A collection of 29 binary datasets, which most of them are used in Probabilistic Circuits literature, can be found at UCLA-StarAI-Binary-Datasets. Moreover, a collection of 5 continuous datasets, commonly present in works regarding Normalizing Flows, can be found at MAF-Continuous-Datasets.
In order to run the experiments, it is necessary to clone the repository.
After downloading the datasets, they must be stored in the experiments/datasets directory to be able to
run the experiments.
Finally, install the development packages from the root directory as follows.
pip install -e .[develop]The experiments scripts are available in the experiments directory and can be launched using the command line
by specifying the dataset and hyperparameters.
The following table shows the available experiments scripts.
| Experiment | Description |
|---|---|
| energy.py | Fit Sum-Product Networks (SPNs) and Normalizing Flows on energy functions. 1 |
| spn.py | Experiments for Sum-Product Networks (SPNs). |
| ratspn.py | Experiments for Randomized And Tensorized Sum-Product Networks (RAT-SPNs). |
| dgcspn.py | Experiments for Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs). |
| flows.py | Experiments for several Normalizing Flows models. |
Footnotes
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Rezende and Mohamed. Variational Inference with Normalizing Flows. ICML (2015). ↩