diff --git a/lang/fr/gklearn/kernels/marginalizedKernel.py b/lang/fr/gklearn/kernels/marginalizedKernel.py
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+++ b/lang/fr/gklearn/kernels/marginalizedKernel.py
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+"""
+@author: linlin
+
+@references:
+
+	[1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between 
+	labeled graphs. In Proceedings of the 20th International Conference on 
+	Machine Learning, Washington, DC, United States, 2003.
+
+	[2] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and 
+	Jean-Philippe Vert. Extensions of marginalized graph kernels. In 
+	Proceedings of the twenty-first international conference on Machine 
+	learning, page 70. ACM, 2004.
+"""
+
+import sys
+import time
+from functools import partial
+from multiprocessing import Pool
+from tqdm import tqdm
+tqdm.monitor_interval = 0
+#import traceback
+
+import networkx as nx
+import numpy as np
+
+from gklearn.utils.kernels import deltakernel
+from gklearn.utils.utils import untotterTransformation
+from gklearn.utils.graphdataset import get_dataset_attributes
+from gklearn.utils.parallel import parallel_gm
+
+
+def marginalizedkernel(*args,
+					   node_label='atom',
+					   edge_label='bond_type',
+					   p_quit=0.5,
+					   n_iteration=20,
+					   remove_totters=False,
+					   n_jobs=None,
+					   chunksize=None,
+					   verbose=True):
+	"""Calculate marginalized graph kernels between graphs.
+
+	Parameters
+	----------
+	Gn : List of NetworkX graph
+		List of graphs between which the kernels are calculated.
+	
+	G1, G2 : NetworkX graphs
+		Two graphs between which the kernel is calculated.
+
+	node_label : string
+		Node attribute used as symbolic label. The default node label is 'atom'.
+
+	edge_label : string
+		Edge attribute used as symbolic label. The default edge label is 'bond_type'.
+
+	p_quit : integer
+		The termination probability in the random walks generating step.
+
+	n_iteration : integer
+		Time of iterations to calculate R_inf.
+
+	remove_totters : boolean
+		Whether to remove totterings by method introduced in [2]. The default 
+		value is False.
+
+	n_jobs : int
+		Number of jobs for parallelization.   
+
+	Return
+	------
+	Kmatrix : Numpy matrix
+		Kernel matrix, each element of which is the marginalized kernel between
+		2 praphs.
+	"""
+	# pre-process
+	n_iteration = int(n_iteration)
+	Gn = args[0][:] if len(args) == 1 else [args[0].copy(), args[1].copy()]
+	Gn = [g.copy() for g in Gn]
+	
+	ds_attrs = get_dataset_attributes(
+		Gn,
+		attr_names=['node_labeled', 'edge_labeled', 'is_directed'],
+		node_label=node_label, edge_label=edge_label)
+	if not ds_attrs['node_labeled'] or node_label == None:
+		node_label = 'atom'
+		for G in Gn:
+			nx.set_node_attributes(G, '0', 'atom')
+	if not ds_attrs['edge_labeled'] or edge_label == None:
+		edge_label = 'bond_type'
+		for G in Gn:
+			nx.set_edge_attributes(G, '0', 'bond_type')
+
+	start_time = time.time()
+	
+	if remove_totters:
+		# ---- use pool.imap_unordered to parallel and track progress. ----
+		pool = Pool(n_jobs)
+		untotter_partial = partial(wrapper_untotter, Gn, node_label, edge_label)
+		if chunksize is None:
+			if len(Gn) < 100 * n_jobs:
+				chunksize = int(len(Gn) / n_jobs) + 1
+			else:
+				chunksize = 100
+		for i, g in tqdm(
+				pool.imap_unordered(
+					untotter_partial, range(0, len(Gn)), chunksize),
+				desc='removing tottering',
+				file=sys.stdout):
+			Gn[i] = g
+		pool.close()
+		pool.join()
+
+#		# ---- direct running, normally use single CPU core. ----
+#		Gn = [
+#			untotterTransformation(G, node_label, edge_label)
+#			for G in tqdm(Gn, desc='removing tottering', file=sys.stdout)
