/**
* 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;
}
/**
* 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);
}
}
/**
* 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);
}
/**
* 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;
}
/**
* 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;*/
}
/**
* 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);
}
/**
* Called after an iteration has finished.
*/
void after_iteration(int iteration, graphchi_context &gcontext) {
training_rmse(iteration, gcontext);
run_validation(pvalidation_engine, gcontext);
}
/**
* 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);
}
