sairate
fa9377e4ae
- 新增现代 C++ 教程的 Preface 章节，包括英文和中文版本 - 添加 C++ Primer 练习代码 - 新增 Learn C++ 教程的 C++ 开发简介章节 - 添加头文件解析文档 - 更新 mkdocs.yml，包含新教程的目录结构 - 修改项目设置，使用 Python 3.10环境
2282 lines
253 KiB
Plaintext
2282 lines
253 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# C++的常用算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 1. 概述"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① 算法主要是由头文件$<algorithm>$、$<functional>$、$<numeric>$组成。\n",
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"\n",
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"② $<algorithm>$是所有STL头文件中最大的一个，范围涉及到比较、交换、查找、遍历操作、复制、修改等等。\n",
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"\n",
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"③ $<numeric>$体积很小，只包含几个在序列上面进行简单数学运算的模板函数。\n",
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"\n",
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"④ $<functional>$定义了一些模板类，用以声明函数对象。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1.1 常用遍历算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.1.1 算法简介"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① for_each //遍历容器\n",
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"\n",
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"② transform //搬运容器到另一个容器中"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.1.2 for_each遍历算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① for_each在实际开发中是最常用遍历算法，需要熟练掌握。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#include<iostream>\n",
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"using namespace std;\n",
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"#include<vector>\n",
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"#include<algorithm>\n",
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"\n",
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"//普通函数\n",
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"void print01(int val)\n",
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"{\n",
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" cout << val << \" \";\n",
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"}\n",
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"\n",
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"//仿函数\n",
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"class print02\n",
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"{\n",
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"public:\n",
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" void operator()(int val)\n",
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" {\n",
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" cout << val << \" \";\n",
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" }\n",
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"};\n",
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"\n",
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"void test01()\n",
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"{\n",
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" vector<int>v;\n",
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"\n",
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" for (int i = 0; i < 10; i++)\n",
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" {\n",
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" v.push_back(i);\n",
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" }\n",
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"\n",
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" for_each(v.begin(), v.end(), print01); //利用普通函数实现遍历操作,放入函数名即可\n",
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" cout << endl;\n",
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"\n",
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" for_each(v.begin(), v.end(), print02()); //仿函数，放入匿名函数对象即可\n",
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" cout << endl;\n",
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" \n",
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"}\n",
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"\n",
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"\n",
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"int main() \n",
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"{\n",
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" test01();\n",
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"\n",
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" system(\"pause\");\n",
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"\n",
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" return 0;\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"运行结果： \n",
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" - 0 1 2 3 4 5 6 7 8 9\n",
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" - 0 1 2 3 4 5 6 7 8 9\n",
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" - 请按任意键继续. . ."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.1.3 transform遍历算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① 功能描述：搬运容器到另一个容器中。\n",
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"\n",
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"② 函数原型：transform(iterator beg1, iterator end1, iterator beg2, _func);\n",
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"\n",
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"1. beg1 源容器开始迭代器\n",
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"2. end1 源容器结束迭代器\n",
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"3. beg2 目标容器开始迭代器\n",
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"4. _func 函数或者函数对象\n",
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"\n",
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"③ 搬运的目标容器必须要提前开辟空间，否则无法正常搬运。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#include<iostream>\n",
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"using namespace std;\n",
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"#include<vector>\n",
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"#include<algorithm>\n",
