''' Author: hhxxrhh 1019962445@qq.com Date: 2024-01-09 11:45:43 LastEditors: hhxxrhh 1019962445@qq.com LastEditTime: 2024-02-01 21:13:02 FilePath: \Desktop\a.py Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE ''' # ## 快速排序 ===========================================（https://blog.csdn.net/flyingluohaipeng/article/details/129134954） # def quickSort(num,l,r): # if(l>=r): # return num # tmp=num[l] # left=l # right=r # while(l<r): # while(num[r]>tmp and l<r): # r-=1 # num[l]=num[r] # while(num[l]<tmp and l<r): # l+=1 # num[r]=num[l] # num[l]=tmp # quickSort(num,left,l-1) # quickSort(num,l+1,right) # num=[10,20,4,3,1,99,25] # quickSort(num,0,len(num)-1) # print(num) #sorted函数======================================== # a=[[1, 1], [1, 2], [2, 3], [10, 0]] # b=sorted(a,key=lambda x:x[1],reverse=True) # dict_={1:10,2:20,5:50,4:40,0:90} # 生成二维矩阵========================================= # >>> dp=[[1]*5 for i in range(3)] # >>> dp # [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1]] # #全排列问题==================================== # data=[1,2,3] # def trace(data,index,ans): # if(index==len(data)-1): # ans.append(data[:]) # for i in range(index,len(data)): # data[i],data[index]=data[index],data[i] # trace(data,index+1,ans) # data[index],data[i]=data[i],data[index] # return # ans=[] # trace(data,0,ans) # print(ans) # # ## 二分查找===================================== # def binary(nums,key): # l=0 # r=len(nums)-1 # while(l<=r): # mid=l+(r-l)//2 # if(nums[mid]==key): # return mid # if(nums[mid]>key): # r=mid-1 # else: # l=mid+1 # return -1 # #左边界====================== # def Left_binary(nums,key): # l=0 # r=len(nums)-1 # while(l<=r): # mid=l+(r-l)//2 # if(nums[mid]>=key): # r=mid-1 # else: # l=mid+1 # if(l>len(nums)-1 or nums[l]!=key): # return -1 # return l # #右边界 # def Right_binary(nums,key): # l=0 # r=len(nums)-1 # while(l<=r): # mid=l+(r-l)//2 # if(nums[mid]<=key): # l=mid+1 # else: # r=mid-1 # if(r<0 or nums[r]!=key): # return -1 # return r # nums=[1,2,3,4,5,8,8,8,8,8,8,10] # print(Left_binary(nums,8)) # print(Right_binary(nums,8)) # nums=[1,2,3,4,5,8,10] # print(binary(nums,8)) ## LRU算法============================================================================= # class Node: # def __init__(self,val): # self.val=val # self.next=None # self.pre=None # class BiLink: # def __init__(self): # self.head=Node(-1) # self.tail=Node(-1) # self.head.next=self.tail # self.tail.pre=self.head # self.size=0 # def removeNode(self,node): # node.pre.next=node.next # node.next.pre=node.pre # self.size-=1 # def removeLast(self): # self.tail.pre=self.tail.pre.pre # self.tail.pre.next=self.tail # self.size-=1 # def addFirst(self,node): # nextNode=self.head.next # self.head.next=node # node.pre=self.head # node.next=nextNode # nextNode.pre=node # self.size+=1 # def printValue(self): # node=self.head # while(node): # print(node.val) # node=node.next # class LRU: # def __init__(self,maxSize=5): # self.mapNode={} # self.link=BiLink() # self.maxSize=maxSize # def put(self,key,value): # if(key in self.mapNode): # self.get(key,value) # return # node=Node(value) # self.mapNode[key]=node # if(self.link.size>=self.maxSize): # self.link.removeLast() # self.link.addFirst(node) # return # def get(self,key): # node=self.mapNode(key) # self.link.removeNode(node) # self.link.addFirst(node) # def printValue(self): # self.link.printValue() # lru=LRU() # lru.put(1,1) # lru.put(2,2) # lru.put(3,3) # lru.put(4,4) # lru.put(5,5) # # lru.printValue() # lru.put(6,6) # lru.put(60,60) # lru.put(61,61) # lru.printValue() # ## node 二叉树的遍历============================= # class Node: # def __init__(self,val): # self.val=val # self.left=None # self.right=None # def buildTree(): # root=Node(1) # root.left=Node(2) # root.right=Node(3) # left=root.left # right=root.right # left.left=Node(4) # left.right=Node(5) # return root # def printTree(root): # queue=[root] # while(len(queue)>0): # tmp=queue # queue=[] # for i in range(len(tmp)): # print(tmp[i].val) # if(tmp[i].left is not None): # queue.append(tmp[i].left) # if(tmp[i].right is not None): # queue.append(tmp[i].right) # return # def inOrder(root): # if(root is None):return # print(root.val) # inOrder(root.left) # inOrder(root.right) # return # root=buildTree() # inOrder(root) # #最长回文子串（连续） 