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| author | Gertjan van den Burg <burg@ese.eur.nl> | 2014-06-26 17:53:51 +0200 |
|---|---|---|
| committer | Gertjan van den Burg <burg@ese.eur.nl> | 2014-06-26 17:53:51 +0200 |
| commit | 244b5d30b3f794a030be5d943fa9f672e50c38ad (patch) | |
| tree | 52e645cb278df0a35d140d0e530012037df32a23 | |
| parent | remove comments from prediction function (diff) | |
| download | gensvm-244b5d30b3f794a030be5d943fa9f672e50c38ad.tar.gz gensvm-244b5d30b3f794a030be5d943fa9f672e50c38ad.zip | |
remove fixed random seed from grid search
| -rw-r--r-- | src/msvmmaj_train_dataset.c | 4 | ||||
| -rw-r--r-- | src/trainMSVMMajdataset.c | 3 | ||||
| -rw-r--r-- | training/satimage.training | 1 |
3 files changed, 1 insertions, 7 deletions
diff --git a/src/msvmmaj_train_dataset.c b/src/msvmmaj_train_dataset.c index 29b410b..7db1379 100644 --- a/src/msvmmaj_train_dataset.c +++ b/src/msvmmaj_train_dataset.c @@ -425,10 +425,6 @@ void consistency_repeats(struct Queue *q, long repeats, TrainType traintype) * seed_model parameter. If seed_model is NULL, random starting values are * used. * - * @todo - * There must be some inefficiencies here because the fold model is allocated - * at every fold. This would be detrimental with large datasets. - * * @param[in] model MajModel with the configuration to train * @param[in] seed_model MajModel with a seed for MajModel::V * @param[in] data MajData with the dataset diff --git a/src/trainMSVMMajdataset.c b/src/trainMSVMMajdataset.c index 35f4611..a6f3e87 100644 --- a/src/trainMSVMMajdataset.c +++ b/src/trainMSVMMajdataset.c @@ -98,8 +98,7 @@ int main(int argc, char **argv) struct Queue *q = Malloc(struct Queue, 1); make_queue(training, q, train_data, test_data); - // srand(time(NULL)); - srand(123456); + srand(time(NULL)); note("Starting training\n"); if (training->traintype == TT) diff --git a/training/satimage.training b/training/satimage.training index bf12e62..d4d08d1 100644 --- a/training/satimage.training +++ b/training/satimage.training @@ -1,5 +1,4 @@ train: ./data/satimage.train -test: ./data/satimage.test p: 1.0 1.25 1.5 1.75 2.0 kappa: -0.9 0.0 0.5 1.0 5.0 lambda: 64 32 16 8 4 2 1 0.5 0.25 0.125 0.0625 0.03125 0.015625 0.0078125 0.00390625 0.001953125 0.0009765625 0.00048828125 0.000244140625 |