int main(int argc, char **argv)
{
std::vector<Demonstration> demos;
Demonstration demo = Demonstration(4,200,0);
std::vector<std::string> vars,vars2;
vars.push_back("t");
vars.push_back("x");
vars.push_back("y");
vars.push_back("z");
demo.getDatapoints().loadFromFile("../../data/data_txyz.txt");
demo.getDatapoints().setVarNames(vars);
cout << endl << "Data is: " << demo.getDatapoints().getData() << endl;
cout << endl << "nbVar is: " << demo.getDatapoints().getNumVARS() << endl;
cout << endl << "nbDatapoints is: " << demo.getDatapoints().getNumPOINTS() << endl;
getchar();
demos.push_back(demo);
GMM_Model *gmm;
gmm = new GMM_Model(demos, 3);
gmm->setVARSNames(vars);
cout << "\n EM rounds = " << gmm->EM_learn();
cout << "\n MU = " << endl << gmm->getCOMPONENTS().at(0).getMU();
cout << "\n MU = " << endl << gmm->getCOMPONENTS().at(1).getMU();
cout << "\n MU = " << endl << gmm->getCOMPONENTS().at(2).getMU();
cout << "\n SIGMA = " << endl << gmm->getCOMPONENTS().at(0).getSIGMA();
cout << "\n SIGMA = " << endl << gmm->getCOMPONENTS().at(1).getSIGMA();
cout << "\n SIGMA = " << endl << gmm->getCOMPONENTS().at(2).getSIGMA();
Datapoints *Repros;
Repros = new Datapoints(1,200);
vars2.push_back("t");
Repros->setVarNames(vars2);
mat data = demo.getDatapoints().getData().submat(0,0,0,199);
Repros->setData(data);
GMR* reg = new GMR(gmm);
reg->regression(Repros);
cout << "\n SIGMA = " << reg->getCOMPONENTS().at(199).getSIGMA();
cout << "\n MU = " << reg->getCOMPONENTS().at(199).getMU();
return 0;
}
int main(int argc, char **argv)
{
// Model variables
uint nFrames = 2;
uint nStates = 3;
uint nVars = 3; //Tohid: t, x, y
uint nDemos = 4;//Tohid: number of Demonstrations, for easier loading from the data files
std::vector<std::string> varNames;
varNames.push_back("t"); // time is input (t) and the attractor position (2D vector [x y]) is the output.
varNames.push_back("x");
varNames.push_back("y");
//..................................................................................
//......... loading the demonstrations and task parameters from the txt files ......
std::vector<Demonstration> demos;
Demonstration demo = Demonstration(3,200,2); //(3 vars, 200 datapoints, 2 frames of references)
std::vector<Parameters> Params;
char filename[256];
cout<<"Loading the demonstrations and the task parameters ...";
for (int m=0; m<nDemos; m++){ //Loading demos in the loop
Parameters param = Parameters(3,2,1); //nVAR, nPAR, nPOINTS (set to 1 to show that the task parameters are fixed all the time)
sprintf(filename, "../../data/pgmm/Data0%d.txt",m+1);
demo.getDatapoints().loadFromFile(filename, true);
demo.getDatapoints().setVarNames(varNames);
sprintf(filename, "../../data/pgmm/Param0%d.txt",m+1);
param.loadFromFile(filename);
Params.push_back(param);
demo.setParams(param);
demos.push_back(demo);
}
cout<<endl<<"Demonstrations and task parameters are loaded successfully !!!!"<<endl;
cout<<"Press any key to continue (learning the PGMM model for the loaded demonstrations)"<<endl;
getchar();
//..................................................................................
