[PrematureStopping] Convert CSignal::cancel_computations() to cancel_…

…computation().

Remove CSignal::cancel_computations() and add TODO when the class does not
inherit directly from CMachine.

src/gpl/shogun/lib/external/gpdtsolve.cpp

@@ -1311,7 +1311,9 @@ float64_t QPproblem::gpdtsolve(float64_t *solution)
     }
     Cache->Iteration();
     nit = nit+1;
-  } while (!optimal() && !(CSignal::cancel_computations()));
+	// TODO: replace with the new signal
+	//} while (!optimal() && !(CSignal::cancel_computations()));
+  } while (!optimal());
   /* End of the problem resolution loop                                      */
   /***************************************************************************/
 

src/gpl/shogun/lib/malsar/malsar_joint_feature_learning.cpp

@@ -51,7 +51,10 @@ malsar_result_t malsar_joint_feature_learning(
 
 	//internal::set_is_malloc_allowed(false);
 	bool done = false;
-	while (!done && iter <= options.max_iter && !CSignal::cancel_computations())
+	// TODO: replace with the new signal
+	// while (!done && iter <= options.max_iter &&
+	// !CSignal::cancel_computations())
+	while (!done && iter <= options.max_iter)
 	{
 		double alpha = double(t_old - 1)/t;
 

src/gpl/shogun/lib/slep/slep_mc_plain_lr.cpp

@@ -91,7 +91,8 @@ slep_result_t slep_mc_plain_lr(
 	bool done = false;
 	CTime time;
 	//internal::set_is_malloc_allowed(false);
-	while ((!done) && (iter<options.max_iter) && (!CSignal::cancel_computations()))
+	// while ((!done) && (iter<options.max_iter) && (!cancel_computation()))
+	while ((!done) && (iter < options.max_iter))
 	{
 		double beta = (alphap-1)/alpha;
 		// compute search points

src/gpl/shogun/lib/slep/slep_mc_tree_lr.cpp

@@ -93,7 +93,8 @@ slep_result_t slep_mc_tree_lr(
 	bool done = false;
 	CTime time;
 	//internal::set_is_malloc_allowed(false);
-	while ((!done) && (iter<options.max_iter) && (!CSignal::cancel_computations()))
+	// while ((!done) && (iter<options.max_iter) && (!cancel_computation()))
+	while ((!done) && (iter < options.max_iter))
 	{
 		double beta = (alphap-1)/alpha;
 		// compute search points

src/gpl/shogun/lib/slep/slep_solver.cpp

@@ -524,7 +524,8 @@ slep_result_t slep_solver(
 	double alphap = 0.0, alpha = 1.0;
 	double fun_x = 0.0;
 
-	while (!done && iter <= options.max_iter && !CSignal::cancel_computations())
+	// while (!done && iter <= options.max_iter && !cancel_computation())
+	while (!done && iter <= options.max_iter)
 	{
 		beta = (alphap-1.0)/alpha;
 

src/shogun/classifier/AveragedPerceptron.cpp

@@ -63,7 +63,7 @@ bool CAveragedPerceptron::train_machine(CFeatures* data)
 
 	//loop till we either get everything classified right or reach max_iter
 
-	while (!(CSignal::cancel_computations()) && (!converged && iter<max_iter))
+	while (!(cancel_computation()) && (!converged && iter < max_iter))
 	{
 		converged=true;
 		SG_INFO("Iteration Number : %d of max %d\n", iter, max_iter);

src/shogun/classifier/LPBoost.cpp

@@ -125,7 +125,7 @@ bool CLPBoost::train_machine(CFeatures* data)
 	int32_t num_hypothesis=0;
 	CTime time;
 
-	while (!(CSignal::cancel_computations()))
+	while (!(cancel_computation()))
 	{
 		int32_t max_dim=0;
 		float64_t violator=find_max_violator(max_dim);

src/shogun/classifier/Perceptron.cpp

@@ -67,7 +67,7 @@ bool CPerceptron::train_machine(CFeatures* data)
 
