o ˷e@sdZddlZddlmZddlZdddZdZej dded Z e e Z e e ee Zd d ZdZe jZeZee eeeZe eZed ed eeje ed ZddddddZeje efieeje eegdddedS)z =========== 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. Nc Cst||r|Sttt|}t|D] }|td|}t||||D]\}} || r3| Sq%qtd)aProgressive 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. z+no node satisfied the termination condition) mathceillog2lenrangepownxbfs_beam_edges NodeNotFound) Gsourcevalue condition initial_widthlog_miwidthuvr}/var/www/ideatree/venv/lib/python3.10/site-packages/../../../share/doc/networkx-2.8.6/examples/algorithms/plot_beam_search.pyprogressive_widening_searchs#   rYdg?)seedcCs t|tkS)N) centralityavg_centrality)rrrrhas_high_centralityWs rz found node z with centrality bluegreyg?) node_color node_size edge_color linewidthsrr)nodelistr$r#)r)__doc__rmatplotlib.pyplotpyplotpltnetworkxr rrgnp_random_graphr eigenvector_centralityrsumvaluesrrrrgetrr found_nodecprint spring_layoutposoptionsdrawdraw_networkx_nodesshowrrrrs4  ?