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"\n",
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"//常用遍历算法 transform\n",
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"\n",
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"class Transform\n",
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"{\n",
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"public:\n",
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" int operator()(int v)\n",
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" {\n",
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" return v;\n",
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" }\n",
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"};\n",
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"\n",
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"class MyPrint\n",
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"{\n",
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"public:\n",
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" void operator()(int val)\n",
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" {\n",
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" cout << val+100 << \" \";\n",
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" }\n",
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"};\n",
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"\n",
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"void test01()\n",
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"{\n",
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" vector<int>v;\n",
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" for (int i = 0; i < 10; i++)\n",
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" {\n",
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" v.push_back(i);\n",
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" }\n",
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" vector<int>vTarget; //目标容器\n",
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" vTarget.resize(v.size()); //目标容器，需要提前开辟空间\n",
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"\n",
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" transform(v.begin(), v.end(), vTarget.begin(), Transform());\n",
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" \n",
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" for_each(vTarget.begin(), vTarget.end(), MyPrint());\n",
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" cout << endl;\n",
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"}\n",
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"\n",
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"int main() \n",
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"{\n",
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" test01();\n",
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"\n",
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" system(\"pause\");\n",
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"\n",
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" return 0;\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"运行结果： \n",
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" - 100 101 102 103 104 105 106 107 108 109\n",
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" - 请按任意键继续. . ."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1.2 常用查找算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.2.1 算法简介"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① find //查找元素\n",
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"\n",
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"② find_if //按条件查找元素\n",
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"\n",
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"③ adjacent_find //查找相邻重复元素\n",
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"\n",
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"④ binary_search //二分查找法\n",
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"\n",
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"⑤ cout //统计元素个数\n",
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"\n",
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"⑥ count_if //按条件统计元素个数"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.2.2 find 查找算法"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"① 功能描述：查找指定元素，找到返回指定元素的迭代器，找不到返回迭代器end()\n",
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"\n",
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"② 函数原型：find(operator beg,iterator end, value);\n",
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"\n",
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"1. 按值查找元素，找到返回指定位置迭代器，找不到返回结束位置迭代器位置。\n",
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"2. beg 开始迭代器\n",
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"3. end 结束迭代器\n",
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"4. value 查找的元素\n",
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"\n",
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"③ 利用find可以坐在容器这种找指定的元素，返回值是迭代器。"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
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"#include<iostream>\n",
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"using namespace std;\n",
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"#include<vector>\n",
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"#include<algorithm>\n",
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"#include<string>\n",
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"\n",
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"//查找 内置数据类型\n",
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"void test01()\n",
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"{\n",
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" vector<int>v;\n",
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" for (int i = 0; i < 10; i++)\n",
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" {\n",
