__init__.py 58.3 KB
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# graph_tool -- a general graph manipulation python module
#
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# Copyright (C) 2007-2012 Tiago de Paula Peixoto <tiago@skewed.de>
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#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

"""
``graph_tool.search`` - Search algorithms
-----------------------------------------

This module includes several search algorithms, which are customizable to
arbitrary purposes. It is mostly a wrapper around the Visitor interface of the
`Boost Graph Library <http://www.boost.org/doc/libs/release/libs/graph/doc/visitor_concepts.html>`_,
and the respective search functions.


Summary
+++++++

.. autosummary::
   :nosignatures:

   bfs_search
   dfs_search
   dijkstra_search
   bellman_ford_search
   astar_search
   BFSVisitor
   DFSVisitor
   DijkstraVisitor
   BellmanFordVisitor
   AStarVisitor
   StopSearch

Examples
++++++++

In this module, most documentation examples will make use of the network
:download:`search_example.xml <search_example.xml>`, shown below.

>>> import numpy.random
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>>> numpy.random.seed(42)
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>>> g = gt.load_graph("search_example.xml")
>>> name = g.vertex_properties["name"]
>>> weight = g.edge_properties["weight"]
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>>> gt.graph_draw(g, vertex_text=name, vertex_font_size=12, vertex_shape="double_circle",
...               vertex_fill_color="#729fcf", vertex_pen_width=3,
...               edge_pen_width=weight, output="search_example.pdf")
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<...>

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.. figure:: search_example.*
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   :alt: search example
   :align: center

   This is the network used in the examples below. The width of the edges
   correspond to the values of the "weight" property map.


Contents
++++++++
"""

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from __future__ import division, absolute_import, print_function
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import sys
if sys.version_info < (3,):
    range = xrange
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from .. dl_import import dl_import
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dl_import("from . import libgraph_tool_search")
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from .. import _prop, _python_type
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from .. decorators import _wraps
import weakref

__all__ = ["bfs_search", "BFSVisitor", "dfs_search", "DFSVisitor",
           "dijkstra_search", "DijkstraVisitor", "bellman_ford_search",
           "BellmanFordVisitor", "astar_search", "AStarVisitor",
           "StopSearch"]


def _wrap_member(g, perms):
    def decorate(func):
        def wrap(*args, **kwargs):
            old_perms = dict(g._Graph__perms)
            g._Graph__perms.update(perms)
            ret = func(*args, **kwargs)
            g._Graph__perms.update(old_perms)
        return wrap
    return decorate


def _perm_wrap(edge_members, vertex_members=[]):
    def decorate(func):
        @_wraps(func)
        def wrap(*args, **kwargs):
            old_members = {}
            g = args[0]
            visitor = kwargs["visitor"]
            for e in edge_members:
                m = getattr(visitor, e)
                old_members[e] = m
                m = _wrap_member(g, {"del_edge": True, "add_edge": True})(m)
                setattr(visitor, e, m)
            for v in vertex_members:
                m = getattr(visitor, v)
                old_members[v] = m
                m = _wrap_member(g, {"add_vertex": False})(m)
                setattr(visitor, v, m)

            perms = dict(g._Graph__perms)
            try:
                g._Graph__perms.update({"del_vertex": False, "del_edge": False,
                                        "add_edge": False})
                ret = func(*args, **kwargs)
            finally:
                g._Graph__perms.update(perms)

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            for n, m in old_members.items():
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                setattr(visitor, n, m)
            return ret
        return wrap
    return decorate


class BFSVisitor(object):
    r"""A visitor object that is invoked at the event-points inside the
    :func:`~graph_tool.search.bfs_search` algorithm. By default, it performs no
    action, and should be used as a base class in order to be useful."""

    def initialize_vertex(self, u):
        """This is invoked on every vertex of the graph before the start of the
        graph search. """
        return

    def discover_vertex(self, u):
        """This is invoked when a vertex is encountered for the first time."""
        return

    def examine_vertex(self, u):
        """This is invoked on a vertex as it is popped from the queue. This
        happens immediately before examine_edge() is invoked on each of the
        out-edges of vertex u."""
        return

    def examine_edge(self, e):
        """This is invoked on every out-edge of each vertex after it is
        discovered."""
        return

    def tree_edge(self, e):
        """This is invoked on each edge as it becomes a member of the edges that
        form the search tree."""
        return

    def non_tree_edge(self, e):
        """This is invoked on back or cross edges for directed graphs and cross
        edges for undirected graphs. """
        return

    def gray_target(self, e):
        """This is invoked on the subset of non-tree edges whose target vertex
        is colored gray at the time of examination. The color gray indicates
        that the vertex is currently in the queue."""
        return

    def black_target(self, e):
        """This is invoked on the subset of non-tree edges whose target vertex
        is colored black at the time of examination. The color black indicates
        that the vertex has been removed from the queue."""
        return

    def finish_vertex(self, u):
        """This invoked on a vertex after all of its out edges have been added
        to the search tree and all of the adjacent vertices have been discovered
        (but before the out-edges of the adjacent vertices have been examined).
        """
        return


@_perm_wrap(["initialize_vertex", "examine_vertex", "finish_vertex"],
            ["initialize_vertex"])
def bfs_search(g, source, visitor=BFSVisitor()):
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    r"""Breadth-first traversal of a directed or undirected graph.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    source : :class:`~graph_tool.Vertex`
        Source vertex.
    visitor : :class:`~graph_tool.search.BFSVisitor` (optional, default: ``BFSVisitor()``)
        A visitor object that is invoked at the event points inside the
        algorithm. This should be a subclass of
        :class:`~graph_tool.search.BFSVisitor`.

    See Also
    --------
    dfs_search: Depth-first search
    dijkstra_search: Dijkstra's search algorithm
    astar_search: :math:`A^*` heuristic search algorithm

    Notes
    -----

    A breadth-first traversal visits vertices that are closer to the source
    before visiting vertices that are further away. In this context "distance"
    is defined as the number of edges in the shortest path from the source
    vertex.

    The time complexity is :math:`O(V + E)`.

    The pseudo-code for the BFS algorithm is listed below, with the annotated
    event points, for which the given visitor object will be called with the
    appropriate method.