//......... Learning the PGMM model parameters using the EM algorithm ......
cout<<"Learning model parameters using EM ...";
PGMM_Model *pgmm;
//pgmm = new PGMM_Model(nVars, nStates, nFrames);
pgmm = new PGMM_Model(demos, nStates, nFrames);
// pgmm->loadPGMMfromMATLAB(priorsName, ZmuName, ZsigmaName); //Tohid: commented, because we do not have Zmu and ZSigma anymore
//cout<<"demos.size() = "<<demos.size()<<endl;
cout << "\n EM rounds = " << pgmm->EM_TensorLearn()<<endl;
cout<<"Learning is done and the model is learned for the provided demonstrations!!!"<<endl;
// saving the learned model parameters
mat MuTmp = zeros(nVars, nStates*nFrames);
mat SigmaTmp = zeros(nVars, nVars*nStates*nFrames);
mat Priors = zeros(1,nStates);
std::vector<GMM_Model> GMMSresult;
GMMSresult = pgmm->getGMMS();
for(uint m=0; m<nFrames; m++){
for (uint i=0;i<nStates; i++){
MuTmp.col(m*nStates+i) = GMMSresult.at(m).getCOMPONENTS().at(i).getMU();
SigmaTmp.cols(m*nVars*nStates+i*nVars, m*nVars*nStates+i*nVars+nVars-1) = GMMSresult.at(m).getCOMPONENTS().at(i).getSIGMA();
}
}
sprintf(filename, "../../data/pgmm/Mu0%d.txt",2);
MuTmp.save(filename, raw_ascii);
sprintf(filename, "../../data/pgmm/Sigma0%d.txt",2);
SigmaTmp.save(filename, raw_ascii);
sprintf(filename, "../../data/pgmm/Priors0%d.txt",2);
Priors = pgmm->getPRIORS();
Priors.save(filename, raw_ascii);
cout<<"the learned model parameters are written into text files succesfully."<<endl;
cout<<"Press any key to continue (performing the GMR for the new set of task parameters)"<<endl;
getchar();
//..................................................................................
// Path for model files
//std::string priorsName = "../../data/pgmm/Priors02.txt";
//std::string MuName = "../../data/pgmm/Mu02.txt";
//std::string SigmaName = "../../data/pgmm/Sigma02.txt";
// Reproduction variables (GMR)
GMR *gmr = new GMR();
Datapoints *repro = new Datapoints(1,1); // Only one datapoint passed at each time step
mat reproData, auxYgmr;
std::vector<std::string> reproVarNames;
reproVarNames.push_back("t"); // time is the query point
repro->setVarNames(reproVarNames);
// loading new parameters for the reproduction from the text file
// In this case we have considered that the parameters are the same all along the reproduction. They can be varying.
std::vector<mat> A_tmp;
mat auxA;
std::vector<colvec> b_tmp;
colvec auxb;
sprintf(filename, "../../data/pgmm/ParamRepro0%d.txt",1);
mat ParamTmp;
ParamTmp.load(filename, raw_ascii);
b_tmp.push_back(trans(ParamTmp.row(0)));
A_tmp.push_back(ParamTmp.rows(1,nVars));
b_tmp.push_back(trans(ParamTmp.row(nVars+1)));
A_tmp.push_back(ParamTmp.rows(nVars+2,2*nVars+1));
for(uint tn = 1 ; tn <= 200 ; tn++){
// Computing the resulting gmm given the set of parameters {A,b}
GMM_Model* gmm;
gmm = pgmm->getGMM(A_tmp, b_tmp);
gmm->setVARSNames(varNames);
// Printing model components
cout << "Resulting GMM given the set of parameters 'A' and 'b'" << endl;
for(uint i = 0 ; i < nStates ; i++){
cout << "State #" << i << ":"<< endl;
gmm->getCOMPONENTS().at(i).getMU().print("Mu = ");
gmm->getCOMPONENTS().at(i).getSIGMA().print("Sigma = ");
}
// Computing GMR
gmr->setGMMModel(gmm);
mat repDataPt;
repDataPt << (tn * 0.01); // time step as input
repDataPt.print("tn = ");
repro->setData(repDataPt);
gmr->regression(repro);
gmr->get_y().print("y = ");
cout << "Please press [ENTER] to continue or [CTRL+C] to finish" << endl;
char key;
std::cin.ignore(1);
}
return 0;
}