//Added this
void hiddenLayer::calculate(inputLayer iL, weights wil)
{
for(int j=0; j < numHidden; j++)
{
//clear value
setNeuron(j, 0);
//get weighted sum of pattern and bias neuron
for( int i=0; i <= iL.getInputs(); i++ )
{
setNeuron(j, (getNeuron(j) + (iL.getNeuron(i) * wil.getWeight(i,j))));
}
//set to result of sigmoid
setNeuron(j, activationFunction(getNeuron(j)));
}
}
//Added this
void outputLayer::calculate(hiddenLayer hL, weights who)
{
for(int k=0; k < numOutput; k++)
{
//clear value
setNeuron(k, 0);
//get weighted sum of pattern and bias neuron
for( int j=0; j <= hL.getNumHidden(); j++ )
{
setNeuron(k, (getNeuron(k) + (hL.getNeuron(j) * who.getWeight(j,k))));
}
//set to result of sigmoid
setNeuron(k, activationFunction(getNeuron(k)));
}
}
Input const Layer::processInput(Input const &input) const {
Input output(size);
for (int i = 0; i < size; i++) {
Output result = getNeuron(i)->processInput(input);
output.setValue(i, result.getValue());
}
return output;
}
void Net::load(ifstream & loadStream) {
clear();
name = loadString(loadStream);
loadStream.read((char*)&data, sizeof(NetData));
//load private data
loadStream.read((char*)(&neuronIndex), sizeof(int));
loadStream.read((char*)(&synapseIndex), sizeof(int));
//load neurons
int count;
loadStream.read((char*)(&count), sizeof(int));
while(count>0){
Neuron * newNode = new Neuron;
newNode->load(loadStream);
neuron.push_back(newNode);
count--;
}
//load synapses
loadStream.read((char*)(&count), sizeof(int));
while(count>0){
Synapse newSynapse;
loadStream.read((char*)(&newSynapse.index), sizeof(int));
loadStream.read((char*)(&newSynapse.weight), sizeof(float));
loadStream.read((char*)(&newSynapse.potentiation), sizeof(float));
loadStream.read((char*)(&newSynapse.fire_frequency), sizeof(char));
loadStream.read((char*)(&newSynapse.fire_timer), sizeof(char));
int idx;
loadStream.read((char*)(&idx), sizeof(int));
newSynapse.from = getNeuron(idx);
loadStream.read((char*)(&idx), sizeof(int));
newSynapse.to = getNeuron(idx);
//cout<<"loading synapse #"<<newSynapse.index<<endl;
synapse.push_back(newSynapse);
count--;
}
}
QVariant ZFlyEmNeuronListModel::data(const QModelIndex &index, int role) const
{
if (!index.isValid()) {
return QVariant();
}
if (index.row() >= m_neuronList.size() || index.row() < 0) {
return QVariant();
}
const ZFlyEmNeuron *neuron = getNeuron(index.row());
if (neuron != NULL) {
return m_presenter->data(*neuron, index.column(), role);
}
/*
if (role == Qt::DisplayRole) {
const ZFlyEmNeuron *neuron = getNeuron(index);
switch (index.column()) {
case 0:
{
QString data = QString("%1").arg(neuron->getId());
return data;
}
case 1:
return neuron->getName().c_str();
case 2:
return neuron->getClass().c_str();
case 3:
if (neuron->getMatched() == NULL) {
return "";
} else {
return QString(":") + neuron->getMatched()->getClass().c_str() +
QString(" (%1)").arg(neuron->getMatched()->getId());
}
case 4:
return neuron->getModelPath().c_str();
default:
return QVariant();
}
} else if (role == Qt::ToolTipRole) {
const ZFlyEmNeuron *neuron = getNeuron(index);
if (neuron != NULL) {
QString data = QString("%1").arg(neuron->getId()) +
" (" + neuron->getModelPath().c_str() + ")";
return data;
}
}
*/
return QVariant();
}
const ZFlyEmNeuron* ZFlyEmNeuronListModel::getNeuron(
const QModelIndex &index) const
{
return getNeuron(index.row(), index.column());
}
