我是C ++新手，我的问题是我只是通过调用.size（）以某种方式（貌似）丢失了向量元素。对于以下代码段（下面附带完整代码），我得到以下输出：
A: number of elements: 560
B: number of elements: 560
B: number of elements: 0
B: number of elements: 0
B: number of elements: 0
C: number of elements: 0
//问题中的分段
cout << "A: number of elements: " << combinations.size() << endl;
float sum_vect[120][2];
for (int i = 0; i < combinations.size(); ++i) {
for (int j = 0; j < 2; ++j) {
sum_vect[i][j] = 0;
}
}
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
for (int i = 0; i < combinations.size(); ++i) {
combination = combinations.at(i);
for (int j = 0; j < order; ++j) {
sum_vect[i][0] += (float)virtual_pos[combination[j]][0];
sum_vect[i][1] += (float)virtual_pos[combination[j]][1];
}
}
vector<int> optimal_ind;
cout << "C: number of elements: " << combinations.size() << endl;
// AUXILIARY FUNCTIONS
void nchoosek_helper(int offset, int n, int k, vector<vector<int>> &combinations, vector<int> combination) {
if (k == 0) {
combinations.push_back(combination);
return;
}
for (int i = offset; i <= n - k; ++i) {
combination.push_back(i);
nchoosek_helper(i+1, n, k-1, combinations, combination);
combination.pop_back();
}
}
double euclidean_norm(double dist1, double dist2){
return sqrt(pow(dist1,2) + pow(dist2,2));
}
//问题中的功能在这里开始
//weirdest: look at A B C
vector<vector<char> > step37::CUDADriver::get_access_pattern(int order){
vector<vector<char> > result;
vector<vector<int> > combinations;
vector<int> combination;
nchoosek_helper(0, 16 ,order, combinations, combination);
cout << "number of combinations: " << combination.size() << endl;
// //mapping lexical index 1-16 to 2D array
int virtual_pos [16][2];
for (int i = 0; i < 16; ++i) {
virtual_pos[i][0] = i%4 * order; //write x
virtual_pos[i][1] = (int)ceil(i/4) * order; //write y
cout << "mapping " << i << "to (" << i%4 * order << "'" << (int)ceil(i/4) * order<< ")" << endl;
}
cout << "A: number of elements: " << combinations.size() << endl;
float sum_vect[120][2];
for (int i = 0; i < combinations.size(); ++i) {
for (int j = 0; j < 2; ++j) {
sum_vect[i][j] = 0;
}
}
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
cout << "B: number of elements: " << combinations.size() << endl;
for (int i = 0; i < combinations.size(); ++i) {
combination = combinations.at(i);
for (int j = 0; j < order; ++j) {
sum_vect[i][0] += (float)virtual_pos[combination[j]][0];
sum_vect[i][1] += (float)virtual_pos[combination[j]][1];
}
}
vector<int> optimal_ind;
cout << "C: number of elements: " << combinations.size() << endl;
cout << "main loop"<< endl;
for (int i = order; i < order*2; ++i) {
for (int j = order; j < order*2; ++j) {
int pos [2];
// pos[0] = i;
// pos[1] = j;
pos[0] = j;
pos[1] = i;
cout << "current position: (" << j << "," << i << ")" << endl;
float min_len = std::numeric_limits<float>::infinity(); //minimum length of combined vector
float min_sum_ind = std::numeric_limits<float>::infinity(); //minimum sum of individual vectors
int min_idx = -1;
for (int k = 0; k < combinations.size(); ++k) {
int curr_vect [2];
curr_vect[0] = sum_vect[k][0] - pos[0] * order;
curr_vect[1] = sum_vect[k][1] - pos[1] * order;
float curr_len = euclidean_norm(curr_vect[0], curr_vect[1]);
float min_sum_tmp = 0;
combination = combinations[k];
for (int l = 0; l < order; ++l) {
min_sum_tmp += euclidean_norm(virtual_pos[combination.at(l)][0] - pos[0],
virtual_pos[combination.at(l)][1]- pos[1]);
}
if (i==4&&j==4){
cout << " ind sum: " << min_sum_tmp << " len: " << curr_len << " sv: (" << sum_vect[k][0] << "," << sum_vect[k][1] <<
") cv: (" << curr_vect[0] << "," << curr_vect[1] << ")" <<endl;
}
if (min_len > curr_len ||
min_len == curr_len && min_sum_tmp < min_sum_ind){
min_len = curr_len;
min_idx = k;
min_sum_ind = min_sum_tmp;
}
}
// cout <<
cout << "pushing minimal idx " << min_idx << endl;
optimal_ind.push_back(min_idx);
}
}
cout << "main loop done"<< endl;
//unpack optimal combinations into relative movements
vector<char> optimal_x((int)pow(order,3));
vector<char> optimal_y((int)pow(order,3));
cout << "number of elements: " << combinations.size() << endl;
for (int i = 0; i <(int)pow(order,2); ++i) {
cout << "optimal idx: " << optimal_ind.at(i) << endl;
combination = combinations.at(optimal_ind.at(i));
for (int j = 0; j < order; ++j) {
int lex_idx = combination.at(j); //some index between 0 and 15 from 4x4 grid
//mvt range in grid relative to thread position: -1 to +
int relative_x = -1 + lex_idx % 4;
int relative_y = -1 + (int) floor(lex_idx / 4);
optimal_x[i * order + j] = relative_x;
optimal_y[i * order + j] = relative_y;
}
}
//DEBUG print
for (int i = 0; i < (int)pow(order,2); ++i) {
combination = combinations.at(optimal_ind.at(i));
cout << "combination: " << i << " ";
for (int j = 0; j < order; ++j) {
cout << combination.at(j) << " ";
}
cout << endl;
}
result.push_back(optimal_x);
result.push_back(optimal_y);
for (int i = 0; i < optimal_x.size(); ++i) {
cout << (int)optimal_x.at(i) << " " << endl;
}
cout << "optimal sizes: " << optimal_x.size() << endl;
cout << "optimal sizes: " << optimal_y.size() << endl;
cout << "result size: " << result.size() << endl;
return result;
}
我真的不明白像.size（）之类的函数如何更改矢量对象（或者它可能只是巧合地同时发生）。应用程序在单线程中运行，但我想无论如何，只要相关的所有内容都包含在函数的范围内，这都无关紧要。显然，当我实际尝试访问组合的某些元素（std :: out_of_range）时，代码会在稍后停止工作。考虑到错误的严重性，我想我必须错过一些非常基本的东西。令人不安的是，如果我使用2作为get_access_pattern（）的参数，一切正常。以上所有内容（使用3和4测试）都会导致此错误。
答案 0 :(得分：3)
您有缓冲区溢出。你的输出：
A: number of elements: 560
您的代码：
cout << "A: number of elements: " << combinations.size() << endl;
float sum_vect[120][2];
for (int i = 0; i < combinations.size(); ++i) {
for (int j = 0; j < 2; ++j) {
sum_vect[i][j] = 0;
}
}
看看＆＃34;我＆＃34;变量在该循环中达到120时。您正在访问sum_vect [120] [j]，这是超出界限的。
当发生缓冲区溢出时，程序将显示未定义的行为。