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/**
* @file gensvm_train.c
* @author G.J.J. van den Burg
* @date 2016-05-01
* @brief Main function for training a GenSVM model.
*
* @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_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)
{
long real_seed;
// copy dataset parameters to model
model->n = data->n;
model->m = data->m;
model->K = data->K;
// allocate model
gensvm_allocate_model(model);
// set the random seed
real_seed = (model->seed == -1) ? time(NULL) : model->seed;
srand(real_seed);
// preprocess kernel
gensvm_kernel_preprocess(model, data);
// reallocate model for kernels
gensvm_reallocate_model(model, data->n, data->r);
// initialize the V matrix (potentially with a seed model)
gensvm_init_V(seed_model, model, data);
// initialize weights
gensvm_initialize_weights(data, model);
// start training
gensvm_optimize(model, data);
}
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