python生成器和yield关键字（完整代码）

下列代码用于先体验普通列表推导式和生成器的差别:

# def add():
#   temp = ["姓名", "学号", "班级", "电话"]
#   dic = {}
#   lst = []
#   for item in temp:
#     inp = input("请输入{}:".format(item))
#     if inp == "exit":
#       print("成功退出输入")
#       return False
#     else:
#       dic[item] = inp
#   lst.append(dic)
#   print("添加成功")
#   return lst
#
# def show(lst):
#   print("-"*30)
#   print("姓名\t\t学号\t\t班级\t\t电话")
#   print("=" * 30)
#   for i in range(len(lst)):
#     for val in lst[i].values():
#       print(val, "\t", end="")
#     print()
#   print("-" * 30)
#
# def search(total_lst):
#   name = input("请输入您要查询的学生姓名：")
#   flag = False
#   tmp = []
#   for i in range(len(total_lst)):
#     if total_lst[i]["姓名"] == name:
#       tmp.append(total_lst[i])
#       show(tmp)
#       flag = True
#   if not flag:
#     print("抱歉，没有找到该学生")
#
# if __name__ == '__main__':
#   total_lst = []
#   while True:
#     flag = add()
#     if flag:
#       total_lst = total_lst + flag
#     else:
#       break
#   show(total_lst)
#   search(total_lst)
#
# def show(lst):
#   print("="*30)
#   print("{:^25s}".format("输出F1赛事车手积分榜"))
#   print("=" * 30)
#   print("{:<10s}".format("排名"), "{:<10s}".format("车手"), "{:<10s}".format("积分"))
#   for i in range(len(lst)):
#     print("{:0>2d}{:<9s}".format(i+1, ""), "{:<10s}".format(lst[i][0]), "{:<10d}".format(lst[i][1]))
#
# if __name__ == '__main__':
#   data = 'lisi 380,jack 256,bob 385,rose 204,alex 212'
#   data = data.split(",")
#   dic = {}
#   da = []
#   for i in range(len(data)):
#     da.append(data[i].split())
#   for i in range(len(da)):
#     dic[da[i][0]] = int(da[i][1])
#   data2 = sorted(dic.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
#   show(data2)


# class Fun:
#   def __init__(self):
#     print("Fun:__init__()")
#   def test(self):
#     print("Fun")
#
# class InheritFun(Fun):
#   def __init__(self):
#     print("InheritedFun.__init__()")
#     super().__init__()
#   def test(self):
#     super().test()
#     print("InheritedFun")
# a = InheritFun()
# a.test()

# from math import *
# class Circle:
#   def __init__(self, radius=1):
#     self.radius = radius
#   def getPerimeter(self):
#     return 2 * self.radius * pi
#   def getArea(self):
#     return self.radius * self.radius * pi
#   def setRadius(self, radius):
#     self.radius = radius
#
# a=Circle(10)
# print("{:.1f},{:.2f}".format(a.getPerimeter(), a.getArea()))

# from math import *
# class Root:
#   def __init__(self, a, b, c):
#     self.a = a
#     self.b = b
#     self.c = c
#   def getDiscriminant(self):
#     return pow(self.b, 2)-4*self.a*self.c
#   def getRoot1(self):
#     return (-self.b+pow(pow(self.b, 2)-4*self.a*self.c, 0.5))/(2*self.a)
#   def getRoot2(self):
#     return (-self.b - pow(pow(self.b, 2) - 4 * self.a * self.c, 0.5)) / (2 * self.a)
# inp = input("请输入a，b，c: ").split(" ")
# inp = list(map(int, inp))
# Root = Root(inp[0], inp[1], inp[2])
# print("判别式为：{:.1f}； x1：{:.1f}; x2：{:.1f}".format(Root.getDiscriminant(), Root.getRoot1(), Root.getRoot2()))

