CStdSerialize *ClassFactory::CreateObject(std::string strClassType, std::string strObjectType, bool bThrowError)
{
CStdSerialize *lpObject=NULL;
strClassType = Std_ToUpper(Std_Trim(strClassType));
if(strClassType == "EXTERNALSTIMULUS")
lpObject = CreateExternalStimulus(strObjectType, bThrowError);
else if(strClassType == "NEURALMODULE")
lpObject = CreateNeuralModule(strObjectType, bThrowError);
else if(strClassType == "DATACOLUMN")
lpObject = CreateDataColumn(strObjectType, bThrowError);
else if(strClassType == "GAIN")
lpObject = CreateGain(strObjectType, bThrowError);
else if(strClassType == "NEURON")
lpObject = CreateNeuron(strObjectType, bThrowError);
else if(strClassType == "SYNAPSE")
lpObject = CreateSynapse(strObjectType, bThrowError);
else if(strClassType == "SYNAPSETYPE")
lpObject = CreateSynapseType(strObjectType, bThrowError);
else if(strClassType == "IONCHANNEL")
lpObject = CreateIonChannel(strObjectType, bThrowError);
else
{
lpObject = NULL;
if(bThrowError)
THROW_PARAM_ERROR(Std_Err_lInvalidClassType, Std_Err_strInvalidClassType, "ClassType", strClassType);
}
return lpObject;
}
void NeuronSpeedTest(uint iterations, uint inputLength, ActivationFunctionPtr actFunction)
{
Neuron* neuron;
uint i;
double* inputVector;
StopWatch stopWatch;
TimeInterval timeInterval;
inputVector = ALLOC_ARRAY(double, inputLength);
for(i = 0; i < inputLength; i++)
{
inputVector[i] = i;
}
neuron = CreateNeuron(inputLength, actFunction);
Reset(&stopWatch);
Start(&stopWatch);
for(i = 0; i < iterations; i++)
{
ComputeNeuronOutput(neuron, inputVector);
}
Stop(&stopWatch);
CalcElapsedMilliSeconds(&stopWatch);
ToTimeIntervalStruct(&stopWatch, &timeInterval);
printf("NEURON SPEED TEST:\nPARAMETRS:\n iterations: %d\n input length: %d\n", iterations, inputLength);
printf("ELAPSED TIME:\n");
printf(" milliseconds: %d\n", timeInterval.Miliseconds);
printf(" seconds: %d\n", timeInterval.Seconds);
printf(" minets: %d\n", timeInterval.Minets);
printf(" hours: %d\n", timeInterval.Hours);
printf("\n");
free(inputVector);
DisposeNeuron(neuron);
}
// Процедура линейного системогенеза, которая преобразует сеть из пулов в сеть из нейронов, как будто это одно и тоже (эволюция нейронных сетей, а не пулов)
void AgentLinearPrimarySystemogenesis(TNeuralNetwork* PrimaryNetwork, TPoolNetwork* PoolNetwork)
{
EraseNeuralNetwork(PrimaryNetwork);
TNeuralPool* CurrentPool = PoolNetwork->PoolsStructure;
TNeuron* CurrentNeuron = PrimaryNetwork->NetworkStructure;
int NeuronsQuantity = 0;
// Проходим по всем пулам и создаем нейроны
while (CurrentPool != NULL)
{
if (CurrentNeuron == NULL)
{
PrimaryNetwork->NetworkStructure = CreateNeuron(++NeuronsQuantity, CurrentPool->Type, CurrentPool->BiasMean, CurrentPool->Layer, true, CurrentPool, CurrentPool->ID, NULL, NULL, NULL);
CurrentNeuron = PrimaryNetwork->NetworkStructure;
}
else
{
CurrentNeuron->next = CreateNeuron(++NeuronsQuantity, CurrentPool->Type, CurrentPool->BiasMean, CurrentPool->Layer, true, CurrentPool, CurrentPool->ID, NULL, NULL, NULL);
CurrentNeuron = CurrentNeuron->next;
}
CurrentPool = CurrentPool->next;
}
CurrentPool = PoolNetwork->PoolsStructure;
CurrentNeuron = PrimaryNetwork->NetworkStructure;
int SynapsesQuantity = 0; // Количество синапсов сети
int PredConnectionQuantity = 0; // Количество предикторных связей
while (CurrentPool != NULL)
{
// Создаем синапсы
TPoolConnection* CurrentConnection = CurrentPool->ConnectednessSet;
TSynapse* CurrentSynapse = CurrentNeuron->InSynapses;
