create a single training function for easy external access

1 files changed, 10 insertions, 35 deletions

diff --git a/src/GenSVMtraintest.c b/src/GenSVMtraintest.c
index 47cc900..73c0ce9 100644
--- a/src/GenSVMtraintest.c
+++ b/src/GenSVMtraintest.c
@@ -12,8 +12,7 @@
 
 #include "gensvm_cmdarg.h"
 #include "gensvm_io.h"
-#include "gensvm_init.h"
-#include "gensvm_optimize.h"
+#include "gensvm_train.h"
 #include "gensvm_pred.h"
 
 #define MINARGS 2
@@ -54,10 +53,8 @@ void exit_with_help()
 	exit(0);
 }
 
-
 int main(int argc, char **argv)
 {
-	bool with_test = false;
 	long i, *predy = NULL;
 	double performance;
 
@@ -68,6 +65,7 @@ int main(int argc, char **argv)
 	     *prediction_outputfile = NULL;
 
 	struct GenModel *model = gensvm_init_model();
+	struct GenModel *seed_model = NULL;
 	struct GenData *traindata = gensvm_init_data();
 	struct GenData *testdata = gensvm_init_data();
 
@@ -77,53 +75,29 @@ int main(int argc, char **argv)
 	parse_command_line(argc, argv, model, &model_inputfile,
 		       	&training_inputfile, &testing_inputfile,
 		       	&model_outputfile, &prediction_outputfile);
-	if (testing_inputfile != NULL)
-		with_test = true;
 
 	// read data from files
 	gensvm_read_data(traindata, training_inputfile);
-	if (with_test)
-		gensvm_read_data(testdata, testing_inputfile);
-
-	// copy dataset parameters to model
-	model->n = traindata->n;
-	model->m = traindata->m;
-	model->K = traindata->K;
 	model->data_file = training_inputfile;
 
-	// allocate model
-	gensvm_allocate_model(model);
-
-	// run pre/post processing in case of kernels
-	gensvm_kernel_preprocess(model, traindata);
-	if (with_test)
-		gensvm_kernel_postprocess(model, traindata, testdata);
-
-	// reallocate model in case of kernel dimension reduction
-	gensvm_reallocate_model(model, traindata->n, traindata->r);
-
-	// initialize weights
-	gensvm_initialize_weights(traindata, model);
-
 	// seed the random number generator
 	srand(time(NULL));
 
 	// load a seed model from file if it is specified
 	if (gensvm_check_argv_eq(argc, argv, "-s")) {
-		struct GenModel *seed_model = gensvm_init_model();
+		seed_model = gensvm_init_model();
 		gensvm_read_model(seed_model, model_inputfile);
-		gensvm_init_V(seed_model, model, traindata);
-		gensvm_free_model(seed_model);
-	} else {
-		gensvm_init_V(NULL, model, traindata);
 	}
 
-	// start training
-	gensvm_optimize(model, traindata);
+	// train the GenSVM model
+	gensvm_train(model, traindata, seed_model);
 
 	// if we also have a test set, predict labels and write to predictions
 	// to an output file if specified
-	if (with_test) {
+	if (testing_inputfile != NULL) {
+		gensvm_read_data(testdata, testing_inputfile);
+		gensvm_kernel_postprocess(model, traindata, testdata);
+
 		// predict labels
 		predy = Calloc(long, testdata->n);
 		gensvm_predict_labels(testdata, model, predy);
@@ -154,6 +128,7 @@ int main(int argc, char **argv)
 
 	// free everything
 	gensvm_free_model(model);
+	gensvm_free_model(seed_model);
 	gensvm_free_data(traindata);
 	gensvm_free_data(testdata);
 	free(training_inputfile);