    ::

        BFS(G, source)
         for each vertex u in V[G]       initialize vertex u
           color[u] := WHITE
           d[u] := infinity
         end for
         color[source] := GRAY
         d[source] := 0
         ENQUEUE(Q, source)              discover vertex source
         while (Q != Ø)
           u := DEQUEUE(Q)               examine vertex u
           for each vertex v in Adj[u]   examine edge (u,v)
             if (color[v] = WHITE)       (u,v) is a tree edge
               color[v] := GRAY
               ENQUEUE(Q, v)             discover vertex v
             else                        (u,v) is a non-tree edge
               if (color[v] = GRAY)
                 ...                     (u,v) has a gray target
               else
                 ...                     (u,v) has a black target
           end for
           color[u] := BLACK             finish vertex u
         end while


    Examples
    --------

    We must define what should be done during the search by subclassing
    :class:`~graph_tool.search.BFSVisitor`, and specializing the appropriate
    methods. In the following we will keep track of the distance from the root,
    and the predecessor tree.

    .. testcode::

        class VisitorExample(gt.BFSVisitor):

            def __init__(self, name, pred, dist):
                self.name = name
                self.pred = pred
                self.dist = dist

            def discover_vertex(self, u):
                print "-->", self.name[u], "has been discovered!"

            def examine_vertex(self, u):
                print self.name[u], "has been examined..."

            def tree_edge(self, e):
                self.pred[e.target()] = int(e.source())
                self.dist[e.target()] = self.dist[e.source()] + 1

    With the above class defined, we can perform the BFS search as follows.

    >>> dist = g.new_vertex_property("int")
    >>> pred = g.new_vertex_property("int")
    >>> gt.bfs_search(g, g.vertex(0), VisitorExample(name, pred, dist))
    --> Bob has been discovered!
    Bob has been examined...
    --> Eve has been discovered!
    --> Chuck has been discovered!
    --> Carlos has been discovered!
    --> Isaac has been discovered!
    Eve has been examined...
    --> Imothep has been discovered!
    --> Carol has been discovered!
    Chuck has been examined...
    Carlos has been examined...
    --> Alice has been discovered!
    Isaac has been examined...
    Imothep has been examined...
    Carol has been examined...
    Alice has been examined...
    --> Oscar has been discovered!
    --> Dave has been discovered!
    Oscar has been examined...
    Dave has been examined...
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    >>> print(dist.a)
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    [0 2 2 1 1 3 1 1 3 2]
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    >>> print(pred.a)
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    [0 3 6 0 0 1 0 0 1 6]


    References
    ----------
    .. [bfs] Edward Moore, "The shortest path through a maze", International
             Symposium on the Theory of Switching, 1959
    .. [bfs-bgl] http://www.boost.org/doc/libs/release/libs/graph/doc/breadth_first_search.html
    .. [bfs-wikipedia] http://en.wikipedia.org/wiki/Breadth-first_search
    """

    if visitor is None:
        visitor = BFSVisitor()

    try:
        libgraph_tool_search.bfs_search(g._Graph__graph,
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                                        weakref.ref(g),
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                                        int(source), visitor)
    except StopSearch:
        pass


class DFSVisitor(object):
    r"""
    A visitor object that is invoked at the event-points inside the
    :func:`~graph_tool.search.dfs_search` algorithm. By default, it performs no
    action, and should be used as a base class in order to be useful.
    """

    def initialize_vertex(self, u):
        """
        This is invoked on every vertex of the graph before the start of the
        graph search.
        """
        return

    def start_vertex(self, u):
        """
        This is invoked on the source vertex once before the start of the
        search.
        """
        return

    def discover_vertex(self, u):
        """This is invoked when a vertex is encountered for the first time."""
        return

    def examine_edge(self, e):
        """
        This is invoked on every out-edge of each vertex after it is discovered.
        """
        return

    def tree_edge(self, e):
        """
        This is invoked on each edge as it becomes a member of the edges that
        form the search tree.
        """
        return

    def back_edge(self, e):
        """
        This is invoked on the back edges in the graph. For an undirected graph
        there is some ambiguity between tree edges and back edges since the edge
        (u,v) and (v,u) are the same edge, but both the
        :meth:`~graph_tool.search.DFSVisitor.tree_edge` and
        :meth:`~graph_tool.search..DFSVisitor.back_edge` functions will be
        invoked. One way to resolve this ambiguity is to record the tree edges,
        and then disregard the back-edges that are already marked as tree
        edges. An easy way to record tree edges is to record predecessors at the
        tree_edge event point.
        """
        return

    def forward_or_cross_edge(self, e):
        """
        This is invoked on forward or cross edges in the graph. In an undirected
        graph this method is never called.
        """
        return

    def finish_vertex(self, e):
        """
        This is invoked on vertex u after finish_vertex has been called for all
        the vertices in the DFS-tree rooted at vertex u. If vertex u is a leaf
        in the DFS-tree, then the
        :meth:`~graph_tool..search.DFSVisitor.finish_vertex` function is called
        on u after all the out-edges of u have been examined.
        """
        return


@_perm_wrap(["initialize_vertex", "discover_vertex", "finish_vertex",
             "start_vertex"], ["initialize_vertex"])
def dfs_search(g, source, visitor=DFSVisitor()):
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    r"""Depth-first traversal of a directed or undirected graph.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    source : :class:`~graph_tool.Vertex`
        Source vertex.
    visitor : :class:`~graph_tool.search.DFSVisitor` (optional, default: ``DFSVisitor()``)
        A visitor object that is invoked at the event points inside the
        algorithm. This should be a subclass of
        :class:`~graph_tool.search.DFSVisitor`.

    See Also
    --------
    bfs_search: Breadth-first search
    dijkstra_search: Dijkstra's search algorithm
    astar_search: :math:`A^*` heuristic search algorithm

    Notes
    -----

    When possible, a depth-first traversal chooses a vertex adjacent to the
    current vertex to visit next. If all adjacent vertices have already been
    discovered, or there are no adjacent vertices, then the algorithm backtracks
    to the last vertex that had undiscovered neighbors. Once all reachable
    vertices have been visited, the algorithm selects from any remaining
    undiscovered vertices and continues the traversal. The algorithm finishes
    when all vertices have been visited.

    The time complexity is :math:`O(V + E)`.

    The pseudo-code for the DFS algorithm is listed below, with the annotated
    event points, for which the given visitor object will be called with the
    appropriate method.

    ::

        DFS(G)
          for each vertex u in V
            color[u] := WHITE                 initialize vertex u
          end for
          time := 0
          call DFS-VISIT(G, source)           start vertex s

        DFS-VISIT(G, u)
          color[u] := GRAY                    discover vertex u
          for each v in Adj[u]                examine edge (u,v)
            if (color[v] = WHITE)             (u,v) is a tree edge
              call DFS-VISIT(G, v)
            else if (color[v] = GRAY)         (u,v) is a back edge
              ...
            else if (color[v] = BLACK)        (u,v) is a cross or forward edge
              ...
          end for
          color[u] := BLACK                   finish vertex u


    Examples
    --------

    We must define what should be done during the search by subclassing
    :class:`~graph_tool.search.DFSVisitor`, and specializing the appropriate
    methods. In the following we will keep track of the discover time, and the
    predecessor tree.