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" v.push_back(i);\n",
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" }\n",
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"\n",
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" //查找 容器中 是否有 5 这个元素\n",
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" vector<int>::iterator it = find(v.begin(), v.end(), 5);\n",
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" if (it == v.end())\n",
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" {\n",
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" cout << \"没有找到！\" << endl;\n",
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" }\n",
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" else\n",
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" {\n",
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" cout << \"找到：\" << *it << endl;\n",
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" }\n",
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"}\n",
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"\n",
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"class Person \n",
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"{\n",
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"public:\n",
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" Person(string name, int age)\n",
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" {\n",
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" this->m_Name = name;\n",
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" this->m_Age = age;\n",
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" }\n",
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"\n",
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" //重载 == 让find底层知道如何对比person数据类型\n",
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" bool operator==(const Person& p)\n",
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" {\n",
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" if (this->m_Name == p.m_Name && this->m_Age == p.m_Age)\n",
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" {\n",
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" return true;\n",
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" }\n",
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" else\n",
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" {\n",
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" return false;\n",
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" }\n",
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" }\n",
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"\n",
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" string m_Name;\n",
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" int m_Age;\n",
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"};\n",
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"\n",
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"//查找 自定义数据类型\n",
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"void test02()\n",
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"{\n",
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" vector<Person>v;\n",
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" //创建数据\n",
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" Person p1(\"aaa\", 10);\n",
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" Person p2(\"bbb\", 20);\n",
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" Person p3(\"ccc\", 30);\n",
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" Person p4(\"ddd\", 40);\n",
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"\n",
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" //放入到容器中\n",
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" v.push_back(p1);\n",
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" v.push_back(p2);\n",
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" v.push_back(p3);\n",
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" v.push_back(p4);\n",
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"\n",
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" Person pp(\"bbb\",20);\n",
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"\n",
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" vector<Person>::iterator it = find(v.begin(), v.end(), pp); //find找里面有没有pp这个对象\n",
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" if (it == v.end())\n",
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" {\n",
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" cout << \"没有找到\" << endl;\n",
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" }\n",
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" else\n",
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" {\n",
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" cout << \"找到元素姓名：\" << it->m_Name << \" 年龄：\" << it->m_Age <<endl;\n",
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" }\n",
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"}\n",
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"\n",
|
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"int main() \n",
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"{\n",
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" test01();\n",
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"\n",
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" test02();\n",
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"\n",
|
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" system(\"pause\");\n",
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"\n",
|
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" return 0;\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"运行结果: \n",
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" - 找到：5\n",
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" - 找到元素姓名：bbb 年龄：20\n",
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" - 请按任意键继续. . ."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.2.3 find_if 查找算法"
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]
