void gmm::batch(const eigen_wsvec_list_t& data, int d, int k) {
if (data.empty()) {
*this = gmm();
return;
}
typedef eigen_wsvec_list_t::const_iterator data_iter;
initialize(data, d, k);
eigen_svec_list_t old_means;
eigen_smat_list_t old_covs;
eigen_solver_list_t old_solvers;
double old_obj = 0, obj = 0;
vector<double> weights(k);
bool converged = false;
int64_t niter = 1;
while (!converged) {
old_covs = covs_;
old_means = means_;
old_solvers = cov_solvers_;
old_obj = obj;
obj = 0;
fill(weights.begin(), weights.end(), 0);
fill(means_.begin(), means_.end(), eigen_svec_t(d));
fill(covs_.begin(), covs_.end(), eigen_smat_t(d, d));
for (data_iter i = data.begin(); i != data.end(); ++i) {
eigen_svec_t cps =
cluster_probs(i->data, old_means, old_covs, old_solvers);
for (int c = 0; c < k; ++c) {
double cp = i->weight * cps.coeff(c);
means_[c] += cp * i->data;
covs_[c] += i->data * (i->data.transpose()) * cp;
weights[c] += cp;
obj -= std::log(std::max(cp, std::numeric_limits<double>::min()));
}
}
for (int c = 0; c < k; ++c) {
means_[c] /= weights[c];
covs_[c] /= weights[c];
double eps = 0.1;
covs_[c] -= means_[c] * means_[c].transpose();
covs_[c] += eps * eye_;
cov_solvers_[c] =
shared_ptr<eigen_solver_t>(new eigen_solver_t(covs_[c]));
}
converged = is_converged(niter++, means_, old_means, obj, old_obj);
}
}
int main(int argc,char** argv){
std::cout<< "=== test GMM and GMC === " << std::endl;
ros::init(argc, argv, "test_statistics");
ros::NodeHandle nh;
std::string path_parameters = "/home/guillaume/MatlabWorkSpace/peg_in_hole_RL/PolicyModelSaved/PolicyModel_txt/gmm_xhu";
stats::GMM gmm(path_parameters);
/*
stats::GMM gmm_c;
std::vector<std::size_t> in = {{0,1,2,3}};
std::vector<std::size_t> out = {{4,5,6}};
arma::vec x(4);
x(0) = 0.1765;
x(1) = 0.3907;
x(2) = 0.0705;
x(3) = -1.9986;
std::cout<< "w(0): " << (gmm.gmm.Weights())[0] << std::endl;
gmm.gmm.Means()[0].print("Mu(0)");
gmm.gmm.Covariances()[0].print("Covariance[0]");
gmm_c.gmm = mlpack::gmm::GMM<double>(gmm.K,3);
std::cout<< "--- condition---" << std::endl;
std::cout<< "in : " << in.size() << std::endl;
std::cout<< "out: " << out.size() << std::endl;
gmm.condition(gmm_c,x,in,out);*/
/*std::cout<< "w(0): " << (gmm_c.gmm.Weights())[114] << std::endl;
gmm_c.gmm.Means()[114].print("Mu(0)");
gmm_c.gmm.Covariances()[114].print("Covariance[0]");
arma::vec e; e.resize(3);
*/
// gmm_c.print();
// gmm_c.expection(e);
//e.print("expectation");
std::cout<< "=== end of test === " << std::endl;
return 0;
}