    .. testcode::

        class VisitorExample(gt.DFSVisitor):

            def __init__(self, name, pred, time):
                self.name = name
                self.pred = pred
                self.time = time
                self.last_time = 0

            def discover_vertex(self, u):
                print "-->", self.name[u], "has been discovered!"
                self.time[u] = self.last_time
                self.last_time += 1

            def examine_edge(self, e):
                print "edge (%s, %s) has been examined..." % \
                    (self.name[e.source()], self.name[e.target()])

            def tree_edge(self, e):
                self.pred[e.target()] = int(e.source())


    With the above class defined, we can perform the DFS search as follows.

    >>> time = g.new_vertex_property("int")
    >>> pred = g.new_vertex_property("int")
    >>> gt.dfs_search(g, g.vertex(0), VisitorExample(name, pred, time))
    --> Bob has been discovered!
    edge (Bob, Eve) has been examined...
    --> Eve has been discovered!
    edge (Eve, Isaac) has been examined...
    --> Isaac has been discovered!
    edge (Isaac, Bob) has been examined...
    edge (Isaac, Chuck) has been examined...
    --> Chuck has been discovered!
    edge (Chuck, Eve) has been examined...
    edge (Chuck, Isaac) has been examined...
    edge (Chuck, Imothep) has been examined...
    --> Imothep has been discovered!
    edge (Imothep, Carol) has been examined...
    --> Carol has been discovered!
    edge (Carol, Eve) has been examined...
    edge (Carol, Imothep) has been examined...
    edge (Imothep, Carlos) has been examined...
    --> Carlos has been discovered!
    edge (Carlos, Eve) has been examined...
    edge (Carlos, Imothep) has been examined...
    edge (Carlos, Bob) has been examined...
    edge (Carlos, Alice) has been examined...
    --> Alice has been discovered!
    edge (Alice, Oscar) has been examined...
    --> Oscar has been discovered!
    edge (Oscar, Alice) has been examined...
    edge (Oscar, Dave) has been examined...
    --> Dave has been discovered!
    edge (Dave, Oscar) has been examined...
    edge (Dave, Alice) has been examined...
    edge (Alice, Dave) has been examined...
    edge (Alice, Carlos) has been examined...
    edge (Imothep, Chuck) has been examined...
    edge (Imothep, Eve) has been examined...
    edge (Chuck, Bob) has been examined...
    edge (Isaac, Eve) has been examined...
    edge (Eve, Imothep) has been examined...
    edge (Eve, Bob) has been examined...
    edge (Eve, Carol) has been examined...
    edge (Eve, Carlos) has been examined...
    edge (Eve, Chuck) has been examined...
    edge (Bob, Chuck) has been examined...
    edge (Bob, Carlos) has been examined...
    edge (Bob, Isaac) has been examined...
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    >>> print(time.a)
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    [0 7 5 6 3 9 1 2 8 4]
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    >>> print(pred.a)
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    [0 3 9 9 7 8 0 6 1 4]



    References
    ----------
    .. [dfs-bgl] http://www.boost.org/doc/libs/release/libs/graph/doc/depth_first_search.html
    .. [dfs-wikipedia] http://en.wikipedia.org/wiki/Depth-first_search
    """

    if visitor is None:
        visitor = DFSVisitor()

    try:
        libgraph_tool_search.dfs_search(g._Graph__graph,
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                                        weakref.ref(g),
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                                        int(source), visitor)
    except StopSearch:
        pass


class DijkstraVisitor(object):
    r"""A visitor object that is invoked at the event-points inside the
    :func:`~graph_tool.search.dijkstra_search` algorithm. By default, it
    performs no action, and should be used as a base class in order to be
    useful.
    """

    def initialize_vertex(self, u):
        """
        This is invoked on every vertex of the graph before the start of the
        graph search.
        """
        return

    def examine_vertex(self, u):
        """
        This is invoked on a vertex as it is popped from the queue. This happens
        immediately before :meth:`~graph_tool.DijsktraVisitor.examine_edge` is
        invoked on each of the out-edges of vertex u.
        """
        return

    def examine_edge(self, e):
        """
        This is invoked on every out-edge of each vertex after it is discovered.
        """
        return

    def discover_vertex(self, u):
        """This is invoked when a vertex is encountered for the first time."""
        return

    def edge_relaxed(self, e):
        """
        Upon examination, if the following condition holds then the edge is
        relaxed (its distance is reduced), and this method is invoked.

        ::

            (u, v) = tuple(e)
            assert(compare(combine(d[u], weight[e]), d[v]))

        """
        return

    def edge_not_relaxed(self, e):
        """
        Upon examination, if the edge is not relaxed (see
        :meth:`~graph_tool.search.DijsktraVisitor.edge_relaxed`) then this
        method is invoked.
        """
        return

    def finish_vertex(self, u):
        """
        This invoked on a vertex after all of its out edges have been added to
        the search tree and all of the adjacent vertices have been discovered
        (but before their out-edges have been examined).
        """
        return


@_perm_wrap(["initialize_vertex", "examine_vertex", "finish_vertex"],
            ["initialize_vertex"])
def dijkstra_search(g, source, weight, visitor=DijkstraVisitor(), dist_map=None,
                    pred_map=None, combine=lambda a, b: a + b,
                    compare=lambda a, b: a < b, zero=0, infinity=float('inf')):
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    r"""Dijsktra traversal of a directed or undirected graph, with non-negative weights.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    source : :class:`~graph_tool.Vertex`
        Source vertex.
    weight : :class:`~graph_tool.PropertyMap`
        Edge property map with weight values.
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    visitor : :class:`~graph_tool.search.DijkstraVisitor` (optional, default: ``DijkstraVisitor()``)
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        A visitor object that is invoked at the event points inside the
        algorithm. This should be a subclass of
        :class:`~graph_tool.search.DijkstraVisitor`.
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    dist_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the distances from the source will be
        stored.
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    pred_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the predecessor map will be
        stored (must have value type "int").
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    combine : binary function (optional, default: ``lambda a, b: a + b``)
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        This function is used to combine distances to compute the distance of a
        path.
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    compare : binary function (optional, default: ``lambda a, b: a < b``)
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        This function is use to compare distances to determine which vertex is
        closer to the source vertex.
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    zero : int or float (optional, default: ``0``)
         Value assumed to correspond to a distance of zero by the combine and
         compare functions.
    infinity : int or float (optional, default: ``float('inf')``)
         Value assumed to correspond to a distance of infinity by the combine and
         compare functions.
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    Returns
    -------
    dist_map : :class:`~graph_tool.PropertyMap`
        A vertex property map with the computed distances from the source.
    pred_map : :class:`~graph_tool.PropertyMap`
        A vertex property map with the predecessor tree.

