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diff --git a/src/gensvm_cross_validation.c b/src/gensvm_cross_validation.c
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+/**
+ * @file gensvm_cross_validation.c
+ * @author G.J.J. van den Burg
+ * @date 2016-10-24
+ * @brief Function for running cross validation on GenModel
+ *
+ * @copyright
+ Copyright 2016, G.J.J. van den Burg.
+
+ This file is part of GenSVM.
+
+ GenSVM is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ GenSVM is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with GenSVM. If not, see <http://www.gnu.org/licenses/>.
+
+ */
+
+#include "gensvm_cross_validation.h"
+
+extern FILE *GENSVM_OUTPUT_FILE;
+
+/**
+ * @brief Run cross validation with a given set of train/test folds
+ *
+ * @details
+ * This cross validation function uses predefined train/test splits. Also, the
+ * the optimal parameters GenModel::V of a previous fold as initial conditions
+ * for GenModel::V of the next fold.
+ *
+ * @note
+ * This function always sets the output stream defined in GENSVM_OUTPUT_FILE
+ * to NULL, to ensure gensvm_optimize() doesn't print too much.
+ *
+ * @param[in] 	model 		GenModel with the configuration to train
+ * @param[in] 	train_folds 	array of training datasets
+ * @param[in] 	test_folds 	array of test datasets
+ * @param[in] 	folds 		number of folds
+ * @param[in] 	n_total 	number of objects in the union of the train
+ * 				datasets
+ * @return 			performance (hitrate) of the configuration on
+ * 				cross validation
+ */
+double gensvm_cross_validation(struct GenModel *model,
+		struct GenData **train_folds, struct GenData **test_folds,
+		int folds, long n_total)
+{
+	int f;
+	long *predy = NULL;
+	double performance, total_perf = 0;
+
+	// make sure that gensvm_optimize() is silent.
+	FILE *fid = GENSVM_OUTPUT_FILE;
+	GENSVM_OUTPUT_FILE = NULL;
+
+	// run cross-validation
+	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)
+		gensvm_optimize(model, train_folds[f]);
+
+		// 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);
+
+	// reset the output stream
+	GENSVM_OUTPUT_FILE = fid;
+
+	return total_perf;
+}