Code Examples
A collection of code examples can be found in the examples directory.
In order to run the code examples, it is necessary clone the repository.
However, additional datasets are not required.
Note that the given examples are not intended to produce state-of-the-art results,
but only to present the library.
The following table contains a description about them and a code complexity ranging from one to three stars. The Complexity column consists of a measure that roughly represents how many features of the library are used, as well as the expected time required to run the script.
Example |
Description |
Complexity |
|---|---|---|
naive_model.py |
Learn, evaluate and print statistics about a naive factorized model. |
⭐ |
spn_plot.py |
Instantiate, prune, marginalize and plot some SPNs. |
⭐ |
clt_plot.py |
Learn a Binary-CLT and plot it. |
⭐ |
cnet_bd.py |
Learn a Binary-CNet using the BDeu score criteria. |
⭐ |
spn_moments.py |
Instantiate and compute moments statistics about the random variables. |
⭐ |
sklearn_interface.py |
Learn and evaluate a SPN using the scikit-learn interface. |
⭐ |
spn_custom_leaf.py |
Learn, evaluate and serialize a SPN with a user-defined leaf distribution. |
⭐ |
clt_to_spn.py |
Learn a Binary-CLT, convert it to a structured decomposable SPN and plot it. |
⭐ |
spn_clt_em.py |
Instantiate a SPN with Binary CLTs, apply EM algorithm and sample some data. |
⭐⭐ |
clt_queries.py |
Learn a Binary-CLT, plot it, run some queries and sample some data. |
⭐⭐ |
ratspn_mnist.py |
Train and evaluate a RAT-SPN on MNIST. |
⭐⭐ |
dgcspn_olivetti.py |
Train, evaluate and complete some images with DGC-SPN on Olivetti-Faces. |
⭐⭐ |
dgcspn_mnist.py |
Train and evaluate a DGC-SPN on MNIST. |
⭐⭐ |
nvp1d_moons.py |
Train and evaluate a 1D RealNVP on Moons dataset. |
⭐⭐ |
ratspn_nvp1d_mnist.py |
Train and evaluate a 1D RealNVP with a RAT-SPN as base distribution. |
⭐⭐ |
maf_cifar10.py |
Train and evaluate a MAF on CIFAR10. |
⭐⭐⭐ |
nvp2d_mnist.py |
Train and evaluate a 2D RealNVP on MNIST. |
⭐⭐⭐ |
nvp2d_cifar10.py |
Train and evaluate a 2D RealNVP on CIFAR10. |
⭐⭐⭐ |
spn_latent_mnist.py |
Train and evaluate a SPN on MNIST using the features extracted by an autoencoder. |
⭐⭐⭐ |