# class Stock:
#   def __init__(self, num, name, pre_price, now_price):
#     self.num = num
#     self.name = name
#     self.pre_price = pre_price
#     self.now_price = now_price
#   def getCode(self):
#     return self.num
#   def getName(self):
#     return self.name
#   def getPriceYesterday(self):
#     return self.pre_price
#   def getPriceToday(self):
#     return self.now_price
#   def getChangePercent(self):
#     return (self.now_price-self.pre_price)/self.pre_price
#
# sCode = input() #输入代码
# sName = input() #输入名称
# priceYesterday = float(input()) #输入昨日价格
# priceToday = float(input()) #输入今日价格
# s = Stock(sCode,sName,priceYesterday,priceToday)
# print("代码:",s.getCode())
# print("名称:",s.getName())
# print("昨日价格:%.2f\n今天价格:%.2f" % (s.getPriceYesterday(),s.getPriceToday()))
# print("价格变化百分比:%.2f%%" % (s.getChangePercent()*100))


# from math import pi
#
# class Shape:
#   def __init__(self, name='None', area=None, perimeter=None):
#     self.name = name
#     self.area = area
#     self.perimeter = perimeter
#   def calArea(self):
#     return self.area
#   def calPerimeter(self):
#     return self.perimeter
#   def display(self):
#     print("名称：%s 面积：%.2f 周长：%.2f" % (self.name, self.area, self.perimeter))
#
# class Rectangle(Shape):
#   def __init__(self, width, height):
#     super().__init__()
#     self.width = width
#     self.height = height
#   def calArea(self):
#     self.area = self.height*self.width
#     return self.area
#   def calPerimeter(self):
#     self.perimeter = (self.height+self.width)*2
#     return self.perimeter
#   def display(self):
#     self.name = "Rectangle"
#     Rectangle.calArea(self)
#     Rectangle.calPerimeter(self)
#     super(Rectangle, self).display()
#
# class Triangle(Shape):
#   def __init__(self, bottom, height, edge1, edge2):
#     super().__init__()
#     self.bottom = bottom
#     self.height = height
#     self.edge1 = edge1
#     self.edge2 = edge2
#   def calArea(self):
#     self.area = (self.bottom*self.height) / 2
#     return self.area
#   def calPerimeter(self):
#     self.perimeter = self.bottom+self.edge2+self.edge1
#     return self.perimeter
#   def display(self):
#     self.name = "Triangle"
#     Triangle.calArea(self)
#     Triangle.calPerimeter(self)
#     super(Triangle, self).display()
#
# class Circle(Shape):
#   def __init__(self, radius):
#     super(Circle, self).__init__()
#     self.radius = radius
#   def calArea(self):
#     self.area = pi*pow(self.radius, 2)
#     return self.area
#   def calPerimeter(self):
#     self.perimeter = 2*pi*self.radius
#     return self.perimeter
#   def display(self):
#     self.name = "Circle"
#     Circle.calArea(self)
#     Circle.calPerimeter(self)
#     super(Circle, self).display()
#
# rectangle = Rectangle(2, 3)
# rectangle.display()
#
# triangle = Triangle(3,4,4,5)
# triangle.display()
#
# circle = Circle(radius=1)
# circle.display()
#
# lst = list(map(lambda x: int(x), ['1', '2', '3']))
# print(lst)

#
# class ListNode(object):
#   def __init__(self):
#     self.val = None
#     self.next = None
#
# #尾插法
# def creatlist_tail(lst):
#   L = ListNode() #头节点
#   first_node = L
#   for item in lst:
#     p = ListNode()
#     p.val = item
#     L.next = p
#     L = p
#   return first_node
# #头插法
# def creatlist_head(lst):
#   L = ListNode() #头节点
#   for item in lst:
#     p = ListNode()
#     p.val = item
#     p.next = L
#     L = p
#   return L
# #打印linklist
# def print_ll(ll):
#   while True:
#     if ll.val:
#       print(ll.val)
#       if ll.next==None: #尾插法停止点
#      编程客栈   break
#     elif not ll.next: #头插法停止点
#       break
#     ll = ll.next
# #题解
# class Solution:
#   def printListFromTailToHead(self, listNode):
#     # write code here
#     res = []
#     while(listNode):
#       res.append(listNode.val)
#       listNode=listNode.next
#     return res[3:0:-1]
#
# if __name__ == "__main__":
#   lst = [1, 2, 3]
#   linklist = creatlist_tail(lst)
#   solution = Solution()
#   res = solution.printListFromTailToHead(linklist)
#   print(res)


