void cGMM::condition(const GMM& gmm_in,const std::vector<std::size_t>& in,const std::vector<std::size_t>& out){
// std::cout<< "cGMM::condition independent x" << std::endl;
std::size_t D_c = in.size();
std::size_t K = gmm_in.K;
arma::colvec weights(K);
std::vector<arma::vec> Means(K);
std::vector<arma::mat> Covariances(K);
// std::cout<< "K: " << K << std::endl;
gaussian_c.resize(K);
// gmm_in.get_means()[0].print("gmm_in.get_means()[0]");
// gmm_in.get_covariances()[0].print("gmm_in.get_covariances()[0]");
for(std::size_t k = 0; k < gmm_in.K;k++){
// P( x_a | X_b)
gaussian_c[k].condition(gmm_in.get_means()[k],gmm_in.get_covariances()[k],in,out);
// std::cout<< "after conidtion["<<0<<"]" << std::endl;
Means[k].resize(D_c);
Covariances[k] = gaussian_c[k].Sigma_1c2;
// std::cout<< "after Covariances["<<k<<"]" << std::endl;
}
gmm_c = GMM(weights,Means,Covariances);
}
double Information::lower_bound_entropy(GMM& gmm) {
double HL = 0;
double tmp = 0;
for(unsigned int i = 0; i < gmm.K;i++){
tmp = 0;
for(unsigned int j = 0; j < gmm.K;j++){
tmp = tmp + gmm.get_weigts()[i] * mvnpdf(gmm.get_means()[i],gmm.get_means()[j],gmm.get_covariances()[i] + gmm.get_covariances()[j]);
}
tmp = log(tmp);
HL = HL + gmm.get_weigts()[i] * tmp;
}
HL = -1*HL;
HL = checkForNan(HL,qHl);
return HL;
}