基础代码

二分查找

#coding=utf-8
def binary_search(list, item):
    low = 0
    high = len(list) - 1

        while low <= high:       # While you haven't narrowed it down to one element ...
        mid = (low + high) // 2  # ... check the middle element
        guess = list[mid]
        if guess == item:
            return mid
        if guess > item:
            high = mid - 1
        else:
            low = mid + 1

    return None                      # Item dosen't exist

my_list = [1, 3, 5, 7, 9]
print (binary_search(my_list, 3))  # 'None' means nil in Python. We use to indicate that the item wasn't found.

2.选择排序

#coding=utf-8

# Finds the smallest value in an array
def findSmallest(arr):
    smallest = arr[0]   # Stores the smallest value
    smallest_index = 0  # Stores the index of the smallest value
    for i in range(1, len(arr)):
        if arr[i] < smallest:
            smallest = arr[i]
            smallest_index = i
    return smallest_index

def selectionSort(arr):
    newArr = []
    for i in range(len(arr)):
        # Finds the smallest element in the array and adds it to the new array
        smallest = findSmallest(arr)
        newArr.append(arr.pop(smallest))
    return newArr

print(selectionSort([5, 3, 6, 2, 10]))

3.递归
3.1 基线条件和递归条件

#coding=utf-8

# 基线条件和递归条件
def countdown(i):
    if i <= 0:    # <---- base case 基线条件
        return
    else:
        countdown(i-1)  # <---- recursive case 递归条件

countdown(5)

3.2 调用栈
这是本节的一个重要概念：调用另一个函数时，当前函数暂停并处于未完成状态

# 调用栈
def greet2(name):
    print("how are you, " + name + "?")

def bye():
    print("ok bye!")

def greet(name):
    print("hello, " + name + "!")
    greet2(name)
    print("getting ready to say bye...")
    bye()

greet("adit")

3.3 递归调用栈-斐波那契

def fact(x):
    if x == 1:
        return 1
    else:
        return x * fact(x-1)

print(fact(5))

4.快速排序
快速排序闭选择排序快很多。
使用了分而治之（D&C）思想。
别忘了，你要使用D&C，因此需要将数组分解，直到满足基线条件

def quickSort(array):
    if len(array) < 2:
        return array  # 基线条件：为空或只包含一个元素的数组是“有序”的
    else:
        pivot = array[0]  # 递归条件
        less = [i for i in array[1:] if i <= pivot ]    # 由所有小于基准值的元素组成的子数组
        greater = [i for i in array[1:] if i > pivot ]  # 由所有大于基准值的元素组成的子数组

        return quickSort(less) + [pivot] + quickSort(greater)

print(quickSort([10, 5, 2, 3]))

5 散列表
散列表由键和值组成
对于同样的输入，散列表必须返回同样的输出，这一点很重要
使用散列表来检查是否重复，速度非常快

6.广度优先搜索

#coding=utf-8
from collections import deque

# 判断一个人是不是芒果销售商
def person_is_seller(name):
    return name[-1] == 'm'  # 检查人的姓名是否以m结尾

graph = {}
graph["you"] = ["alice", "bob", "claire"]
graph["bob"] = ["anuj", "peggy"]
graph["alice"] = ["peggy"]
graph["claire"] = ["thom", "jonny"]
graph["anuj"] = []
graph["peggy"] = []
graph["thom"] = []
graph["jonny"] = []

def search(name):
    search_queue = deque()       # 创建一个队列
    search_queue += graph[name]  # 将你的邻居都加入到这个搜索队列中
    searched = []                # 这个数组用于记录检查过的人
    while search_queue:                  # 只要队列不为空
        person = search_queue.popleft()   # 就取出其中的第一个人
        if not person in searched:       # 仅当这个人没检查过时才检查
            if person_is_seller(person):  # # 检查这个人是否是芒果销售商
                print(person + " is a mango seller!")  # 是芒果销售商
                return True
            else:
                search_queue += graph[person]  # 不是芒果销售商。将这个人的朋友都加入搜索队列
                searched.append(person)        # 将这个人标记为检查过
    return False     # 如果到达了这里，就说明队列中没人是芒果销售商

print(search("you"))

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