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},
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||
{
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"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
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"① 功能描述：按条件查找\n",
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"\n",
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"② 函数原型：find_if(iterator beg, iterator end, _Pred);\n",
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"\n",
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||
"1. 按值查找元素，找到返回指定位置迭代器，找不到返回结束迭代器位置。\n",
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"2. beg 开始迭代器\n",
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"3. end 结束迭代器\n",
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"4. _Pred 函数或者谓词 (返回bool类型的仿函数)"
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
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"#include<vector>\n",
|
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"#include<algorithm>\n",
|
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"#include<string>\n",
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"\n",
|
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"//1、查找 内置数据类型\n",
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"\n",
|
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"class GreaterFive\n",
|
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"{\n",
|
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"public:\n",
|
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" bool operator()(int val)\n",
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" {\n",
|
||
" return val > 5; //当val大于5时，返回真\n",
|
||
" }\n",
|
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"};\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
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" for (int i = 0; i < 10; i++)\n",
|
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" {\n",
|
||
" v.push_back(i);\n",
|
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" }\n",
|
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" vector<int>::iterator it = find_if(v.begin(), v.end(), GreaterFive());\n",
|
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"\n",
|
||
" if (it == v.end())\n",
|
||
" {\n",
|
||
" cout << \"没有找到\";\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" cout << \"找到大于5的数字为：\" << *it << endl;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"//查找 自定义数据类型\n",
|
||
"\n",
|
||
"class Person \n",
|
||
"{\n",
|
||
"public:\n",
|
||
" Person(string name, int age)\n",
|
||
" {\n",
|
||
" this->m_Name = name;\n",
|
||
" this->m_Age = age;\n",
|
||
" }\n",
|
||
" string m_Name;\n",
|
||
" int m_Age;\n",
|
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"};\n",
|
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"\n",
|
||
"class Greater20\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(Person& p)\n",
|
||
" {\n",
|
||
" return p.m_Age > 20;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test02()\n",
|
||
"{\n",
|
||
" vector<Person>v;\n",
|
||
"\n",
|
||
" //创建数据\n",
|
||
" Person p1(\"aaa\", 10);\n",
|
||
" Person p2(\"bbb\", 20);\n",
|
||
" Person p3(\"ccc\", 30);\n",
|
||
" Person p4(\"ddd\", 40);\n",
|
||
"\n",
|
||
" v.push_back(p1);\n",
|
||
" v.push_back(p2);\n",
|
||
" v.push_back(p3);\n",
|
||
" v.push_back(p4);\n",
|
||
"\n",
|
||
" //找年龄大于20的人\n",
|
||
" vector<Person>::iterator it = find_if(v.begin(), v.end(), Greater20());\n",
|
||
"\n",
|
||
" if (it == v.end())\n",
|
||
" {\n",
|
||
" cout << \"没有找到\" << endl;\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" cout << \"找到姓名：\" << it->m_Name << \" 年龄：\" << it->m_Age << endl;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" test02();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 找到大于5的数字为：6\n",
|
||
" - 找到姓名：ccc 年龄：30\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.2.4 adjacent_find 查找算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：查找相邻重复元素\n",
|
||
"\n",
|
||
"② 函数原型：adjacent_find(iterator beg, iterator end);\n",
|
||
"\n",
|
||
"1. 查找相邻重复元素，返回相邻元素的第一个位置的迭代器\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"\n",
|
||
"③ 面试题中如果出现查找相邻重复元素，记得用STL中的adjacent_find算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
||
" v.push_back(0);\n",
|
||
" v.push_back(2);\n",
|
||
" v.push_back(0);\n",
|
||
" v.push_back(3);\n",
|
||
" v.push_back(1);\n",
|
||
" v.push_back(4);\n",
|
||
" v.push_back(3); //相邻，3重复，adjacent_find()返回的是这个，第一个元素的迭代器\n",
|
||
" v.push_back(3); \n",
|
||
"\n",
|
||
" vector<int>::iterator pos = adjacent_find(v.begin(), v.end());\n",
|
||
"\n",
|
||
" if (pos == v.end())\n",
|
||
" {\n",
|
||
" cout << \"没有找到相邻重复元素\";\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" cout << \"找到相邻重复元素：\" << *pos << endl;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 找到相邻重复元素：3\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.2.5 binary_search 查找算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：查找指定元素是否存在。\n",
|
||
"\n",
|
||
"② 函数原型：bool binary_search(iterator beg, iterator end, value);\n",
|
||
"\n",
|
||
"1. 查找指定的元素，查到返回true，否则false\n",
|
||
"2. 注意：在无序序列中不可用\n",
|
||
"3. --beg 开始迭代器\n",
|
||
"4. --end 结束迭代器\n",
|
||
"5. --value 查找迭代器\n",
|
||
"\n",
|
||
"③ 二分法查找效率很高，值得注意的是查找的容器中元素必须是有序序列。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"\n",
|
||
"//常用查找算法 binary_search\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v.push_back(i);\n",
|
||
" }\n",
|
||
"\n",
|
||
" //查找容器中是否有9元素\n",
|
||
" //注意：容器必须是有序的序列，上面的vector是一个升序的序列，如果vector是一个无序的序列，那么有可能找到，有可能找不到\n",
|
||
" bool ret = binary_search(v.begin(), v.end(),9);\n",
|
||
"\n",
|
||
" if (ret)\n",
|
||
" {\n",
|
||
" cout << \"找到元素\" << endl;\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" cout << \"没有找到元素：\" << endl;\n",
|
||
" }\n",
|
||
"\n",
|
||
" vector<int>v2;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v2.push_back(i);\n",
|
||
" }\n",
|
||
" v2.push_back(2); //又插入了一个元素，vector变成无序序列了，那么结果就是未知的了\n",