    See Also
    --------
    bfs_search: Breadth-first search
    dfs_search: Depth-first search
    astar_search: :math:`A^*` heuristic search algorithm

    Notes
    -----

    Dijkstra's algorithm finds all the shortest paths from the source vertex to
    every other vertex by iteratively "growing" the set of vertices S to which
    it knows the shortest path. At each step of the algorithm, the next vertex
    added to S is determined by a priority queue. The queue contains the
    vertices in V - S prioritized by their distance label, which is the length
    of the shortest path seen so far for each vertex. The vertex u at the top of
    the priority queue is then added to S, and each of its out-edges is relaxed:
    if the distance to u plus the weight of the out-edge (u,v) is less than the
    distance label for v then the estimated distance for vertex v is
    reduced. The algorithm then loops back, processing the next vertex at the
    top of the priority queue. The algorithm finishes when the priority queue is
    empty.

    The time complexity is :math:`O(V \log V)`.

    The pseudo-code for Dijkstra's algorithm is listed below, with the annotated
    event points, for which the given visitor object will be called with the
    appropriate method.

    ::

        DIJKSTRA(G, source, weight)
          for each vertex u in V                      initialize vertex u
            d[u] := infinity
            p[u] := u
          end for
          d[source] := 0
          INSERT(Q, source)                           discover vertex s
          while (Q != Ø)
            u := EXTRACT-MIN(Q)                       examine vertex u
            for each vertex v in Adj[u]               examine edge (u,v)
              if (weight[(u,v)] + d[u] < d[v])        edge (u,v) relaxed
                d[v] := weight[(u,v)] + d[u]
                p[v] := u
                DECREASE-KEY(Q, v)
              else                                    edge (u,v) not relaxed
                ...
              if (d[v] was originally infinity)
                INSERT(Q, v)                          discover vertex v
            end for                                   finish vertex u
          end while
          return d

    Examples
    --------

    We must define what should be done during the search by subclassing
    :class:`~graph_tool.search.DijkstraVisitor`, and specializing the
    appropriate methods. In the following we will keep track of the discover
    time, and the predecessor tree.

    .. testcode::

        class VisitorExample(gt.DijkstraVisitor):

            def __init__(self, name, time):
                self.name = name
                self.time = time
                self.last_time = 0

            def discover_vertex(self, u):
                print "-->", self.name[u], "has been discovered!"
                self.time[u] = self.last_time
                self.last_time += 1

            def examine_edge(self, e):
                print "edge (%s, %s) has been examined..." % \
                    (self.name[e.source()], self.name[e.target()])

            def edge_relaxed(self, e):
                print "edge (%s, %s) has been relaxed..." % \
                    (self.name[e.source()], self.name[e.target()])


    With the above class defined, we can perform the Dijkstra search as follows.

    >>> time = g.new_vertex_property("int")
    >>> dist, pred = gt.dijkstra_search(g, g.vertex(0), weight, VisitorExample(name, time))
    --> Bob has been discovered!
    edge (Bob, Eve) has been examined...
    edge (Bob, Eve) has been relaxed...
    --> Eve has been discovered!
    edge (Bob, Chuck) has been examined...
    edge (Bob, Chuck) has been relaxed...
    --> Chuck has been discovered!
    edge (Bob, Carlos) has been examined...
    edge (Bob, Carlos) has been relaxed...
    --> Carlos has been discovered!
    edge (Bob, Isaac) has been examined...
    edge (Bob, Isaac) has been relaxed...
    --> Isaac has been discovered!
    edge (Eve, Isaac) has been examined...
    edge (Eve, Imothep) has been examined...
    edge (Eve, Imothep) has been relaxed...
    --> Imothep has been discovered!
    edge (Eve, Bob) has been examined...
    edge (Eve, Carol) has been examined...
    edge (Eve, Carol) has been relaxed...
    --> Carol has been discovered!
    edge (Eve, Carlos) has been examined...
    edge (Eve, Chuck) has been examined...
    edge (Isaac, Bob) has been examined...
    edge (Isaac, Chuck) has been examined...
    edge (Isaac, Eve) has been examined...
    edge (Chuck, Eve) has been examined...
    edge (Chuck, Isaac) has been examined...
    edge (Chuck, Imothep) has been examined...
    edge (Chuck, Bob) has been examined...
    edge (Carlos, Eve) has been examined...
    edge (Carlos, Imothep) has been examined...
    edge (Carlos, Bob) has been examined...
    edge (Carlos, Alice) has been examined...
    edge (Carlos, Alice) has been relaxed...
    --> Alice has been discovered!
    edge (Imothep, Carol) has been examined...
    edge (Imothep, Carlos) has been examined...
    edge (Imothep, Chuck) has been examined...
    edge (Imothep, Eve) has been examined...
    edge (Alice, Oscar) has been examined...
    edge (Alice, Oscar) has been relaxed...
    --> Oscar has been discovered!
    edge (Alice, Dave) has been examined...
    edge (Alice, Dave) has been relaxed...
    --> Dave has been discovered!
    edge (Alice, Carlos) has been examined...
    edge (Carol, Eve) has been examined...
    edge (Carol, Imothep) has been examined...
    edge (Oscar, Alice) has been examined...
    edge (Oscar, Dave) has been examined...
    edge (Dave, Oscar) has been examined...
    edge (Dave, Alice) has been examined...
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    >>> print(time.a)
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    [0 7 6 3 2 9 1 4 8 5]
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    >>> print(pred.a)
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    [0 3 6 0 0 1 0 0 1 6]
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    >>> print(dist.a)
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    [  0.           8.91915887   9.27141329   4.29277116   4.02118246
      12.23513866   3.23790211   3.45487436  11.04391549   7.74858396]

    References
    ----------
    .. [dijkstra] E. Dijkstra, "A note on two problems in connexion with
        graphs", Numerische Mathematik, 1:269-271, 1959.
    .. [dijkstra-bgl] http://www.boost.org/doc/libs/release/libs/graph/doc/dijkstra_shortest_paths_no_color_map.html
    .. [dijkstra-wikipedia] http://en.wikipedia.org/wiki/Dijkstra's_algorithm
    """

    if visitor is None:
        visitor = DijkstraVisitor()
    if dist_map is None:
        dist_map = g.new_vertex_property(weight.value_type())
    if pred_map is None:
        pred_map = g.new_vertex_property("int")
    if pred_map.value_type() != "int32_t":
        raise ValueError("pred_map must be of value type 'int32_t', not '%s'." % \
                             pred_map.value_type())

