diff --git a/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py b/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py
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+++ b/lang/fr/gklearn/tests/ged/test_get_nb_edit_operations_symbolic_cml.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul  6 12:08:24 2020
+
+@author: ljia
+"""
+import random
+import numpy as np
+
+def test_get_nb_edit_operations_symbolic_cml():
+	"""Test get_nb_edit_operations_symbolic_cml().
+	"""
+	"""**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]
+	
+	"""**2.  Compute graph edit distance.**"""
+	
+# 	try:
+	# Initialize label costs randomly.
+	node_label_costs, edge_label_costs = _initialize_label_costs(dataset)
+	
+	# Compute GEDs.
+	pi_forward, pi_backward, dis, node_labels, edge_labels = _compute_ged(dataset, node_label_costs, edge_label_costs)
+	
+	
+	# Compute numbers of edit operations.
+	
+	from gklearn.ged.util.util import get_nb_edit_operations_symbolic_cml
+	
+	n_edit_operations = get_nb_edit_operations_symbolic_cml(graph1, graph2, pi_forward, pi_backward, node_labels, edge_labels)
+	
+	assert np.abs((np.dot(np.concatenate((node_label_costs, edge_label_costs)), n_edit_operations) - dis) / dis) < 10e-6
+	
+# 	except Exception as exception:
+# 		assert False, exception
+		
+		
+def _initialize_label_costs(dataset):
+	node_label_costs = _initialize_node_label_costs(dataset)
+	edge_label_costs = _initialize_edge_label_costs(dataset)
+	return node_label_costs, edge_label_costs
+	
+	
+def _initialize_node_label_costs(dataset):
+	# Get list of node labels.
+	nls = dataset.get_all_node_labels()
+	# Generate random costs.
+	nb_nl = int((len(nls) * (len(nls) - 1)) / 2 + 2 * len(nls))
+	rand_costs = random.sample(range(1, 10 * nb_nl + 1), nb_nl)
+	rand_costs /= np.max(rand_costs)
+			
+	return rand_costs
+
+
+def _initialize_edge_label_costs(dataset):
+	# Get list of edge labels.
+	els = dataset.get_all_edge_labels()
+	# Generate random costs.
+	nb_el = int((len(els) * (len(els) - 1)) / 2 + 2 * len(els))
+	rand_costs = random.sample(range(1, 10 * nb_el + 1), nb_el)
+	rand_costs /= np.max(rand_costs)
+			
+	return rand_costs
+
+
+def _compute_ged(dataset, node_label_costs, edge_label_costs):
+	from gklearn.ged.env import GEDEnv
+	from gklearn.ged.util.util import label_costs_to_matrix
+	import networkx as nx
+			
+	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.
+						  )
+	for g in dataset.graphs:
+		ged_env.add_nx_graph(g, '') # add graphs
+
+	node_labels = ged_env.get_all_node_labels()
+	edge_labels = ged_env.get_all_edge_labels()
+	listID = ged_env.get_all_graph_ids() # get list IDs of graphs
+	ged_env.set_label_costs(label_costs_to_matrix(node_label_costs, len(node_labels)), 
+					  label_costs_to_matrix(edge_label_costs, len(edge_labels)))
+	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.
+	
+	# make the map label correct (label remove map as np.inf)
+	nodes1 = [n for n in dataset.graphs[0].nodes()]
+	nodes2 = [n for n in dataset.graphs[1].nodes()]
+	nb1 = nx.number_of_nodes(dataset.graphs[0])
+	nb2 = nx.number_of_nodes(dataset.graphs[1])
+	pi_forward = [nodes2[pi] if pi < nb2 else np.inf for pi in pi_forward]
+	pi_backward = [nodes1[pi] if pi < nb1 else np.inf for pi in pi_backward]
+	
+	return pi_forward, pi_backward, dis, node_labels, edge_labels
+		
+
+if __name__ == "__main__":
+	test_get_nb_edit_operations_symbolic_cml()
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