New translations compute_graph_edit_distance.py (Chinese Simplified)

lang/zh/gklearn/examples/ged/compute_graph_edit_distance.py

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+# -*- coding: utf-8 -*-
+"""compute_graph_edit_distance.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Wfgn7WVuyOQQgwOvdUQBz0BzEVdp0YM3
+
+**This script demonstrates how to compute a graph edit distance.**
+---
+
+**0.   Install `graphkit-learn`.**
+"""
+
+"""**1.   Get dataset.**"""
+
+from gklearn.utils import Dataset
+
+# Predefined dataset name, use dataset "MUTAG".
+ds_name = 'MUTAG'
+
+# Initialize a Dataset.
+dataset = Dataset()
+# Load predefined dataset "MUTAG".
+dataset.load_predefined_dataset(ds_name)
+graph1 = dataset.graphs[0]
+graph2 = dataset.graphs[1]
+print(graph1, graph2)
+
+"""**2.  Compute graph edit distance.**"""
+
+from gklearn.ged.env import GEDEnv
+
+
+ged_env = GEDEnv() # initailize GED environment.
+ged_env.set_edit_cost('CONSTANT', # GED cost type.
+                      edit_cost_constants=[3, 3, 1, 3, 3, 1] # edit costs.
+					  )  
+ged_env.add_nx_graph(graph1, '') # add graph1
+ged_env.add_nx_graph(graph2, '') # add graph2
+listID = ged_env.get_all_graph_ids() # get list IDs of graphs
+ged_env.init(init_type='LAZY_WITHOUT_SHUFFLED_COPIES') # initialize GED environment.
+options = {'initialization_method': 'RANDOM', # or 'NODE', etc.
+           'threads': 1 # parallel threads.
+		   }
+ged_env.set_method('BIPARTITE', # GED method.
+                   options # options for GED method.
+				   )
+ged_env.init_method() # initialize GED method.
+
+ged_env.run_method(listID[0], listID[1]) # run.
+
+pi_forward = ged_env.get_forward_map(listID[0], listID[1]) # forward map.
+pi_backward = ged_env.get_backward_map(listID[0], listID[1]) # backward map.
+dis = ged_env.get_upper_bound(listID[0], listID[1])	# GED bewteen two graphs.
+print(pi_forward)
+print(pi_backward)
+print(dis)