|
||
" bool ret2 = binary_search(v2.begin(), v2.end(), 9);\n",
|
||
"\n",
|
||
" if (ret2)\n",
|
||
" {\n",
|
||
" cout << \"找到元素\";\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" cout << \"没有找到元素\" << endl;\n",
|
||
" }\n",
|
||
"}\n",
|
||
"\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果： \n",
|
||
" - 找到元素\n",
|
||
" - 没有找到元素\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.2.6 count 查找算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：统计元素个数。\n",
|
||
"\n",
|
||
"② 函数原型：count(iterator big, iterator end, value);\n",
|
||
"\n",
|
||
"1. 统计元素出现次数\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. value 统计的元素\n",
|
||
"\n",
|
||
"③ 统计自定义数据类型时候，需要配合重载operator=="
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"\n",
|
||
"//1、统计内置数据类型\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
||
" v.push_back(10);\n",
|
||
" v.push_back(40);\n",
|
||
" v.push_back(30);\n",
|
||
" v.push_back(40);\n",
|
||
" v.push_back(20);\n",
|
||
" v.push_back(40);\n",
|
||
"\n",
|
||
" int num = count(v.begin(), v.end(), 40);\n",
|
||
"\n",
|
||
" cout << \"40的元素个数为：\" << num << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"//2、统计自定义数据类型\n",
|
||
"\n",
|
||
"class Person\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" Person(string name, int age)\n",
|
||
" {\n",
|
||
" this->m_Name = name;\n",
|
||
" this->m_Age = age;\n",
|
||
" }\n",
|
||
"\n",
|
||
" // 重载==\n",
|
||
" bool operator==(const Person& p)\n",
|
||
" {\n",
|
||
" if (this->m_Age == p.m_Age)\n",
|
||
" {\n",
|
||
" return true;\n",
|
||
" }\n",
|
||
" else\n",
|
||
" {\n",
|
||
" return false;\n",
|
||
" }\n",
|
||
" }\n",
|
||
" string m_Name;\n",
|
||
" int m_Age;\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test02()\n",
|
||
"{\n",
|
||
" vector<Person>v;\n",
|
||
" Person p1(\"刘备\", 35);\n",
|
||
" Person p2(\"关羽\", 35);\n",
|
||
" Person p3(\"张飞\", 35);\n",
|
||
" Person p4(\"赵云\", 30);\n",
|
||
" Person p5(\"曹操\", 40);\n",
|
||
" \n",
|
||
" //将人员插入到容器中\n",
|
||
" v.push_back(p1);\n",
|
||
" v.push_back(p2);\n",
|
||
" v.push_back(p3);\n",
|
||
" v.push_back(p4);\n",
|
||
" v.push_back(p5);\n",
|
||
"\n",
|
||
" Person p(\"诸葛亮\", 35);\n",
|
||
"\n",
|
||
" int num = count(v.begin(), v.end(), p);\n",
|
||
"\n",
|
||
" cout << \"和诸葛亮同岁数的人有多少：\" << num << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" test02();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果： \n",
|
||
" - 40的元素个数为：3\n",
|
||
" - 和诸葛亮同岁数的人有多少：3\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.2.7 count_if 查找算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：按条件统计元素个数。\n",
|
||
"\n",
|
||
"② 函数原型：count_if(iterator beg, iterator end, _Pred)\n",
|
||
"\n",
|
||
"1. 按条件统计元素出现次数\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. _Pred 谓词"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"\n",
|
||
"//1、统计内置数据类型\n",
|
||
"\n",
|
||
"class Greater20\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(int val)\n",
|
||
" {\n",
|
||
" return val > 20;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
||
" v.push_back(10);\n",
|
||
" v.push_back(40);\n",
|
||
" v.push_back(30);\n",
|
||
" v.push_back(20);\n",
|
||
" v.push_back(40);\n",
|
||
"\n",
|
||
" int num = count_if(v.begin(), v.end(), Greater20());\n",
|
||
"\n",
|
||
" cout << \"在容器中大于20的元素个数为：\" << num << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"//2、统计自定义数据类型\n",
|
||
"\n",
|
||
"class Person\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" Person(string name, int age)\n",
|
||
" {\n",
|
||
" this->m_Name = name;\n",
|
||
" this->m_Age = age;\n",
|
||
" }\n",
|
||
" string m_Name;\n",
|
||
" int m_Age;\n",
|
||
"};\n",
|
||
"\n",
|
||
"class AgeGreater20\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(const Person &p)\n",
|
||
" {\n",
|
||
" return p.m_Age > 20;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test02()\n",
|
||
"{\n",
|
||
" vector<Person>v;\n",
|
||
" Person p1(\"刘备\", 35);\n",
|
||
" Person p2(\"关羽\", 35);\n",
|
||
" Person p3(\"张飞\", 35);\n",
|
||
" Person p4(\"赵云\", 40);\n",
|
||
" Person p5(\"曹操\", 20);\n",
|
||
" \n",
|
||
" //将人员插入到容器中\n",
|
||
" v.push_back(p1);\n",
|
||
" v.push_back(p2);\n",
|
||
" v.push_back(p3);\n",
|
||
" v.push_back(p4);\n",
|
||
" v.push_back(p5);\n",
|
||
"\n",
|
||
" //统计 大于20岁人员个数\n",
|
||
"\n",
|
||
" int num = count_if(v.begin(), v.end(), AgeGreater20());\n",
|
||
"\n",
|
||
" cout << \"大于20岁的人员个数为：\" << num << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" //test02();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果： \n",
|
||
" - 在容器中大于20的元素个数为：3\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1.3 常用排序算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.1 算法简介"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① sort //对容器内元素进行排序\n",
|
||
"\n",
|
||
"② random_shuffle //洗牌 指定范围内的元素随机调整次序\n",
|
||
"\n",
|
||
"③ merge //容器元素合并，并存储到另一容器中\n",
|
||
"\n",
|
||
"④ reverse //反转指定范围的元素"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.2 sort排序算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：对容器内元素进行排序\n",
|
||
"\n",
|
||
"② 函数原型：sort(iterator beg, iterator end, _Pred);\n",
|
||
"\n",
|
||
"1. 按值查找元素，找到返回指定位置迭代器，找不到返回结束位置迭代器位置\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. _Pred 谓词\n",
|
||
"\n",
|
||
"③ sort属于开发中最常用的算法之一，需熟练掌握。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"\n",
|
||
"//1、统计内置数据类型\n",
|
||
"\n",
|
||
"//普通函数\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v;\n",
|
||
" v.push_back(10);\n",
|
||
" v.push_back(30);\n",
|
||
" v.push_back(50);\n",
|
||
" v.push_back(20);\n",
|
||
" v.push_back(40);\n",
|
||
"\n",
|
||
" //利用sort升序\n",
|
||
" sort(v.begin(), v.end()); //默认是升序排列\n",
|
||
" for_each(v.begin(), v.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" //改变为降序\n",
|
||
" sort(v.begin(), v.end(), greater<int>());\n",
|
||
" for_each(v.begin(), v.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"//2、统计自定义数据类型\n",
|
||
"\n",
|
||
"class Person\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" Person(string name, int age)\n",
