这篇文章主要介绍了python生成器和yield关键字怎么用，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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_search3(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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