 
 	//loop till we either get everything classified right or reach max_iter
-	while (!(CSignal::cancel_computations()) && (!converged && iter<max_iter))
+	while (!(cancel_computation()) && (!converged && iter < max_iter))
 	{
 		converged=true;
 		for (auto example_idx : features->index_iterator())

src/shogun/classifier/mkl/MKL.cpp

@@ -477,7 +477,7 @@ bool CMKL::train_machine(CFeatures* data)
 
 
 			mkl_iterations++;
-			if (perform_mkl_step(sumw, suma) || CSignal::cancel_computations())
+			if (perform_mkl_step(sumw, suma) || cancel_computation())
 				break;
 		}
 

src/shogun/classifier/mkl/MKLMulticlass.cpp

@@ -371,7 +371,7 @@ bool CMKLMulticlass::train_machine(CFeatures* data)
 	int32_t numberofsilpiterations=0;
 	bool final=false;
 
-	while (!(CSignal::cancel_computations()) && !final)
+	while (!(cancel_computation()) && !final)
 	{
 
 		//curweights.clear();

src/shogun/classifier/svm/LibLinear.cpp

@@ -316,7 +316,7 @@ void CLibLinear::solve_l2r_l1l2_svc(
 
 	auto pb = progress(range(10));
 	CTime start_time;
-	while (iter < max_iterations && !CSignal::cancel_computations())
+	while (iter < max_iterations && !cancel_computation())
 	{
 		if (m_max_train_time > 0 && start_time.cur_time_diff() > m_max_train_time)
 		  break;
@@ -526,7 +526,7 @@ void CLibLinear::solve_l1r_l2_svc(
 
 	auto pb = progress(range(10));
 	CTime start_time;
-	while (iter < max_iterations && !CSignal::cancel_computations())
+	while (iter < max_iterations && !cancel_computation())
 	{
 		if (m_max_train_time > 0 && start_time.cur_time_diff() > m_max_train_time)
 		  break;
@@ -898,7 +898,7 @@ void CLibLinear::solve_l1r_lr(
 
 	auto pb = progress(range(10));
 	CTime start_time;
-	while (iter < max_iterations && !CSignal::cancel_computations())
+	while (iter < max_iterations && !cancel_computation())
 	{
 		if (m_max_train_time > 0 && start_time.cur_time_diff() > m_max_train_time)
 		  break;

src/shogun/classifier/svm/MPDSVM.cpp

@@ -95,7 +95,7 @@ bool CMPDSVM::train_machine(CFeatures* data)
 	}
 
 	// go ...
-	while (niter++ < maxiter && !CSignal::cancel_computations())
+	while (niter++ < maxiter && !cancel_computation())
 	{
 		int32_t maxpidx=-1;
 		float64_t maxpviol = -1;

src/shogun/classifier/svm/NewtonSVM.cpp

@@ -82,7 +82,7 @@ bool CNewtonSVM::train_machine(CFeatures* data)
 	float64_t obj, *grad=SG_MALLOC(float64_t, x_d+1);
 	float64_t t;
 
-	while(!CSignal::cancel_computations())
+	while (!cancel_computation())
 	{
 		iter++;
 

src/shogun/classifier/svm/OnlineSVMSGD.cpp

@@ -106,7 +106,7 @@ bool COnlineSVMSGD::train(CFeatures* data)
 		is_log_loss = true;
 
 	int32_t vec_count;
-	for(int32_t e=0; e<epochs && (!CSignal::cancel_computations()); e++)
+	for (int32_t e = 0; e < epochs && (!cancel_computation()); e++)
 	{
 		vec_count=0;
 		count = skip;

src/shogun/classifier/svm/SGDQN.cpp

@@ -143,7 +143,7 @@ bool CSGDQN::train(CFeatures* data)
 	if ((loss_type == L_LOGLOSS) || (loss_type == L_LOGLOSSMARGIN))
 		is_log_loss = true;
 
-	for(int32_t e=0; e<epochs && (!CSignal::cancel_computations()); e++)
+	for (int32_t e = 0; e < epochs && (!cancel_computation()); e++)
 	{
 		count = skip;
 		bool updateB=false;

src/shogun/classifier/svm/SVMLight.cpp

@@ -647,7 +647,11 @@ int32_t CSVMLight::optimize_to_convergence(int32_t* docs, int32_t* label, int32_
   for (;((iteration<100 || (!mkl_converged && callback) ) || (retrain && (!terminate))); iteration++){
 #else
 
-	  for (;((!CSignal::cancel_computations()) && ((iteration<3 || (!mkl_converged && callback) ) || (retrain && (!terminate)))); iteration++){
+  for (; ((!cancel_computation()) &&
+	      ((iteration < 3 || (!mkl_converged && callback)) ||
+	       (retrain && (!terminate))));
+	   iteration++)
+  {
 #endif
 
 	  if(use_kernel_cache)

src/shogun/classifier/svm/SVMSGD.cpp

@@ -115,7 +115,7 @@ bool CSVMSGD::train_machine(CFeatures* data)
 	if ((loss_type == L_LOGLOSS) || (loss_type == L_LOGLOSSMARGIN))
 		is_log_loss = true;
 
