first working version of new kernel GenSVM

1 files changed, 154 insertions, 1 deletions

diff --git a/src/gensvm_train_dataset.c b/src/gensvm_train_dataset.c
index 3034bb4..eee4bf9 100644
--- a/src/gensvm_train_dataset.c
+++ b/src/gensvm_train_dataset.c
@@ -435,6 +435,12 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype)
  * 				cross validation
  */
 double cross_validation(struct GenModel *model, struct GenData *data,
+		long folds)
+{
+	return 0.0;
+}
+/*
+double cross_validation(struct GenModel *model, struct GenData *data,
 	       	long folds)
 {
 	FILE *fid;
@@ -487,7 +493,7 @@ double cross_validation(struct GenModel *model, struct GenData *data,
 
 	return total_perf;
 }
-
+*/
 /**
  * @brief Run the grid search for a cross validation dataset
  *
@@ -542,6 +548,147 @@ void start_training_cv(struct Queue *q)
 	gensvm_free_model(model);
 }
 
+
+bool kernel_changed(struct Task *newtask, struct Task *oldtask)
+{
+	if (oldtask == NULL)
+		return true;
+	int i;
+	if (newtask->kerneltype != oldtask->kerneltype) {
+		return true;
+	} else if (newtask->kerneltype == K_POLY) {
+		for (i=0; i<3; i++)
+			if (newtask->kernelparam[i] != oldtask->kernelparam[i])
+				return true;
+		return false;
+	} else if (newtask->kerneltype == K_RBF) {
+		if (newtask->kernelparam[0] != oldtask->kernelparam[0])
+			return true;
+		return false;
+	} else if (newtask->kerneltype == K_SIGMOID) {
+		for (i=0; i<2; i++)
+			if (newtask->kernelparam[i] != oldtask->kernelparam[i])
+				return true;
+		return false;
+	}
+	return false;
+}
+
+
+void start_training(struct Queue *q)
+{
+	int f, folds;
+	double perf, current_max = 0;
+	struct Task *task = get_next_task(q);
+	struct Task *prevtask = NULL;
+	struct GenModel *model = gensvm_init_model();
+	clock_t main_s, main_e, loop_s, loop_e;
+
+	// in principle this can change between tasks, but this shouldn't be 
+	// the case TODO
+	folds = task->folds;
+
+	model->n = 0;
+	model->m = task->train_data->m;
+	model->K = task->train_data->K;
+	gensvm_allocate_model(model);
+	gensvm_seed_model_V(NULL, model, task->train_data);
+
+	long *cv_idx = Calloc(long, task->train_data->n);
+	gensvm_make_cv_split(task->train_data->n, task->folds, cv_idx);
+
+	struct GenData **train_folds = Malloc(struct GenData *, task->folds);
+	struct GenData **test_folds = Malloc(struct GenData *, task->folds);
+	for (f=0; f<folds; f++) {
+		train_folds[f] = gensvm_init_data();
+		test_folds[f] = gensvm_init_data();
+		gensvm_get_tt_split(task->train_data, train_folds[f],
+			       	test_folds[f], cv_idx, f);
+	}
+
+	main_s = clock();
+	while (task) {
+		print_progress_string(task, q->N);
+		make_model_from_task(task, model);
+	
+		if (kernel_changed(task, prevtask)) {
+			note("*");
+			for (f=0; f<folds; f++) {
+				gensvm_kernel_preprocess(model,
+					       	train_folds[f]);
+				gensvm_kernel_postprocess(model,
+					       	train_folds[f], test_folds[f]);
+			}
+			note("*");
+		}
+
+		loop_s = clock();
+		perf = gensvm_cross_validation(model, train_folds, test_folds,
+				folds, task->train_data->n);
+		loop_e = clock();
+		current_max = maximum(current_max, perf);
+
+		note("\t%3.3f%% (%3.3fs)\t(best = %3.3f%%)\n", perf,
+				elapsed_time(loop_s, loop_e), current_max);
+
+		q->tasks[task->ID]->performance = perf;
+		prevtask = task;
+		task = get_next_task(q);
+	}
+	main_e = clock();
+
+	note("\nTotal elapsed training time: %8.8f seconds\n",
+			elapsed_time(main_s, main_e));
+
+	gensvm_free_model(model);
+	for (f=0; f<folds; f++) {
+		gensvm_free_data(train_folds[f]);
+		gensvm_free_data(test_folds[f]);
+	}
+	free(train_folds);
+	free(test_folds);
+}
+
+
+double gensvm_cross_validation(struct GenModel *model,
+	      	struct GenData **train_folds, struct GenData **test_folds,
+		int folds, long n_total)
+{
+	FILE *fid;
+
+	int f;
+	long *predy;
+	double performance, total_perf = 0;
+
+	for (f=0; f<folds; f++) {
+		// reallocate model in case dimensions differ with data
+		gensvm_reallocate_model(model, train_folds[f]->n,
+				train_folds[f]->r);
+
+		// initialize object weights
+		gensvm_initialize_weights(train_folds[f], model);
+
+		// train the model (surpressing output)
+		fid = GENSVM_OUTPUT_FILE;
+		GENSVM_OUTPUT_FILE = NULL;
+		gensvm_optimize(model, train_folds[f]);
+		GENSVM_OUTPUT_FILE = fid;
+
+		// calculate prediction performance on test set
+		predy = Calloc(long, test_folds[f]->n);
+		gensvm_predict_labels(test_folds[f], model, predy);
+		performance = gensvm_prediction_perf(test_folds[f], predy);
+		total_perf += performance * test_folds[f]->n;
+
+		free(predy);
+	}
+
+	total_perf /= ((double) n_total);
+
+	return total_perf;
+}	
+
+
 /**
  * @brief Run the grid search for a train/test dataset
  *
@@ -563,6 +710,11 @@ void start_training_cv(struct Queue *q)
  */
 void start_training_tt(struct Queue *q)
 {
+	return;
+}
+/*
+void start_training_tt(struct Queue *q)
+{
 	FILE *fid;
 
 	long c = 0;
@@ -628,6 +780,7 @@ void start_training_tt(struct Queue *q)
 	free(task);
 	gensvm_free_model(seed_model);
 }
+*/
 
 /**
  * @brief Free the Queue struct