This release is a pre-release and may not be stable for production use.
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
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
1 maintainer
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