-	for(int32_t e=0; e<epochs && (!CSignal::cancel_computations()); e++)
+	for (int32_t e = 0; e < epochs && (!cancel_computation()); e++)
 	{
 		count = skip;
 		for (int32_t i=0; i<num_vec; i++)

src/shogun/classifier/vw/VowpalWabbit.cpp

@@ -164,7 +164,7 @@ bool CVowpalWabbit::train_machine(CFeatures* feat)
 	}
 
 	features->start_parser();
-	while (!(CSignal::cancel_computations()) && (env->passes_complete < env->num_passes))
+	while (!(cancel_computation()) && (env->passes_complete < env->num_passes))
 	{
 		while (features->get_next_example())
 		{

src/shogun/distance/Distance.cpp

@@ -333,8 +333,9 @@ SGMatrix<T> CDistance::get_distance_matrix()
 
 					pb.print_progress();
 
-					if (CSignal::cancel_computations())
-						break;
+					// TODO: replace with new signal
+					// if (CSignal::cancel_computations())
+					//	break;
 				}
 			}
 		}

src/shogun/distributions/HMM.cpp

@@ -5586,7 +5586,9 @@ bool CHMM::baum_welch_viterbi_train(BaumWelchViterbiType type)
 	float64_t prob_train=CMath::ALMOST_NEG_INFTY;
 	iteration_count=iterations;
 
-	while (!converged(prob, prob_train) && (!CSignal::cancel_computations()))
+	// TODO: replace with the new signal
+	// while (!converged(prob, prob_train) && (!CSignal::cancel_computations()))
+	while (!converged(prob, prob_train))
 	{
 		CMath::swap(working, estimate);
 		prob=prob_train;

src/shogun/features/DotFeatures.cpp

@@ -88,8 +88,10 @@ void CDotFeatures::dense_dot_range(float64_t* output, int32_t start, int32_t sto
 #ifdef WIN32
 		for (int32_t i=t_start; i<t_stop; i++)
 #else
-		for (int32_t i=t_start; i<t_stop &&
-				!CSignal::cancel_computations(); i++)
+		// TODO: replace with the new signal
+		// for (int32_t i=t_start; i<t_stop &&
+		//		!CSignal::cancel_computations(); i++)
+		for (int32_t i = t_start; i < t_stop; i++)
 #endif
 		{
 			if (alphas)
@@ -100,11 +102,6 @@ void CDotFeatures::dense_dot_range(float64_t* output, int32_t start, int32_t sto
 		}
 	}
 	pb.complete();
-
-#ifndef WIN32
-		if ( CSignal::cancel_computations() )
-			SG_INFO("prematurely stopped.           \n")
-#endif
 }
 
 void CDotFeatures::dense_dot_range_subset(int32_t* sub_index, int32_t num, float64_t* output, float64_t* alphas, float64_t* vec, int32_t dim, float64_t b)
@@ -137,8 +134,10 @@ void CDotFeatures::dense_dot_range_subset(int32_t* sub_index, int32_t num, float
 #ifdef WIN32
 		for (int32_t i=t_start; i<t_stop; i++)
 #else
-		for (int32_t i=t_start; i<t_stop &&
-				!CSignal::cancel_computations(); i++)
+		// TODO: replace with the new signal
+		// for (int32_t i=t_start; i<t_stop &&
+		//		!CSignal::cancel_computations(); i++)
+		for (int32_t i = t_start; i < t_stop; i++)
 #endif
 		{
 			if (alphas)
@@ -149,11 +148,6 @@ void CDotFeatures::dense_dot_range_subset(int32_t* sub_index, int32_t num, float
 		}
 	}
 	pb.complete();
-
-#ifndef WIN32
-		if ( CSignal::cancel_computations() )
-			SG_INFO("prematurely stopped.           \n")
-#endif
 }
 
