大前提是先按大小排好序
def binary_search(list, item):
  # low and high keep track of which part of the list you'll search in.
  low = 0
  high = len(list) - 1
  # While you haven't narrowed it down to one element ...
  while low <= high:
    # ... check the middle element
    mid = (low + high) // 2
    guess = list[mid]
    # Found the item.
    if guess == item:
      return mid
    # The guess was too high.
    if guess > item:
      high = mid - 1
    # The guess was too low.
    else:
      low = mid + 1
  # Item doesn't exist
  return None
my_list = [2, 3, 5, 7, 11,13]
print(binary_search(my_list, 13)) # => 1
# 返回所在位置
# 'None' means nil in Python. We use to indicate that the item wasn't found.
print(binary_search(my_list, -1)) # => None

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

def quicksort(array):
  if len(array) < 2:
    # base case, arrays with 0 or 1 element are already "sorted"
    return array
  else:
    # recursive case
    pivot = array[0]
    # sub-array of all the elements less than the pivot
    less = [i for i in array[1:] if i <= pivot]
    # 这是什么操作（python还有这种写法？）
    # 从1开始，往后循环
    # sub-array of all the elements greater than the pivot
    greater = [i for i in array[1:] if i > pivot]
    return quicksort(less) + [pivot] + quicksort(greater)
print(quicksort([10, 5, 2, 3, 8]))

book = {"apple": 0.67, "milk": 1.49, "avocado": 1.49}
book["Lisa"] = 100
print(book)

voted = {}
def check_voter(name):
  if voted.get(name):
    print("kick them out!")
  else:
    voted[name] = True
    print("let them vote!")
check_voter("tom")
check_voter("mike")
check_voter("mike")

from collections import deque
def person_is_seller(name):
      return name[-1] == 'm'
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() #定义其为队列
    search_queue += graph[name]
    # This array is how you keep track of which people you've searched before.
    searched = []
    while search_queue:
        person = search_queue.popleft()
        # Only search this person if you haven't already searched them.
        if person not in searched:
            if person_is_seller(person):
                print(person + " is a mango seller!")
                return True
            else:
                search_queue += graph[person]
                # Marks this person as searched
                searched.append(person)
    return False
search("you")

# the graph
# 用于确定父连接
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"] = {}
# the costs table
# 用于初始化
infinity = float("inf")
costs = {}
costs["a"] = 6
costs["b"] = 2
costs["fin"] = infinity
# the parents table
parents = {}
parents["a"] = "start"
parents["b"] = "start"
parents["fin"] = None
processed = []
def find_lowest_cost_node(costs):
    lowest_cost = float("inf")  #函数初始化
    lowest_cost_node = None
    # Go through each node.
    for node in costs:
        cost = costs[node]
        # If it's the lowest cost so far and hasn't been processed yet...
        if cost < lowest_cost and node not in processed:
            # ... set it as the new lowest-cost node.
            lowest_cost = cost
            lowest_cost_node = node
    return lowest_cost_node
# Find the lowest-cost node that you haven't processed yet.
node = find_lowest_cost_node(costs)
# If you've processed all the nodes, this while loop is done.
while node is not None:
    cost = costs[node]
    # Go through all the neighbors of this node.
    neighbors = graph[node]
    for n in neighbors.keys():
        new_cost = cost + neighbors[n]
        # If it's cheaper to get to this neighbor by going through this node...
        if costs[n] > new_cost:
            # ... update the cost for this node.
            costs[n] = new_cost
            # This node becomes the new parent for this neighbor.
            parents[n] = node
    # Mark the node as processed.
    processed.append(node)
    # Find the next node to process, and loop.
    node = find_lowest_cost_node(costs)
print("Cost from the start to each node:")
print(costs)

# You pass an array in, and it gets converted to a set.
states_needed = set(["mt", "wa", "or", "id", "nv", "ut", "ca", "az"])
stations = {}
stations["kone"] = set(["id", "nv", "ut"])
stations["ktwo"] = set(["wa", "id", "mt"])
stations["kthree"] = set(["or", "nv", "ca"])
stations["kfour"] = set(["nv", "ut"])
stations["kfive"] = set(["ca", "az"])
final_stations = set()
#   第一遍时ktwo因为长度与kone的范围一样而没被纳入
while states_needed: #在遍历中，直到states_needed全部清空
  best_station = None
  states_covered = set()
  for station, states_for_station in stations.items():  ##没看懂
    covered = states_needed & states_for_station  ## 取交集
    if len(covered) > len(states_covered):
      best_station = station
      states_covered = covered
  states_needed -= states_covered
  final_stations.add(best_station)
print(final_stations)