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    try:
        zero = _python_type(dist_map.value_type())(zero)
    except OverflowError:
        zero = (weight.a.max() + 1) * g.num_vertices()
        zero = _python_type(dist_map.value_type())(zero)

    try:
        infinity = _python_type(dist_map.value_type())(infinity)
    except OverflowError:
        infinity = (weight.a.max() + 1) * g.num_vertices()
        infinity = _python_type(dist_map.value_type())(infinity)

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    try:
        libgraph_tool_search.dijkstra_search(g._Graph__graph,
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                                             weakref.ref(g),
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                                             int(source),
                                             _prop("v", g, dist_map),
                                             _prop("v", g, pred_map),
                                             _prop("e", g, weight), visitor,
                                             compare, combine, zero, infinity)
    except StopSearch:
        pass

    return dist_map, pred_map


class BellmanFordVisitor(object):
    r"""A visitor object that is invoked at the event-points inside the
    :func:`~graph_tool.search.bellman_ford_search` algorithm. By default, it
    performs no action, and should be used as a base class in order to be
    useful.
    """

    def examine_edge(self, e):
        """
        This is invoked on every edge in the graph ``|V|`` times.
        """
        return

    def edge_relaxed(self, e):
        """
        This is invoked when the distance label for the target vertex is
        decreased. The edge (u,v) that participated in the last relaxation for
        vertex v is an edge in the shortest paths tree.
        """
        return

    def edge_not_relaxed(self, e):
        """
        This is invoked if the distance label for the target vertex is not
        decreased.
        """
        return

    def edge_minimized(self, e):
        """
        This is invoked during the second stage of the algorithm, during the
        test of whether each edge was minimized. If the edge is minimized then
        this function is invoked.
        """
        return

    def edge_not_minimized(self, e):
        """
        This is invoked during the second stage of the algorithm, during the
        test of whether each edge was minimized. If the edge was not minimized,
        this function is invoked. This happens when there is a negative cycle in
        the graph.
        """
        return


@_perm_wrap([], [])
def bellman_ford_search(g, source, weight, visitor=BellmanFordVisitor(),
                        dist_map=None, pred_map=None,
                        combine=lambda a, b: a + b,
                        compare=lambda a, b: a < b, zero=0,
                        infinity=float('inf')):
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    r"""Bellman-Ford traversal of a directed or undirected graph, with negative weights.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    source : :class:`~graph_tool.Vertex`
        Source vertex.
    weight : :class:`~graph_tool.PropertyMap`
        Edge property map with weight values.
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    visitor : :class:`~graph_tool.search.DijkstraVisitor` (optional, default: ``DijkstraVisitor()``)
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        A visitor object that is invoked at the event points inside the
        algorithm. This should be a subclass of
        :class:`~graph_tool.search.DijkstraVisitor`.
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    dist_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the distances from the source will be
        stored.
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    pred_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the predecessor map will be
        stored (must have value type "int").
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    combine : binary function (optional, default: ``lambda a, b: a + b``)
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        This function is used to combine distances to compute the distance of a
        path.
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    compare : binary function (optional, default: ``lambda a, b: a < b``)
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        This function is use to compare distances to determine which vertex is
        closer to the source vertex.
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    zero : int or float (optional, default: ``0``)
         Value assumed to correspond to a distance of zero by the combine and
         compare functions.
    infinity : int or float (optional, default: ``float('inf')``)
         Value assumed to correspond to a distance of infinity by the combine and
         compare functions.
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    Returns
    -------
    minimized : bool
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        True if all edges were successfully minimized, or False if there is a
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        negative loop in the graph.
    dist_map : :class:`~graph_tool.PropertyMap`
        A vertex property map with the computed distances from the source.
    pred_map : :class:`~graph_tool.PropertyMap`
        A vertex property map with the predecessor tree.

    See Also
    --------
    bfs_search: Breadth-first search
    dfs_search: Depth-first search
    dijsktra_search: Dijkstra search
    astar_search: :math:`A^*` heuristic search

    Notes
    -----

    The Bellman-Ford algorithm [bellman-ford]_ solves the single-source shortest
    paths problem for a graph with both positive and negative edge weights. If
    you only need to solve the shortest paths problem for positive edge weights,
    :func:`~graph_tool.search.dijkstra_search` provides a more efficient
    alternative. If all the edge weights are all equal, then
    :func:`~graph_tool.search.bfs_search` provides an even more efficient
    alternative.

    The Bellman-Ford algorithm proceeds by looping through all of the edges in
    the graph, applying the relaxation operation to each edge. In the following
    pseudo-code, ``v`` is a vertex adjacent to ``u``, ``w`` maps edges to their
    weight, and ``d`` is a distance map that records the length of the shortest
    path to each vertex seen so far. ``p`` is a predecessor map which records
    the parent of each vertex, which will ultimately be the parent in the
    shortest paths tree

    ::

        RELAX(u, v, w, d, p)
          if (w(u,v) + d[u] < d[v])
            d[v] := w(u,v) + d[u]       relax edge (u,v)
            p[v] := u
          else
            ...                         edge (u,v) is not relaxed


    The algorithm repeats this loop ``|V|`` times after which it is guaranteed
    that the distances to each vertex have been reduced to the minimum possible
    unless there is a negative cycle in the graph. If there is a negative cycle,
    then there will be edges in the graph that were not properly minimized. That
    is, there will be edges ``(u,v)`` such that ``w(u,v) + d[u] < d[v]``. The
    algorithm loops over the edges in the graph one final time to check if all
    the edges were minimized, returning true if they were and returning false
    otherwise.

    ::


        BELLMAN-FORD(G)
          for each vertex u in V
            d[u] := infinity
            p[u] := u
          end for
          for i := 1 to |V|-1
            for each edge (u,v) in E          examine edge (u,v)
              RELAX(u, v, w, d, p)
            end for
          end for
          for each edge (u,v) in E
            if (w(u,v) + d[u] < d[v])
              return (false, , )              edge (u,v) was not minimized
            else
              ...                             edge (u,v) was minimized
          end for
          return (true, p, d)

    The time complexity is :math:`O(V E)`.

    Examples
    --------

    We must define what should be done during the search by subclassing
    :class:`~graph_tool.search.BellmanFordVisitor`, and specializing the
    appropriate methods. In the following we will keep track of the edge
    minimizations.

