diff options
Diffstat (limited to 'src/gensvm_train_dataset.c')
| -rw-r--r-- | src/gensvm_train_dataset.c | 86 |
1 files changed, 43 insertions, 43 deletions
diff --git a/src/gensvm_train_dataset.c b/src/gensvm_train_dataset.c index 4f42040..a70b457 100644 --- a/src/gensvm_train_dataset.c +++ b/src/gensvm_train_dataset.c @@ -6,7 +6,7 @@ * * @details * The GenSVM algorithm takes a number of parameters. The functions in - * this file are used to find the optimal parameters. + * this file are used to find the optimal parameters. */ #include <math.h> @@ -31,7 +31,7 @@ extern FILE *GENSVM_OUTPUT_FILE; * * @details * A Training instance describes the grid to search over. This funtion - * creates all tasks that need to be performed and adds these to + * creates all tasks that need to be performed and adds these to * a Queue. Each task contains a pointer to the train and test datasets * which are supplied. Note that the tasks are created in a specific order of * the parameters, to ensure that the GenModel::V of a previous parameter @@ -39,13 +39,13 @@ extern FILE *GENSVM_OUTPUT_FILE; * parameter set. * * @param[in] training Training struct describing the grid search - * @param[in] queue pointer to a Queue that will be used to + * @param[in] queue pointer to a Queue that will be used to * add the tasks to * @param[in] train_data GenData of the training set * @param[in] test_data GenData of the test set * */ -void make_queue(struct Training *training, struct Queue *queue, +void make_queue(struct Training *training, struct Queue *queue, struct GenData *train_data, struct GenData *test_data) { long i, j, k; @@ -74,7 +74,7 @@ void make_queue(struct Training *training, struct Queue *queue, task->test_data = test_data; task->folds = training->folds; task->kerneltype = training->kerneltype; - task->kernelparam = Calloc(double, training->Ng + + task->kernelparam = Calloc(double, training->Ng + training->Nc + training->Nd); queue->tasks[i] = task; } @@ -86,7 +86,7 @@ void make_queue(struct Training *training, struct Queue *queue, cnt = 1; i = 0; while (i < N ) - for (j=0; j<training->Np; j++) + for (j=0; j<training->Np; j++) for (k=0; k<cnt; k++) { queue->tasks[i]->p = training->ps[j]; i++; @@ -95,9 +95,9 @@ void make_queue(struct Training *training, struct Queue *queue, cnt *= training->Np; i = 0; while (i < N ) - for (j=0; j<training->Nl; j++) + for (j=0; j<training->Nl; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->lambda = + queue->tasks[i]->lambda = training->lambdas[j]; i++; } @@ -116,7 +116,7 @@ void make_queue(struct Training *training, struct Queue *queue, while (i < N ) for (j=0; j<training->Nw; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->weight_idx = + queue->tasks[i]->weight_idx = training->weight_idxs[j]; i++; } @@ -126,7 +126,7 @@ void make_queue(struct Training *training, struct Queue *queue, while (i < N ) for (j=0; j<training->Ne; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->epsilon = + queue->tasks[i]->epsilon = training->epsilons[j]; i++; } @@ -136,7 +136,7 @@ void make_queue(struct Training *training, struct Queue *queue, while (i < N && training->Ng > 0) for (j=0; j<training->Ng; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->kernelparam[0] = + queue->tasks[i]->kernelparam[0] = training->gammas[j]; i++; } @@ -146,7 +146,7 @@ void make_queue(struct Training *training, struct Queue *queue, while (i < N && training->Nc > 0) for (j=0; j<training->Nc; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->kernelparam[1] = + queue->tasks[i]->kernelparam[1] = training->coefs[j]; i++; } @@ -156,7 +156,7 @@ void make_queue(struct Training *training, struct Queue *queue, while (i < N && training->Nd > 0) for (j=0; j<training->Nd; j++) for (k=0; k<cnt; k++) { - queue->tasks[i]->kernelparam[2] = + queue->tasks[i]->kernelparam[2] = training->degrees[j]; i++; } @@ -285,7 +285,7 @@ struct Queue *create_top_queue(struct Queue *q) } nq->N = N; nq->i = 0; - + return nq; } @@ -298,12 +298,12 @@ struct Queue *create_top_queue(struct Queue *q) * The best performing tasks in the supplied Queue are found by taking those * Task structs that have a performance greater or equal to the 95% percentile * of the performance of all tasks. These tasks are then gathered in a new - * Queue. For each of the tasks in this new Queue the cross validation run is - * repeated a number of times. + * Queue. For each of the tasks in this new Queue the cross validation run is + * repeated a number of times. * * For each of the Task configurations that are repeated the mean performance, * standard deviation of the performance and the mean computation time are - * reported. + * reported. * * Finally, the overall best tasks are written to the specified output. These * tasks are selected to have both the highest mean performance, as well as the @@ -317,7 +317,7 @@ struct Queue *create_top_queue(struct Queue *q) * an interval is found which contains tasks. If one or more tasks are found, * this loop stops. * - * @param[in] q Queue of Task structs which have already been + * @param[in] q Queue of Task structs which have already been * run and have a Task::performance value * @param[in] repeats Number of times to repeat the best * configurations for consistency @@ -385,7 +385,7 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) matrix_set(perf, repeats, i, r, p); mean[i] += p/((double) repeats); note("%3.3f\t", p); - // this is done because if we reuse the V it's not a + // this is done because if we reuse the V it's not a // consistency check gensvm_seed_model_V(NULL, model, task->train_data); for (f=0; f<task->folds; f++) { @@ -420,21 +420,21 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) pi = prctile(mean, N, (100.0-p)); pr = prctile(std, N, p); pt = prctile(time, N, p); - for (i=0; i<N; i++) + for (i=0; i<N; i++) if ((pi - mean[i] < 0.0001) && - (std[i] - pr < 0.0001) && + (std[i] - pr < 0.0001) && (time[i] - pt < 0.0001)) { note("(%li)\tw = %li\te = %f\tp = %f\t" "k = %f\tl = %f\t" "mean: %3.3f\tstd: %3.3f\t" "time: %3.3f\n", - nq->tasks[i]->ID, + nq->tasks[i]->ID, nq->tasks[i]->weight_idx, - nq->tasks[i]->epsilon, + nq->tasks[i]->epsilon, nq->tasks[i]->p, - nq->tasks[i]->kappa, + nq->tasks[i]->kappa, nq->tasks[i]->lambda, - mean[i], + mean[i], std[i], time[i]); breakout = true; @@ -458,15 +458,15 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) * @brief Check if the kernel parameters change between tasks * * @details - * In the current strategy for training the kernel matrix is decomposed once, - * and tasks with the same kernel settings are performed sequentially. When a - * task needs to be done with different kernel parameters, the kernel matrix - * needs to be recalculated. This function is used to check whether this is + * In the current strategy for training the kernel matrix is decomposed once, + * and tasks with the same kernel settings are performed sequentially. When a + * task needs to be done with different kernel parameters, the kernel matrix + * needs to be recalculated. This function is used to check whether this is * the case. * * @param[in] newtask the next task * @param[in] oldtask the old task - * @return whether the kernel needs to be reevaluated + * @return whether the kernel needs to be reevaluated */ bool kernel_changed(struct Task *newtask, struct Task *oldtask) { @@ -502,10 +502,10 @@ bool kernel_changed(struct Task *newtask, struct Task *oldtask) * cross_validation(), the optimal weights of one parameter set are used as * initial estimates for GenModel::V in the next parameter set. Note that to * optimally exploit this feature of the optimization algorithm, the order in - * which tasks are considered is important. This is considered in + * which tasks are considered is important. This is considered in * make_queue(). - * - * The performance found by cross validation is stored in the Task struct. + * + * The performance found by cross validation is stored in the Task struct. * * @param[in,out] q Queue with Task instances to run */ @@ -519,7 +519,7 @@ void start_training(struct Queue *q) struct GenModel *model = gensvm_init_model(); clock_t main_s, main_e, loop_s, loop_e; - // in principle this can change between tasks, but this shouldn't be + // in principle this can change between tasks, but this shouldn't be // the case TODO folds = task->folds; @@ -594,17 +594,17 @@ void start_training(struct Queue *q) * @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. + * 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. * * @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 + * @param[in] n_total number of objects in the union of the train * datasets - * @return performance (hitrate) of the configuration on + * @return performance (hitrate) of the configuration on * cross validation */ double gensvm_cross_validation(struct GenModel *model, @@ -643,7 +643,7 @@ double gensvm_cross_validation(struct GenModel *model, total_perf /= ((double) n_total); return total_perf; -} +} /** @@ -735,9 +735,9 @@ void copy_model(struct GenModel *from, struct GenModel *to) * @brief Print the description of the current task on screen * * @details - * To track the progress of the grid search the parameters of the current task - * are written to the output specified in GENSVM_OUTPUT_FILE. Since the - * parameters differ with the specified kernel, this function writes a + * To track the progress of the grid search the parameters of the current task + * are written to the output specified in GENSVM_OUTPUT_FILE. Since the + * parameters differ with the specified kernel, this function writes a * parameter string depending on which kernel is used. * * @param[in] task the Task specified |