void SOMAW<TNeuron, TSOMParameters>::updateNeuron(TNeuron &neuron, MatVector<float> &data, float alpha, float phi) {
for (uint i = 0; i < data.size(); i++) {
//update neuron weights
float distance = fabs(data[i] - neuron.weights[i]);
neuron.avgDistance[i] = neuron.avgDistance[i] + this->parameters.k1 * ((neuron.dsWeights[i] * distance) - neuron.avgDistance[i]);
}
float average = neuron.avgDistance.sum() / neuron.avgDistance.size();
//update neuron ds weights
for (uint i = 0; i < data.size(); i++) {
//update neuron weights
float distance = fabs(data[i] - neuron.weights[i]);
if (neuron.dsWeights[i] * distance > average)
neuron.dsWeights[i] = this->parameters.k2 * neuron.dsWeights[i];
else
neuron.dsWeights[i] = this->parameters.k3 * neuron.dsWeights[i];
}
float sum = neuron.dsWeights.sum();
for (uint i = 0; i < data.size(); i++) {
neuron.dsWeights[i] = neuron.dsWeights[i] / sum;
}
//update neuron weights
for (uint i = 0; i < data.size(); i++) {
neuron.weights[i] = neuron.weights[i] + phi * alpha * (data[i] - neuron.weights[i]);
}
}
float SOMAW<TNeuron, TSOMParameters>::presentPaternToNeuron(const MatVector<float> &data, TNeuron &neuron) {
float distance = 0;
for (uint i = 0; i < data.size(); i++) {
distance += qrt(neuron.dsWeights[i]) * qrt(fabs(data[i] - neuron.weights[i]));
}
return (1 / (distance + 0.0000001));
}
double maxDistance(MatVector distancesTable, vector<locationNode> locationList) {
double maxDist=0;
int numNodes=distancesTable.size();
for (int i=0; i<numNodes; i++) {
for (int j=0; j<numNodes; j++) {
if (distancesTable[i][j]>maxDist) maxDist=distancesTable[i][j];
}
}
double maxArea=0;
numNodes=locationList.size();
for (int i=0; i<numNodes; i++) {
if (locationList[i].area>maxArea) maxArea=locationList[i].area;
}
return maxDist+maxArea;
}