    .. testcode::

        class VisitorExample(gt.BellmanFordVisitor):

            def __init__(self, name):
                self.name = name

            def edge_minimized(self, e):
                print "edge (%s, %s) has been minimized..." % \
                    (self.name[e.source()], self.name[e.target()])

            def edge_not_minimized(self, e):
                print "edge (%s, %s) has not been minimized..." % \
                    (self.name[e.source()], self.name[e.target()])


    With the above class defined, we can perform the Bellman-Ford search as
    follows.

    >>> nweight = g.copy_property(weight)
    >>> nweight.a[6] *= -1   # include negative weight in edge (Carlos, Alice)
    >>> minimized, dist, pred = gt.bellman_ford_search(g, g.vertex(0), nweight, VisitorExample(name))
    edge (Bob, Eve) has been minimized...
    edge (Bob, Chuck) has been minimized...
    edge (Bob, Carlos) has been minimized...
    edge (Bob, Isaac) has been minimized...
    edge (Alice, Oscar) has been minimized...
    edge (Alice, Dave) has been minimized...
    edge (Alice, Carlos) has been minimized...
    edge (Carol, Eve) has been minimized...
    edge (Carol, Imothep) has been minimized...
    edge (Carlos, Eve) has been minimized...
    edge (Carlos, Imothep) has been minimized...
    edge (Chuck, Eve) has been minimized...
    edge (Chuck, Isaac) has been minimized...
    edge (Chuck, Imothep) has been minimized...
    edge (Dave, Oscar) has been minimized...
    edge (Eve, Isaac) has been minimized...
    edge (Eve, Imothep) has been minimized...
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    >>> print(minimized)
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    True
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    >>> print(pred.a)
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    [3 3 9 1 6 1 3 6 1 3]
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    >>> print(dist.a)
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    [-28.42555934 -37.34471821 -25.20438243 -41.97110592 -35.20316571
     -34.02873843 -36.58860946 -33.55645565 -35.2199616  -36.0029274 ]

    References
    ----------
    .. [bellman-ford] R. Bellman, "On a routing problem", Quarterly of Applied
       Mathematics, 16(1):87-90, 1958.
    .. [bellman-ford-bgl] http://www.boost.org/doc/libs/release/libs/graph/doc/bellman_ford_shortest.html
    .. [bellman-ford-wikipedia] http://en.wikipedia.org/wiki/Bellman-Ford_algorithm
    """

    if visitor is None:
        visitor = BellmanFordVisitor()
    if dist_map is None:
        dist_map = g.new_vertex_property(weight.value_type())
    if pred_map is None:
        pred_map = g.new_vertex_property("int")
    if pred_map.value_type() != "int32_t":
        raise ValueError("pred_map must be of value type 'int32_t', not '%s'." % \
                             pred_map.value_type())
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    try:
        zero = _python_type(dist_map.value_type())(zero)
    except OverflowError:
        zero = (weight.a.max() + 1) * g.num_vertices()
        zero = _python_type(dist_map.value_type())(zero)

    try:
        infinity = _python_type(dist_map.value_type())(infinity)
    except OverflowError:
        infinity = (weight.a.max() + 1) * g.num_vertices()
        infinity = _python_type(dist_map.value_type())(infinity)

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    minimized = False
    try:
        minimized = \
            libgraph_tool_search.bellman_ford_search(g._Graph__graph,
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                                                     weakref.ref(g),
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                                                     int(source),
                                                     _prop("v", g, dist_map),
                                                     _prop("v", g, pred_map),
                                                     _prop("e", g, weight),
                                                     visitor, compare, combine,
                                                     zero, infinity)
    except StopSearch:
        pass

    return minimized, dist_map, pred_map


class AStarVisitor(object):
    r"""A visitor object that is invoked at the event-points inside the
    :func:`~graph_tool.search.astar_search` algorithm. By default, it
    performs no action, and should be used as a base class in order to be
    useful.
    """

    def initialize_vertex(self, u):
        """
        This is invoked on every vertex of the graph before the start of the
        graph search.
        """
        return

    def examine_vertex(self, u):
        """ This is invoked on a vertex as it is popped from the queue (i.e. it
        has the lowest cost on the ``OPEN`` list). This happens immediately
        before examine_edge() is invoked on each of the out-edges of vertex u.
        """
        return

    def examine_edge(self, e):
        """
        This is invoked on every out-edge of each vertex after it is examined.
        """
        return

    def discover_vertex(self, u):
        """
        This is invoked when a vertex is first discovered and is added to the
        ``OPEN`` list.
        """
        return

    def edge_relaxed(self, e):
        """
        Upon examination, if the following condition holds then the edge is
        relaxed (its distance is reduced), and this method is invoked.

        ::

            (u, v) = tuple(e)
            assert(compare(combine(d[u], weight[e]), d[v]))

        """
        return

    def edge_not_relaxed(self, e):
        """
        Upon examination, if the edge is not relaxed (see
        :meth:`~graph_tool.search.AStarVisitor.edge_relaxed`) then this
        method is invoked.
        """
        return

    def black_target(self, e):
        """ This is invoked when a vertex that is on the ``CLOSED`` list is
        "rediscovered" via a more efficient path, and is re-added to the
        ``OPEN`` list.
        """
        return

    def finish_vertex(self, u):
        """
        This is invoked on a vertex when it is added to the CLOSED list,
        which happens after all of its out edges have been examined.
        """
        return


@_perm_wrap(["initialize_vertex", "examine_vertex", "finish_vertex"],
            ["initialize_vertex"])
def astar_search(g, source, weight, visitor=AStarVisitor(),
                 heuristic=lambda v: 1, dist_map=None, pred_map=None,
                 cost_map=None, combine=lambda a, b: a + b,
                 compare=lambda a, b: a < b, zero=0,
                 infinity=float('inf'), implicit=False):
    r"""
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    Heuristic :math:`A^*` search on a weighted, directed or undirected graph for the case where all edge weights are non-negative.
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    Parameters
    ----------
    g : :class:`~graph_tool.Graph`
        Graph to be used.
    source : :class:`~graph_tool.Vertex`
        Source vertex.
    weight : :class:`~graph_tool.PropertyMap`
        Edge property map with weight values.
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    visitor : :class:`~graph_tool.search.AStarVisitor` (optional, default: ``AStarVisitor()``)
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        A visitor object that is invoked at the event points inside the
        algorithm. This should be a subclass of
        :class:`~graph_tool.search.AStarVisitor`.
    heuristic : unary function (optional, default: ``lambda v: 1``)
        The heuristic function that guides the search. It should take a single
        argument which is a :class:`~graph_tool.Vertex`, and output an estimated
        distance from the source vertex.
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    dist_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the distances from the source will be
        stored.
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    pred_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the predecessor map will be
        stored (must have value type "int").
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    cost_map : :class:`~graph_tool.PropertyMap` (optional, default: ``None``)
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        A vertex property map where the vertex costs will be stored. It must
        have the same value type as ``dist_map``. This parameter is only used if
        ``implicit`` is True.
    combine : binary function (optional, default: ``lambda a, b: a + b``)
        This function is used to combine distances to compute the distance of a
        path.
    compare : binary function (optional, default: ``lambda a, b: a < b``)
        This function is use to compare distances to determine which vertex is
        closer to the source vertex.
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    implicit : bool (optional, default: ``False``)
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        If true, the underlying graph will be assumed to be implicit
        (i.e. constructed during the search).
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    zero : int or float (optional, default: ``0``)
         Value assumed to correspond to a distance of zero by the combine and
         compare functions.
    infinity : int or float (optional, default: ``float('inf')``)
         Value assumed to correspond to a distance of infinity by the combine and
         compare functions.
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    Returns
    -------
    dist_map : :class:`~graph_tool.PropertyMap`
        A vertex property map with the computed distances from the source.