# -*- coding:utf-8 -*-
# class Solution:
#   def __init__(self):
#     self.stack1 = []
#     self.stack2 = []
#   def push(self, node):
#     # write code here
#     self.stack1.append(node)
#   def pop(self):
#     # return xx
#     if self.stack2:
#       return self.stack2.pop()
#     else:
#       for i in range(len(self.stack1)):
#         self.stack2.append(self.stack1.pop())
#       return self.stack2.pop()
#
# if __name__ == '__main__':
#   solution = Solution()
#   solution.push(1)
#   solution.push(2)
#   print(solution.pop())
#   print(solution.pop())


# # binary search
# def binary_search(lst, x):
#   lst.sort()
#   if len(lst) > 0:
#     pivot = len(lst) // 2
#     if lst[pivot] == x:
#       return True
#     elif lst[pivot] > x:
#       return binary_search(lst[:pivot], x)
#     elif lst[pivot] < x:
#       return binary_search(lst[pivot+1:], x)
#   return False
#
# def binary_search2(lst, x):
#   lst.sort()
#   head = 0
#   tail = len(lst)
#   pivot = len(lst) // 2
#   while head <= tail:
#     if lst[pivot]>x:
#       tail = pivot
#       pivot = (head+tail) // 2
#     elif lst[pivot]<x:
#       head = pivot
#       pivot = (head+tail) // 2
#     elif lst[pivot] == x:
#       return True
#   return False
# if __name__ == '__main__':
#   lst = [5, 3, 1, 8, 9]
#   print(binary_search(lst, 3))
#   print(binary_search(lst, 100))
#
#   print(binary_search(lst, 8))
#   print(binary_search(lst, 100))


# 括号匹配
# def bracket_matching(ans):
#   stack = []
#   flag = True
#   left = ['(', '{', '[']
#   right = [')', '}', ']']
#   for i in range(len(ans)):
#     if ans[i] in left:
#       stack.append(ans[i])
#     else:
#       tmp = stack.pop()
#       if left.index(tmp) != right.index(ans[i]):
#         flag = False
#   if stack:
#     flag = False
#   return flag
#
# print(bracket_matching('({})()[[][]'))
# print(bracket_matching('({})()[[]]'))


# def longestValidParentheses(s):
#   maxlen = 0
#   stack = []
#   for i in range(len(s)):
#     if s[i] == '(':
#       stack.append(s[i])
#     if s[i] == ')' and len(stack) != 0:
#       stack.pop()
#       maxlen += 2
#   return maxlen
# print(longestValidParentheses('()(()'))


# def GetLongestParentheses(s):
#   maxlen = 0
#   start = -1
#   stack = []
#   for i in range(len(s)):
#     if s[i]=='(':
#       stack.append(i)
#     else:
#       if not stack:
#         start = i
#       else:
#         stack.pop()
#         if not stack:
#           maxlen = max(maxlen, i-start)
#         else:
#           maxlen = max(maxlen, i-stack[-1])
#   return maxlen
# print(GetLongestParentheses('()(()'))
# print(GetLongestParentheses('()(()))'))
# print(GetLongestParentheses(')()())'))

# import torch
# a = torch.tensor([[[1,0,3],
#          [4,6,5]]])
# print(a.size())
# b = torch.squeeze(a)
# print(b, b.size())
# b = torch.squeeze(a,-1)
# print(b, b.size())
# b = torch.unsqueeze(a,2)
# print(b, b.size())
#
# print('-----------------')
# x = torch.zeros(2, 1, 2, 1, 2)
# print(x.size())
# y = torch.squeeze(x)
# print(y.size())
# y = torch.squeeze(x, 0)
# print(y.size())
# y = torch.squeeze(x, 1)
# print(y.size())


