int main (int argc, char** argv)
{
learned_factors_t* learned = NULL;
recommended_items_t* r_items = NULL;
struct k_fold_parameters k_fold_params;
void * param_to_find = NULL;
int is_float;
clock_t start = clock();
clock_t end;
if (parse_arguments (argc, argv, &k_fold_params, ¶m_to_find, &is_float) != 0)
{
exit (-1);
}
k_fold_params.model.parameters = k_fold_params.params;
if (param_to_find != NULL)
if (is_float)
{
d_find_minimum_tsearch ( (float*) param_to_find, 0.1, 1, 0.1, 40, &k_fold_params,&RMSE_mean);
}
else
{
i_find_minimum_tsearch ( (int*) param_to_find, 1, 100, 30, &k_fold_params,&RMSE_mean);
}
else
{
error_t e= RMSE_mean (k_fold_params);
printf (" %f", e.RMSE );
printf (" %f \n", e.MAE );
}
free_recommended_items (r_items);
free_learned_factors (learned);
k_fold_params.social_relations_file_path = NULL;
free (k_fold_params.file_path);
free (k_fold_params.social_relations_file_path);
end = clock();
printf ("Time : %f s \n", (double) (end - start) / CLOCKS_PER_SEC);
system ("pause");
return 0;
}
int
main(void) {
learned_factors_t* learned;
training_set_t* tset;
recommended_items_t* r_items = NULL;
learning_model_t model = {0};
//Model configuration
//Setup model parameters
model_parameters_t params = { 0 };
params.items_number = 3;
params.users_number = 2;
params.training_set_size = 5;
params.dimensionality = 40;
params.iteration_number = 60;
params.lambda = 0.055;
params.step = 0.0095;
params.lambda_bias = 0.02;
params.step_bias = 0.001;
//Use the basic matrix factorization model
model.learning_algorithm = learn_mf_bias;
model.rating_estimator = estimate_rating_mf_bias;
model.parameters = params;
//Learning
//Initialize a training set
tset = init_training_set(params);
set_known_rating(0, 0, 1, tset);
set_known_rating(0, 1, 0, tset);
set_known_rating(0, 2, 6, tset);
set_known_rating(1, 1, 0, tset);
set_known_rating(1, 0, 2, tset);
compile_training_set(tset);
learned = learn(tset, model);
//Rating estimation
printf( "users [0] item [0], rating = %f \n",
estimate_rating_from_factors(0, 0, learned, model));
printf( "users [0] item [1], rating = %f \n",
estimate_rating_from_factors(0, 1, learned, model));
printf( "users [0] item [2], rating = %f \n",
estimate_rating_from_factors(0, 2, learned, model));
printf( "users [1] item [1], rating = %f \n",
estimate_rating_from_factors(1, 1, learned, model));
printf( "users [1] item [0], rating = %f \n",
estimate_rating_from_factors(1, 0, learned, model));
r_items = recommend_items(0, 3, learned, tset, model);
print_recommended_items(r_items->items->head, 0);
free_recommended_items(r_items);
free_learned_factors(learned);
free_training_set(tset);
return 0;
}
