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discrete pairwise undirected graphical models

Project description

Copyright (c) 2020 Nico Piatkowski

pxpy

The python library for discrete pairwise undirected graphical models. Runs on Linux with GLIBC >= 2.28 and Windows 10.

Inference

  • Loopy belief propagation
  • Junction tree
  • Stochastic Clenshaw-Curtis quadrature

Sampling

  • Gibbs Sampling
  • Perturb+Map Sampling

Parameter learning

  • Accelerated proximal gradient
  • built-in L1 / L2 regularization
  • Support for custom regularization

Structure learning

  • Chow-Liu trees
  • Soft-thresolding
  • High-order clique structures

Misc

  • Support for deep Boltzmann tree models (DBT)
  • Support for spatio-temporal compressible reparametrization (STRF)
  • Runs on x86_64 (linux, windows) and aarch64 (linux)
  • Graph drawing via graphviz
  • Discretization

<https://randomfields.org>

Alpha Changelog

  • 1.0a56: Improved: Numerical stability of discretization
  • 1.0a55: Added: Load/store of discretization models; aarch64 support (tested on Jetson TX1)
  • 1.0a54: Improved: Init speed
  • 1.0a53: Improved: Init speed
  • 1.0a52: Improved: Graph splitting; init speed
  • 1.0a51: Fixed: Multi-core normalization; Split-edge weight centering
  • 1.0a50: Improved: Support for external inference engines; Changed required GLIBC version to 2.29
  • 1.0a49: Fixed: External loader
  • 1.0a48: Added: Shell script “pxpy_environ” for populating various environment variables. Improved: multi-core support.
  • 1.0a47: Added: draw_neighbors(..). Improved: Discretization
  • 1.0a44: Improved: Discretization
  • 1.0a42: Improved: Updated some default values
  • 1.0a41: Improved: Fixed subtle bug in parameter initialization
  • 1.0a40: Added: Loading string data via genfromstrcsv(..) (built-in string<->int mapper)
  • 1.0a36: Improved: Randomized clique search
  • 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

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