pxpy 1.0a71
discrete pairwise undirected graphical models
Requires Python: >=3.6

Copyright (c) 2021 Nico Piatkowski

pxpy
=====================================================================
The python library for discrete pairwise undirected graphical models.
Runs on Linux with GLIBC >= 2.28 and Windows 10. Exported ONNX models
run on any architecture that has an ONNX runtime with opset 13.

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>

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Alpha Changelog
=====================================================================
* 1.0a71: Improved: ONNX and QUBO export
* 1.0a70: Added: QUBO export
* 1.0a69: Added: ONNX PAM export
* 1.0a68: Improved: Data type selection, ONNX export
* 1.0a67: Improved: Integer MRF; ONNX export
* 1.0a66: Improved: Data type selection, various minor fixes. Added: Integer model file io.
* 1.0a65: Improved: Manual model construction
* 1.0a64: Improved: GPU code. Added: ONNX export
* 1.0a63: Added: Experimental annealed rejection sampler for structure sampling
* 1.0a62: Improved: Model loading
* 1.0a61: Improved: Setting target for "star" structure; reduced python version to 3.6
* 1.0a60: 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