// Пока мы не прошли все входные связи текущего пула
while (CurrentConnection != NULL)
{
if (CurrentConnection->Enabled)
{
if (CurrentSynapse == NULL) // Если нет еще ни одного синапса у текущего нейрона
{
CurrentNeuron->InSynapses = CreateSynapse(++SynapsesQuantity, CurrentConnection->WeightMean, CurrentConnection->Enabled, GetPointer2Neuron(PrimaryNetwork, CurrentConnection->PrePool->ID), CurrentNeuron, NULL);
CurrentSynapse = CurrentNeuron->InSynapses;
}
else
{
CurrentSynapse->next = CreateSynapse(++SynapsesQuantity, CurrentConnection->WeightMean, CurrentConnection->Enabled, GetPointer2Neuron(PrimaryNetwork, CurrentConnection->PrePool->ID), CurrentNeuron, NULL);
CurrentSynapse = CurrentSynapse->next;
}
}
CurrentConnection = CurrentConnection->next;
}
// Создаем все предикторные связи
TPoolPredConnection* CurrentPoolPredConnection = CurrentPool->PredConnectednessSet;
TPredConnection* CurrentPredConnection= CurrentNeuron->PredConnections;
// Пока мы не прошли все входные связи текущего пула
while (CurrentPoolPredConnection != NULL)
{
if (CurrentPoolPredConnection->Enabled)
{
if (CurrentPredConnection == NULL) // Если нет еще ни одной связи у текущего нейрона
{
CurrentNeuron->PredConnections = CreatePredConnection(++PredConnectionQuantity, CurrentPoolPredConnection->Enabled, GetPointer2Neuron(PrimaryNetwork, CurrentPoolPredConnection->PrePool->ID), CurrentNeuron, NULL);
CurrentPredConnection = CurrentNeuron->PredConnections;
}
else
{
CurrentPredConnection->next = CreatePredConnection(++PredConnectionQuantity, CurrentPoolPredConnection->Enabled, GetPointer2Neuron(PrimaryNetwork, CurrentPoolPredConnection->PrePool->ID), CurrentNeuron, NULL);
CurrentPredConnection = CurrentPredConnection->next;
}
}
CurrentPoolPredConnection = CurrentPoolPredConnection->next;
}
CurrentPool = CurrentPool->next;
CurrentNeuron = CurrentNeuron->next;
}
// Заполняем данные про построенную сеть
PrimaryNetwork->NeuronQuantity = NeuronsQuantity;
PrimaryNetwork->SynapseQuantity = SynapsesQuantity;
PrimaryNetwork->PredConnectionQuantity = PredConnectionQuantity;
}
// Построение ПОЛНОЙ (без отбора) первичной нейронной сети из генотипа (структуры пулов)
void BuildPrimaryNetwork(TNeuralNetwork* PrimaryNetwork, TPoolNetwork* PoolNetwork, double DevelopSynapseProbability, double DevelopPredConnectionProbability)
{
int NeuronsQuantity = 0; // Количество нейронов в первичной сети
TNeuralPool* CurrentPool= PoolNetwork->PoolsStructure; // Ссылка на текущий пул
TNeuron* CurrentNeuron = PrimaryNetwork->NetworkStructure; // Ссылка на текущий нейрон
int MaxCapacity = 0; // Максимальная размерность пула
// Находим максимальную размерность пула
for (int PoolNumber = 0; PoolNumber < PoolNetwork->PoolQuantity; PoolNumber++)
{
if (CurrentPool->Capacity > MaxCapacity)
MaxCapacity = CurrentPool->Capacity;
CurrentPool = CurrentPool->next;
}
// Создаем двумерный массив ссылок на все нейроны первичной сети, разбитых на принадлежность к пулу (чтобы потом их не искать, когда добавляем связи)
//printf("%i ", MaxCapacity);
//TNeuron* PoolAndNetworkStructure[PoolNetwork->PoolQuantity][MaxCapacity];
TNeuron*** PoolAndNetworkStructure = new TNeuron**[PoolNetwork->PoolQuantity];
for (int i=0; i<PoolNetwork->PoolQuantity; ++i)
PoolAndNetworkStructure[i] = new TNeuron*[MaxCapacity];
CurrentPool = PoolNetwork->PoolsStructure;
// Проходим по всем пулам и создаем нейроны
for (int PoolNumber = 0; PoolNumber < PoolNetwork->PoolQuantity; PoolNumber++)
