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New translations weisfeiler_lehman.py (French)

l10n_v0.2.x
linlin 4 years ago
parent
commit
fb33dea84f
1 changed files with 13 additions and 13 deletions
  1. +13
    -13
      lang/fr/gklearn/kernels/weisfeiler_lehman.py

+ 13
- 13
lang/fr/gklearn/kernels/weisfeiler_lehman.py View File

@@ -125,12 +125,12 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
def __subtree_kernel_do(self, Gn):
"""Calculate Weisfeiler-Lehman kernels between graphs.
"""Compute Weisfeiler-Lehman kernels between graphs.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
List of graphs between which the kernels are computed.
Return
------
@@ -152,7 +152,7 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
# number of occurence of each label in G
all_num_of_each_label.append(dict(Counter(labels_ori)))
# calculate subtree kernel with the 0th iteration and add it to the final kernel.
# Compute subtree kernel with the 0th iteration and add it to the final kernel.
self.__compute_gram_matrix(gram_matrix, all_num_of_each_label, Gn)
# iterate each height
@@ -198,7 +198,7 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
# all_labels_ori.update(labels_comp)
all_num_of_each_label.append(dict(Counter(labels_comp)))
# calculate subtree kernel with h iterations and add it to the final kernel
# Compute subtree kernel with h iterations and add it to the final kernel
self.__compute_gram_matrix(gram_matrix, all_num_of_each_label, Gn)
return gram_matrix
@@ -244,12 +244,12 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
def _wl_spkernel_do(Gn, node_label, edge_label, height):
"""Calculate Weisfeiler-Lehman shortest path kernels between graphs.
"""Compute Weisfeiler-Lehman shortest path kernels between graphs.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
List of graphs between which the kernels are computed.
node_label : string
node attribute used as label.
edge_label : string
@@ -312,7 +312,7 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
for node in G.nodes(data = True):
node[1][node_label] = set_compressed[set_multisets[node[0]]]
# calculate subtree kernel with h iterations and add it to the final kernel
# Compute subtree kernel with h iterations and add it to the final kernel
for i in range(0, len(Gn)):
for j in range(i, len(Gn)):
for e1 in Gn[i].edges(data = True):
@@ -326,12 +326,12 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
def _wl_edgekernel_do(Gn, node_label, edge_label, height):
"""Calculate Weisfeiler-Lehman edge kernels between graphs.
"""Compute Weisfeiler-Lehman edge kernels between graphs.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
List of graphs between which the kernels are computed.
node_label : string
node attribute used as label.
edge_label : string
@@ -390,7 +390,7 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
for node in G.nodes(data = True):
node[1][node_label] = set_compressed[set_multisets[node[0]]]
# calculate subtree kernel with h iterations and add it to the final kernel
# Compute subtree kernel with h iterations and add it to the final kernel
for i in range(0, len(Gn)):
for j in range(i, len(Gn)):
for e1 in Gn[i].edges(data = True):
@@ -403,12 +403,12 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
def _wl_userkernel_do(Gn, node_label, edge_label, height, base_kernel):
"""Calculate Weisfeiler-Lehman kernels based on user-defined kernel between graphs.
"""Compute Weisfeiler-Lehman kernels based on user-defined kernel between graphs.
Parameters
----------
Gn : List of NetworkX graph
List of graphs between which the kernels are calculated.
List of graphs between which the kernels are computed.
node_label : string
node attribute used as label.
edge_label : string
@@ -463,7 +463,7 @@ class WeisfeilerLehman(GraphKernel): # @todo: total parallelization and sp, edge
for node in G.nodes(data = True):
node[1][node_label] = set_compressed[set_multisets[node[0]]]
# calculate kernel with h iterations and add it to the final kernel
# Compute kernel with h iterations and add it to the final kernel
gram_matrix += base_kernel(Gn, node_label, edge_label)
return gram_matrix


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