稀疏矩阵的压缩存储

#include <vector>#include <iostream>using namespace std;template<class T>class SparseMatrix{public:// 稀疏矩阵的压缩存储SparseMatrix(int* array, size_t row, size_t col, const T& invalid): _row(row), _col(col), _invalid(invalid){for (size_t i = 0; i < row; ++i){for (size_t j = 0; j < col; ++j){if (array[i*col+j] != invalid){Trituple<T> a(i, j, array[i*col + j]);_sm.push_back(a);}}}}SparseMatrix(){}// 访问稀疏矩阵中row行col中的元素T& Access(int row, int col){////遍历数组两种方法////1.for循环////2.迭代器//vector<Trituple<T>>::iterator it = _sm.begin();//while (it != _sm.end())//{//if (it->_row == row&&it->_col == col)//{//return it->_data;//}//it++;//}//return _invalid;for (size_t i = 0; i < _sm.size(); ++i){if (_sm[i]._row == row&&_sm[i]._col == col){return _sm[i]._data;}return _invalid;}}// 还原稀疏矩阵template<class T>friend ostream& operator<<(ostream& _cout, SparseMatrix<T>& s)//要申明为友元函数{int index = 0;for (size_t i = 0; i < s._row; ++i){for (size_t j = 0; j < s._col; ++j){if (index<s._sm.size() && i == s._sm[index]._row&&j == s._sm[index]._col){_cout << s._sm[index++]._data << " ";}else{_cout << s._invalid << " ";}}_cout << endl;}return _cout;}// 实现稀疏矩阵的逆置，并给出时间复杂度//直接存储是有问题的，打印是按照行优先打印存储也要行优先存储SparseMatrix<T> Transprot(){SparseMatrix<T> sm;//定义一个逆转后的结构体sm._col = _row;sm._row = _col;sm._invalid = _invalid;    /*使用迭代器*///for (int i = 0; i < _col; ++i)//{//vector<Trituple<T>>::iterator it = _sm.begin();//while (it != _sm.end())//{//if (i == it->_col)//{//sm._sm.push_back(Trituple<T>(it->_col, it->_row, it->_data));//}//it++;//}//}//return sm;//时间复杂度O（M*N）for (size_t i = 0; i < _col; ++i){size_t index = 0;while (index < _sm.size()){if (_sm[index]._col == i){//三元组tmp存入满足下表i的元素Trituple<T> tmp(_sm[index]._col, _sm[index]._row, _sm[index]._data);sm._sm.push_back(tmp);}++index;//此处要++index而不是index++}}return sm;}// 实现稀疏矩阵的快速逆置，并给出时间复杂度SparseMatrix<T> FastTransprot(){SparseMatrix<T> sm;//定义一个逆转后的结构体sm._col = _row;sm._row = _col;sm._invalid = _invalid;sm._sm.reserve(_sm.size());//开辟有效元素个空间//1、统计原矩阵中每一列有多少个有效元素int* pCount = new int[_col];memset(pCount, 0, _col*sizeof(pCount[0]));for (int i = 1; i < _sm.size(); ++i){pCount[_sm[i]._col]++;}//2、原矩阵的每一列在新矩阵中的起始位置int* pAddr = new int[_col];memset(pAddr, 0, _col*sizeof(pAddr[0]));for (int j = 1; j < _col; ++j){pAddr[j] = pAddr[j - 1] + pCount[j - 1];}//3、放置元素到新的空间for (int i = 0; i < _sm.size(); ++i){//Trituple<T> t(_sm[i]._col, _sm[i]._row, _sm[i]._data);//sm._sm[pAddr[_sm[i]._col]] = t;//pAddr[_sm[i]._col] ++;int & addr = pAddr[_sm[i]._col];sm._sm[addr] = Trituple<T>(_sm[i]._col, _sm[i]._row, _sm[i]._data);addr++;}return sm;}//// 实现稀疏矩阵的加法操作//SparseMatrix<T> operator+(const SparseMatrix<T>& sp);//三元组template<class T>struct Trituple{Trituple(size_t row, size_t col, const T& data): _row(row), _col(col), _data(data){}Trituple()//无参数构造函数{}size_t _row;size_t _col;T _data;};private:vector<Trituple<T>> _sm;size_t _row;size_t _col;T _invalid;};void TestSparseMatrix(){int array[6][5] ={ { 1, 0, 3, 0, 5 },    { 0, 0, 0, 0, 0 },    { 0, 0, 0, 0, 0 },    { 2, 0, 4, 0, 6 },    { 0, 0, 0, 0, 0 },    { 0, 0, 0, 0, 0 } };SparseMatrix<int> sp((int*)array, sizeof(array) / sizeof(array[0]), sizeof(array[0]) / sizeof(array[0][0]), 0);cout << sp << endl;cout << endl;SparseMatrix<int> sp1 = sp.Transprot();cout << sp1 << endl;SparseMatrix<int> sp2 = sp.FastTransprot();cout << sp2 << endl;}int main(){TestSparseMatrix();system("pause");return 0;}