{
// Создаем все нейроны пула
for (int NeuronNumber = 0; NeuronNumber < CurrentPool->Capacity; NeuronNumber++)
{
if (CurrentNeuron == NULL) // Если в сети нет еще ни одного нейрона
{
PrimaryNetwork->NetworkStructure = CreateNeuron(++NeuronsQuantity, CurrentPool->Type, NormalDistribution(CurrentPool->BiasMean, CurrentPool->BiasVariance), CurrentPool->Layer, true, CurrentPool, CurrentPool->ID, NULL, NULL, NULL);
CurrentNeuron = PrimaryNetwork->NetworkStructure;
}
else
{
CurrentNeuron->next = CreateNeuron(++NeuronsQuantity, CurrentPool->Type, NormalDistribution(CurrentPool->BiasMean, CurrentPool->BiasVariance), CurrentPool->Layer, true, CurrentPool, CurrentPool->ID, NULL, NULL, NULL);
CurrentNeuron = CurrentNeuron->next;
}
PoolAndNetworkStructure[PoolNumber][NeuronNumber] = CurrentNeuron;
}
CurrentPool = CurrentPool->next;
}
CurrentPool = PoolNetwork->PoolsStructure;
CurrentNeuron = PrimaryNetwork->NetworkStructure;
int SynapsesQuantity = 0; // Количество синапсов сети
int PredConnectionQuantity = 0; // Количество предикторных связей
// Проходимсся по всем пулам и создаем синапсы
for (int PoolNumber = 0; PoolNumber < PoolNetwork->PoolQuantity; ++PoolNumber)
{
for (int NeuronNumber = 0; NeuronNumber < CurrentPool->Capacity; ++NeuronNumber)
{
// Создаем синапсы
TPoolConnection* CurrentConnection = CurrentPool->ConnectednessSet;
TSynapse* CurrentSynapse = CurrentNeuron->InSynapses;
// Пока мы не прошли все входные связи текущего пула
while (CurrentConnection != NULL)
{
/*// Определяем количество входных связей для текущего нейрона от нейронов пресинаптического пула
int CurrentSynapsesQuantity = rand() % 0.2*CurrentConnection->PrePool->Capacity;
int PreNeuronNumber[CurrentSynapsesQuantity]; // Номера пресинаптических нейронов из текущего пресинаптического пула
GenerateRandomNumbers(1, CurrentConnection->PrePool->Capacity, CurrentSynapsesQuantity, PreNeuronNumber);
BubbleSort(PreNeuronNumber, CurrentSynapsesQuantity);
for (int i=0; i<CurrentSynapsesQuantity; i++) // Создаем все текущие синапсы
{
if (CurrentSynapse = NULL) // Если нет еще ни одного синапса у текущего нейрона
{
CurrentNeuron->InSynapses = CreateSynapse(++SynapsesQuantity, NormalDistribution(CurrentConnection->WeightMean, CurrentConnection->WeightVariance), CurrentConnection->Enabled, PoolAndNetworkStructure[CurrentConnection->PrePool->ID-1][PreNeuronNumber[i]-1], CurrentNeuron, NULL);
CurrentSynapse = CurrentNeuron->InSynapses;
}
else
{
CurrentSynapse->next = CreateSynapse(++SynapsesQuantity, NormalDistribution(CurrentConnection->WeightMean, CurrentConnection->WeightVariance), CurrentConnection->Enabled, PoolAndNetworkStructure[CurrentConnection->PrePool->ID-1][PreNeuronNumber[i]-1], CurrentNeuron, NULL);
CurrentSynapse = CurrentSynapse->next;
}
}*/
// Проходимся по всем потенциальным пресинаптическим нейронам и определяем развивается ли связь
if (CurrentConnection->Enabled)
for (int CurrentPreNeuronNumber = 0; CurrentPreNeuronNumber < CurrentConnection->PrePool->Capacity; ++CurrentPreNeuronNumber )
{
if (UniformDistribution(0, 1) <= DevelopSynapseProbability)
{
if (CurrentSynapse == NULL) // Если нет еще ни одного синапса у текущего нейрона
{
CurrentNeuron->InSynapses = CreateSynapse(++SynapsesQuantity, NormalDistribution(CurrentConnection->WeightMean, CurrentConnection->WeightVariance), CurrentConnection->Enabled, PoolAndNetworkStructure[CurrentConnection->PrePool->ID-1][CurrentPreNeuronNumber], CurrentNeuron, NULL);