    See Also
    --------
    bfs_search: Breadth-first search
    dfs_search: Depth-first search
    dijkstra_search: Dijkstra's search algorithm

    Notes
    -----

    The :math:`A^*` algorithm is a heuristic graph search algorithm: an
    :math:`A^*` search is "guided" by a heuristic function. A heuristic function
    :math:`h(v)` is one which estimates the cost from a non-goal state (v) in
    the graph to some goal state, t. Intuitively, :math:`A^*` follows paths
    (through the graph) to the goal that are estimated by the heuristic function
    to be the best paths. Unlike best-first search, :math:`A^*` takes into
    account the known cost from the start of the search to v; the paths
    :math:`A^*` takes are guided by a function :math:`f(v) = g(v) + h(v)`, where
    :math:`h(v)` is the heuristic function, and :math:`g(v)` (sometimes denoted
    :math:`c(s, v)`) is the known cost from the start to v. Clearly, the
    efficiency of :math:`A^*` is highly dependent on the heuristic function with
    which it is used.

    The time complexity is :math:`O((E + V) \log V)`.

    The pseudo-code for the :math:`A^*` algorithm is listed below, with the
    annotated event points, for which the given visitor object will be called
    with the appropriate method.

    ::

        A*(G, source, h)
          for each vertex u in V                 initialize vertex u
            d[u] := f[u] := infinity
            color[u] := WHITE
          end for
          color[s] := GRAY
          d[s] := 0
          f[s] := h(source)
          INSERT(Q, source)                      discover vertex source
          while (Q != Ø)
            u := EXTRACT-MIN(Q)                  examine vertex u
            for each vertex v in Adj[u]          examine edge (u,v)
              if (w(u,v) + d[u] < d[v])
                d[v] := w(u,v) + d[u]            edge (u,v) relaxed
                f[v] := d[v] + h(v)
                if (color[v] = WHITE)
                  color[v] := GRAY
                  INSERT(Q, v)                   discover vertex v
                else if (color[v] = BLACK)
                  color[v] := GRAY
                  INSERT(Q, v)                   reopen vertex v
                end if
              else
                ...                              edge (u,v) not relaxed
            end for
            color[u] := BLACK                    finish vertex u
          end while


    Examples
    --------

    We will use an irregular two-dimensional lattice as an example, where the
    heuristic function will be based on the euclidean distance to the target.

    The heuristic function will be defined as:

    .. testcode::

        def h(v, target, pos):
            return sqrt(sum((pos[v].a - pos[target].a) ** 2))

    where ``pos`` is the vertex position in the plane.

    We must define what should be done during the search by subclassing
    :class:`~graph_tool.search.AStarVisitor`, and specializing the appropriate
    methods. In the following we will keep track of the discovered vertices, and
    which edges were examined, as well as the predecessor tree. We will also
    abort the search when a given target vertex is found, by raising the
    :class:`~graph_tool.search.StopSearch` exception.

    .. testcode::

        class VisitorExample(gt.AStarVisitor):

            def __init__(self, touched_v, touched_e, target):
                self.touched_v = touched_v
                self.touched_e = touched_e
                self.target = target

            def discover_vertex(self, u):
                self.touched_v[u] = True

            def examine_edge(self, e):
                self.touched_e[e] = True

            def edge_relaxed(self, e):
                if e.target() == self.target:
                    raise gt.StopSearch()


    With the above class defined, we can perform the :math:`A^*` search as
    follows.

    .. testsetup::

        from numpy.random import seed, random
        import matplotlib.cm
        seed(42)

    >>> points = random((500, 2)) * 4
    >>> points[0] = [-0.01, 0.01]
    >>> points[1] = [4.01, 4.01]
    >>> g, pos = gt.triangulation(points, type="delaunay")
    >>> weight = g.new_edge_property("double") # Edge weights corresponding to
    ...                                        # Euclidean distances
    >>> for e in g.edges():
    ...    weight[e] = sqrt(sum((pos[e.source()].a -
    ...                          pos[e.target()].a) ** 2))
    >>> touch_v = g.new_vertex_property("bool")
    >>> touch_e = g.new_edge_property("bool")
    >>> target = g.vertex(1)
    >>> dist, pred = gt.astar_search(g, g.vertex(0), weight,
    ...                              VisitorExample(touch_v, touch_e, target),
    ...                              heuristic=lambda v: h(v, target, pos))

    We can now observe the best path found, and how many vertices and edges were
    visited in the process.

    >>> ecolor = g.new_edge_property("string")
    >>> ewidth = g.new_edge_property("double")
    >>> ewidth.a = 1
    >>> for e in g.edges():
    ...    ecolor[e] = "blue" if touch_e[e] else "black"
    >>> v = target
    >>> while v != g.vertex(0):
    ...     p = g.vertex(pred[v])
    ...     for e in v.out_edges():
    ...         if e.target() == p:
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    ...             ecolor[e] = "#a40000"
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    ...             ewidth[e] = 3
    ...     v = p
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    >>> gt.graph_draw(g, pos=pos, output_size=(300, 300), vertex_fill_color=touch_v,
    ...               vcmap=matplotlib.cm.binary, edge_color=ecolor,
    ...               edge_pen_width=ewidth, output="astar-delaunay.pdf")
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    <...>

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    .. figure:: astar-delaunay.*
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       :align: center

       The shortest path is shown in red. The visited edges are shown in blue,
       and the visited vertices in black.


