ó ,µ[c@sMdZddlZddlZdd„Zd„ZedkrIeƒndS(sÎ =========== Beam Search =========== Beam search with dynamic beam width. The progressive widening beam search repeatedly executes a beam search with increasing beam width until the target node is found. iÿÿÿÿNic Cs§||ƒr|Stjtjt|ƒdƒƒ}x`t|ƒD]R}|td|ƒ}x6tj||||ƒD]\}} || ƒrp| SqpWq>Wtjdƒ‚dS(sãProgressive widening beam search to find a node. The progressive widening beam search involves a repeated beam search, starting with a small beam width then extending to progressively larger beam widths if the target node is not found. This implementation simply returns the first node found that matches the termination condition. `G` is a NetworkX graph. `source` is a node in the graph. The search for the node of interest begins here and extends only to those nodes in the (weakly) connected component of this node. `value` is a function that returns a real number indicating how good a potential neighbor node is when deciding which neighbor nodes to enqueue in the breadth-first search. Only the best nodes within the current beam width will be enqueued at each step. `condition` is the termination condition for the search. This is a function that takes a node as input and return a Boolean indicating whether the node is the target. If no node matches the termination condition, this function raises :exc:`NodeNotFound`. `initial_width` is the starting beam width for the beam search (the default is one). If no node matching the `condition` is found with this beam width, the beam search is restarted from the `source` node with a beam width that is twice as large (so the beam width increases exponentially). The search terminates after the beam width exceeds the number of nodes in the graph. is+no node satisfied the termination conditionN( tmathtceiltlogtlentrangetpowtnxtbfs_beam_edgest NodeNotFound( tGtsourcetvaluet conditiont initial_widthtlog_mtitwidthtutv((s9share/doc/networkx-2.2/examples/algorithms/beam_search.pytprogressive_widening_searchs# !%  cs˜tjddƒ}tj|ƒ‰tˆjƒƒt|ƒ‰‡‡fd†}d}ˆj}|}t||||ƒ}ˆ|}dj||ƒGHdS(såSearch for a node with high centrality. In this example, we generate a random graph, compute the centrality of each node, then perform the progressive widening search in order to find a node of high centrality. idgà?csˆ|ˆkS(N((R(tavg_centralityt centrality(s9share/doc/networkx-2.2/examples/algorithms/beam_search.pythas_high_centralityXsis"found node {0} with centrality {1}N( Rtgnp_random_graphteigenvector_centralitytsumtvaluesRtgetRtformat(R RR R R t found_nodetc((RRs9share/doc/networkx-2.2/examples/algorithms/beam_search.pytmainLs  t__main__(t__doc__RtnetworkxRRRt__name__(((s9share/doc/networkx-2.2/examples/algorithms/beam_search.pyt s    9