CurrentSynapse = CurrentNeuron->InSynapses;
}
else
{
CurrentSynapse->next = CreateSynapse(++SynapsesQuantity, NormalDistribution(CurrentConnection->WeightMean, CurrentConnection->WeightVariance), CurrentConnection->Enabled, PoolAndNetworkStructure[CurrentConnection->PrePool->ID-1][CurrentPreNeuronNumber], CurrentNeuron, NULL);
CurrentSynapse = CurrentSynapse->next;
}
}
}
CurrentConnection = CurrentConnection->next;
}
//Создаем предикторные связи
TPoolPredConnection* CurrentPredConnection = CurrentPool->PredConnectednessSet;
TPredConnection* CurrentPredSynapse = CurrentNeuron->PredConnections;
// Пока мы не прошли все входные предикторные связи текущего пула
while (CurrentPredConnection != NULL)
{
/*// Определяем количество входных предикторных связей для текущего нейрона от нейронов пресинаптического пула
int CurrentPredSynapsesQuantity = rand() % 0.2*CurrentPredConnection->PrePool->Capacity;
int PreNeuronNumber[CurrentPredSynapsesQuantity]; // Номера пресинаптических нейронов из текущего пресинаптического пула
GenerateRandomNumbers(1, CurrentPredConnection->PrePool->Capacity, CurrentPredSynapsesQuantity, PreNeuronNumber);
BubbleSort(PreNeuronNumber, CurrentPredSynapsesQuantity);
for (int i=0; i<CurrentPredSynapsesQuantity; i++) // Создаем все текущие предикторные связи
{
if (CurrentPredSynapse = NULL) // Если нет еще ни одной предикторной связи у текущего нейрона
{
CurrentNeuron->PredConnections = CreatePredConnection(++PredConnectionQuantity, CurrentPredConnection->Enabled, PoolAndNetworkStructure[CurrentPredConnection->PrePool->ID-1][PreNeuronNumber[i]-1], CurrentNeuron, NULL);
CurrentPredSynapse = CurrentNeuron->PredConnections;
}
else
{
CurrentPredSynapse->next = CreatePredConnection(++PredConnectionQuantity, CurrentPredConnection->Enabled, PoolAndNetworkStructure[CurrentPredConnection->PrePool->ID-1][PreNeuronNumber[i]-1], CurrentNeuron, NULL);
CurrentPredSynapse = CurrentPredSynapse->next;
}
}*/
// Проходимся по всем потенциальным пресинаптическим нейронам для текущей предикторной связи и определяем развивается ли связь
if (CurrentPredConnection->Enabled)
for (int CurrentPreNeuronNumber = 0; CurrentPreNeuronNumber < CurrentPredConnection->PrePool->Capacity; ++CurrentPreNeuronNumber)
{
if (UniformDistribution(0, 1) <= DevelopPredConnectionProbability)
{
if (CurrentPredSynapse == NULL) // Если нет еще ни одного синапса у текущего нейрона
{
CurrentNeuron->PredConnections = CreatePredConnection(++PredConnectionQuantity, CurrentPredConnection->Enabled, PoolAndNetworkStructure[CurrentPredConnection->PrePool->ID-1][CurrentPreNeuronNumber], CurrentNeuron, NULL);
CurrentPredSynapse = CurrentNeuron->PredConnections;
}
else
{
CurrentPredSynapse->next = CreatePredConnection(++PredConnectionQuantity, CurrentPredConnection->Enabled, PoolAndNetworkStructure[CurrentPredConnection->PrePool->ID-1][CurrentPreNeuronNumber], CurrentNeuron, NULL);
CurrentPredSynapse = CurrentPredSynapse->next;
}
}
}
CurrentPredConnection = CurrentPredConnection->next;
}
CurrentNeuron = CurrentNeuron->next;
}
CurrentPool = CurrentPool->next;
}
for (int i=0; i<PoolNetwork->PoolQuantity; ++i)
delete [](PoolAndNetworkStructure[i]);
delete []PoolAndNetworkStructure;
// Заполняем данные про построенную сеть
PrimaryNetwork->NeuronQuantity = NeuronsQuantity;
PrimaryNetwork->SynapseQuantity = SynapsesQuantity;
PrimaryNetwork->PredConnectionQuantity = PredConnectionQuantity;
}
