns::GradedThresholded);

	network->AddPopulation(layer1);
	network->AddPopulation(layer2);

	FullConnectivity* full = new FullConnectivity();
	FullConnectivity* full2;
	FullConnectivityNoLocalHypercolumns* full3NoLocal;

	layer2->AddPre(layer1,full);

	bool thresholded = true;
	ProjectionModifierTriesch* eTriesch = new ProjectionModifierTriesch(0.002f,0.2f,0.05f,1.0f/float(nrOutputRateUnits), thresholded);//0.05,0.2,0.005,1.0/(float)nrOutputRateUnits, thresholded);

	if(isTriesch)
		full->AddProjectionsEvent(eTriesch);

	//float eta1 = 3, eta2= 2.4, eta3 = 1.5, alpha = 0.005, beta = 200;
	float eta1 = 0.5, eta2= 0.02, eta3 = 0.02, alpha = 0.0005, beta = 10;//alpha = 1.0/8.0, beta = 10;
	bool lateral = false;

	ProjectionModifierFoldiak* eFoldiak = new ProjectionModifierFoldiak(eta1, eta2, eta3, alpha, beta, lateral);
	lateral = true;
	alpha = 0.75;
	ProjectionModifierFoldiak* eFoldiakLateral = new ProjectionModifierFoldiak(eta1, eta2, eta3, alpha, beta, lateral);
	//ProjectionModifierBCM* eBCM = new ProjectionModifierBCM(0.1,0.05,20);

	if(!isTriesch)
	{
		full2 = new FullConnectivity();
		layer2->AddPre(layer2,full2);
		full->AddProjectionsEvent(eFoldiak);
		full2->AddProjectionsEvent(eFoldiakLateral);
	}
	else
	{
		full3NoLocal = new FullConnectivityNoLocalHypercolumns();
		//full3NoLocal->AddProjectionsEvent(eBCM);
		full3NoLocal->AddProjectionsEvent(eFoldiakLateral);
		layer2->AddPre(layer2,full3NoLocal);
	}

	// implements N here
	SoftMax* softmax = new SoftMax(SoftMax::WTAThresholded,0.5);//(10.0, SoftMax::ProbWTA);
	WTA* wta = new WTA();
	//layer2->AddPopulationModifier(wta);
	layer2->AddPopulationModifier(softmax);

	network->Initialize();

	//////////////////////////////
	// Meters
	char* name1 = new char[50];
	char* name2 = new char[50];
	sprintf(name1,"Projection_triesch_n%d.csv",mpiRank);
	Meter* connMeter = new Meter(name1, Storage::CSV);
	connMeter->AttachProjection(layer2->GetIncomingProjections()[0],0);
	network->AddMeter(connMeter);

	sprintf(name2,"Layer2Activity_triesch.csv");

	Meter* layerMeter = new Meter(name2, Storage::CSV);
	layerMeter->AttachPopulation(layer2);
	network->AddMeter(layerMeter);
	// end Meters
	//////////////////////////////

	vector<vector<float> > trainData = dataSources.GetBars(sizeX,sizeY, nrItems);

	int iterations = 1;
	int iterSameStimuli = 100;

	if(!isTriesch)
		iterSameStimuli = 10;

	layer1->SwitchOnOff(false);	// fixed during training phase

	for(int j=0;j<iterations;j++)
	{
		for(int i=0;i<trainData.size();i++)
		{
//.........这里部分代码省略.........

// Switching
void NetworkTests::NetworkTestSwitching(int mpiRank, int mpiSize)
{
	int nrHypercolumns = 5;
	int nrRateUnits = 10;
	int nrItems = 2;

	DataSources sources;
	srand(2);
	vector<vector<float> > data = sources.GetRandomHCs(nrHypercolumns,nrRateUnits,nrItems);//sources.GetRandom(size,0.1,nrItems);
	
	// setup recurrent network

	Network* network = new Network();
	network->SetMPIParameters(mpiRank,mpiSize);

	PopulationColumns* layer1 = new PopulationColumns(network,nrHypercolumns,nrRateUnits,PopulationColumns::Graded);
	FullConnectivity* full = new FullConnectivity();//FullConnectivity(false,"");

	layer1->AddPre(layer1,full);
	network->AddPopulation(layer1);

	ProjectionModifierBcpnnOnline* eBcpnn = new ProjectionModifierBcpnnOnline();
	ProjectionModifierTriesch* eTriesch = new ProjectionModifierTriesch();
	ProjectionModifierHebbSimple* eHebb = new ProjectionModifierHebbSimple();
	ProjectionModifierBCM* eBCM = new ProjectionModifierBCM();

	full->AddProjectionsEvent(eBcpnn);		// incl adding transfer fcn
	//full->AddProjectionsEvent(eTriesch);		// incl adding transfer fcn
	//full->AddProjectionsEvent(eHebb);
	//full->AddProjectionsEvent(eBCM);

	PopulationModifierAdaptation2* eAdaptation = new PopulationModifierAdaptation2();
	//eAdaptation->SetParameters(0,0); 		// adaptation off initially	
	eAdaptation->SetParameters(0); 		// adaptation off initially
	layer1->AddPopulationModifier(eAdaptation);
	
	WTA* wta = new WTA();
	layer1->AddPopulationModifier(wta);//wta);//softmax);

	network->Initialize();
	eAdaptation->Initm_Aj(1); // initialize m_Aj vector

	// set up meters
	char* name1 = new char[30];
	char* name2 = new char[30];
	char* name3 = new char[30];
	sprintf(name1,"Projections_n%d.csv",mpiRank);
	sprintf(name2,"Layer1ActivityWTA.csv");
	sprintf(name3,"Layer1Activity.csv");

	Meter* connMeter = new Meter(name1, Storage::CSV);
	connMeter->AttachProjection(layer1->GetIncomingProjections()[0],0);
	network->AddMeter(connMeter);

	Meter* layerMeter = new Meter(name3, Storage::CSV);
	layerMeter->AttachPopulation(layer1);
	network->AddMeter(layerMeter);

	Meter* eventLayerMeter=new Meter(name2, Storage::CSV);
	eventLayerMeter->AttachPopulationModifier(eAdaptation);
	network->AddMeter(eventLayerMeter);

	int nrIters = 10;
	int stimuliOn = 10;

	layer1->SwitchOnOff(false); // fixed input

	// store patterns
	for(unsigned int i=0;i<nrIters;i++)
	{
		for(unsigned int j=0;j<data.size();j++)
		{
			for(unsigned int k=0;k<stimuliOn; k++)
			{
				layer1->SetValuesAll(data[j]);
				network->Simulate();
			}
		}
	}


	// random stimulation
	vector<float> randVec(data[0].size());
	for(unsigned int i=0;i<randVec.size();i++)
		randVec[i] = 0.5f*float(rand()/RAND_MAX);

	// mixture
	vector<float> mixVec(data[0].size());
	for(unsigned int i=0;i<mixVec.size();i++)
		mixVec[i] = 1*(data[0][i] + data[1][i]);

	layer1->SetValuesAll(mixVec);//randVec);

	// Test without adaptation turned on

	layer1->SwitchOnOff(true);
	//eHebb->SetEtaHebb(0.0);
	eBCM->SwitchOnOff(false);
	eBcpnn->SwitchOnOff(false);
//.........这里部分代码省略.........