[opengm] 03/386: replaced REDME.md content with TODO list
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Wed Aug 31 08:34:58 UTC 2016
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to branch debian/master
in repository opengm.
commit 8d3aac3ae75663f27c2e0f01d7eab68d2256f229
Author: Jan Funke <funke at ini.ch>
Date: Fri May 16 14:14:09 2014 +0200
replaced REDME.md content with TODO list
---
README.md | 120 +++++++-------------------------------------------------------
1 file changed, 13 insertions(+), 107 deletions(-)
diff --git a/README.md b/README.md
index 3a9b274..1d6dde0 100644
--- a/README.md
+++ b/README.md
@@ -1,113 +1,19 @@
-OpenGM 2
-========
+TODO
+====
-[](https://travis-ci.org/opengm/opengm)
+Thorsten:
+ Dataset
------------------------------------------------------------------------------------------------
+Jan:
-**Manual for OpenGM 2.0.2** -> http://hci.iwr.uni-heidelberg.de//opengm2/download/opengm-2.0.2-beta-manual.pdf
+ StructMaxMargin
+ Optimizer
+ BundleMethodOptimizer
+ SubgradientOptimizer
-**Code-Documentation for OpenGM 2.0.2** -> http://hci.iwr.uni-heidelberg.de//opengm2/doxygen/opengm-2.0.2-beta/html/index.html
+Jörg:
-OpenGM is a C++ template library for discrete factor graph models and distributive operations on these models. It includes state-of-the-art optimization and inference algorithms beyond message passing. OpenGM handles large models efficiently, since (i) functions that occur repeatedly need to be stored only once and (ii) when functions require different parametric or non-parametric encodings, multiple encodings can be used alongside each other, in the same model, using included and custom [...]
-
-Features
-
- Factor Graph Models (Kschischang et al. 2001)
- Graphs of any order and structure, from second order grid graphs to irregular higher-order models
- Arbitrary (commutative and associative) operations, including sum, product, conjunction and disjunction
- Flexible number of labels (different variables can have differently many labels)
- Function sharing across factors
- Function type abstraction. Different (built-in and custom) encodings can be used alongside each other
- Functions
- Explicit function (multi-dimensional table)
- Sparse function (sparse multi-dimensional table)
- Potts functions (different types, including higher-order)
- Truncated absolute difference
- Truncated squared difference
- Views that treat one graphical model as a function within another graphical model
- Algorithms
- Loopy Belief Propagation (Pearl 1988, Yedidia et al. 2000)
- parallel and sequential min-sum and max-product message passing (also for higher-order models)
- message damping (Wainwright 2008)
- Tree-reweighted Belief Propagation (TRBP) (Wainwright et al. 2005)
- parallel and sequential min-sum and max-product message passing (also for higher-order models)
- message damping (Wainwright 2008)
- A-star branch-and-bound search (Bergtholdt et al. 2009)
- Dual Decomposition
- With sub-gradient methods (Kappes et al. 2010)
- With bundle methods (Kappes et al. 2012)
- Automated decomposition of arbitrary factor graphs
- Arbitrary sub-solvers via templates
- Graph Cut (Boykov et al. 2001).
- Push-Relabel (Goldberg and Tarjan 1986)
- Edmonds-Karp (Edmonds and Karp 1972)
- Kolmogorov (Boykov and Kolmogorov 2004)
- QPBO
- MQPBO
- Linear Programming Relaxations over the Local Polytope
- TRWS
- ADSAL
- CombiLP
- Integer Linear Programming
- Multicut (Kappes et al. 2011)
- Reduced-Inference (Kappes et al. 2013)
- Alpha-Expansion
- Alpha-Beta-Swap
- Alpha-Fusion
- Inf and Flip
- Iterated Conditional Modes (ICM) (Besag 1986)
- Lazy Flipper (Andres et al. 2010)
- Kerninghan Lin
- MCMC Metropolis-Hastings algorithms (Metropolis et al. 1953)
- Gibbs sampling (Geman and Geman 1984)
- Swendsen-Wang sampling (Swendsen and Wang 1987)
- Wrappers around other graphical model libraries
- MRF-LIB
- LIB-DAI
- TRW-S
- QPBO
- GCO
- FastPD
- AD3
- DAOOPT
- MPLP, MPLP-C
- Binary HDF5 file format
- Command Line Optimizer with built-in protocol mode for runtime and convergence analyses
- Python Module with OpenGM C++ API exported to Python with boost::python
- Allmost the complete C++ API is exported to Python
- Allmost all C++ inference algorithms wrapped to Python
- Vectorized API to add multiple functions and factors at once
- Add functions via numpy ndarrays
- Add functions via all default opengm function types
- Extendibility through interfaces for
- custom pure python function types
- custom pure python visitor for inference
- visualization of current inference state with matplotlib
- Visualize Factor Graph (needs networkx and graphviz)
- High performance
- Graphical models with more than 10,000,000 factors
- Specialized functions for optimized cache usage
- Extendibility through interfaces for
- custom algorithms
- custom functions
- custom label spaces
-
-
-
-opengm/opengm - master
-
-[](https://travis-ci.org/opengm/opengm)
-
-DerThorsten/opengm - master (opengm-python dev.)
-
-[](https://travis-ci.org/DerThorsten/opengm)
-
-Copyright (C) 2012 Bjoern Andres, Thorsten Beier and Joerg H.~Kappes.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ SampleLearning
+ LossGenerator
+ HammingLossGenerator
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