|
||
" {\n",
|
||
" this->m_Name = name;\n",
|
||
" this->m_Age = age;\n",
|
||
" }\n",
|
||
" string m_Name;\n",
|
||
" int m_Age;\n",
|
||
"};\n",
|
||
"\n",
|
||
"class AgeGreater20\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(const Person &p)\n",
|
||
" {\n",
|
||
" return p.m_Age > 20;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test02()\n",
|
||
"{\n",
|
||
" vector<Person>v;\n",
|
||
" Person p1(\"刘备\", 35);\n",
|
||
" Person p2(\"关羽\", 35);\n",
|
||
" Person p3(\"张飞\", 35);\n",
|
||
" Person p4(\"赵云\", 40);\n",
|
||
" Person p5(\"曹操\", 20);\n",
|
||
" \n",
|
||
" //将人员插入到容器中\n",
|
||
" v.push_back(p1);\n",
|
||
" v.push_back(p2);\n",
|
||
" v.push_back(p3);\n",
|
||
" v.push_back(p4);\n",
|
||
" v.push_back(p5);\n",
|
||
"\n",
|
||
" //统计 大于20岁人员个数\n",
|
||
" \n",
|
||
" int num = count_if(v.begin(), v.end(), AgeGreater20());\n",
|
||
"\n",
|
||
" cout << \"大于20岁的人员个数为：\" << num << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" //test02();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 10 20 30 40 50\n",
|
||
" - 50 40 30 20 10\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.3 random_shuffle排序算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：洗牌算法指定范围内的元素随机调整次序。\n",
|
||
"\n",
|
||
"② 函数原型：random_shuffle(iterator beg, iterator end);\n",
|
||
"\n",
|
||
"1. 指定范围内的元素随机调整次序\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"\n",
|
||
"③ random_shuffle洗牌算法比较实用，使用时记得加随机数种子。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"#include<ctime>\n",
|
||
"\n",
|
||
"class myPrint\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" void operator()(int val)\n",
|
||
" {\n",
|
||
" cout << val << \" \";\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" srand((unsigned int)time(NULL)); //生成一个随机数种子，利用时间生成，确保每次生成的随机数种子不同\n",
|
||
"\n",
|
||
" vector<int>v;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v.push_back(i);\n",
|
||
" }\n",
|
||
" //利用洗牌 算法 打乱顺序\n",
|
||
" random_shuffle(v.begin(), v.end());\n",
|
||
"\n",
|
||
" for_each(v.begin(), v.end(),myPrint());\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 1 5 7 9 4 2 6 8 3 0\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.4 merge排序算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：两个容器元素合并，并存储到另一容器中。\n",
|
||
"\n",
|
||
"② 函数原型：merge(iterator beg1, iterator end1, iterator beg2, iterator end2, iterator dest);\n",
|
||
"\n",
|
||
"1. 容器元素合并，并存储到另一容器中\n",
|
||
"2. 注意：两个容器必须是有序的\n",
|
||
"3. beg1 容器1开始迭代器\n",
|
||
"4. end2 容器1结束迭代器\n",
|
||
"5. beg2 容器2开始迭代器\n",
|
||
"6. end2 容器2结束迭代器\n",
|
||
"7. dest 目标容器开始迭代器\n",
|
||
"\n",
|
||
"③ merge合并后的容器也是有序的"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"#include<ctime>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" vector<int>v2;\n",
|
||
"\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i);\n",
|
||
" v2.push_back(i + 1);\n",
|
||
" }\n",
|
||
"\n",
|
||
" //目标容器\n",
|
||
" vector<int>vTarget;\n",
|
||
" vTarget.resize(v1.size() + v2.size()); //提前给目标容器分配空间\n",
|
||
" \n",
|
||
" merge(v1.begin(), v1.end(), v2.begin(), v2.end(), vTarget.begin());\n",
|
||
" for_each(vTarget.begin(), vTarget.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.3.5 reverse排序算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：将容器内元素进行反转\n",
|
||
"\n",
|
||
"② 函数原型：reverse(iterator beg, iterator end);\n",
|
||
"\n",
|
||
"1. 反转指定范围的元素\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"\n",
|
||
"③ reverse反转区间内的元素，面试题可能涉及到"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"#include<ctime>\n",
|
||
"\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" v1.push_back(10);\n",
|
||
" v1.push_back(30);\n",
|
||
" v1.push_back(50);\n",
|
||
" v1.push_back(20);\n",
|
||
" v1.push_back(40);\n",
|
||
"\n",
|
||
" cout << \"反转前：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" cout << \"反转后：\" << endl;\n",
|
||
" reverse(v1.begin(), v1.end());\n",
|
||
" for_each(v1.begin(), v1.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 反转前：\n",
|
||
" - 10 30 50 20 40\n",
|
||
" - 反转后：\n",
|
||
" - 40 20 50 30 10\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1.4 常用的拷贝和替换算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.4.1 算法简介"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 算法简介：\n",
|
||
"\n",
|
||
"1. copy //容器内指定范围的元素拷贝到另一容器中\n",
|
||
"2. replace //将容器内指定范围的旧元素修改为新元素\n",
|
||
"3. replace_if //容器内指定范围满足条件的元素替换为新元素\n",
|
||
"4. swap //互换两个容器的元素"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.4.2 copy算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：容器内指定范围的元素拷贝到另一容器中。\n",
|
||
"\n",
|
||
"② 函数原型：copy(iterator beg, iterator end, iterator dest);\n",
|
||
"\n",
|
||
"1. 按值查找元素，找到返回指定位置迭代器，找不到返回结束迭代器位置\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. dest 目标起始迭代器\n",
|
||
"\n",
|
||
"③ 利用copy算法在拷贝时，目标容器记得提前开辟空间。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i);\n",
|
||
" }\n",
|
||
" vector<int>v2;\n",
|
||
" v2.resize(v1.size());\n",
|
||
" copy(v1.begin(),v1.end(),v2.begin());\n",
|
||
"\n",
|
||
" for_each(v2.begin(), v2.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.4.3 replace算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：将容器内指定范围的旧元素修改为新元素。\n",
|
||
"\n",
|
||
"② 函数原型：repalece(iterator beg, iterator end, oldvalue, newvalue);\n",
|
||
"\n",
|
||
"1. 将区间内旧元素替换成新元素\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. oldvalue 旧元素\n",
|
||
"5. newvalue 新元素\n",
|
||
"\n",
|
||
"③ replace会替换区间内满足条件的元素。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"\n",
|
||
"class MyPrint\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" void operator()(int val)\n",
|
||
" {\n",
|
||
" cout << val << \" \";\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i);\n",
|
||
" }\n",
|
||
" v1.push_back(20);\n",
|
||
" v1.push_back(30);\n",
|
||
" v1.push_back(50);\n",
|
||
" v1.push_back(30);\n",
|
||
" v1.push_back(40);\n",
|
||
" v1.push_back(20);\n",
|
||
" v1.push_back(10);\n",
|
||
" v1.push_back(20);\n",
|
||
"\n",
|
||
" cout << \"替换前：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" //将容器里面所有的20替换成2000\n",
