python-算法基础

1.时间复杂度和空间复杂度

2.查找算法

2.1 二分查询法

2.1.1 非递归代码

def erfen(data,target):
    low = 0
    high = len(data) - 1
    while low < high:
        mid = (low + high) // 2
        if data[mid] == target:
            return mid
        elif data[mid] < target:
            low = mid + 1
        else:
            high = mid - 1
    return

2.1.2 递归代码

def diguierfen(data,target):
    low = 0
    high = len(data) - 1
    if low < high:
        mid = (low + high) // 2
        if data[mid] == target:
            return mid
        elif data[mid] < target:
            diguierfen(data[mid+1:high],target)
        else:
            diguierfen(data[0:mid-1],target)
    else:
        return

 

3.排序算法

3.1 冒泡排序

def maopao(data):
    length = len(data)-1
    for i in range(length):
        for j in range(length-i):
            if data[j]>data[j+1]:
                data[j],data[j+1]=data[j+1],data[j]

思路：存在n个数排序时

第1轮 n-1次

第2轮 n-2次

第n-1轮 1次

外层循环n-1次，内存循环从n-1每次减少1直到1

for i in range(length):
    for j in range(length-i):

外层是从0开始到n-1，内层是从0开始，每次减少1

i=0时，表示第1轮循环，需要比较n-1次

i=1时，表示第2轮循环，需要比较n-2次

3.2 优化版的冒泡排序

def maopao(data):
    length = len(data)-1
    for i in range(length):
        exchange = False
        for j in range(length-i):
            print("#")
            if data[j]>data[j+1]:
                data[j],data[j+1]=data[j+1],data[j]
                exchange = True
        if not exchange:
            break

 3.3 选择排序

def xuanze(data):
    length = len(data) - 1
    for i in range(length):
        minindex = i
        for j in range(i+1,length+1):
            if data[minindex]>data[j]:
                data[minindex],data[j] = data[j],data[minindex]

定义第一个索引为最小值，每次遍历找出最小值，每次都是和自己后面一个值比较

3.4 快速排序

4.二叉树遍历

class note:
    def __init__(self,data=None,left=None,right=None):
        self.data = data
        self.left = left
        self.right = right


class Tree:
    def __init__(self,root):
        self.root = root

    def pre(self,tree):
        if tree is None:
            return
        print(tree.data)
        self.pre(tree.left)
        self.pre(tree.right)

    def mid(self,tree):
        if tree is None:
            return
        self.mid(tree.left)
        print(tree.data)
        self.mid(tree.right)

    def after(self,tree):
        if tree is None:
            return
        self.after(tree.left)
        self.after(tree.right)
        print(tree.data)

if __name__=="__main__":
    # node1 = note(data=1)
    # node2 = note(node1, 0, 2)
    # node3 = note(data=3)
    # node4 = note(data=4)
    # node5 = note(node3, node4, 5)
    # node6 = note(node2, node5, 6)
    # node7 = note(node6, 0, 7)
    # node8 = note(data=8)
    # root = note(node7, node8, 100)
    root = note('D',note('E'),note('F'))
    # root = note('D', note('B', note('A'), note('C')), note('E', right=note('G', note('F'))))
    t = Tree(root)
    t.pre(t.root)
    t.mid(t.root)
    t.after(t.root)

分别为前中后序遍历