# from typing import List
# class Solution:
#   def duplicate(self, numbers: List[int]) -> int:
#     # write code here
#     dic = dict()
#     for i in range(len(numbers)):
#       if numbers[i] not in dic.keys():
#         dic[numbers[i]] = 1
#       else:
#         dic[numbers[i]] += 1
#     for key, value in dic.items():
#       if value > 1:
#         return key
#     return -1
# if __name__ == '__main__':
#   solution = Solution()
#   print(solution.duplicate([2,3,1,0,2,5,3]))

# class TreeNode:
#   def __init__(self, data=0):
#     self.val = data
#     self.left = None
#     self.right = None
#
#
# class Solution:
#   def TreeDepth(self , pRoot: TreeNode) -> int:
#     # write code here
#     if pRoot is None:
#       return 0
#     count = 0
#     now_layer =[pRoot]
#     next_layer = []
#     while now_layer:
#       for i in now_layer:
#         if i.left:
#           next_layer.append(i.left)
#         if i.right:
#           next_layer.append(i.right)
#       count +=1
#       now_layer, next_layer = next_layer,[]
#     return count
#
# if __name__ == '__main__':
#   inp = [1,2,3,4,5,'#',6,'#','#',7]
#   bt = TreeNode(1)
#
#   bt.left = TreeNode(2)
#   bt.right = TreeNode(3)
#
#   bt.left.left = TreeNode(4)
#   bt.left.right = TreeNode(5)
#   bt.right.left = None
#   bt.right.right = TreeNode(6)
#
#   bt.left.left.left = None
#   bt.left.left.right = None
#   bt.left.riwww.cppcns.comght.left = TreeNode(7)
#
#   solution = Solution()
#   print('深度：', solution.TreeDepth(bt))

# class ListNode:
#   def __init__(self):
#     self.val = None
#     self.next = None
#
# def creatlist_tail(lst):
#   L = ListNode()
#   first_node = L
#   for item in lst:
#     p = ListNode()
#     p.val = item
#     L.next = p
#     L = p
#   return first_node
#
# def show(node:ListNode):
#   print(node.val,end=' ')
#   if node.next is not None:
#     node = show(node.next)
#
# class Solution:
#   def ReverseList(self, head: ListNode) -> ListNode:
#     # write code here
#     res = None
#     while head:
#       nextnode = head.next
#       head.next = res
#       res = head
#       head = nextnode
#     return res
#
# if __name__ == '__main__':
#   lst = [1,2,3]
#   linklist = creatlist_tail(lst)
#   show(linklist)
#   print()
#   solution = Solution()
#   show(solution.ReverseList(linklist))


# 字典推导式

# a = ['a', 'b', 'c']
# b = [4, 5, 6]
# dic = {k:v for k,v in zip(a,b)}
# print(dic)

#列表推导式

# l = [i for i in range(10)]
# print(l)
#
#
#
# # 生成器推导式
# l1 = (i for i in range(10))
# print(type(l1)) # 输出结果：<class 'generator'>
# for i in l1:
#   print(i)

# print('{pi:0>10.1f}'.format(pi=3.14159855))
# print("'","center".center(40),"'")
# print("center".center(40,'-'))
# print("center".zfill(40))
# print("center".ljust(40,'-'))
# print("center".rjust(40,'-'))

