From d507e2ef7243da3c38480936d7bf8cac216c25f2 Mon Sep 17 00:00:00 2001 From: linlin Date: Mon, 19 Oct 2020 15:25:00 +0200 Subject: [PATCH] New translations marginalizedKernel.py (Chinese Simplified) --- lang/zh/gklearn/kernels/marginalizedKernel.py | 32 +++++++++++++-------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/lang/zh/gklearn/kernels/marginalizedKernel.py b/lang/zh/gklearn/kernels/marginalizedKernel.py index 950f1a6..b6d7fb0 100644 --- a/lang/zh/gklearn/kernels/marginalizedKernel.py +++ b/lang/zh/gklearn/kernels/marginalizedKernel.py @@ -39,15 +39,15 @@ def marginalizedkernel(*args, n_jobs=None, chunksize=None, verbose=True): - """Calculate marginalized graph kernels between graphs. + """Compute marginalized graph 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. G1, G2 : NetworkX graphs - Two graphs between which the kernel is calculated. + Two graphs between which the kernel is computed. node_label : string Node attribute used as symbolic label. The default node label is 'atom'. @@ -59,7 +59,7 @@ def marginalizedkernel(*args, The termination probability in the random walks generating step. n_iteration : integer - Time of iterations to calculate R_inf. + Time of iterations to compute R_inf. remove_totters : boolean Whether to remove totterings by method introduced in [2]. The default @@ -83,11 +83,11 @@ def marginalizedkernel(*args, 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: + if not ds_attrs['node_labeled'] or node_label is 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: + if not ds_attrs['edge_labeled'] or edge_label is None: edge_label = 'bond_type' for G in Gn: nx.set_edge_attributes(G, '0', 'bond_type') @@ -133,7 +133,7 @@ def marginalizedkernel(*args, # # ---- direct running, normally use single CPU core. ---- ## pbar = tqdm( ## total=(1 + len(Gn)) * len(Gn) / 2, -## desc='calculating kernels', +## desc='Computing kernels', ## file=sys.stdout) # for i in range(0, len(Gn)): # for j in range(i, len(Gn)): @@ -152,12 +152,12 @@ def marginalizedkernel(*args, def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): - """Calculate marginalized graph kernel between 2 graphs. + """Compute marginalized graph kernel between 2 graphs. Parameters ---------- G1, G2 : NetworkX graphs - 2 graphs between which the kernel is calculated. + 2 graphs between which the kernel is computed. node_label : string node attribute used as label. edge_label : string @@ -165,7 +165,7 @@ def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): p_quit : integer the termination probability in the random walks generating step. n_iteration : integer - time of iterations to calculate R_inf. + time of iterations to compute R_inf. Return ------ @@ -188,12 +188,12 @@ def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): # # 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 +# # Compute 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 +# # Compute 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 @@ -219,7 +219,7 @@ def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): # neighbor2] # ref [1] equation (8) # R_inf[:] = R_inf_new # -# # add elements of R_inf up and calculate kernel +# # add elements of R_inf up and compute kernel. # for node1 in g1.nodes(data=True): # for node2 in g2.nodes(data=True): # s = p_init_G1 * p_init_G2 * deltakernel( @@ -267,11 +267,11 @@ def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): neighbor_n1[neighbor1][edge_label], neighbor_n2[neighbor2][edge_label]) - # calculate R_inf with a simple interative method + # Compute 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 + # Compute R_inf for each pair of nodes for node1 in g1.nodes(): neighbor_n1 = g1[node1] # the transition probability distribution in the random walks @@ -288,7 +288,7 @@ def _marginalizedkernel_do(g1, g2, node_label, edge_label, p_quit, n_iteration): (t_dict[(node1, node2, neighbor1, neighbor2)] * \ R_inf_old[(neighbor1, neighbor2)]) # ref [1] equation (8) - # add elements of R_inf up and calculate kernel + # add elements of R_inf up and compute 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])