|
||
" replace(v1.begin(), v1.end(), 20, 20000);\n",
|
||
" cout << \"替换后：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 替换前：\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9 20 30 50 30 40 20 10 20\n",
|
||
" - 替换后：\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9 20000 30 50 30 40 20000 10 20000\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.4.4 replace_if算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：将区间内满足条件的元素，替换成指定元素。\n",
|
||
"\n",
|
||
"② 函数原型：replace_if(iterator beg, iterator end, _pred, newvalue);\n",
|
||
"\n",
|
||
"1. 按条件替换元素，满足条件的替换成指定元素\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. _pred 谓词\n",
|
||
"5. newvalue 替换的新元素\n",
|
||
"\n",
|
||
"③ replace_if 按条件查找，可以利用仿函数灵活筛选满足的条件。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"\n",
|
||
"class MyPrint\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" void operator()(int val)\n",
|
||
" {\n",
|
||
" cout << val << \" \";\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"class Greater30\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(int val)\n",
|
||
" {\n",
|
||
" return val >= 30;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" v1.push_back(20);\n",
|
||
" v1.push_back(30);\n",
|
||
" v1.push_back(50);\n",
|
||
" v1.push_back(30);\n",
|
||
" v1.push_back(40);\n",
|
||
" v1.push_back(20);\n",
|
||
" v1.push_back(10);\n",
|
||
" v1.push_back(20);\n",
|
||
"\n",
|
||
" cout << \"替换前：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" //将容器里面大于等于30的 替换成30000\n",
|
||
" replace_if(v1.begin(), v1.end(), Greater30(),30000);\n",
|
||
" cout << \"替换后：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 替换前：\n",
|
||
" - 20 30 50 30 40 20 10 20\n",
|
||
" - 替换后：\n",
|
||
" - 20 30000 30000 30000 30000 20 10 20\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.4.5 swap算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：互换两个容器的元素。\n",
|
||
"\n",
|
||
"② 函数原型：swap(container c1, container c2);\n",
|
||
"\n",
|
||
"1. 互换两个容器的元素\n",
|
||
"2. c1容器1\n",
|
||
"3. c2容器2\n",
|
||
"\n",
|
||
"③ swap交换容器时，注意交换的容器要同种类型。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<algorithm>\n",
|
||
"#include<string>\n",
|
||
"#include<functional>\n",
|
||
"\n",
|
||
"class MyPrint\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" void operator()(int val)\n",
|
||
" {\n",
|
||
" cout << val << \" \";\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"class Greater30\n",
|
||
"{\n",
|
||
"public:\n",
|
||
" bool operator()(int val)\n",
|
||
" {\n",
|
||
" return val >= 30;\n",
|
||
" }\n",
|
||
"};\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" vector<int>v2;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i);\n",
|
||
" v2.push_back(i + 100);\n",
|
||
" }\n",
|
||
"\n",
|
||
" cout << \"交换前：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
" for_each(v2.begin(), v2.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" swap(v1,v2);\n",
|
||
" cout << \"交换后：\" << endl;\n",
|
||
" for_each(v1.begin(), v1.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
" for_each(v2.begin(), v2.end(), MyPrint());\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 交换前：\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9\n",
|
||
" - 100 101 102 103 104 105 106 107 108 109\n",
|
||
" - 交换后：\n",
|
||
" - 100 101 102 103 104 105 106 107 108 109\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1.5 常用的算术生成算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.5.1 算法简介"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 算术生成算法属于小型算法，使用时包含的头文件为 #include $<numeric>$\n",
|
||
"\n",
|
||
"② 算法简介：\n",
|
||
"\n",
|
||
"1. accumulate //计算容器元素累积总和\n",
|
||
"2. fill //向容器中添加元素"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.5.2 accumulate生成算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 计算区间内 容器元素累加总和。\n",
|
||
"\n",
|
||
"② 函数原型：accumulate(iterator beg, iterator end, value);\n",
|
||
"\n",
|
||
"1. 计算容器元素累加总和\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. value 起始值\n",
|
||
"\n",
|
||
"③ accumulate使用时，头文件注意是numeric，这个算法很实用。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<numeric>\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" for (int i = 0; i <= 100; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i);\n",
|
||
" }\n",
|
||
"\n",
|
||
" int total1 = accumulate(v1.begin(), v1.end(), 0); //起始累加值为0\n",
|
||
" int total2 = accumulate(v1.begin(), v1.end(), 10000); //5050加上10000\n",
|
||
" cout << \"total1 = \" << total1 << endl;\n",
|
||
" cout << \"total2 = \" << total2 << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - total1 = 5050\n",
|
||
" - total2 = 15050\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.5.3 fill生成算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：向容器中填充指定的元素。\n",
|
||
"\n",
|
||
"② 函数原型：fill(iterator beg, iterator end, value);\n",
|
||
"\n",
|
||
"1. 向容器中填充元素\n",
|
||
"2. beg 开始迭代器\n",
|
||
"3. end 结束迭代器\n",
|
||
"4. value 填充的值\n",
|
||
"\n",
|
||
"③ 利用fill可以将容器区间内元素填充为指定值。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<numeric>\n",
|
||
"#include<algorithm>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" v1.resize(10);\n",
|
||
" for_each(v1.begin(), v1.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
" \n",
|
||
" //后期重新填充\n",
|
||
" fill(v1.begin(), v1.end(), 100);\n",
|
||
" for_each(v1.begin(), v1.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 0 0 0 0 0 0 0 0 0 0\n",
|
||
" - 100 100 100 100 100 100 100 100 100 100\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1.6 常用集合算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.6.1 算法简介"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① set_itersection //求两个容器的交集\n",
|
||
"\n",
|
||
"② set_union //求两个容器的并集\n",
|
||
"\n",
|
||
"③ set_difference // 求两个容器的差集"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.6.2 set_intersection算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：求两个容器的交集。\n",
|
||
"\n",
|
||
"② 函数原型：set_intersection(iterator beg1, iterator end1, iterator beg2, iterator end2, iterator dest);\n",
|
