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;
}
string buildEnvironment(vector<locationNode> &locationList, MatVector &distancesTable, int numNodes, string mapType, string probabilityDistribution, string object) {
ostringstream stream2String;
string msg;
//Initialize random generators
default_random_engine generator;
uniform_real_distribution<double> distribution(0.0,1.0); //numbers in [0,1]
//Pass the random numbers according the map number
//so, choosing different map generates different random numbers
for (int i=0; i<selectedMap*numNodes*numNodes*numNodes; i++) {
double dump=distribution(generator);
}
vector<double> roomProbabilities;
if (probabilityDistribution!="File")
generateRoomProbabilities(probabilityDistribution, numNodes, roomProbabilities); //Generated with random
else {
loadRoomProbabilities(object, numNodes, roomProbabilities, "/home/izquierdocr/projects/internetQuery/data/experimentQueryWeightedProbabilityWebPb.txt"); //Loaded from files
//loadRoomProbabilities(object, numNodes, roomProbabilities, "/home/izquierdocr/projects/internetQuery/data/bordaUserPb.txt"); //Loaded from users
}
for (int i=0; i<numNodes; i++) {
locationNode location;
location.ID=i;
location.objectProbability=roomProbabilities[numNodes-i-1]; //Only for evaluating cross probabilities. This can be changed to [i]. Choosen only for having other order than 0,1,2...
location.priority=0;
locationList.push_back(location);
}
doubleVector rowMat;
rowMat.assign(numNodes,0);
for (int i=0; i<numNodes; i++) {
distancesTable.push_back(rowMat);
}
if (mapType=="R") { //Type: House(H), Office (O), Random (R)
for (int i=0; i<numNodes; i++) {
locationList[i].area = ( DISTANCES_MEAN + DISTANCES_SIGMA * (distribution(generator)-0.5) )/2; //Assume areas represent in general 1/2 of distances between rooms (Here area defined as robot movement inside rooms
}
//Generate distances randomly
for (int i=1; i<numNodes; i++) {
for (int j=0; j<i; j++) {
distancesTable[i][j]= DISTANCES_MEAN + DISTANCES_SIGMA * (distribution(generator)-0.5);
distancesTable[j][i]=distancesTable[i][j];
}
}
}
if (mapType=="H") { //Type: House(H), Office (O), Random (R)
//Load distances from files
//int numMap = MAX_MAPS*(double(rand()) / RAND_MAX)+1; //Random house map
int numMap = selectedMap;
loadMapData(numNodes, numMap, mapType, distancesTable);
for (int i=0; i<numNodes; i++) {
locationList[i].area = areas[i];
//locationList[i].area = 1; //Testing with equal areas
}
}
if (mapType=="O") { //Type: House(H), Office (O), Random (R)
//Not yet office maps
}
//Print node data
cout << "Locations (ID,Priority,Probability,Area)" << endl;
stream2String << "Locations (ID,Priority,Probability,Area)" << endl;
for (int i=0; i<locationList.size(); i++) {
cout << locationList[i].ID << ", ";
cout << locationList[i].priority << ", ";
cout << locationList[i].objectProbability << ", ";
cout << locationList[i].area << endl;
stream2String << locationList[i].ID << ",";
stream2String << locationList[i].priority << ",";
stream2String << locationList[i].objectProbability << ",";
stream2String << locationList[i].area << endl;
}
//Print distances data
cout << "Distances table" << endl;
stream2String << "Distances table" << endl;
for (int i=0; i<numNodes; i++) {
for (int j=0; j<numNodes; j++) {
cout << distancesTable[i][j] << ",";
stream2String << distancesTable[i][j] << ",";
}
cout << endl;
stream2String << endl;
}
msg = stream2String.str();
return msg;
}
