linthompsamp.py 文件源码

 复制代码def _linthompsamp_score(self, context):
        """Thompson Sampling"""
        action_ids = list(six.viewkeys(context))
        context_array = np.asarray([context[action_id]
                                    for action_id in action_ids])
        model = self._model_storage.get_model()
        B = model['B']  # pylint: disable=invalid-name
        mu_hat = model['mu_hat']
        v = self.R * np.sqrt(24 / self.epsilon
                             * self.context_dimension
                             * np.log(1 / self.delta))
        mu_tilde = self.random_state.multivariate_normal(
            mu_hat.flat, v**2 * np.linalg.inv(B))[..., np.newaxis]
        estimated_reward_array = context_array.dot(mu_hat)
        score_array = context_array.dot(mu_tilde)

        estimated_reward_dict = {}
        uncertainty_dict = {}
        score_dict = {}
        for action_id, estimated_reward, score in zip(
                action_ids, estimated_reward_array, score_array):
            estimated_reward_dict[action_id] = float(estimated_reward)
            score_dict[action_id] = float(score)
            uncertainty_dict[action_id] = float(score - estimated_reward)
        return estimated_reward_dict, uncertainty_dict, score_dict