Spf network kit install#
Spf network kit code#

Simon Roesler - Karlsruhe Institute of Technology.

See also the list of contributors who participated in this project.

Nicola Di Mauro - University of Bari Aldo Moro.

Robert Peharz - University of Cambridge.

If you find SPFlow useful please cite us in your = , 'Mixed sum-product networks: A deep architecture for hybrid domains.' In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). Molina, Alejandro, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, and Kristian Kersting. 'Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions.' In AAAI, pp. Molina, Alejandro, Sriraam Natarajan, and Kristian Kersting. 'Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning'.

Antonio Vergari, Nicola Di Mauro, and Floriana Esposito.

'Learning Accurate Cutset Networks by Exploiting Decomposability'.

Nicola Di Mauro, Antonio Vergari, and Floriana Esposito.

'Learning Bayesian Random Cutset Forests'.

Nicola Di Mauro, Antonio Vergari, and Teresa M.A.

'Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks'.

Nicola Di Mauro, Antonio Vergari, Teresa M.A.The Expectations function allows you to directly compute first oder moments given an SPN and (optionally) a list of features for which you need the expectation and an array of evidence.ДЄll other aspects of the SPN library can be extended in a similar same way. There are three main functions implemented for that. SPNs allow you to compute first and higher order moments of the represented probability function by directly evaluating the tree structure. nan print( mpe( cnet_random, train_data_mpe)) Expectations and Moments MPE import mpe train_data_mpe = train_data. Ll = log_likelihood( cnet_random, train_data) Ll = log_likelihood( cnet_naive_mle, train_data) # learning a CNet with random conditioning cnet_random = learn_cnet( train_data, # learning a CNet with a naive mle conditioning cnet_naive_mle = learn_cnet( train_data, binomial( 1,, size =( 100, 10))Дs_context = Context( parametric_types =[ Bernoulli, Bernoulli, Bernoulli, Bernoulli, Inference import log_likelihood train_data = np. LearningWrappers import learn_parametric, learn_cnet from spn. CLTree import create_cltree_leaf from spn. With SPFlow we can learn both the structure and the parameters of CNets, a particular kind of SPNs with CLTs as leaf providing exact MPE inference, with:ДЇrom spn. Inference import log_likelihood ds_context = Context( parametric_types =). LearningWrappers import learn_parametric from spn.

Spf network kit install#

To install the latest released version of SPFlow using pip These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Spf network kit code#

It efficiently implements several probabilistic inference routines like computing marginals, conditionals and (approximate) most probable explanations (MPEs)ДЄlong with sampling as well as utilities for serializing,plotting and structure statistics on an SPN.ДЇurthermore, SPFlow is extremely extensible and customizable, allowing users to promptly create new inference and learning routinesД«y injecting custom code into a light-weight functional-oriented API framework. The library allows one to quickly create SPNs both from data and through a domain specific language (DSL). Learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).

SPFlow, an open-source Python library providing a simple interface to inference, SPFlow: An Easy and Extensible Library for Sum-Product Networks