linlin
5 years ago
1 changed files with 5 additions and 5 deletions
Unified View
Diff Options
+ 5
- 5
lang/fr/gklearn/ged/learning/cost_matrices_learner.py
View File
| @@ -49,7 +49,7 @@ class CostMatricesLearner(CostsLearner): | |||||
| np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, | np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, | ||||
| np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] | np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] | ||||
| prob = cp.Problem(cp.Minimize(cost_fun), constraints) | prob = cp.Problem(cp.Minimize(cost_fun), constraints) | ||||
| self.__execute_cvx(prob) | |||||
| self._execute_cvx(prob) | |||||
| edit_costs_new = x.value | edit_costs_new = x.value | ||||
| residual = np.sqrt(prob.value) | residual = np.sqrt(prob.value) | ||||
| elif not self._triangle_rule and not self._allow_zeros: # @todo | elif not self._triangle_rule and not self._allow_zeros: # @todo | ||||
| @@ -57,7 +57,7 @@ class CostMatricesLearner(CostsLearner): | |||||
| cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) | cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec) | ||||
| constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]] | constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]] | ||||
| prob = cp.Problem(cp.Minimize(cost_fun), constraints) | prob = cp.Problem(cp.Minimize(cost_fun), constraints) | ||||
| self.__execute_cvx(prob) | |||||
| self._execute_cvx(prob) | |||||
| edit_costs_new = x.value | edit_costs_new = x.value | ||||
| residual = np.sqrt(prob.value) | residual = np.sqrt(prob.value) | ||||
| elif self._triangle_rule and not self._allow_zeros: # @todo | elif self._triangle_rule and not self._allow_zeros: # @todo | ||||
| @@ -67,7 +67,7 @@ class CostMatricesLearner(CostsLearner): | |||||
| np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, | np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0, | ||||
| np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] | np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0] | ||||
| prob = cp.Problem(cp.Minimize(cost_fun), constraints) | prob = cp.Problem(cp.Minimize(cost_fun), constraints) | ||||
| self.__execute_cvx(prob) | |||||
| self._execute_cvx(prob) | |||||
| edit_costs_new = x.value | edit_costs_new = x.value | ||||
| residual = np.sqrt(prob.value) | residual = np.sqrt(prob.value) | ||||
| else: | else: | ||||
| @@ -113,7 +113,7 @@ class CostMatricesLearner(CostsLearner): | |||||
| elif abs(cost - self._cost_list[-2][i]) / cost > self._epsilon_ec: | elif abs(cost - self._cost_list[-2][i]) / cost > self._epsilon_ec: | ||||
| self._ec_changed = True | self._ec_changed = True | ||||
| break | break | ||||
| # if abs(cost - edit_cost_list[-2][i]) > self.__epsilon_ec: | |||||
| # if abs(cost - edit_cost_list[-2][i]) > self._epsilon_ec: | |||||
| # ec_changed = True | # ec_changed = True | ||||
| # break | # break | ||||
| self._residual_changed = False | self._residual_changed = False | ||||
| @@ -135,7 +135,7 @@ class CostMatricesLearner(CostsLearner): | |||||
| print('-------------------------------------------------------------------------') | print('-------------------------------------------------------------------------') | ||||
| print('States of iteration', self._itrs + 1) | print('States of iteration', self._itrs + 1) | ||||
| print('-------------------------------------------------------------------------') | print('-------------------------------------------------------------------------') | ||||
| # print('Time spend:', self.__runtime_optimize_ec) | |||||
| # print('Time spend:', self._runtime_optimize_ec) | |||||
| print('Total number of iterations for optimizing:', self._itrs + 1) | print('Total number of iterations for optimizing:', self._itrs + 1) | ||||
| print('Total number of updating edit costs:', self._num_updates_ecs) | print('Total number of updating edit costs:', self._num_updates_ecs) | ||||
| print('Was optimization of edit costs converged:', self._converged) | print('Was optimization of edit costs converged:', self._converged) | ||||