seed V based on scale of X

5 files changed, 40 insertions, 22 deletions

diff --git a/include/libMSVMMaj.h b/include/libMSVMMaj.h
index b7261dc..a9bd789 100644
--- a/include/libMSVMMaj.h
+++ b/include/libMSVMMaj.h
@@ -29,12 +29,14 @@ void msvmmaj_simplex_gen(long K, double *U);
 void msvmmaj_category_matrix(struct MajModel *model, struct MajData *data);
 void msvmmaj_simplex_diff(struct MajModel *model, struct MajData *dataset);
 
-void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data, double *ZV);
+void msvmmaj_calculate_errors(struct MajModel *model, struct MajData *data,
+	       	double *ZV);
 void msvmmaj_calculate_huber(struct MajModel *model);
 
 void msvmmaj_step_doubling(struct MajModel *model);
 
-void msvmmaj_seed_model_V(struct MajModel *from_model, struct MajModel *to_model);
+void msvmmaj_seed_model_V(struct MajModel *from_model,
+	       	struct MajModel *to_model, struct MajData *data);
 void msvmmaj_initialize_weights(struct MajData *data, struct MajModel *model);
 
 #endif
diff --git a/src/libMSVMMaj.c b/src/libMSVMMaj.c
index ca0916b..488b76d 100644
--- a/src/libMSVMMaj.c
+++ b/src/libMSVMMaj.c
@@ -216,19 +216,34 @@ void msvmmaj_calculate_huber(struct MajModel *model)
  * @param[in] 		from_model 	MajModel from which to copy V
  * @param[in,out] 	to_model 	MajModel to which V will be copied
  */
-void msvmmaj_seed_model_V(struct MajModel *from_model, struct MajModel *to_model)
+void msvmmaj_seed_model_V(struct MajModel *from_model,
+	       	struct MajModel *to_model, struct MajData *data)
 {
-	long i, j;
-	long m = to_model->m;
-	long K = to_model->K;
-	double value;
-
+	long i, j, k;
+	double cmin, cmax, value;
+	
+	long n = data->n;
+	long m = data->m;
+	long K = data->K;
+	
 	if (from_model == NULL) {
-		for (i=0; i<m+1; i++)
-			for (j=0; j<K-1; j++)
-				matrix_set(to_model->V, K-1, i, j, -1.0+2.0*rnd());
+		for (i=0; i<m+1; i++) {
+			cmin = 1e100;
+			cmax = -1e100;
+			for (k=0; k<n; k++) {
+				value = matrix_get(data->Z, m+1, k, i);
+				cmin = minimum(cmin, value);
+				cmax = maximum(cmax, value);
+			}
+			for (j=0; j<K-1; j++) {
+				cmin = (abs(cmin) < 1e-10) ? -1 : cmin;
+				cmax = (abs(cmax) < 1e-10) ? 1 : cmax;
+				value = 1.0/cmin + (1.0/cmax - 1.0/cmin)*rnd();
+				matrix_set(to_model->V, K-1, i, j, value);
+			}
+		}
 	} else {
-		for (i=0; i<m+1; i++)
+		for (i=0; i<m+1; i++) 
 			for (j=0; j<K-1; j++) {
 				value = matrix_get(from_model->V, K-1, i, j);
 				matrix_set(to_model->V, K-1, i, j, value);
diff --git a/src/msvmmaj_train.c b/src/msvmmaj_train.c
index 2333544..4aea85f 100644
--- a/src/msvmmaj_train.c
+++ b/src/msvmmaj_train.c
@@ -88,13 +88,14 @@ void msvmmaj_optimize(struct MajModel *model, struct MajData *data)
 		Lbar = L;
 		L = msvmmaj_get_loss(model, data, ZV);
 
-		if (it%100 == 0)
+		if (it%100 == 0) 
 			note("iter = %li, L = %15.16f, Lbar = %15.16f, "
 			     "reldiff = %15.16f\n", it, L, Lbar, (Lbar - L)/L);
 		it++;
 	}
 