# s = "python is easy to learn, easy to use."
# print(s.find('to',0,len(s)))
# print(s.find('es'))

# num = [1,2,3]
# print("+".join(str(i) for i in num),"=",sum(num))
# print(''.center(40,'-'))

#
# import torch
# from torch import nn
# import numpy as np
#
# # 一维BN
# d1 = torch.rand([2,3,4]) #BCW
# bn1 = nn.BatchNorm1d(3, momentum=1)
# res = bn1(d1)
# print(res.shape)
#
# #二维BN（常用）
# d2 = torch.rand([2,3,4,5]) #BCHW
# bn2 = nn.BatchNorm2d(3, momentum=1)
# res = bn2(d2)
# print(res.shape)
# print(bn2.running_mean) #3个chanel均值
# print(bn2.running_var) #3个chanel方差
#
#
# a = np.array(d2.tolist())
# mean = np.mean(a,axis=(0,2,3))
# print(mean)
#
#
# def batchnorm_forward(x, gamma, beta, bn_param):
#   """
#   Forward pass for batch normalization
#
#   Input:
#   - x: Data of shape (N, D)
#   - gamma: Scale parameter of shape (D,)
#   - beta: Shift parameter of shape (D,)
#   - bn_param: Dictionary with the following keys:
#    - mode: 'train' or 'test'
#    - eps: Constant for numeric stability
#    - momentum: Constant for running mean / variance
#    - running_mean: Array of shape(D,) giving running mean of features
#    - running_var Array of shape(D,) giving running variance of features
#   Returns a tuple of:
#   - out: of shape (N, D)
#   - cache: A tuple of values needed in the backward pass
#   """
#   mode = bn_param['mode']
#   eps = bn_param.get('eps', 1e-5)
#   momentum = bn_param.get('momentum', 0.9)
#
#   N, D = x.shape
#   running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype))
#   running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype))
#
#   out, cache = None, None
#
#   if mode == 'train':
#     sample_mean = np.mean(x, axis=0) # np.mean([[1,2],[3,4]])->[2,3]
#     sample_var = np.var(x, axis=0)
#     out_ = (x - sample_mean) / np.sqrt(sample_var + eps)
#
#     running_mean = momentum * running_mean + (1 - momentum) * sample_mean
#     running_var = momentum * running_var + (1 - momentum) * sample_var
#
#     out = gamma * out_ + beta
#     cache = (out_, x, sample_var, sample_mean, eps, gamma, beta)
#   elif mode == 'test':
#     # scale = gamma / np.sqrt(running_var + eps)
#     # out = x * scale + (beta - running_mean * scale)
#     x_hat = (x - running_mean) / (np.sqrt(running_var + eps))
#     out = gamma * x_hat + beta
#   else:
#     raise ValueError('Invalid forward batchnorm mode "%s"' % mode)
#
#   # Store the updated running means back into bn_param
#   bn_param['running_mean'] = running_mean
#   bn_param['running_var'] = running_var
#
#   return out, cache
#


# import numpy as np
# import matplotlib.pyplot as plt
#
#
# def py_cpu_nms(dets, thresh):
#
#  x1 = dets[:, 0]
#  y1 = dets[:, 1]
#  x2 = dets[:, 2]
#  y2 = dets[:, 3]
#  scores = dets[:, 4]
#  areas = (x2-x1+1)*(y2-y1+1)
#  res = []
#  index = scores.argsort()[::-1]
#  while index.size>0:
#    i = index[0]
#    res.append(i)
#    x11 = np.maximum(x1[i],x1[index[1:]])
#    y11 = np.maximum(y1[i], y1[index[1:]])
#    x22 = np.minimum(x2[i],x2[index[1:]])
#    y22 = np.minimum(y2[i],y2[index[1:]])
#
#    w = np.maximum(0,x22-x11+1)
#    h = np.maximum(0,y22-y11+1)
#
#    overlaps = w * h
#    iou = overlaps/(areas[i]+areas[编程客栈index[1:]]-overlaps)
#
#    idx = np.where(iou<=thresh)[0]
#    index = index[idx+1]
#  print(res)
#  return res
#
# def plot_boxs(box,c):
#   x1 = box[:, 0]
#   y1 = box[:, 1]
#   x2 = box[:, 2]
#   y2 = box[:, 3]
#
#   plt.plot([x1,x2],[y1,y1],c)
#   plt.plot([x1,x2],[y2,y2],c)
#   plt.plot([x1,x1],[y1,y2],c)
#   plt.plot([x2,x2],[y1,y2],c)
#
# if __name__ == '__main__':
#   boxes = np.array([[100, 100, 210, 210, 0.72],
#            [250, 250, 420, 420, 0.8],
#            [220, 220, 320, 330, 0.92],
#            [230, 240, 325, 330, 0.81],
#            [220, 230, 315, 340, 0.9]])
#   plt.figure()
#   ax1 = plt.subplot(121)
#   ax2 = plt.subplot(122)
#   plt.sca(ax1)
#   plot_boxs(boxes,'k')
#
#   res = py_cpu_nms(boxes,0.7)
#   plt.sca(ax2)
#   plot_boxs(boxes[res],'r')
#   plt.show()


