Model predictions on the test set

This folder contain the deep neural network predictions on the test set. All files are in
the format .npy and can be read using numpy.load(). Each one should contain a

All the content within this folder can be generate using the following sequence of commands:

(without a GPU it should take about 25 minutes. With GPU acceleration it should take
less then one minute)

```bash
cd /path/to/automatic-ecg-diagnosis
PFOLDER="./dnn_predicts"
MFOLDER="./model"
DFOLDER="./data"

To generate the predictions on the test set corresponding to the main model used allong the paper use:

python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/model.hdf5 --output_file $PFOLDER/model.npy

We also train several networks with the same architecture and configuration

but with different initial seeds. In order to generate the neural network

prediction on the test set for each of these models:

mkdir $FNAME/other_seeds
for n in 1 2 3 4 5 6 7 8 9 10
do
python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/other_seeds/model_$n.hdf5 --output_file $PFOLDER/other_seeds/model_$n.npy
done

Finally, to asses the effect of how we structure our problem, we have considered alternative s

cenarios where we use 90\%-5\%-5\% splits, stratified randomly,

by patient or in chronological order. The predictions of those models in the test set

can be obtained using:

mkdir $PFOLDER/other_splits
for n in date_order individual_patients normal_order
do
python predict.py $DFOLDER/ecg_tracings.hdf5 $MFOLDER/other_splits/model_$n.hdf5 --output_file $PFOLDER/other_splits/model_$n.npy
done
```

Where the DFOLDER should give the path to the folder containing the test dataset and MFOLDER should point to the
folder containing pre-trained models. The test dataset can be downloaded from here and the
pretrained models can be downloaded from here here