void ConvertStatetoVect(MexVector<uint32_t> &SeedState){
			if (SeedState.size() != 4)
				SeedState.resize(4);
			SeedState[0] = w;
			SeedState[1] = x;
			SeedState[2] = y;
			SeedState[3] = z;
		}
示例#2
0
	InternalVars(InputArgs &IArgs) :
		N                     (IArgs.a.size()),
		M                     (IArgs.NStart.size()),
		i                     (0),
		Time                  (IArgs.InitialState.Time),
		nSteps                (onemsbyTstep*NoOfms),
		NoOfSpikes            (0),
		CurrentQIndex         (IArgs.InitialState.CurrentQIndex),
		OutputControl         (IArgs.OutputControl),
		OutputControlString   (IArgs.OutputControlString),
		StorageStepSize       (IArgs.StorageStepSize),
		StatusDisplayInterval (IArgs.StatusDisplayInterval),
		Network               (N),
		Neurons               (M),
		InterestingSyns       (IArgs.InterestingSyns),
		V                     (IArgs.InitialState.V),
		U                     (IArgs.InitialState.U),
		Iin1(N),	// Iin is defined separately as an atomic vect.
		Iin2(N),
		WeightDeriv           (IArgs.InitialState.WeightDeriv),
		IextInterface         (),
		SpikeQueue            (),
		LSTNeuron             (IArgs.InitialState.LSTNeuron),
		LSTSyn                (IArgs.InitialState.LSTSyn),
		AuxArray                (M),
		PreSynNeuronSectionBeg  (N, -1),
		PreSynNeuronSectionEnd  (N, -1),
		PostSynNeuronSectionBeg (N, -1),
		PostSynNeuronSectionEnd (N, -1),

		BinningBuffer     (CacheBuffering*onemsbyTstep*DelayRange / 4),
		BufferInsertIndex (onemsbyTstep*DelayRange, 0),
		AddressOffset     (onemsbyTstep*DelayRange, 0),

		onemsbyTstep       (IArgs.onemsbyTstep),
		NoOfms             (IArgs.NoOfms),
		DelayRange         (IArgs.DelayRange),
		CacheBuffering     (128),
		I0                 (IArgs.I0),
		CurrentDecayFactor1(IArgs.CurrentDecayFactor1),
		CurrentDecayFactor2(IArgs.CurrentDecayFactor2){
		// Setting up Network and Neurons
		Network.resize(M);
		MexTransform(IArgs.NStart.begin(), IArgs.NStart.end(), Network.begin(), FFL([ ](Synapse &Syn, int   &NStart)->void{Syn.NStart        = NStart       ; }));
		MexTransform(IArgs.NEnd  .begin(), IArgs.NEnd  .end(), Network.begin(), FFL([ ](Synapse &Syn, int   &NEnd  )->void{Syn.NEnd          = NEnd         ; }));
		MexTransform(IArgs.Weight.begin(), IArgs.Weight.end(), Network.begin(), FFL([ ](Synapse &Syn, float &Weight)->void{Syn.Weight        = Weight       ; }));
		MexTransform(IArgs.Delay .begin(), IArgs.Delay .end(), Network.begin(), FFL([&](Synapse &Syn, float &Delay )->void{Syn.DelayinTsteps = Delay*IArgs.onemsbyTstep + 0.5f; }));

		Neurons.resize(N);
		MexTransform(IArgs.a.begin(), IArgs.a.end(), Neurons.begin(), FFL([](Neuron &Neu, float &a)->void{Neu.a = a; }));
		MexTransform(IArgs.b.begin(), IArgs.b.end(), Neurons.begin(), FFL([](Neuron &Neu, float &b)->void{Neu.b = b; }));
		MexTransform(IArgs.c.begin(), IArgs.c.end(), Neurons.begin(), FFL([](Neuron &Neu, float &c)->void{Neu.c = c; }));
		MexTransform(IArgs.d.begin(), IArgs.d.end(), Neurons.begin(), FFL([](Neuron &Neu, float &d)->void{Neu.d = d; }));

