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New translations sylvester_equation.py (Chinese Simplified)

l10n_v0.2.x
linlin 5 years ago
parent
63c24beef2
commit
e6b5342df1
1 changed files with 30 additions and 30 deletions
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      lang/zh/gklearn/kernels/sylvester_equation.py

+ 30
- 30
lang/zh/gklearn/kernels/sylvester_equation.py View File

@@ -16,18 +16,18 @@ import numpy as np
import networkx as nx
from control import dlyap
from gklearn.utils.parallel import parallel_gm, parallel_me
from gklearn.kernels import RandomWalk
from gklearn.kernels import RandomWalkMeta


class SylvesterEquation(RandomWalk):
class SylvesterEquation(RandomWalkMeta):
def __init__(self, **kwargs):
RandomWalk.__init__(self, **kwargs)
super().__init__(**kwargs)

def _compute_gm_series(self):
self._check_edge_weight(self._graphs)
self._check_edge_weight(self._graphs, self._verbose)
self._check_graphs(self._graphs)
if self._verbose >= 2:
import warnings
@@ -38,7 +38,7 @@ class SylvesterEquation(RandomWalk):
# compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
if self._q == None:
if self._q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
if self._verbose >= 2:
@@ -54,16 +54,16 @@ class SylvesterEquation(RandomWalk):
# norm[norm == 0] = 1
# A_wave_list.append(A_tilde / norm)

if self._p == None: # p is uniform distribution as default.
if self._p is None: # p is uniform distribution as default.
from itertools import combinations_with_replacement
itr = combinations_with_replacement(range(0, len(self._graphs)), 2)
if self._verbose >= 2:
iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout)
iterator = tqdm(itr, desc='Computing kernels', file=sys.stdout)
else:
iterator = itr
for i, j in iterator:
kernel = self.__kernel_do(A_wave_list[i], A_wave_list[j], lmda)
kernel = self._kernel_do(A_wave_list[i], A_wave_list[j], lmda)
gram_matrix[i][j] = kernel
gram_matrix[j][i] = kernel
@@ -76,7 +76,7 @@ class SylvesterEquation(RandomWalk):
def _compute_gm_imap_unordered(self):
self._check_edge_weight(self._graphs)
self._check_edge_weight(self._graphs, self._verbose)
self._check_graphs(self._graphs)
if self._verbose >= 2:
import warnings
@@ -85,7 +85,7 @@ class SylvesterEquation(RandomWalk):
# compute Gram matrix.
gram_matrix = np.zeros((len(self._graphs), len(self._graphs)))
if self._q == None:
if self._q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
if self._verbose >= 2:
@@ -94,7 +94,7 @@ class SylvesterEquation(RandomWalk):
iterator = self._graphs
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel?

if self._p == None: # p is uniform distribution as default.
if self._p is None: # p is uniform distribution as default.
def init_worker(A_wave_list_toshare):
global G_A_wave_list
G_A_wave_list = A_wave_list_toshare
@@ -113,7 +113,7 @@ class SylvesterEquation(RandomWalk):
def _compute_kernel_list_series(self, g1, g_list):
self._check_edge_weight(g_list + [g1])
self._check_edge_weight(g_list + [g1], self._verbose)
self._check_graphs(g_list + [g1])
if self._verbose >= 2:
import warnings
@@ -124,24 +124,24 @@ class SylvesterEquation(RandomWalk):
# compute kernel list.
kernel_list = [None] * len(g_list)
if self._q == None:
if self._q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout)
iterator = tqdm(g_list, desc='compute adjacency matrices', file=sys.stdout)
else:
iterator = range(len(g_list))
iterator = g_list
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator]

if self._p == None: # p is uniform distribution as default.
if self._p is None: # p is uniform distribution as default.
if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout)
iterator = tqdm(range(len(g_list)), desc='Computing kernels', file=sys.stdout)
else:
iterator = range(len(g_list))
for i in iterator:
kernel = self.__kernel_do(A_wave_1, A_wave_list[i], lmda)
kernel = self._kernel_do(A_wave_1, A_wave_list[i], lmda)
kernel_list[i] = kernel
else: # @todo
@@ -153,7 +153,7 @@ class SylvesterEquation(RandomWalk):
def _compute_kernel_list_imap_unordered(self, g1, g_list):
self._check_edge_weight(g_list + [g1])
self._check_edge_weight(g_list + [g1], self._verbose)
self._check_graphs(g_list + [g1])
if self._verbose >= 2:
import warnings
@@ -162,17 +162,17 @@ class SylvesterEquation(RandomWalk):
# compute kernel list.
kernel_list = [None] * len(g_list)
if self._q == None:
if self._q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
if self._verbose >= 2:
iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout)
iterator = tqdm(g_list, desc='compute adjacency matrices', file=sys.stdout)
else:
iterator = range(len(g_list))
iterator = g_list
A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel?

if self._p == None: # p is uniform distribution as default.
if self._p is None: # p is uniform distribution as default.
def init_worker(A_wave_1_toshare, A_wave_list_toshare):
global G_A_wave_1, G_A_wave_list
G_A_wave_1 = A_wave_1_toshare
@@ -186,7 +186,7 @@ class SylvesterEquation(RandomWalk):
len_itr = len(g_list)
parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr,
init_worker=init_worker, glbv=(A_wave_1, A_wave_list), method='imap_unordered',
n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose)
n_jobs=self._n_jobs, itr_desc='Computing kernels', verbose=self._verbose)
else: # @todo
pass
@@ -201,7 +201,7 @@ class SylvesterEquation(RandomWalk):
def _compute_single_kernel_series(self, g1, g2):
self._check_edge_weight([g1] + [g2])
self._check_edge_weight([g1] + [g2], self._verbose)
self._check_graphs([g1] + [g2])
if self._verbose >= 2:
import warnings
@@ -209,13 +209,13 @@ class SylvesterEquation(RandomWalk):
lmda = self._weight
if self._q == None:
if self._q is None:
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list actually contains the transposes of the adjacency matrices.
A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose()
A_wave_2 = nx.adjacency_matrix(g2, self._edge_weight).todense().transpose()
if self._p == None: # p is uniform distribution as default.
kernel = self.__kernel_do(A_wave_1, A_wave_2, lmda)
if self._p is None: # p is uniform distribution as default.
kernel = self._kernel_do(A_wave_1, A_wave_2, lmda)
else: # @todo
pass
else: # @todo
@@ -224,7 +224,7 @@ class SylvesterEquation(RandomWalk):
return kernel
def __kernel_do(self, A_wave1, A_wave2, lmda):
def _kernel_do(self, A_wave1, A_wave2, lmda):
S = lmda * A_wave2
T_t = A_wave1
@@ -242,4 +242,4 @@ class SylvesterEquation(RandomWalk):
def _wrapper_kernel_do(self, itr):
i = itr[0]
j = itr[1]
return i, j, self.__kernel_do(G_A_wave_list[i], G_A_wave_list[j], self._weight)
return i, j, self._kernel_do(G_A_wave_list[i], G_A_wave_list[j], self._weight)

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