create a single training function for easy external access

1 files changed, 51 insertions, 0 deletions

diff --git a/src/gensvm_train.c b/src/gensvm_train.c
new file mode 100644
index 0000000..4c0f332
--- /dev/null
+++ b/src/gensvm_train.c
@@ -0,0 +1,51 @@
+/**
+ * @file gensvm_train.c
+ * @author Gertjan van den Burg
+ * @date May, 2016
+ * @brief Main function for training a GenSVM model.
+ *
+ */
+
+#include "gensvm_train.h"
+
+/**
+ * @brief Utility function for training a GenSVM model
+ *
+ * @details
+ * This function organizes model allocation, kernel preprocessing, instance 
+ * weight initialization, and model training. It is the function that should 
+ * be used for training a single GenSVM model. Note that optionally a seed 
+ * model can be passed to the function to seed the V matrix with. When no such 
+ * model is used this parameter should be set to NULL.
+ *
+ * @param[in] 	model 		a GenModel instance
+ * @param[in] 	data 		a GenData instance with the training data
+ * @param[in] 	seed_model 	an optional GenModel to seed the V matrix
+ *
+ */
+void gensvm_train(struct GenModel *model, struct GenData *data,
+		struct GenModel *seed_model)
+{
+	// copy dataset parameters to model
+	model->n = data->n;
+	model->m = data->m;
+	model->K = data->K;
+
+	// initialize the V matrix (potentially with a seed model)
+	gensvm_init_V(seed_model, model, data);
+
+	// allocate model
+	gensvm_allocate_model(model);
+
+	// preprocess kernel
+	gensvm_kernel_preprocess(model, data);
+
+	// reallocate model for kernels
+	gensvm_reallocate_model(model, data->n, data->r);
+
+	// initialize weights
+	gensvm_initialize_weights(data, model);
+
+	// start training
+	gensvm_optimize(model, data);
+}