		// Setting Initial Conditions of V and U
		if (U.istrulyempty()){
			U.resize(N);
			for (int j = 0; j<N; ++j)
				U[j] = Neurons[j].b*(Neurons[j].b - 5.0f - sqrt((5.0f - Neurons[j].b)*(5.0f - Neurons[j].b) - 22.4f)) / 0.08f;
		}
		else if (U.size() != N){
			// GIVE ERROR MESSAGE HERE
			return;
		}

		if (V.istrulyempty()){
			V.resize(N);
			for (int j = 0; j<N; ++j){
				V[j] = (Neurons[j].b - 5.0f - sqrt((5.0f - Neurons[j].b)*(5.0f - Neurons[j].b) - 22.4f)) / 0.08f;
			}
		}
		else if (V.size() != N){
			// GIVE ERROR MESSAGE HEREx
			return;
		}

		// Setting Initial Conditions for INTERNAL CURRENT 1
		if (IArgs.InitialState.Iin1.size() == N){
			for (int j = 0; j < N; ++j){
				Iin1[j] = (long long int)(IArgs.InitialState.Iin1[j] * (1i64 << 32));
			}
		}
		else if (IArgs.InitialState.Iin1.size()){
			// GIVE ERROR MESSAGE HERE
			return;
		}
		//else{
		//	Iin1 is already initialized to zero by tbb::zero_allocator<long long>
		//}

		// Setting Initial Conditions for INTERNAL CURRENT 2
		if (IArgs.InitialState.Iin2.size() == N){
			for (int j = 0; j < N; ++j){
				Iin2[j] = (long long int)(IArgs.InitialState.Iin2[j] * (1i64 << 32));
			}
		}
		else if (IArgs.InitialState.Iin2.size()){
			// GIVE ERROR MESSAGE HERE
			return;
		}
		//else{
		//	Iin2 is already initialized to zero by tbb::zero_allocator<long long>
		//}

		// Setting Initial Conditions for Weight Derivative
		if (WeightDeriv.istrulyempty()){
			WeightDeriv.resize(M, 0.0f);
		}
		else if (WeightDeriv.size() != M){
			// Return Exception
			return;
		}

		// Initializing InternalVars for IextInternal giving it
		//   1. The IExtInterface::InternalVarsStruct in InternalVars
		//   2. The IExtInterface::InputVarsStruct in InputVars
		//   3. The IExtInterface::SingleStateStruct (Initial State) in InputVars
		//   4. InputVars itself to take parameters such as N, onemsbyTstep, 
		//      Noofms etc.
		
		IExtInterface::initInternalVariables(
			IextInterface,
			IArgs.IextInterface,
			IArgs.InitialState.IextInterface,
			IArgs
		);
		
		// Setting Initial Conditions of SpikeQueue
		if (IArgs.InitialState.SpikeQueue.istrulyempty()){
			SpikeQueue = MexVector<MexVector<int> >(onemsbyTstep * DelayRange, MexVector<int>());
		}
		else if (IArgs.InitialState.SpikeQueue.depth() == 1 && IArgs.InitialState.SpikeQueue.LevelSize(0) == onemsbyTstep*DelayRange) {
			IArgs.InitialState.SpikeQueue.getVectTree(SpikeQueue);
		}
		else {
			//GIVE ERROR MESSAGE HERE
			return;
		}

		// Setting Initial Conditions for LSTs
		if (LSTNeuron.istrulyempty()){
			LSTNeuron = MexVector<int>(N, -1);
		}
		else if (LSTNeuron.size() != N){
			//GIVE ERROR MESSAGE HERE
			return;
		}
		if (LSTSyn.istrulyempty()){
			LSTSyn = MexVector<int>(M, -1);
		}
		else if (LSTSyn.size() != M){
			//GIVE ERROR MESSAGE HERE
			return;
		}
	}