# 2 3 3 4
# 1 2 3
# 4 5 6
# 1 2 3 4
# 5 6 7 8
# 9 10 11 12
# lst1, lst2 = [], []
# n1,m1,n2,m2 = map(int,input().split())
# for i in range(n1):
#   nums = list(map(int,input().split())) #输入一行数据
#   lst1.append(nums)
# for i in range(n2):
#   nums = list(map(int,input().split()))
#   lst2.append(nums)
# res = []
# for i in range(n1):
#   res.append([])
#   for j in range(m2):
#     lst4 = []
#     lst3 = lst1[i]
#     for k in range(n2):
#       lst4.append(lst2[k][j])
#     res_num = sum(map(lambda x,y:x*y,lst3,lst4))
#     res[i].append(res_num)
# print(res)
#
# import numpy as np
# print('numpy:',np.dot(lst1,lst2))


#定义残差块
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
#
# class ResBlock(nn.Module):
#   def __init__(self,inchanel,outchanel,stride=1):
#     super(ResBlock,self).__init__()
#     self.left = nn.Sequential(
#       nn.Conv2d(inchanel,outchanel,kernel_size=3,stride=stride,padding=1,bias=False),
#       nn.BatchNorm2d(outchanel),
#       nn.ReLU(inplace=True),
#       nn.Conv2d(outchanel,outchanel,kernel_size=3,stride=1,padding=1,bias=False),
#       nn.BatchNorm2d(outchanel)
#     )
#     self.shortcut = nn.Sequential()
#     if stride!=1 or inchanel!=outchanel:
#       self.shortcut = nn.Sequential(
#         nn.Conv2d(inchanel,outchanel,kernel_size=1,stride=stride,padding=1,bias=False),
#         nn.BatchNorm2d(outchanel)
#       )
#   def forward(self,x):
#     out = self.left(x)
#     out = out + self.shortcut(x)
#     out = F.relu(out)
#
#     return out
#
# class ResNet(nn.Module):
#   def __init__(self,Resblock,num_classes=10):
#     super(ResNet,self).__init__()
#     self.inchanel = 64
#     self.conv1 = nn.Sequential(
#       nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1,bias=False),
#       nn.BatchNorm2d(64),
#       nn.ReLU()
#     )
#     self.layer1 = self.make_layer(ResBlock,64,2,1)
#     self.layer2 = self.make_layer(ResBlock, 128, 2, 2)
#     self.layer3 = self.make_layer(ResBlock, 256, 2, 2)
#     self.layer4 = self.make_layer(ResBlock, 512, 2, 2)
#     self.fc = nn.Linear(512,num_classes)
#
#   def make_layer(self,ResBlock,channels,num_blocks,stride):
#     strides = [stride] + [1] * (num_blocks-1)
#     layers = []
#     for stride in strides:
#       layers.append(ResBlock(self.inchanel,channels,stride))
#       self.inchanel=channels
#     return nn.Sequential(*layers)
#   def forward(self,x):
#     out = self.conv1(x)
#     out = self.layer1(out)
#     out = self.layer2(out)
#     out = self.layer3(out)
#     out = self.layer4(out)
#     out = F.avg_pool2d(out,4)
#     out = out.view(out.size(0),-1)
#     out = self.fc(out)
#     return out


