speeding motor

def topkprob(self, vec, k=5): vec_sort = np.sort(vec)[-1::-1] topk = vec_sort[:k] ary = np.arange(k) return np.prod([np.exp(topk[i]) / np.sum(np.exp(topk[i:])) for i in ary]) def listwise_cost(self, list_ans, list_pred): return - np.sum(self.topkprob(list_ans) * np.log(self.topkprob(list_pred)))...

> sorry for your confusion. as written in the readme, ListNet implementation is not the exact paper. https://github.com/shiba24/learning2rank#listnet > If you want to use ListNet, maybe you can look at...

@whuissyxa I have the same question too , have you got it?