[TOC]
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Various minor improvements of documentation
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Update documentation layout
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Clean up documentation of Python classes and functions in infrared namespace
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Improve application example notebook
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Add applications examples: graph coloring, automata and network parsimony
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Elaborate documentation of def_constraint_class, def_function_class
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New defaults and additional parameters for targeted sampling (multi-dimensional Boltzmann sampling)
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Add error checking in def_constraint_class, def_function_class
- Rename class ired:ConstraintNetwork to ired:FeatureNetwork
This release improves support for stochastic optimization based on resampling (among some minor improvements and adapations)
- Introduce support for resampling by restricted traceback. The functionality is directly supported by the Infrared engine without need for recomputation of the partition function.
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Resampling functionality is provided by methods resample() of ir.Sampler and ir.PFClusterTree
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Add ir.mc_optimize for optimization
- Update the bookchapter tutorial - use new resampling mechanism
This release facilitates the modeling of sampling and optimization problems from a much broader range of applications by
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introducing a new modeling syntax which facilitates the definition of constraint models and supporting the definition of new constraints and functions with simple Syntax in Python
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enabling modeling in a truly declarative and compositional style
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supporting domain bounds, which can be performance critical for many applications
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optimizing space requirements
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enabling optimization in addition to sampling, where both can be based on the same constraint model
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improving the API documentation
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adding tutorials and examples in Jupyter notebooks that demonstrate the modeling of design problems in the framework
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Introduce new modeling syntax to define constraint network models based on a new class
Model
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Old class ConstraintNetwork is merged with new class Model
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Support targeting of only selected features
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Revise syntax for the definition of function and constraints
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Add support for optimization (in addition to sampling)
- allow to select evaluation algebra
- introduce weighted functions
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Support IUPAC sequence constraints with a specific form of constraint simplification
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Rename functions and constraints in module rna
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Support named variables in constraint models
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Support upper and lower bound of variable domains; add class
FiniteDomain
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Remove dependency on
boost::graph
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Implement traceback for constraint optimization
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Optimize space consumption of materialized functions and constraints by using a specialized replacement
SimpleMap
of standard sparse containers likestd::hash_map
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Add evaluation in arctic semiring for constraint optimization
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Revisit API documentation
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Adapt tutorial notebook to new Python interface
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Add example for multi target design from Python code (Jupyter notebook)
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Add Jupyter notebook as supplement of a book chapter on Infrared with new example code
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Add tags to sync with book chapter
- Update setup.py
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Fix bug of repeated dependencies in the specification of models
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Fix undefined behavior in the materialization of functions/constraints
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Export algebra for constraint optimization
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Redefine constraints and functions in Python
Materialization of functions /and/ constraints
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supports the definition of functions and constraints in Python without performance penalties
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the existing mechanism for functions was extended to constraints
- Update behavior of
BoltzmannSampler.plot_td()
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Re-enable API documentation via doxygen
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Update tutorial jupyter notebook
Use pybind11
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Change build mechanism to
setup.py
andcmake
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Enable installation via pip and conda
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Refactor and streamline the Python interface
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Support tree decomposition by networkx, improve the tree decomposition interface
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Add access to partition function and provide consistency check in
- Add tutorial of the Python interface (Jupyter notebook)
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Add tests, fix handling of inconsistent networks
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Revert to using
boost::python
(to resolve a bug in the use ofpybind11
)
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Improve installation via autotools
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Replace dependency on
libhtd
bynetworkx
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Generalize Python classes for Boltzmann sampler construction and provide them in Python module
infrared
for simpler use of the lib -
Implement multi-dimensional Boltzmann sampling and provide this functionality via module
infrared
- Add/cleanup Doxygen documentation of Python modules
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Improve program comments, support doxygen (
make doxygen-doc
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Revise code, fix make check
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Optimize infrared evaluation/sampling engine
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Optimize tree decomposition using
libhtd
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Cleanup treedecomp and redprint Python code and add features
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Bugfixes
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Implement some tests
Initial release