# import torch
# import torch.nn as nn
# import torch.nn.functional as F
#
# class ASPP(nn.Module):
#   def __init__(self,in_channel=512,depth=256):
#     super(ASPP,self).__init__()
#     self.mean = nn.AdaptiveAvgPool2d((1,1))
#     self.conv = nn.Conv2d(in_channel,depth,1,1)
#     self.atrous_block1 = nn.Conv2d(in_channel,depth,1,1)
#     self.atrous_block6 = nn.Conv2d(in_channel,depth,3,1,padding=6,dilation=6)
#     self.atrous_block12 = nn.Conv2d(in_channel,depth,3,1,padding=12,dilation=12)
#     self.atrous_block18 = nn.Conv2d(in_channel,depth,3,1,padding=18,dilation=18)
#     self.conv1x1_output = nn.Conv2d(depth*5,depth,1,1)
#   def forward(self,x):
#     size = x[2:]
#     pool_feat = self.mean(x)
#     pool_feat = self.conv(pool_feat)
#     pool_feat = F.upsample(pool_feat,size=size,mode='bilinear')
#
#     atrous_block1 = self.atrous_block1(x)
#     atrous_block6 = self.atrous_block6(x)
#     atrous_block12 = self.atrous_block12(x)
#     atrous_block18 = self.atrous_block18(x)
#
#     out = self.conv1x1_output(torch.cat([pool_feat,atrous_block1,atrous_block6,
#                       atrous_block12,atrous_block18],dim=1))
#     return out

#牛顿法求三次根
# def sqrt(n):
#   k = n
#   while abs(k*k-n)>1e-6:
#     k = (k + n/k)/2
#   print(k)
#
# def cube_root(n):
#   k = n
#   while abs(k*k*k-n)>1e-6:
#     k = k + (k*k*k-n)/3*k*k
#   print(k)
# sqrt(2)
# cube_root(8)

# -*- coding:utf-8 -*-
# import random
#
# import numpy as np
# from matplotlib import pyplot
#
#
# class K_Means(object):
#   # k是分组数；tolerance‘中心点误差'；max_iter是迭代次数
#   def __init__(self, k=2, tolerance=0.0001, max_iter=300):
#     self.k_ = k
#     self.tolerance_ = tolerance
#     self.max_iter_ = max_iter
#
#   def fit(self, data):
#     self.centers_ = {}
#     for i in range(self.k_):
#       self.centers_[i] = data[random.randint(0,len(data))]
#     # print('center', self.centers_)
#     for i in range(self.max_iter_):
#       self.clf_ = {} #用于装归属到每个类中的点[k,len(data)]
#       for i in range(self.k_):
#         self.clf_[i] = []
#       # print("质点:",self.centers_)
#       for feature in data:
#         distances = [] #装中心点到每个点的距离[k]
#         for center in self.centers_:
#           # 欧拉距离
#           distances.append(np.linalg.norm(feature - self.centers_[center]))
#         classification = distances.index(min(distances))
#         self.clf_[classification].append(feature)
#
#       # print("分组情况:",self.clf_)
#       prev_centers = dict(self.centers_)
#
#       for c in self.clf_:
#         self.centers_[c] = np.average(self.clf_[c], axis=0)
#
#       # '中心点'是否在误差范围
#       optimized = True
#       for center in self.centers_:
#         org_centers = prev_centers[center]www.cppcns.com
#         cur_centers = self.centers_[center]
#         if np.sum((cur_centers - org_centers) / org_centers * 100.0) > self.tolerance_:
#           optimized = False
#       if optimized:
#         break
#
#   def predict(self, p_data):
#     distances = [np.linalg.norm(p_data - self.centers_[center]) for center in self.centers_]
#     index = distances.index(min(distances))
#     return index
#
#
# if __name__ == '__main__':
#   x = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])
#   k_means = K_Means(k=2)
#   k_means.fit(x)
#   for center in k_means.centers_:
#     pyplot.scatter(k_means.centers_[center][0], k_means.centers_[center][1], marker='*', s=150)
#
#   for cat in k_means.clf_:
#     for point in k_means.clf_[cat]:
#       pyplot.scatter(point[0], point[1], c=('r' if cat == 0 else 'b'))
#
#   predict = [[2, 1], [6, 9]]
#   for feature in predict:
#     cat = k_means.predict(feature)
#     pyplot.scatter(feature[0], feature[1], c=('r' if cat == 0 else 'b'), marker='x')
#
#   pyplot.show()

