需求:获取树结构的节点深度. 实现util.js: // 获取节点深度 参数为树结构array function getMaxFloor(treeData){ let deep = 0; function eachData(data, index) { data.forEach(elem => { if (index > deep) { deep = index; } if (elem.children.length > 0) { eachData(elem.children, deep
1.主要思想:根据已有数据,规则性的造数据 select * FROM(select lId,strName,lId as lParentId,-1 as orderIdx from tbClassify WHERE lParentId = 0 UNION ALL(select t1.* from tbClassify t1 join(select lId from tbClassify where lParentId=0 order by orderIdx) t2 ont1.lParentI
在NLP中深度学习模型何时需要树形结构? 前段时间阅读了Jiwei Li等人[1]在EMNLP2015上发表的论文<When Are Tree Structures Necessary for Deep Learning of Representations?>,该文主要对比了基于树形结构的递归神经网络(Recursive neural network)和基于序列结构的循环神经网络(Recurrent neural network),在4类NLP任务上进行实验,来讨论深度学习模型何时需要树形结