+#		]
+
+	Kmatrix = np.zeros((len(Gn), len(Gn)))
+
+	# ---- use pool.imap_unordered to parallel and track progress. ----
+	def init_worker(gn_toshare):
+				global G_gn
+				G_gn = gn_toshare
+	do_partial = partial(wrapper_marg_do, node_label, edge_label,
+						 p_quit, n_iteration)   
+	parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker, 
+				glbv=(Gn,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose)
+
+
+#	# ---- direct running, normally use single CPU core. ----
+##	pbar = tqdm(
+##		total=(1 + len(Gn)) * len(Gn) / 2,
+##		desc='calculating kernels',
+##		file=sys.stdout)
+#	for i in range(0, len(Gn)):
+#		for j in range(i, len(Gn)):
+##			print(i, j)
+#			Kmatrix[i][j] = _marginalizedkernel_do(Gn[i], Gn[j], node_label,
+#												   edge_label, p_quit, n_iteration)
+#			Kmatrix[j][i] = Kmatrix[i][j]
+##			pbar.update(1)
+
+	run_time = time.time() - start_time
+	if verbose:
+		print("\n --- marginalized kernel matrix of size %d built in %s seconds ---"
+			  % (len(Gn), run_time))
+
+	return Kmatrix, run_time
+
+
+def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration):
+	"""Calculate marginalized graph kernel between 2 graphs.
+
+	Parameters
+	----------
+	G1, G2 : NetworkX graphs
+		2 graphs between which the kernel is calculated.
+	node_label : string
+		node attribute used as label.
+	edge_label : string
+		edge attribute used as label.
+	p_quit : integer
+		the termination probability in the random walks generating step.
+	n_iteration : integer
+		time of iterations to calculate R_inf.
+
+	Return
+	------
+	kernel : float
+		Marginalized Kernel between 2 graphs.
+	"""
+	# init parameters
+	kernel = 0
+	num_nodes_G1 = nx.number_of_nodes(g1)
+	num_nodes_G2 = nx.number_of_nodes(g2)
+	# the initial probability distribution in the random walks generating step
+	# (uniform distribution over |G|)
+	p_init_G1 = 1 / num_nodes_G1
+	p_init_G2 = 1 / num_nodes_G2
+
+	q = p_quit * p_quit
+	r1 = q
+
+#	# initial R_inf
+#	# matrix to save all the R_inf for all pairs of nodes
+#	R_inf = np.zeros([num_nodes_G1, num_nodes_G2])
+#
+#	# calculate R_inf with a simple interative method
+#	for i in range(1, n_iteration):
+#		R_inf_new = np.zeros([num_nodes_G1, num_nodes_G2])
+#		R_inf_new.fill(r1)
+#
+#		# calculate R_inf for each pair of nodes
+#		for node1 in g1.nodes(data=True):
+#			neighbor_n1 = g1[node1[0]]
+#			# the transition probability distribution in the random walks
+#			# generating step (uniform distribution over the vertices adjacent
+#			# to the current vertex)
+#			if len(neighbor_n1) > 0:
+#				p_trans_n1 = (1 - p_quit) / len(neighbor_n1)
+#				for node2 in g2.nodes(data=True):
+#					neighbor_n2 = g2[node2[0]]
+#					if len(neighbor_n2) > 0:
+#						p_trans_n2 = (1 - p_quit) / len(neighbor_n2)
+#		
+#						for neighbor1 in neighbor_n1:
+#							for neighbor2 in neighbor_n2:
+#								t = p_trans_n1 * p_trans_n2 * \
+#									deltakernel(g1.node[neighbor1][node_label],
+#												g2.node[neighbor2][node_label]) * \
+#									deltakernel(
+#										neighbor_n1[neighbor1][edge_label],
+#										neighbor_n2[neighbor2][edge_label])
+#		
+#								R_inf_new[node1[0]][node2[0]] += t * R_inf[neighbor1][
+#									neighbor2]  # ref [1] equation (8)
+#		R_inf[:] = R_inf_new
+#
+#	# add elements of R_inf up and calculate kernel
+#	for node1 in g1.nodes(data=True):
+#		for node2 in g2.nodes(data=True):
+#			s = p_init_G1 * p_init_G2 * deltakernel(
+#				node1[1][node_label], node2[1][node_label])
+#			kernel += s * R_inf[node1[0]][node2[0]]  # ref [1] equation (6)
+	
+	
+	R_inf = {} # dict to save all the R_inf for all pairs of nodes
+	# initial R_inf, the 1st iteration.
