Ejemplo n.º 1
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext) {
     rbm_alpha *= rbm_mult_step_dec;
     training_rmse(iteration, gcontext);
     if (iteration >= 2)
         run_validation(pvalidation_engine, gcontext);
     else Rcpp::Rcout<<std::endl;
 }
Ejemplo n.º 2
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext) {
   //print rmse every other iteration, since 2 iterations are considered one NMF round
   int now = iteration % 2;
   if (now == 0){
     training_rmse(iteration/2, gcontext);
     run_validation(pvalidation_engine, gcontext);
   }
 }
Ejemplo n.º 3
0
 /**
  * Called before an iteration is started.
  */
 void before_iteration(int iteration, graphchi_context &gcontext) {
     Rcpp::Rcerr << "before_iteration: resetting MRR" << std::endl;
     reset_mrr(gcontext.execthreads);
     last_training_objective = training_objective;
     objective_vec = zeros(gcontext.execthreads);
     stat_vec = zeros(gcontext.execthreads);
     node_without_edges = 0;
     if (gcontext.iteration == 0)
         run_validation(pvalidation_engine, gcontext);
 }
Ejemplo n.º 4
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext) {
     training_objective = sum(objective_vec);
     Rcpp::Rcout<<"  Training objective:" << std::setw(10) << training_objective << std::endl;
     if (halt_on_mrr_decrease > 0 && halt_on_mrr_decrease < cur_iteration && training_objective < last_training_objective) {
         Rcpp::Rcout << "Stopping engine because of validation objective decrease" << std::endl;
         gcontext.set_last_iteration(gcontext.iteration);
     }
     Rcpp::Rcerr << "after_iteration: running validation engine" << std::endl;
     run_validation(pvalidation_engine, gcontext);
     if (verbose)
         Rcpp::Rcout<<"Average step size: " << sum(stat_vec)/(double)M << "Node without edges: " << node_without_edges << std::endl;
     sgd_gamma *= sgd_step_dec;
 }
Ejemplo n.º 5
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext)
 {
   training_objective = sum(objective_vec);
   std::cout<<"  Training objective:" << std::setw(10) << training_objective << std::endl;
   if (halt_on_mrr_decrease > 0 && halt_on_mrr_decrease < cur_iteration && training_objective < last_training_objective)
   {
     logstream(LOG_WARNING) << "Stopping engine because of validation objective decrease" << std::endl;
     gcontext.set_last_iteration(gcontext.iteration);
   }
   logstream(LOG_DEBUG) << "after_iteration: running validation engine" << std::endl;
   run_validation(pvalidation_engine, gcontext);
   sgd_gamma *= sgd_step_dec;
 }
  /**
   * Called after an iteration has finished.
   */
  void after_iteration(int iteration, graphchi_context &gcontext) {
    sgd_gamma *= sgd_step_dec;
    training_rmse(iteration, gcontext);
    run_validation(pvalidation_engine, gcontext);
   /* std::vector<double> lbo (D,0);
    std::vector<double> rbo (D,1);
    double copy[N];   
    for(int j = 0; j < D; j++)
    {
        for(unsigned int i = M; i < M + N; i++)
            copy[i - M] = latent_factors_inmem.at(i).pvec[j];
        std::sort(copy, copy+N);
        lbo.at(j) = copy[0];
        rbo.at(j) = copy[N-1];
    }
    std::cout << "left bound: ("; 
    for(int i = 0; i < D; i++)
	std::cout << lbo.at(i) << ", ";
    std::cout << ").  right bound: (";
    for(int i = 0; i < D; i++)
	std::cout << rbo.at(i) << ", ";
    std::cout << ")." << std::endl;*/
  }
Ejemplo n.º 7
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext) {
   sgd_gamma *= sgd_step_dec;
   training_rmse(iteration, gcontext);
   run_validation(pvalidation_engine, gcontext);
 }
Ejemplo n.º 8
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, graphchi_context &gcontext) {
   training_rmse(iteration, gcontext);
   run_validation(pvalidation_engine, gcontext);
 }
Ejemplo n.º 9
0
 /**
  * Called after an iteration has finished.
  */
 void after_iteration(int iteration, CE_Graph_context &gcontext) {
   biassgd_gamma *= biassgd_step_dec;
   training_rmse(iteration, gcontext);
   run_validation(pvalidation_engine, gcontext);
 }