1012 The Best Rank (25分)

本文介绍了一个学生排名系统的设计与实现,该系统针对计算机科学专业一年级学生的成绩进行评估,通过比较四类排名(C语言编程、数学、英语及平均成绩),确定每位学生的最佳排名,并采用优先级排序确保准确性。

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To evaluate the performance of our first year CS majored students, we consider their grades of three courses only: C - C Programming Language, M - Mathematics (Calculus or Linear Algrbra), and E - English. At the mean time, we encourage students by emphasizing on their best ranks – that is, among the four ranks with respect to the three courses and the average grade, we print the best rank for each student.
For example, The grades of C, M, E and A - Average of 4 students are given as the following:
StudentID C M E A
310101  98 85 88 90
310102  70 95 88 84
310103  82 87 94 88
310104  91 91 91 91
Then the best ranks for all the students are No.1 since the 1st one has done the best in C Programming Language, while the 2nd one in Mathematics, the 3rd one in English, and the last one in average.

Input Specification:

Each input file contains one test case. Each case starts with a line containing 2 numbers N and M (≤2000), which are the total number of students, and the number of students who would check their ranks, respectively. Then N lines follow, each contains a student ID which is a string of 6 digits, followed by the three integer grades (in the range of [0, 100]) of that student in the order of C, M and E. Then there are M lines, each containing a student ID.

Output Specification:

For each of the M students, print in one line the best rank for him/her, and the symbol of the corresponding rank, separated by a space.
The priorities of the ranking methods are ordered as A > C > M > E. Hence if there are two or more ways for a student to obtain the same best rank, output the one with the highest priority.
If a student is not on the grading list, simply output N/A.

按照不同的分数进行排序,分为四个序列,用map实现由id到rank的映射。对于四个分数的排序,不需要写四个cmp函数,只需要在cmp函数中用一个全局变量来控制按哪个分数排序即可。

#include<iostream>
#include<vector>
#include<unordered_map>
#include<algorithm>
using namespace std;
struct stu {
	string id;
	int score[4] = { 0 };
};
vector<stu>rec;//借助vector实现排序
unordered_map<string, int>refl[4];
char tag[4] = { 'A','C','M','E' };
int choice;
bool cmp(stu s1, stu s2) {
	return s1.score[choice] > s2.score[choice];
}
int main() {
	int n, m;
	cin >> n >> m;
	for (int i = 0; i < n; i++) {
		stu temp;
		cin >> temp.id >> temp.score[1] >> temp.score[2] >> temp.score[3];
		temp.score[0] = (temp.score[1] + temp.score[2] + temp.score[3]) / 3;
		rec.push_back(temp);
	}
	for (int i = 0; i < 4; i++) {
		choice = i;
		sort(rec.begin(), rec.end(), cmp);
		int preS = rec[0].score[i], rank, preR = 1;
		refl[i][rec[0].id] = 1;
		for (int j = 1; j < rec.size(); j++) {
			if (rec[j].score[i] == preS)rank = preR;
			else {
				rank = j + 1;
				preS = rec[j].score[i];
				preR = rank;
			}
			refl[i][rec[j].id] = rank;
		}
	}
	for (int i = 0; i < m; i++) {
		string id;
		cin >> id;
		if (refl[0].count(id) == 0) {
			cout << "N/A" << endl;
			continue;
		}
		int minR = refl[0][id], pos = 0;
		for (int j = 1; j < 4; j++) {
			if (refl[j][id] < minR) {
				pos = j;
				minR = refl[j][id];
			}
		}
		cout << refl[pos][id] << " " << tag[pos] << endl;
	}
	return 0;
}
下面这个代码报错了,应该怎么改: %%Matlab Genetic Algorithm for Sin Prediction clear; clc; %population size Npop=50; %create the population Pop=rand(Npop,1)*2*pi; %define fitness fit=@(x) sin(x); %fitness score score=fit(Pop); %maximum number of generations maxgen=100; %weights w=0.7; %probability p_crossover=0.9; p_mutation=0.2; %loop for number of generations for gen=1:maxgen %ranking %rank the population in descending order [~,rank]=sort(score); %rank the population in ascending order rank=flipud(rank); %normalised rank NormalisedRank=rank/sum(rank); %selection %cumulative sum of the normalised rank cumulativeSum=cumsum(NormalisedRank); %randomly select the two parents %from the populations based on their %normalised rank randnum=rand; parent1=find(cumulativeSum>randnum,1); randnum=rand; parent2=find(cumulativeSum>randnum,1); %crossover %randomly select the crossover point pc=randi([1 Npop-1]); %create the offsprings offspring1=[Pop(parent1,1:pc) Pop(parent2,pc+1:end)]; offspring2=[Pop(parent2,1:pc) Pop(parent1,pc+1:end)]; %perform crossover with a probability if(rand<p_crossover) Pop=[Pop; offspring1; offspring2]; end %mutation %randomly select the point of mutation pm=randi([1 Npop]); %mutate the value under the chosen point Pop(pm)=rand*2*pi; %perform mutation with a probability if (rand<p_mutation) Pop(pm)=rand*2*pi; end %evaluate new population score=fit(Pop); %elitism %sort the population in ascending order %of their fitness score [score,rank]=sort(score); elite=Pop(rank(1),:); Pop(rank(Npop),:)=elite; %replace old population Pop=Pop(1:Npop,:); end %print the best solution disp('Best Solution: '); disp(elite);
02-06
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