+	for node1 in g1.nodes():
+		for node2 in g2.nodes():
+#			R_inf[(node1[0], node2[0])] = r1
+			if len(g1[node1]) > 0:
+				if len(g2[node2]) > 0:
+					R_inf[(node1, node2)] = r1
+				else:
+					R_inf[(node1, node2)] = p_quit
+			else:
+				if len(g2[node2]) > 0:
+					R_inf[(node1, node2)] = p_quit
+				else:
+					R_inf[(node1, node2)] = 1
+			
+	# compute all transition probability first.
+	t_dict = {}
+	if n_iteration > 1:
+		for node1 in g1.nodes():
+			neighbor_n1 = g1[node1]
+			# the transition probability distribution in the random walks
+			# generating step (uniform distribution over the vertices adjacent
+			# to the current vertex)
+			if len(neighbor_n1) > 0:
+				p_trans_n1 = (1 - p_quit) / len(neighbor_n1)
+				for node2 in g2.nodes():
+					neighbor_n2 = g2[node2]
+					if len(neighbor_n2) > 0:
+						p_trans_n2 = (1 - p_quit) / len(neighbor_n2)
+						for neighbor1 in neighbor_n1:
+							for neighbor2 in neighbor_n2:
+								t_dict[(node1, node2, neighbor1, neighbor2)] = \
+									p_trans_n1 * p_trans_n2 * \
+									deltakernel(g1.nodes[neighbor1][node_label],
+												g2.nodes[neighbor2][node_label]) * \
+									deltakernel(
+										neighbor_n1[neighbor1][edge_label],
+										neighbor_n2[neighbor2][edge_label])
+
+	# calculate R_inf with a simple interative method
+	for i in range(2, n_iteration + 1):
+		R_inf_old = R_inf.copy()
+
+		# calculate R_inf for each pair of nodes
+		for node1 in g1.nodes():
+			neighbor_n1 = g1[node1]
+			# the transition probability distribution in the random walks
+			# generating step (uniform distribution over the vertices adjacent
+			# to the current vertex)
+			if len(neighbor_n1) > 0:
+				for node2 in g2.nodes():
+					neighbor_n2 = g2[node2]
+					if len(neighbor_n2) > 0:   
+						R_inf[(node1, node2)] = r1
+						for neighbor1 in neighbor_n1:
+							for neighbor2 in neighbor_n2:
+								R_inf[(node1, node2)] += \
+									(t_dict[(node1, node2, neighbor1, neighbor2)] * \
+									R_inf_old[(neighbor1, neighbor2)])  # ref [1] equation (8)
+
+	# add elements of R_inf up and calculate kernel
+	for (n1, n2), value in R_inf.items():
+		s = p_init_G1 * p_init_G2 * deltakernel(
+				g1.nodes[n1][node_label], g2.nodes[n2][node_label])
+		kernel += s * value  # ref [1] equation (6)
+
+	return kernel
+		
+		
+def wrapper_marg_do(node_label, edge_label, p_quit, n_iteration, itr):
+	i= itr[0]
+	j = itr[1]
+	return i, j, _marginalizedkernel_do(G_gn[i], G_gn[j], node_label, edge_label, p_quit, n_iteration)
+	
+
+def wrapper_untotter(Gn, node_label, edge_label, i):
+	return i, untotterTransformation(Gn[i], node_label, edge_label)