From 1756ce6589225b2477fd3b8ab28e8285703731f2 Mon Sep 17 00:00:00 2001 From: linlin Date: Mon, 19 Oct 2020 15:29:16 +0200 Subject: [PATCH] New translations spectral_decomposition.py (French) --- lang/fr/gklearn/kernels/spectral_decomposition.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/lang/fr/gklearn/kernels/spectral_decomposition.py b/lang/fr/gklearn/kernels/spectral_decomposition.py index 7efc005..abb3dcd 100644 --- a/lang/fr/gklearn/kernels/spectral_decomposition.py +++ b/lang/fr/gklearn/kernels/spectral_decomposition.py @@ -66,7 +66,7 @@ class SpectralDecomposition(RandomWalkMeta): iterator = itr for i, j in iterator: - kernel = self.__kernel_do(q_T_list[i], q_T_list[j], P_list[i], P_list[j], D_list[i], D_list[j], self._weight, self._sub_kernel) + kernel = self._kernel_do(q_T_list[i], q_T_list[j], P_list[i], P_list[j], D_list[i], D_list[j], self._weight, self._sub_kernel) gram_matrix[i][j] = kernel gram_matrix[j][i] = kernel @@ -162,7 +162,7 @@ class SpectralDecomposition(RandomWalkMeta): iterator = range(len(g_list)) for i in iterator: - kernel = self.__kernel_do(q_T1, q_T_list[i], P1, P_list[i], D1, D_list[i], self._weight, self._sub_kernel) + kernel = self._kernel_do(q_T1, q_T_list[i], P1, P_list[i], D1, D_list[i], self._weight, self._sub_kernel) kernel_list[i] = kernel else: # @todo @@ -190,9 +190,9 @@ class SpectralDecomposition(RandomWalkMeta): P_list = [] D_list = [] if self._verbose >= 2: - iterator = tqdm(range(len(g_list)), desc='spectral decompose', file=sys.stdout) + iterator = tqdm(g_list, desc='spectral decompose', file=sys.stdout) else: - iterator = range(len(g_list)) + iterator = g_list for G in iterator: # don't normalize adjacency matrices if q is a uniform vector. Note # A actually is the transpose of the adjacency matrix. @@ -252,7 +252,7 @@ class SpectralDecomposition(RandomWalkMeta): if self._p is None: # p is uniform distribution as default. q_T1 = 1 / nx.number_of_nodes(g1) q_T2 = 1 / nx.number_of_nodes(g2) - kernel = self.__kernel_do(q_T1, q_T2, P1, P2, D1, D2, self._weight, self._sub_kernel) + kernel = self._kernel_do(q_T1, q_T2, P1, P2, D1, D2, self._weight, self._sub_kernel) else: # @todo pass else: # @todo @@ -261,7 +261,7 @@ class SpectralDecomposition(RandomWalkMeta): return kernel - def __kernel_do(self, q_T1, q_T2, P1, P2, D1, D2, weight, sub_kernel): + def _kernel_do(self, q_T1, q_T2, P1, P2, D1, D2, weight, sub_kernel): # use uniform distribution if there is no prior knowledge. kl = kron(np.dot(q_T1, P1), np.dot(q_T2, P2)).todense() # @todo: this is not needed when p = q (kr = kl.T) for undirected graphs. @@ -280,4 +280,4 @@ class SpectralDecomposition(RandomWalkMeta): def _wrapper_kernel_do(self, itr): i = itr[0] j = itr[1] - return i, j, self.__kernel_do(G_q_T_list[i], G_q_T_list[j], G_P_list[i], G_P_list[j], G_D_list[i], G_D_list[j], self._weight, self._sub_kernel) \ No newline at end of file + return i, j, self._kernel_do(G_q_T_list[i], G_q_T_list[j], G_P_list[i], G_P_list[j], G_D_list[i], G_D_list[j], self._weight, self._sub_kernel) \ No newline at end of file