-	note("optimization finished, iter = %li, error = %15.16f\n", it-1,
+	note("optimization finished, iter = %li, loss = %15.16f, "
+			"rel. diff. = %15.16f\n", it-1, L,
 			(Lbar - L)/L);
 	note("number of support vectors: %li\n", msvmmaj_num_sv(model, data));
 
diff --git a/src/msvmmaj_train_dataset.c b/src/msvmmaj_train_dataset.c
index 8d23684..7fa7316 100644
--- a/src/msvmmaj_train_dataset.c
+++ b/src/msvmmaj_train_dataset.c
@@ -335,7 +335,7 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype)
 			model->m = task->train_data->m;
 			model->K = task->train_data->K;
 			msvmmaj_allocate_model(model);
-			msvmmaj_seed_model_V(NULL, model);
+			msvmmaj_seed_model_V(NULL, model, task->train_data);
 		}
 
 		time[i] = 0.0;
@@ -356,7 +356,7 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype)
 			note("%3.3f\t", p);
 			// this is done because if we reuse the V it's not a 
 			// consistency check
-			msvmmaj_seed_model_V(NULL, model);
+			msvmmaj_seed_model_V(NULL, model, task->train_data);
 		}
 		for (r=0; r<repeats; r++) {
 			std[i] += pow(matrix_get(
@@ -512,7 +512,7 @@ void start_training_cv(struct Queue *q)
 	model->m = task->train_data->m;
 	model->K = task->train_data->K;
 	msvmmaj_allocate_model(model);
-	msvmmaj_seed_model_V(NULL, model);
+	msvmmaj_seed_model_V(NULL, model, task->train_data);
 
 	main_s = clock();
 	while (task) {
@@ -576,7 +576,7 @@ void start_training_tt(struct Queue *q)
 	seed_model->m = task->train_data->m;
 	seed_model->K = task->train_data->K;
 	msvmmaj_allocate_model(seed_model);
-	msvmmaj_seed_model_V(NULL, seed_model);
+	msvmmaj_seed_model_V(NULL, seed_model, task->train_data);
 	
 	main_s = clock();
 	while (task) {
@@ -594,7 +594,7 @@ void start_training_tt(struct Queue *q)
 
 		msvmmaj_allocate_model(model);
 		msvmmaj_initialize_weights(task->train_data, model);
-		msvmmaj_seed_model_V(seed_model, model);
+		msvmmaj_seed_model_V(seed_model, model, task->train_data);
 
 		fid = MSVMMAJ_OUTPUT_FILE;
 		MSVMMAJ_OUTPUT_FILE = NULL;
@@ -607,7 +607,7 @@ void start_training_tt(struct Queue *q)
 		if (task->test_data->y != NULL) 
 			total_perf = msvmmaj_prediction_perf(task->test_data,
 				       	predy);
-		msvmmaj_seed_model_V(model, seed_model);
+		msvmmaj_seed_model_V(model, seed_model, task->train_data);
 
 		msvmmaj_free_model(model);
 		free(predy);
diff --git a/src/trainMSVMMaj.c b/src/trainMSVMMaj.c
index af07fe7..5377b43 100644
--- a/src/trainMSVMMaj.c
+++ b/src/trainMSVMMaj.c
@@ -113,10 +113,10 @@ int main(int argc, char **argv)
 	if (msvmmaj_check_argv_eq(argc, argv, "-m")) {
 		struct MajModel *seed_model = msvmmaj_init_model();
 		msvmmaj_read_model(seed_model, model_filename);
-		msvmmaj_seed_model_V(seed_model, model);
+		msvmmaj_seed_model_V(seed_model, model, data);
 		msvmmaj_free_model(seed_model);
 	} else {
-		msvmmaj_seed_model_V(NULL, model);
+		msvmmaj_seed_model_V(NULL, model, data);
 	}
 
 	// start training