From: boyu <r02222047@ntu.edu.tw>
Date: Tue, 7 Jul 2015 16:16:29 +0000 (+0800)
Subject: Fix bug and memory leaks in 2.0
X-Git-Tag: v201~2
X-Git-Url: https://granicus.if.org/sourcecode?a=commitdiff_plain;h=b007df4a6f1dd014f011721146db38e3891c6e98;p=liblinear

Fix bug and memory leaks in 2.0
	- For SVR, model_->w was not initialized.
	  Now it is initialized in train().
	- For parameter search, subprob of CV and its x, y are not freed
	  at the end of find_parameter_C().
	- There were problems in checking prev_w for stopping condition.
	  It wrongly allocated memory for num_unchange_w < 0.
	  Flow control is modified.
	- Reposition w_new in tron to reduce peak memory usage.
	- Now print nr_fold when parameter search starts.
---

diff --git a/linear.cpp b/linear.cpp
index 7ad136f..deb0169 100644
--- a/linear.cpp
+++ b/linear.cpp
@@ -2343,6 +2343,8 @@ model* train(const problem *prob, const parameter *param)
 	if(check_regression_model(model_))
 	{
 		model_->w = Malloc(double, w_size);
+		for(i=0; i<w_size; i++)
+			model_->w[i] = 0;
 		model_->nr_class = 2;
 		model_->label = NULL;
 		train_one(prob, param, model_->w, 0, 0);
@@ -2616,7 +2618,13 @@ void find_parameter_C(const problem *prob, const parameter *param, int nr_fold,
 			else
 				total_w_size = subprob[i].n * submodel->nr_class;
 
-			if(prev_w[i] != NULL && num_unchanged_w >= 0)
+			if(prev_w[i] == NULL)
+			{
+				prev_w[i] = Malloc(double, total_w_size);
+				for(j=0; j<total_w_size; j++)
+					prev_w[i][j] = submodel->w[j];
+			}
+			else if(num_unchanged_w >= 0)
 			{
 				double norm_w_diff = 0;
 				for(j=0; j<total_w_size; j++)
@@ -2631,7 +2639,6 @@ void find_parameter_C(const problem *prob, const parameter *param, int nr_fold,
 			}
 			else
 			{
-				prev_w[i] = Malloc(double, total_w_size);
 				for(j=0; j<total_w_size; j++)
 					prev_w[i][j] = submodel->w[j];
 			}
@@ -2667,8 +2674,13 @@ void find_parameter_C(const problem *prob, const parameter *param, int nr_fold,
 	free(perm);
 	free(target);
 	for(i=0; i<nr_fold; i++)
+	{
+		free(subprob[i].x);
+		free(subprob[i].y);
 		free(prev_w[i]);
+	}
 	free(prev_w);
+	free(subprob);
 }
 
 double predict_values(const struct model *model_, const struct feature_node *x, double *dec_values)
diff --git a/train.c b/train.c
index 4df8594..53f6dbb 100644
--- a/train.c
+++ b/train.c
@@ -149,6 +149,7 @@ void do_find_parameter_C()
 		start_C = param.C;
 	else
 		start_C = -1.0;
+	printf("Doing parameter search with %d-fold cross validation.\n", nr_fold);
 	find_parameter_C(&prob, &param, nr_fold, start_C, max_C, &best_C, &best_rate);
 	printf("Best C = %lf  CV accuracy = %g%%\n", best_C, 100.0*best_rate);
 }
diff --git a/tron.cpp b/tron.cpp
index 2cd2834..3ea60f6 100644
--- a/tron.cpp
+++ b/tron.cpp
@@ -69,7 +69,6 @@ void TRON::tron(double *w)
 	int search = 1, iter = 1, inc = 1;
 	double *s = new double[n];
 	double *r = new double[n];
-	double *w_new = new double[n];
 	double *g = new double[n];
 
 	// calculate gradient norm at w=0 for stopping condition.
@@ -91,6 +90,7 @@ void TRON::tron(double *w)
 
 	iter = 1;
 
+	double *w_new = new double[n];
 	while (iter <= max_iter && search)
 	{
 		cg_iter = trcg(delta, g, s, r);