DiscreteMRNNParams DiscreteMRNNParams::FromSerialized(std::istream& in) {
std::string magic;
in >> magic;
if (magic != "MRNN") {
throw std::runtime_error("Not a MRNN layer, but " + magic);
}
size_t in_sz;
in >> in_sz;
DiscreteMRNNParams rnn(0, 0);
for (size_t i = 0; i < in_sz; ++i) {
rnn.whx_.push_back(std::make_shared<Param>(utils::ReadMatrixTxt(in)));
}
for (size_t i = 0; i < in_sz; ++i) {
rnn.whh_.push_back(std::make_shared<Param>(utils::ReadMatrixTxt(in)));
}
//rnn.h_->value() = utils::ReadMatrixTxt(in);
//rnn.bh_->value() = utils::ReadMatrixTxt(in);
return rnn;
}
RNN RNNBreedNetworks(RNN Parent1, RNN Parent2, double mutation_probability, double mutation_range)
{
if (mutation_probability > 1.0f)
{
mutation_probability = 1.0f;
std::cout << "Warning: keep mutation probability between 0.0 and 1.0. Capping to 1.0" << std::endl;
}
else if (mutation_probability < 0.0f)
{
mutation_probability = 0.0f;
std::cout << "Warning: keep mutation probability between 0.0 and 1.0. Flooring to 0.0" << std::endl;
}
//Make sure the networks are the same size
if (Parent1.Num_Layers != Parent2.Num_Layers)
{
std::cout << "Error! Cannot breed due to network formating!" << std::endl;
RNN rnn(0, 0);
return rnn;
}
if (Parent1.Num_Layers != Parent2.Num_Layers || Parent1.InputVectorSize != Parent2.InputVectorSize)
{
std::cout << "Error! Cannot breed due to network formating!" << std::endl;
RNN rnn(0, 0);
return rnn;
}
//Genetic algorithm
RNN offspringnetwork(Parent1.InputVectorSize, Parent1.Num_Layers); //initialize offspring network
//crossover the genes of the weights
for (int i = 1; i < Parent1.Num_Layers; i++) //we start at 1 because weights[0] is a filler matrix and does not contain any elements
{
int crossoverpoint = rand() % Parent1.Weights[i].Elements.size();
for (int j = 0; j < crossoverpoint; j++)
{
offspringnetwork.Weights[i].Elements[j] = Parent1.Weights[i].Elements[j]; //one part of the gene is from parent 1
}
for (int j = crossoverpoint; j < offspringnetwork.Weights[i].Elements.size(); j++)
{
offspringnetwork.Weights[i].Elements[j] = Parent2.Weights[i].Elements[j]; //the other part of the gene is from parent 2
}
//randomly mutate genes
for (int k = 0; k < offspringnetwork.Weights[i].Elements.size(); k++)
{
int random_int = rand() % (int)((1.01f - mutation_probability) * 1000); //we round mutation_probability * 1000 to an integer to ensure it is not a double
for (int j = 0; j < 10; j++) //we're choosing out of 1000 to get precision up to the hundredth place, so we must take 10 samples to get a probability out of 100
{
//random selection of gene
if (random_int == rand() % (int)(mutation_probability * 1000))
{
offspringnetwork.Weights[i].Elements[k] += (rand() % (int)(mutation_range * 20000 - mutation_range * 10000)) / 10000.0f; //mutate the gene
}
}
}
}
//crossover the genes of the biases and recurrent weights
for (int i = 0; i < Parent1.Num_Layers; i++)
{
int crossoverpoint = rand() % Parent1.Biases[i].Elements.size();
for (int j = 0; j < crossoverpoint; j++)
{
offspringnetwork.Biases[i].Elements[j] = Parent1.Biases[i].Elements[j]; //one part of the gene is from parent 1
offspringnetwork.RecurrentWeights[i].Elements[j] = Parent1.RecurrentWeights[i].Elements[j];
}
for (int j = crossoverpoint; j < offspringnetwork.Biases[i].Elements.size(); j++)
{
offspringnetwork.Biases[i].Elements[j] = Parent2.Biases[i].Elements[j]; //the other part of the gene is from parent 2
offspringnetwork.RecurrentWeights[i].Elements[j] = Parent2.RecurrentWeights[i].Elements[j];
}
//randomly mutate genes
for (int k = 0; k < offspringnetwork.Biases[i].Elements.size(); k++)
{
int random_int = rand() % (int)((1.01f - mutation_probability) * 1000); //we round mutation_probability * 1000 to an integer to ensure it is not a double
for (int j = 0; j < 10; j++) //we're choosing out of 1000 to get precision up to the hundredth place, so we must take 10 samples to get a probability out of 100
{
//random selection of gene
if (random_int == rand() % (int)(mutation_probability * 1000))
{
offspringnetwork.Biases[i].Elements[k] += (rand() % (int)(mutation_range * 20000 - mutation_range * 10000)) / 10000.0f; //mutate the gene
offspringnetwork.RecurrentWeights[i].Elements[k] += (rand() % (int)(mutation_range * 20000 - mutation_range * 10000)) / 10000.0f; //mutate the gene
}
}
}
}
return offspringnetwork;
}
