btVector3 randomBVec3InRange( std::pair< btVector3, btVector3 > range =
std::pair< btVector3, btVector3 >(
btVector3( -1, -1, -1),
btVector3( 1, 1, 1) ) )
{
return(
btVector3(
randomFloatInRange(
std::pair< float, float >( range.first[0], range.second[0] ) ),
randomFloatInRange(
std::pair< float, float >( range.first[1], range.second[1] ) ),
randomFloatInRange(
std::pair< float, float >( range.first[2], range.second[2] ) ) ) );
}
void init(){
mouseX = 0;
mouseY = 0;
selectedIndex = -1;
//use time to get more random initial SOM arrangement
srand(time(NULL));
TSPFileReader trainingVectorFactory;
vector<neuron> TrainingNeurons;
//TrainingNeurons = trainingVectorFactory.retrieveTrainingVectors("./maps/RGB.tsp");
neuron myNewNeuron;
//data randomly generated
for(int i=0; i<300; i++){
for (int j=0;j<3;j++){
myNewNeuron.push_back(randomFloatInRange(0,255));
}
TrainingNeurons.push_back(myNewNeuron);
myNewNeuron.clear();
}
//clustered data added
for(int i=0; i<100; i++){
for(int j=0;j<3;j++){
//add only high-blue values
if(j==0){
myNewNeuron.push_back(randomFloatInRange(0,255));
}else{
myNewNeuron.push_back(0);
}
}
TrainingNeurons.push_back(myNewNeuron);
myNewNeuron.clear();
}
//clustered data added
for(int i=0; i<100; i++){
for(int j=0;j<3;j++){
//add only high-blue values
if(j==1){
myNewNeuron.push_back(randomFloatInRange(0,255));
}else{
myNewNeuron.push_back(0);
}
}
TrainingNeurons.push_back(myNewNeuron);
myNewNeuron.clear();
}
//clustered data added
for(int i=0; i<100; i++){
for(int j=0;j<3;j++){
//add only high-blue values
if(j==2){
myNewNeuron.push_back(randomFloatInRange(0,255));
}else{
myNewNeuron.push_back(0);
}
}
TrainingNeurons.push_back(myNewNeuron);
myNewNeuron.clear();
}
cam = new camera();
cam->translateRight(0.75);
cam->translateUp(0.75);
cam->translateForward(-1.75);
//colourcube: 3d data, 3d mesh(10x10x10), 1000 training iterations
mySOM.init(TrainingNeurons,7,7,7,50);
terminateProgram = false;
}
