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Tweak documentation styling
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.gitignore

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.vscode/
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wheelhouse/
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_wlplan/
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# graphs generated by pyvis
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*.html

README.md

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WLPlan
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======
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[![PyPI version](https://badge.fury.io/py/wlplan.svg)](https://badge.fury.io/py/wlplan)
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[![License](https://img.shields.io/pypi/l/wlplan)](https://badge.fury.io/py/wlplan)
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WLPlan is a package for generating embeddings of PDDL planning problems for machine learning tasks.
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## Installation

src/main.cpp

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/* Planning */
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auto planning_m = m.def_submodule("planning");
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auto atom = py::class_<planning::Atom>(planning_m, "Atom", R"(
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Parameters
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auto atom = py::class_<planning::Atom>(planning_m, "Atom",
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R"(Parameters
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----------
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predicate : Predicate
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Predicate object.
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objects : List[Object]
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List of object names.
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)");
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auto domain = py::class_<planning::Domain>(planning_m, "Domain", R"(
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Parameters
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auto domain = py::class_<planning::Domain>(planning_m, "Domain",
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R"(Parameters
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----------
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name : str
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Domain name.
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constant_objects : List[Object], optional
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List of constant objects.
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)");
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auto object = py::class_<planning::Object>(planning_m, "Object", R"(
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Object is a type alias for a str.
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auto object = py::class_<planning::Object>(planning_m, "Object",
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R"(Object is a type alias for a str.
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)");
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auto predicate = py::class_<planning::Predicate>(planning_m, "Predicate", R"(
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Parameters
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auto predicate = py::class_<planning::Predicate>(planning_m, "Predicate",
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R"(Parameters
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----------
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name : str
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Predicate name.
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arity : int
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Predicate arity.
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)");
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auto problem = py::class_<planning::Problem>(planning_m, "Problem", R"(
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Parameters
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auto problem = py::class_<planning::Problem>(planning_m, "Problem",
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R"(Parameters
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----------
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domain : Domain
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Domain object.
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negative_goals : List[Atom]
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List of negative goal atoms.
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)");
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auto state = py::class_<planning::State>(planning_m, "State", R"(
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State is a type alias for a list of Atoms.
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auto state = py::class_<planning::State>(planning_m, "State",
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R"(State is a type alias for a list of Atoms.
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)");
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predicate
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/* Data */
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auto data_m = m.def_submodule("data");
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auto dataset = py::class_<data::Dataset>(data_m, "Dataset", R"(
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WLPlan dataset object.
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auto dataset = py::class_<data::Dataset>(data_m, "Dataset",
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R"(WLPlan dataset object.
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Datasets contain a domain and a list of problem states.
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data : List[ProblemStates]
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List of problem states.
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)");
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auto problem_states = py::class_<data::ProblemStates>(data_m, "ProblemStates", R"(
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Stores a problem and training states for the problem.
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auto problem_states = py::class_<data::ProblemStates>(data_m, "ProblemStates",
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R"(Stores a problem and training states for the problem.
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Upon initialisation, the problem and states are checked for consistency.
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/* Graph */
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auto graph_m = m.def_submodule("graph");
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auto graph = py::class_<graph::Graph>(graph_m, "Graph", R"(
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WLPlan graph object.
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auto graph = py::class_<graph::Graph>(graph_m, "Graph",
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R"(WLPlan graph object.
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Graphs have integer node colours and edge labels.
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wlplan/__version__.py

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__version__ = "0.2.2"
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__version__ = "0.2.3"

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