动态规划================================================= # def find(data): # ans=1 # dp=[[0]*len(data) for i in range(len(data))] # for i in range(len(data)): # dp[i][i]=1 # for i in range(len(data)-1,-1,-1): # for j in range(i+1,len(data)): # if(data[i]==data[j] and j-i<=1): # dp[i][j]=1 # elif(data[i]==data[j] and dp[i+1][j-1]==1): # dp[i][j]=1 # if(dp[i][j]==1 and j-i+1>ans): # ans=j-i+1 # return ans # data="aababaac" # print(find(data)) # ## k-means================================================================================= # import numpy as np # class Kmeans: # def __init__(self, k=2, tolerance=0.01, max_iter=300): # self.k = k # self.tol = tolerance # self.max_iter = max_iter # self.classifications = None # self.centroids = None # def fit(self, data): # """ # :param data: numpy数组，约定shape为：(数据数量，数据维度) # :type data: numpy.ndarray # """ # # 初始化聚类中心（维度：k个 * features种数） # self.centroids = np.zeros([self.k, data.shape[1]]) # for i in range(self.k): # self.centroids[i] = data[i] # for i in range(self.max_iter): # # 清空聚类列表 # self.classifications = [[] for i in range(self.k)] # # 对每个点与聚类中心进行距离计算 # for feature_set in data: # # 预测分类 # classification = self.predict(feature_set) # # 加入类别 # self.classifications[classification].append(feature_set) # # 记录前一次的结果 # prev_centroids = np.ndarray.copy(self.centroids) # # 更新中心 # for classification in range(self.k): # self.centroids[classification] = np.average(self.classifications[classification], axis=0) #axis=0 保留行 1：保留列 # # 检测相邻两次中心的变化情况 # for c in range(self.k): # if np.sum(np.abs(prev_centroids[c] - self.centroids[c])) > self.tol: # break # # 如果都满足条件（上面循环没break），则返回 # else: # return # def predict(self, data): # # 距离 # distances = np.sum(np.abs(data - self.centroids),axis=1) # print(distances) # # 最小距离索引 # return distances.argmin() # data=[[1,2,3,4,5],[6,7,8,9,0],[1,2,3,4,50],[1,1,1,1,1]] # data=np.array(data) # algo=Kmeans() # algo.fit(data) # print(algo.predict(data[0])) # ##union-find 算法 # ##attention # # ## 两个序列的中位数//寻找两个序列的第k小的数 log复杂度 有序数组==================================== # def getAnswer(start1,end1,num1,start2,end2,num2,k): # len1=end1-start1+1 # len2=end2-start2+1 # if(len1==0): # return num2[start2+k-1] # elif(len2==0): # return num1[start1+k-1] # if(k==1): # return min(num1[start1],num2[start2]) # p1=start1+min(k//2,len1)-1 # p2=start2+min(k//2,len2)-1 # if(num1[p1]<num2[p2]): # return getAnswer(p1+1,end1,num1,start2,end2,num2,k-(p1-start1+1)) # else: # return getAnswer(start1,end1,num1,p2+1,end2,num2,k-(p2-start2+1)) # a=[1,2,5,10,19,20] # b=[3,4,5,67,100,200] # left=(len(a)+len(b)+1)//2 # right=(len(a)+len(b)+2)//2 # print((getAnswer(0,len(a)-1,a,0,len(b)-1,b,left)+getAnswer(0,len(a)-1,a,0,len(b)-1,b,right))/2) # #数据流中的中位数（堆实现）=============================================================== # from heapq import heappush,heappushpop,heappop # class Solution: # def __init__(self): # self.maxHeap=[] # self.minHeap=[] # def insert(self,num): # if(len(self.maxHeap)==len(self.minHeap)): # heappush(self.maxHeap, -heappushpop(self.minHeap,num)) # else: # heappush(self.minHeap,-heappushpop(self.maxHeap,-num)) # def getMid(self): # if(len(self.maxHeap)==len(self.minHeap)): # return (heappop(self.minHeap)-heappop(self.maxHeap))/2 # else: # return -heappop(self.maxHeap) # tmp=Solution() # for i in range(9): # tmp.insert(i) # print(tmp.maxHeap,tmp.minHeap) # print(tmp.getMid())
#满二叉树 赋值邻居节点 时间复杂度o(n) 空间复杂度O(1)===========================================
# # // 1
# # // 2 -> 3
# # // 4 -> 5 -> 6 -> 7
# # struct TreeNode {
# # int val;
# # TreeNode left;
# # TreeNode right;
# # TreeNode sibling;
# # };
# def solution(root):
# tmp=root
# while(tmp):
# nextTmp=tmp.left
# while(tmp):
# tmp.left.sibling=tmp.right
# if(tmp.sibling):
# tmp.right.sibling=tmp.sibling.left
# tmp=tmp.sibling
# tmp=nextTmp
# //题目:设计一个数据结构，实现方法set(i,val)/get(i)/setall(val)，三个方法的时间复杂度都是O(1)
# class solution:
# def __init__(self):
# self.setall=-1
# self.dict={}
# self.dictValue={}
# self.maxTime=-1
# self.allValue=-1
# def set(self,i,val):
# self.dict[i]=self.maxTime
# self.maxTime+=1
# self.dictValue[i]=val
# def setall(self,val):
# self.setall=self.maxTime+1
# self.maxTime+=1
# self.allValue=val
# def get(self,i):
# if(self.dict[i]>self.setall):
# return self.dictValue[i]
# else:
# return self.allValue
