float mmf::OptSO3MMFvMF::computeAssignment(uint32_t& N) { N = this->cld_.counts().sum(); // Compute log of the cluster weights and push them to GPU Eigen::VectorXf pi = Eigen::VectorXf::Ones(K()*6)*1000; std::cout << this->t_ << std::endl; std::cout<<"counts: "<<this->cld_.counts().transpose()<<std::endl; if (this->t_ == 0) { pi.fill(1.); } else if (this->t_ >24) { std::cout << "truncating noisy MFs: " << std::endl; for (uint32_t k=0; k<K()*6; ++k) { float count = this->cld_.counts().middleRows((k/6)*6,6).sum(); pi(k) = count > 0.10*N ? count : 1.e-20; std::cout << count << " < " << 0.1*N << std::endl; } } else { // estimate the axis and MF proportions // pi += this->cld_.counts(); // estimate only the MF proportions for (uint32_t k=0; k<K()*6; ++k) pi(k) += this->cld_.counts().middleRows((k/6)*6,6).sum(); if (estimateTau_) { for (uint32_t k=0; k<K()*6; ++k) if (this->cld_.counts()(k) == 0) { taus_(k) = 0.; // uniform } else { Eigen::Vector3f mu = Eigen::Vector3f::Zero(); mu((k%6)/2) = (k%6)%2==0?-1.:1.; mu = Rs_[k/6]*mu; taus_(k) = jsc::vMF<3>::MLEstimateTau( this->cld_.xSums().col(k).cast<double>(), mu.cast<double>(), this->cld_.counts()(k)); } } else { taus_.fill(60.); } } std::cout<<"pi: "<<pi.transpose()<<std::endl; pi = (pi.array() / pi.sum()).array().log(); std::cout<<"pi: "<<pi.transpose()<<std::endl; if (estimateTau_) { std::cout << pi.transpose() << std::endl; std::cout << taus_.transpose() << std::endl; for (uint32_t k=0; k<K()*6; ++k) { pi(k) -= jsc::vMF<3>::log2SinhOverZ(taus_(k)) - log(2.*M_PI); } } pi_.set(pi); Rot2Device(); Eigen::VectorXf residuals = Eigen::VectorXf::Zero(K()*6); MMFvMFCostFctAssignmentGPU((float*)residuals.data(), d_cost, &N, d_N_, cld_.d_x(), d_weights_, cld_.d_z(), d_mu_, pi_.data(), cld_.N(), K()); return residuals.sum(); };