||
"\n",
|
||
"1. 求两个集合的交集\n",
|
||
"2. 注意：两个集合必须是有序序列\n",
|
||
"3. beg1 容器1 开始迭代器\n",
|
||
"4. end1 容器1 结束迭代器\n",
|
||
"5. beg2 容器2 开始迭代器\n",
|
||
"6. end2 容器2 结束迭代器\n",
|
||
"7. dest 目标容器开始迭代器\n",
|
||
"\n",
|
||
"③ 求交集的两个集合必须都是有序序列。\n",
|
||
"\n",
|
||
"④ 目标容器开辟空间需要从两个容器中取小值。\n",
|
||
"\n",
|
||
"⑤ set_intersection返回值是交集中最后一个元素的位置。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<numeric>\n",
|
||
"#include<algorithm>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" vector<int>v2;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i); //0~9\n",
|
||
" v2.push_back(i + 5); //5~14\n",
|
||
" }\n",
|
||
" vector<int>vTarget;\n",
|
||
" //目标容器需要提前开辟空间\n",
|
||
" //最特殊情况 大容器包含小容器 开辟空间 取小容器的size即可\n",
|
||
" vTarget.resize(min(v1.size(), v2.size()));\n",
|
||
"\n",
|
||
" //获取交集\n",
|
||
" vector<int>::iterator itEnd = set_intersection(v1.begin(), v1.end(), v2.begin(), v2.end(), vTarget.begin());\n",
|
||
" for_each(vTarget.begin(), vTarget.end(), myPrint); //取交集的元素少于开辟的容量，空的部分用0补充了\n",
|
||
" cout << endl;\n",
|
||
" for_each(vTarget.begin(), itEnd, myPrint); //itEnd为取交集的最后一个元素的迭代器进行返回\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() {\n",
|
||
"\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果： \n",
|
||
" - 5 6 7 8 9 0 0 0 0 0\n",
|
||
" - 5 6 7 8 9\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.6.3 set_union集合算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：求两个集合的并集。\n",
|
||
"\n",
|
||
"② 函数原型：set_union(iterator beg1, iterator end1, iterator beg2, iterator end2, iterator dest);\n",
|
||
"\n",
|
||
"1. 求两个集合的并集。\n",
|
||
"2. 注意：两个集合必须是有序序列。\n",
|
||
"3. beg1 容器1 开始迭代器\n",
|
||
"4. end1 容器1 结束迭代器\n",
|
||
"5. beg2 容器2 开始迭代器\n",
|
||
"6. end2 容器2 结束迭代器\n",
|
||
"7. dest 目标容器开始迭代器\n",
|
||
"\n",
|
||
"③ 求并集的两个集合必须得是有序序列。\n",
|
||
"\n",
|
||
"④ 目标容器开辟空间需要两个容器相加。\n",
|
||
"\n",
|
||
"⑤ set_union返回值是并集中最后一个元素的为真。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<numeric>\n",
|
||
"#include<algorithm>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" vector<int>v2;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i); //0~9\n",
|
||
" v2.push_back(i + 5); //5~14\n",
|
||
" }\n",
|
||
" vector<int>vTarget;\n",
|
||
" //目标容器需要提前开辟空间\n",
|
||
" //最特殊情况 两个容器没有交集，并集就是两个容器size相加\n",
|
||
" vTarget.resize(v1.size()+v2.size());\n",
|
||
"\n",
|
||
" //获取交集\n",
|
||
" vector<int>::iterator itEnd = set_union(v1.begin(), v1.end(), v2.begin(), v2.end(), vTarget.begin());\n",
|
||
" for_each(vTarget.begin(), vTarget.end(), myPrint); //用最特殊的情况开辟的内存，由于一共有20个数，开辟了20个内存，并集后多余的位置为0\n",
|
||
" cout << endl;\n",
|
||
" for_each(vTarget.begin(), itEnd, myPrint); //itEnd为取并集后返回的最后一个元素的迭代器进行返回\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 0 0 0 0\n",
|
||
" - 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 1.6.4 set_difference集合算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"① 功能描述：求两个集合的差集\n",
|
||
"\n",
|
||
"② 函数原型：set_difference(iterator beg1, iterator end1, iterator beg2, iterator end2, iterator dest);\n",
|
||
"\n",
|
||
"1. 求两个集合的差集\n",
|
||
"2. 注意：两个集合必须是有序序列\n",
|
||
"3. beg1 容器1 开始迭代器\n",
|
||
"4. end1 容器1 结束迭代器\n",
|
||
"5. beg2 容器2 开始迭代器\n",
|
||
"6. end2 容器2 结束迭代器\n",
|
||
"7. dest 目标容器开始迭代器\n",
|
||
"\n",
|
||
"③ 当集合前后顺序不同时，集合的差集不一样，如下图所示，V1与V2的差集、V2与V1的差集，是不一样的。"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {
|
||
"image.png": {
|
||
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"
|
||
}
|
||
},
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"④ 求差集的两个集合必须是有序序列。\n",
|
||
"\n",
|
||
"⑤ 目标容器的开辟空间需要从两个容器取较大值。\n",
|
||
"\n",
|
||
"⑥ set_difference返回值是差集中最后一个元素的位置。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#include<iostream>\n",
|
||
"using namespace std;\n",
|
||
"#include<vector>\n",
|
||
"#include<numeric>\n",
|
||
"#include<algorithm>\n",
|
||
"\n",
|
||
"void myPrint(int val)\n",
|
||
"{\n",
|
||
" cout << val << \" \";\n",
|
||
"}\n",
|
||
"\n",
|
||
"void test01()\n",
|
||
"{\n",
|
||
" vector<int>v1;\n",
|
||
" vector<int>v2;\n",
|
||
" for (int i = 0; i < 10; i++)\n",
|
||
" {\n",
|
||
" v1.push_back(i); //0~9\n",
|
||
" v2.push_back(i + 5); //5~14\n",
|
||
" }\n",
|
||
" vector<int>vTarget;\n",
|
||
" //目标容器需要提前开辟空间\n",
|
||
" //最特殊情况 两个容器没有交集，取两个容器中大的size作为目标容器开辟空间\n",
|
||
" vTarget.resize(max(v1.size(),v2.size()));\n",
|
||
"\n",
|
||
" cout << \"v1和v2的差集为：\" << endl;\n",
|
||
" vector<int>::iterator itEnd = set_difference(v1.begin(), v1.end(), v2.begin(), v2.end(), vTarget.begin());\n",
|
||
" for_each(vTarget.begin(), vTarget.end(), myPrint); \n",
|
||
" cout << endl;\n",
|
||
" for_each(vTarget.begin(), itEnd, myPrint); \n",
|
||
" cout << endl;\n",
|
||
"\n",
|
||
" cout << \"v2和v1的差集为：\" << endl;\n",
|
||
" itEnd = set_difference(v2.begin(), v2.end(), v1.begin(), v1.end(), vTarget.begin());\n",
|
||
" for_each(vTarget.begin(), vTarget.end(), myPrint);\n",
|
||
" cout << endl;\n",
|
||
" for_each(vTarget.begin(), itEnd, myPrint);\n",
|
||
" cout << endl;\n",
|
||
"}\n",
|
||
"\n",
|
||
"int main() \n",
|
||
"{\n",
|
||
" test01();\n",
|
||
"\n",
|
||
" system(\"pause\");\n",
|
||
"\n",
|
||
" return 0;\n",
|
||
"}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"运行结果：\n",
|
||
" - v1和v2的差集为：\n",
|
||
" - 0 1 2 3 4 0 0 0 0 0\n",
|
||
" - 0 1 2 3 4\n",
|
||
" - v2和v1的差集为：\n",
|
||
" - 10 11 12 13 14 0 0 0 0 0\n",
|
||
" - 10 11 12 13 14\n",
|
||
" - 请按任意键继续. . ."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.6.3",
|
||
"language": "python",
|
||
"name": "python3.6.3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.3"
|
||
},
|
||
"toc": {
|
||
"base_numbering": 1,
|
||
"nav_menu": {},
|
||
"number_sections": false,
|
||
"sideBar": true,
|
||
"skip_h1_title": false,
|
||
"title_cell": "Table of Contents",
|
||
"title_sidebar": "Contents",
|
||
"toc_cell": false,
|
||
"toc_position": {
|
||
"height": "calc(100% - 180px)",
|
||
"left": "10px",
|
||
"top": "150px",
|
||
"width": "320px"
|
||
},
|
||
"toc_section_display": true,
|
||
"toc_window_display": true
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|