 SGMatrix<float64_t> CDotFeatures::get_computed_dot_feature_matrix()

src/shogun/features/hashed/HashedWDFeaturesTransposed.cpp

@@ -291,11 +291,6 @@ void CHashedWDFeaturesTransposed::dense_dot_range(float64_t* output, int32_t sta
 	}
 #endif
 	SG_FREE(index);
-
-#ifndef WIN32
-		if ( CSignal::cancel_computations() )
-			SG_INFO("prematurely stopped.           \n")
-#endif
 }
 
 void CHashedWDFeaturesTransposed::dense_dot_range_subset(int32_t* sub_index, int num, float64_t* output, float64_t* alphas, float64_t* vec, int32_t dim, float64_t b)
@@ -382,11 +377,6 @@ void CHashedWDFeaturesTransposed::dense_dot_range_subset(int32_t* sub_index, int
 		SG_FREE(index);
 	}
 #endif
-
-#ifndef WIN32
-		if ( CSignal::cancel_computations() )
-			SG_INFO("prematurely stopped.           \n")
-#endif
 }
 
 void* CHashedWDFeaturesTransposed::dense_dot_range_helper(void* p)

src/shogun/kernel/Kernel.cpp

@@ -1302,8 +1302,9 @@ template <class T> void* CKernel::get_kernel_matrix_helper(void* p)
 
 				pb->print_progress();
 
-				if (CSignal::cancel_computations())
-					break;
+				// TODO: replace with the new signal
+				// if (CSignal::cancel_computations())
+				//	break;
 			}
 		}
 

src/shogun/kernel/string/WeightedDegreePositionStringKernel.cpp

@@ -1254,26 +1254,28 @@ void CWeightedDegreePositionStringKernel::compute_batch(
 	{
 
 	   auto pb = progress(range(num_feat), *this->io);
-	   for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
-			{
-				init_optimization(num_suppvec, IDX, alphas, j);
-				S_THREAD_PARAM_WDS<DNATrie> params;
-				params.vec=vec;
-				params.result=result;
-				params.weights=weights;
-				params.kernel=this;
-				params.tries=&tries;
-				params.factor=factor;
-				params.j=j;
-				params.start=0;
-				params.end=num_vec;
-				params.length=length;
-				params.max_shift=max_shift;
-				params.shift=shift;
-				params.vec_idx=vec_idx;
-				compute_batch_helper((void*) &params);
-
-			    pb.print_progress();
+	   // TODO: replace with the new signal
+	   // for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+	   for (int32_t j = 0; j < num_feat; j++)
+	   {
+		   init_optimization(num_suppvec, IDX, alphas, j);
+		   S_THREAD_PARAM_WDS<DNATrie> params;
+		   params.vec = vec;
+		   params.result = result;
+		   params.weights = weights;
+		   params.kernel = this;
+		   params.tries = &tries;
+		   params.factor = factor;
+		   params.j = j;
+		   params.start = 0;
+		   params.end = num_vec;
+		   params.length = length;
+		   params.max_shift = max_shift;
+		   params.shift = shift;
+		   params.vec_idx = vec_idx;
+		   compute_batch_helper((void*)&params);
+
+		   pb.print_progress();
 		    }
 		    pb.complete();
 	}
@@ -1282,7 +1284,9 @@ void CWeightedDegreePositionStringKernel::compute_batch(
 	{
 
 		auto pb = progress(range(num_feat), *this->io);
-		for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		// TODO: replace with the new signal
+		// for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		for (int32_t j = 0; j < num_feat; j++)
 		{
 			init_optimization(num_suppvec, IDX, alphas, j);
 			pthread_t* threads = SG_MALLOC(pthread_t, num_threads-1);

src/shogun/kernel/string/WeightedDegreeStringKernel.cpp

@@ -884,8 +884,9 @@ void CWeightedDegreeStringKernel::compute_batch(
 
 	if (num_threads < 2)
 	{
-
-		for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		// TODO: replace with the new signal
+		// for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		for (int32_t j = 0; j < num_feat; j++)
 		{
 			init_optimization(num_suppvec, IDX, alphas, j);
 			S_THREAD_PARAM_WD params;
@@ -909,8 +910,9 @@ void CWeightedDegreeStringKernel::compute_batch(
 #ifdef HAVE_PTHREAD
 	else
 	{
-
-		for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		// TODO: replace with the new signal
+		// for (int32_t j=0; j<num_feat && !CSignal::cancel_computations(); j++)
+		for (int32_t j = 0; j < num_feat; j++)
 		{
 			init_optimization(num_suppvec, IDX, alphas, j);
 			pthread_t* threads = SG_MALLOC(pthread_t, num_threads-1);