C++ Network::Reset方法代码示例

int main()
{
    while (true)
    {
        scanf("%d%d",&n,&m);
        if (n == 0 && m == 0)   break;
        g.Reset(n+2,0,n+1);
        g.InsertEdge(0,1,n*2);
        for (int i = 1;i <= n;i++)
        {
            int cnt,v;
            scanf("%d",&cnt);
            for (int j = 1;j <= cnt;j++)
            {
                scanf("%d",&v);
                g.InsertEdge(i,v,1);
            }
            g.InsertEdge(i,n+1,2);
        }
        cout << g.Dinic() << endl;
    }
}

//.........这里部分代码省略.........
	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++)
		{
			/*if(!isTriesch)
			{
				// in order to settle recurrent activity
				eFoldiak->SwitchOnOff(false);
				eFoldiakLateral->SwitchOnOff(false);
			}*/

			for(int k=0;k<iterSameStimuli;k++)
			{
			/*	if(!isTriesch && k==iterSameStimuli-1)
				{
					eFoldiak->SwitchOnOff(true);
					eFoldiakLateral->SwitchOnOff(true);
				}
*/
				for(int m=0;m<1;m++)
				{
					layer1->SetValuesAll(trainData[i]);
					//for(int n=0;n<3;n++)
					network->Simulate();
				}
			}

			// allow units to reset
			network->Reset();

			/*if(i%50 == 0)
			{
				network->RecordAll();
				if(mpiRank == 0)
					cout<<"Storing.";
			}*/
		}	
	}

	network->RecordAll();
}

//.........这里部分代码省略.........
				}

				if(j==plastStopIter)
				{
					bFeedforward->SwitchOnOff(false);//SetAlpha(0.0);
					bInhib->SwitchOnOff(false);//SetImpactBeta(0.0);
					softmax->SetType(SoftMax::WTA);
				}
			}

			// next time step
			network->Simulate();

			cout.flush();
		}

		connMeter.RecordAll(0);

		if(mpiRank == 0)
		{
			cout<<"\n";
			cout.flush();
			layerMeter.RecordAll(0);
			layer3Meter.RecordAll(0);
		}
	}

	// save data
	if(mpiRank == 0)
		vqMeter.RecordAll(0);

	// 2. Testing phase
	//network->Simulate();
	network->Reset(); // Clears values
	//network->Simulate();
	//network->Simulate();

	layer1->SwitchOnOff(false);
	layer3->SwitchOnOff(true);
	
	bFeedforward->SwitchOnOff(false);//->SetAlpha(0.0);
	bClassification->SwitchOnOff(false);
	kClassification->SwitchOnOff(false);
	bInhib->SwitchOnOff(false);//SetImpactBeta(0.0);
	softmax->SetType(SoftMax::WTA);

	vector<vector<float> > testingData = storageH5.LoadDataFloatCSV(filenameTesting,nrTestImages,true);//storageH5.LoadDataFloatHDF5(filename,"trainingData",0,nrTrainImages);//storageH5.LoadDataFloatHDF5(filename,"trainingData",partsOfDataToUseAsInput,0,nrTrainImages);
	vector<vector<float> > testingLabels = storageLabels.LoadDataFloatCSV(filenameTestingLabels,nrTestImages,true);

	// A. Training data
	for(int i=0;i<nrTrainImages;i++)
	{
		if(mpiRank == 0)
		{
			cout<<i;
			cout.flush();
		}

		binData = toBinary(trainingData[i],trainingData[i].size(), nrInputRateUnits);//vector<float> binData = toBinary(currentTrainingData[0],currentTrainingData[0].size(),nrInputRateUnits);//trainingData[i],trainingData[i].size(), nrInputRateUnits);
		binDataOut = toBinary(trainingLabels[i],trainingLabels[i].size(), nrOutputRateUnits);
		
		layer1->SetValuesAll(binData);
		
		network->Simulate();
	}