1. 标准化(Standardization or Mean Removal and Variance Scaling) 变换后各维特征有0均值,单位方差.也叫z-score规范化(零均值规范化).计算方式是将特征值减去均值,除以标准差. sklearn.preprocessing.scale(X) 一般会把train和test集放在一起做标准化,或者在train集上做标准化后,用同样的标准化器去标准化test集,此时可以用scaler scaler = sklearn.preprocessin
203题是在链表中删除一个固定的值,83题是在链表中删除重复的数值,但要保留一个:82也是删除重复的数值,但重复的都删除,不保留. 比如[1.2.2.3],83题要求的结果是[1.2.3],82题要求的结果是[1,3]. 这种题用递归去做比较方便思考,特别是这种重复的数值.递归就是只遍历当前的节点的情况,之前的不用管,之后的以当前节点为出发点思考问题. 203. Remove Linked List Elements class Solution { public: ListNode* remo
1.查询表中重复数据.select * from peoplewhere peopleId in (select peopleId from people group by peopleId having count(peopleId) > 1)2.删除表中多余的重复记录,重复记录是根据单个字段(peopleId)来判断,只留有rowid最小的记录delete from people where peopleId in (select peopleId
js中的NaN不和任何值相等,包括自身. 所以可以使用x!=x来判断x是否是NaN,当且仅当x为NaN时,表达式的结果为true. NaN != NaN //true 可以依此删除数组中的'NaN'. Array.prototype.delNaN = function () { var arr = []; for (var i = 0; i < this.length; i++) { if (this[i] === this[i]) { arr.push(this[i]); } } return
DECLARE @tablename VARCHAR(30),@sql VARCHAR(500)DECLARE cur_delete_table CURSOR READ_ONLY FORWARD_ONLY FORSELECT name FROM sysobjects WHERE name LIKE 'PUB%' AND type='U'OPEN cur_delete_tableFETCH NEXT FROM cur_delete_table INTO @tablenameWHILE @@FETC