# def pred(key, value):
#   if key == 'math':
#     return value>=40
#   else:
#     return value>=60
# def func(dic,pred):
#   # temp = []
#   # for item in dic:
#   #   if not pred(item,dic[item]):
#   #     temp.append(item)
#   # for item in temp:
#   #   del dic[item]
#   # return dic
#
#   for k in list(dic.keys()):
#     if dic[k]<60:
#       del dic[k]
#   return dic
#
# if __name__ == '__main__':
#   dic={'math':66,'c':78,'c++':59,'python':55}
#   dic = func(dic,pred)
#   print(dic)

#
# class TreeNode:
#   def __init__(self):
#     self.left = None
#     self.right = None
#     self.data = None
#
# def insert(tree,x):
#   temp = TreeNode()
#   temp.data = x
#   if tree.data>x:
#     if tree.left == None:
#       tree.left = temp
#     else:
#       insert(tree.left,x)
#   else:
#     if tree.right == None:
#       tree.right = temp
#     else:
#       insert(tree.right,x)
#
# def print_tree(node):
#   if node is None:
#     return 0
#   print_tree(node.left)
#   print(node.data)
#   print_tree(node.right)
#
#
# def sort(lst):
#   tree = TreeNode()
#   tree.data = lst[0]
#   for i in range(1, len(lst)):
#     insert(tree,lst[i])
#   print_tree(tree)
#
# sort([5,2,4])


# from collections import Iterable, Iterator
#
#
# class Person(object):
#   """定义一个人类"""
#
#   def __init__(self):
#     self.name = list()
#     self.name_num = 0
#
#   def add(self, name):
#     self.name.append(name)
#
#   def __iter__(self):
#     return self
#   def __next__(self):
#     # 记忆性返回数据
#     if self.name_num < len(self.name):
#       ret = self.name[self.name_num]
#       self.name_num += 1
#       return ret
#     else:
#       raise StopIteration
#
# person1 = Person()
# person1.add("张三")
# person1.add("李四")
# person1.add("王五")
#
# print("判断是否是可迭代的对象：", isinstance(person1, Iterable))
# print("判断是否是迭代器：", isinstance(person1,Iterator))
# for name in person1:
#   print(name)

# nums = []
# a = 0
# b = 1
# i = 0
# while i < 10:
#   nums.append(a)
#   a,b = b,a+b
#   i += 1
# for i in nums:
#   print(i)
#
# class Fb():
#   def __init__(self):
#     self.a = 0
#     self.b = 1
#     self.i = 0
#   def __iter__(self):
#     return self
#   def __next__(self):
#     res = self.a
#     if self.i<10:
#       self.a,self.b = self.b,self.a+self.b
#       self.i += 1
#       return res
#     else:
#       raise StopIteration
#
# fb = Fb()
# for i in fb:
#   print(i)


import time

def get_time(func):
  def wraper(*args, **kwargs):
    start_time = time.time()
    result = func(*args, 编程客栈**kwargs)
    end_time = time.time()
    print("Spend:", end_time - start_time)
    return result
  return wraper

@get_time
def _list(n):
  l = [i*i*i for i in range(n)]


@get_time
def _generator(n):
  ge = (i*i*i for i in range(n))

@get_time
def _list_print(l1):
  for i in l1:
    print(end='')

@get_time
def _ge_print(ge):
  for i in ge:
    print(end='')

n = 100000
print('list 生成耗时：')
_list(n)
print('生成器 生成耗时：')
_generator(n)


l1 = [i*i*i for i in range(n)]
ge = (i*i*i for i in range(n))
# print(l1)
# print(ge)
print('list遍历耗时：')
_list_print(l1)
print('生成器遍历耗时：')
_ge_print(ge)

结论：

生成速度：生成器>列表

for_in_循环遍历：1、速度方面：列表>生成器；2、内存占用方面：列表<生成器

总的来说，生成器就是用于降低内存消耗的。

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