最近在看《算法图解》，书写的很棒，于是我决定把上面提到的算法依次实现一下，代码放在文章中做留存。
第一章：二分查找

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# bisect_search.py
import random
low = 0
hight = 100
numberlist = list(range(0, 100))
guess_number = random.randint(0,100)
print(guess_number)
while True:
    mid = round((low+ hight) / 2)
    if guess_number == numberlist[mid]:
        print('Guess number is ',guess_number)
        break
    elif guess_number > numberlist[mid]:
        low = mid
    elif guess_number < numberlist[mid]:
        hight = mid
    else:
        print('Has someting woring.')

第二章：选择排序

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# Selection sort
def findSmallest(arr):
    smallest = arr[0]
    smallest_index = 0
    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)):
        smallest = findSmallest(arr)
        newArr.append(arr.pop(smallest))
    return newArr


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

第四章：快速排序

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def quicksort(array):
    if len(array) < 2:
        return array
    else:
        pivot = array[0]
        less = [i for i in array[1:] if i <= pivot]
        greater = [i for i in array[1:] if i > pivot]
        return quicksort(less) + [pivot] + quicksort(greater)


print(quicksort([1,5,8,9,3,4,7,10]))

第六章：广度优先收索

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# 广度优先收索
from collections import deque

def person_is_seller(name):
    return name[-1] == "m"

def search(name):
    search_queue = deque()
    search_queue += graph[name]
    searched = []
    while search_queue:
        person = search_queue.popleft()
        if not person in searched:
            if person_is_seller(person):
                print (person + " is a mongo seller!")
                return True
            else:
                search_queue += graph[person]
                searched.append(person)
    return False

search("you")

第七章：狄克斯特拉算法

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import math
# 创建图
graph = {}
graph["start"] = {}
graph["start"]["a"] = 6
graph["start"]["b"] = 2

graph["a"] = {}
graph["a"]["fin"] = 1
graph["b"] = {}
graph["b"]["a"] = 3
graph["b"]["fin"] = 5
graph["fin"] = {}

print(graph)

# 创建开销表

costs = {}
costs["a"] = 6
costs["b"] = 2 
costs["fin"] = math.inf
print(math.inf,type(math.inf))

# 创建父节点
parents = {}
parents["a"] = "start"
parents["b"] = "start"
parents["fin"] = None
# 用来记录处理过的节点
processed = []

def find_lower_cost_node(costs):
    lowest_cost = math.inf
    lowest_cost_node = None
    for node in costs:
        cost = costs[node]
        if cost < lowest_cost and node not in processed:
            lowest_cost = cost
            lowest_cost_node = node
    return lowest_cost_node

node = find_lower_cost_node(costs)

while node is not None:
    cost = costs[node]
    neighbors = graph[node]
    for n in neighbors.keys():
        new_cost = cost + neighbors[n]
        if costs[n] > new_cost:
            costs[n] = new_cost
            parents[n] = node
    processed.append(node)
    node = find_lower_cost_node(costs)

print(costs)