linlin
5 years ago
1 changed files with 7 additions and 7 deletions
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+ 7
- 7
lang/fr/gklearn/kernels/spectral_decomposition.py
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| @@ -66,7 +66,7 @@ class SpectralDecomposition(RandomWalkMeta): | |||||
| iterator = itr | iterator = itr | ||||
| for i, j in iterator: | 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[i][j] = kernel | ||||
| gram_matrix[j][i] = kernel | gram_matrix[j][i] = kernel | ||||
| @@ -162,7 +162,7 @@ class SpectralDecomposition(RandomWalkMeta): | |||||
| iterator = range(len(g_list)) | iterator = range(len(g_list)) | ||||
| for i in iterator: | 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 | kernel_list[i] = kernel | ||||
| else: # @todo | else: # @todo | ||||
| @@ -190,9 +190,9 @@ class SpectralDecomposition(RandomWalkMeta): | |||||
| P_list = [] | P_list = [] | ||||
| D_list = [] | D_list = [] | ||||
| if self._verbose >= 2: | 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: | else: | ||||
| iterator = range(len(g_list)) | |||||
| iterator = g_list | |||||
| for G in iterator: | for G in iterator: | ||||
| # don't normalize adjacency matrices if q is a uniform vector. Note | # don't normalize adjacency matrices if q is a uniform vector. Note | ||||
| # A actually is the transpose of the adjacency matrix. | # 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. | if self._p is None: # p is uniform distribution as default. | ||||
| q_T1 = 1 / nx.number_of_nodes(g1) | q_T1 = 1 / nx.number_of_nodes(g1) | ||||
| q_T2 = 1 / nx.number_of_nodes(g2) | 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 | else: # @todo | ||||
| pass | pass | ||||
| else: # @todo | else: # @todo | ||||
| @@ -261,7 +261,7 @@ class SpectralDecomposition(RandomWalkMeta): | |||||
| return kernel | 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. | # use uniform distribution if there is no prior knowledge. | ||||
| kl = kron(np.dot(q_T1, P1), np.dot(q_T2, P2)).todense() | 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. | # @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): | def _wrapper_kernel_do(self, itr): | ||||
| i = itr[0] | i = itr[0] | ||||
| j = itr[1] | 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) | |||||
| 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) | |||||