    The :math:`A^*` algorithm is very useful for searching *implicit* graphs,
    i.e. graphs which are not entirely stored in memory and are generated
    "on-the-fly" during the search. In the following example we will carry out a
    search in a hamming hypercube of 10 bits witch has random weights on its
    edges in the range :math:`[0,1]`. The vertices of the hypercube will be
    created during the search.

    The heuristic function will use the Hamming distance between vertices:

    .. testcode::

        def h(v, target, state):
            return sum(abs(state[v].a - target)) / 2


    In the following visitor we will keep growing the graph on-the-fly, and
    abort the search when a given target vertex is found, by raising the
    :class:`~graph_tool.search.StopSearch` exception.

    .. testcode::

        from numpy.random import random

        class HammingVisitor(gt.AStarVisitor):

            def __init__(self, g, target, state, weight, dist, cost):
                self.g = g
                self.state = state
                self.target = target
                self.weight = weight
                self.dist = dist
                self.cost = cost
                self.visited = {}

            def examine_vertex(self, u):
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                for i in range(len(self.state[u])):
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                    nstate = list(self.state[u])
                    nstate[i] ^= 1
                    if tuple(nstate) in self.visited:
                        v = self.visited[tuple(nstate)]
                    else:
                        v = self.g.add_vertex()
                        self.visited[tuple(nstate)] = v
                        self.state[v] = nstate
                        self.dist[v] = self.cost[v] = float('inf')
                    for e in u.out_edges():
                        if e.target() == v:
                            break
                    else:
                        e = self.g.add_edge(u, v)
                        self.weight[e] = random()
                self.visited[tuple(self.state[u])] = u

            def edge_relaxed(self, e):
                if self.state[e.target()] == self.target:
                    self.visited[tuple(self.target)] = e.target()
                    raise gt.StopSearch()

    With the above class defined, we can perform the :math:`A^*` search as
    follows.

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    >>> numpy.random.seed(42)
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    >>> g = gt.Graph(directed=False)
    >>> state = g.new_vertex_property("vector<bool>")
    >>> v = g.add_vertex()
    >>> state[v] = [0] * 10
    >>> target = [1] * 10
    >>> weight = g.new_edge_property("double")
    >>> dist = g.new_vertex_property("double")
    >>> cost = g.new_vertex_property("double")
    >>> visitor = HammingVisitor(g, target, state, weight, dist, cost)
    >>> dist, pred = gt.astar_search(g, g.vertex(0), weight, visitor, dist_map=dist,
    ...                              cost_map=cost, heuristic=lambda v: h(v, array(target), state),
    ...                              implicit=True)

    We can now observe the best path found, and how many vertices and edges were
    visited in the process.

    >>> ecolor = g.new_edge_property("string")
    >>> vcolor = g.new_vertex_property("string")
    >>> ewidth = g.new_edge_property("double")
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    >>> ewidth.a = 1
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    >>> for e in g.edges():
    ...    ecolor[e] = "black"
    >>> for v in g.vertices():
    ...    vcolor[v] = "white"
    >>> v = visitor.visited[tuple(target)]
    >>> while v != g.vertex(0):
    ...     vcolor[v] = "black"
    ...     p = g.vertex(pred[v])
    ...     for e in v.out_edges():
    ...         if e.target() == p:
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    ...             ecolor[e] = "#a40000"
    ...             ewidth[e] = 3
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    ...     v = p
    >>> vcolor[v] = "black"
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    >>> gt.graph_draw(g, output_size=(300, 300), vertex_fill_color=vcolor, edge_color=ecolor,
    ...               edge_pen_width=ewidth, output="astar-implicit.pdf")
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    <...>

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    .. figure:: astar-implicit.*
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       :align: center

       The shortest path is shown in red, and the vertices which belong to it
       are in black. Note that the number of vertices visited is much smaller
       than the total number :math:`2^{10} = 1024`.

    References
    ----------

    .. [astar] Hart, P. E.; Nilsson, N. J.; Raphael, B. "A Formal Basis for the
       Heuristic Determination of Minimum Cost Paths". IEEE Transactions on
       Systems Science and Cybernetics SSC4 4 (2): 100–107, 1968.
       :doi:`10.1109/TSSC.1968.300136`
    .. [astar-bgl] http://www.boost.org/doc/libs/release/libs/graph/doc/astar_search.html
    .. [astar-wikipedia] http://en.wikipedia.org/wiki/A*_search_algorithm
    """

    if visitor is None:
        visitor = AStarVisitor()
    if dist_map is None:
        dist_map = g.new_vertex_property(weight.value_type())
    if pred_map is None:
        pred_map = g.new_vertex_property("int")
    if pred_map.value_type() != "int32_t":
        raise ValueError("pred_map must be of value type 'int32_t', not '%s'." % \
                             pred_map.value_type())

    dist_type = dist_map.python_value_type()
    if dist_type is not object:
        h = lambda v: dist_type(heuristic(v))
    else:
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        h = heuristic
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    try:
        zero = _python_type(dist_map.value_type())(zero)
    except OverflowError:
        zero = (weight.a.max() + 1) * g.num_vertices()
        zero = _python_type(dist_map.value_type())(zero)

    try:
        infinity = _python_type(dist_map.value_type())(infinity)
    except OverflowError:
        infinity = (weight.a.max() + 1) * g.num_vertices()
        infinity = _python_type(dist_map.value_type())(infinity)


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    try:
        if not implicit:
            g._Graph__perms.update({"del_vertex": False, "del_edge": False,
                                    "add_edge": False})

            libgraph_tool_search.astar_search(g._Graph__graph,
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                                              weakref.ref(g),
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                                              int(source), _prop("v", g, dist_map),
                                              _prop("v", g, pred_map),
                                              _prop("e", g, weight), visitor,
                                              compare, combine, zero, infinity,
                                              h)
        else:
            if cost_map is None:
                cost_map = g.new_vertex_property(dist_map.value_type())
            elif cost_map.value_type() != dist_map.value_type():
                raise ValueError("The cost_map value type must be the same as" +
                                 " dist_map.")
            g._Graph__perms.update({"del_vertex": False})
            libgraph_tool_search.astar_search_implicit\
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                (g._Graph__graph, weakref.ref(g), int(source),
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                 _prop("v", g, dist_map), _prop("v", g, pred_map),
                 _prop("v", g, cost_map), _prop("e", g, weight), visitor,
                 compare, combine, zero, infinity, h)
    except StopSearch:
        g._Graph__perms.update({"del_vertex": True, "del_edge": True,
                                "add_edge": True})
    finally:
        g._Graph__perms.update({"del_vertex": True, "del_edge": True,
                                "add_edge": True})

    return dist_map, pred_map


class StopSearch(Exception):
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    """If this exception is raised from inside any search visitor object, the search is aborted."""
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    pass
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