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
#                 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.right.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[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]
#                 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, **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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