Metadata-Version: 2.1
Name: PySDD
Version: 0.2.7
Summary: Sentential Decision Diagrams
Home-page: https://github.com/wannesm/PySDD
Author: Wannes Meert, Arthur Choi
Author-email: wannes.meert@cs.kuleuven.be
License: Apache 2.0
Project-URL: PySDD documentation, http://pysdd.readthedocs.io/en/latest/
Project-URL: PySDD source, https://github.com/wannesm/PySDD
Description: =====
        PySDD
        =====
        
        Python wrapper package to interactively use Sententical Decision Diagrams (SDD).
        
        Full documentation available on http://pysdd.readthedocs.io.
        
        ------------
        Dependencies
        ------------
        
        * Python >=3.6
        * Cython
        * SDD package >=2.0: http://reasoning.cs.ucla.edu/sdd/
        
        Optional:
        
        * cysignals
        * numpy
        
        
        ------------
        Installation
        ------------
        
        .. code-block:: shell
        
           $ pip install PySDD
        
        
        --------------
        Python package
        --------------
        
        The wrapper can be used as a Python package and allows for interactive use.
        
        The following example builds an SDD for the formula ``a∧b ∨ b∧c ∨  c∧d``.
        
        .. code-block:: python
        
            from pysdd.sdd import SddManager, Vtree, WmcManager
            vtree = Vtree(var_count=4, var_order=[2,1,4,3], vtree_type="balanced")
            sdd = SddManager.from_vtree(vtree)
            a, b, c, d = [sdd.literal(i) for i in range(1, 5)]
        
            # Build SDD for formula
            formula = (a & b) | (b & c) | (c & d)
        
            # Model Counting
            wmc = formula.wmc(log_mode=False)
            print(f"Model Count: {wmc.propagate()}")
            wmc.set_literal_weight(a, 0.5)
            print(f"Weighted Model Count: {wmc.propagate()}")
        
            # Visualize SDD and Vtree
            with open("output/sdd.dot", "w") as out:
                print(formula.dot(), file=out)
            with open("output/vtree.dot", "w") as out:
                print(vtree.dot(), file=out)
        
        The SDD and Vtree are visualized using Graphviz DOT:
        
        .. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/sdd.png
        .. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/vtree.png
        
        
        
        More examples are available in the ``examples`` directory.
        An interactive Jupyter notebook is available in
        `notebooks/examples.ipynb <notebooks/examples.ipynb>`_
        
        
        ----------------------
        Command Line Interface
        ----------------------
        
        A Python CLI application is installed if you use pip, ``pysdd``. Or it can be used
        directly from the source directory where it is called ``pysdd-cli.py``.
        This script mimicks the original sdd binary and adds additional features (e.g. weighted model counting)
        
        .. code-block:: shell
        
            $ pysdd -h
            $ ./pysdd-cli.py -h
            usage: pysdd-cli.py [-h] [-c FILE | -d FILE | -s FILE] [-v FILE] [-W FILE]
                            [-V FILE] [-R FILE] [-S FILE] [-m] [-t TYPE] [-r K] [-q]
                            [-p] [--log_mode]
        
            Sentential Decision Diagram, Compiler
        
            optional arguments:
              -h, --help  show this help message and exit
              -c FILE     set input CNF file
              -d FILE     set input DNF file
              -s FILE     set input SDD file
              -v FILE     set input VTREE file
              -W FILE     set output VTREE file
              -V FILE     set output VTREE (dot) file
              -R FILE     set output SDD file
              -S FILE     set output SDD (dot) file
              -m          minimize the cardinality of compiled sdd
              -t TYPE     set initial vtree type (left/right/vertical/balanced/random)
              -r K        if K>0: invoke vtree search every K clauses. If K=0: disable
                          vtree search. By default (no -r option), dynamic vtree search is
                          enabled
              -q          perform post-compilation vtree search
              -p          verbose output
              --log_mode  weights in log
        
            Weighted Model Counting is performed if the NNF file containts a line
            formatted as follows: "c weights PW_1 NW_1 ... PW_n NW_n".
        
        
        -----------------
        Memory management
        -----------------
        
        Python's memory management is not used for the internal datastructures.
        Use the SDD library's garbage collection commands (e.g. ref, deref) to
        perform memory management.
        
        
        -----------------------
        Compilation from source
        -----------------------
        
        .. code-block:: shell
        
           $ pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
        
        The repository should contain all the required files and libraries (unless
        you use Windows). If you want to compile from source, note that some c-source
        files from the SDD package have been updated to work with this wrapper and are
        included in this repository. Do not overwrite these new files with the original
        files.
        
        * Download the SDD package from http://reasoning.cs.ucla.edu/sdd/.
        * Install the SDD package in the PySDD package in directories
          ``pysdd/lib/sdd-2.0`` and ``pysdd/lib/sddlib-2.0`` without overwriting
          the already available files.
        * Run ``python3 setup.py build_ext --inplace`` or ``make build`` to compile the
          library in the current directory. If you want to install the library such
          that the library is available for your local installation or in your virtual
          environment, use ``python3 setup.py install``.
        
        For some Linux platforms, it might be necessary to recompile the libsdd-2.0 code with
        the gcc option ``-fPIC`` and replace the ``pysdd/lib/sdd-2.0/lib/Linux/libsdd.a``
        library with your newly compiled version.
        
        The Windows platform is not supported. There is some initial support but we cannot
        offer guarantees or detailed instructions (but are happy to accept pull requests).
        
        
        ----------
        References
        ----------
        
        This package is inspired by the SDD wrapper used in the probabilistic
        programming language `ProbLog <https://dtai.cs.kuleuven.be/problog/>`_.
        
        References:
        
        * Wannes Meert, PySDD,
          in `Recent Trends in Knowledge Compilation
          <http://drops.dagstuhl.de/opus/volltexte/2018/8589/pdf/dagrep_v007_i009_p062_17381.pdf>`_,
          Report from Dagstuhl Seminar 17381, Sep 2017.
          Eds. A. Darwiche, P. Marquis, D. Suciu, S. Szeider.
        
        Other languages:
        
        * C: http://reasoning.cs.ucla.edu/sdd/
        * Java: https://github.com/jessa/JSDD
        
        
        -------
        Contact
        -------
        
        * Wannes Meert, KU Leuven, https://people.cs.kuleuven.be/wannes.meert
        * Arthur Choi, UCLA, http://web.cs.ucla.edu/~aychoi/
        
        
        -------
        License
        -------
        
        Python SDD wrapper:
        
        Copyright 2017-2018, KU Leuven and Regents of the University of California.
        Licensed under the Apache License, Version 2.0.
        
        
        SDD package:
        
        Copyright 2013-2018, Regents of the University of California
        Licensed under the Apache License, Version 2.0.
        
Keywords: sdd,knowledge compilation
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Provides-Extra: all
