pxpy 1.0a29

discrete pairwise undirected graphical models

Copyright (c) 2020 Nico Piatkowski

pxpy

The python library for discrete pairwise undirected graphical models.

Inference: * Loopy belief propagation (GPU support) * Junction tree * Stochastic Clenshaw-Curtis quadrature

Sampling: * Gibbs Sampling * Perturb+Map Sampling

Parameter learning: * Accelerated proximal gradient * built-in L1 / L2 regularization * Supports arbitrary custom regularization

Structure learning: * Chow-Liu trees * Soft-thresolding * High-order clique structures

Misc: * Support for spatio-temporal compressible reparametrization (STRF) * Runs on x86_64 (linux, windows), ARMv8 (linux), and MSP430 (bare metal) * Basic graph drawing via graphviz * Discretization

<https://randomfields.org>

Changelog

• 1.0a29: Added: Randomized clique search

• 1.0a28: Improved: Handling NaN-values during discretization (now interpreted as missing)

• 1.0a27: Improved: Accelerated structure estimation

• 1.0a26: Improved: Progress computation. Added: Online entropy computation for large cliques

• 1.0a25: Improved: Memory management

• 1.0a24: Improved: Structure estimation, backend. Added: Third-order structure estimation; simple graphviz output

• 1.0a23: Improved: Structure estimation

• 1.0a22: Improved: Discretization engine, support for external inference engine. Added: default to 32bit computation (disable via env PX_USE64BIT)

• 1.0a21: Improved: Support for external inference engine

• 1.0a20: Added: Support for external inference engine (access via env PX_EXTINF)

• 1.0a19: Improved: Manual model creation

• 1.0a18: Added: Debug mode (linux only, enable via env PX_DEBUGMODE)

• 1.0a17: Improved: API, tests, regularization. Added: AIC and BIC computation

• 1.0a16: Improved: Memory management, access to optimizer state in optimization hooks. Added: Support for training resumption

• 1.0a15: Improved: API

• 1.0a14: Improved: Memory management

• 1.0a13: Improved: Memory management (fixed leak in conditional sampling/marginals)

• 1.0a12: Improved: Access to vertex and pairwise marginals

• 1.0a11: Added: Access to single variable marginals

• 1.0a10: Improved: Library build process

• 1.0a9: Added: Conditional sampling

• 1.0a8: Imroved: Maximum-a-posteriori (MAP) estimation. Added: Custom graph construction

• 1.0a7: Added: Conditional marginal inference, support for Ising/minimal statistics

• 1.0a6: Added: Manual model creation, support for training data with missing values (represented by pxpy.MISSING_VALUE)

• 1.0a5: Improved: Model management

• 1.0a4: Added: Model access in regularization and proximal hooks

• 1.0a3: Improved: GLIBC requirement, removed libgomp dependency

• 1.0a2: Added: Python 3.5 compatibility

• 1.0a1: Initial release