稀疏矩阵的存储，及矩阵的转置

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include“Matrix”

#pragma once#include<iostream>#include<vector>using namespace std;template<class T>struct  Triple{    T _value;    size_t _row;    size_t _col;};template<class T>class SparseMatrix{public:    SparseMatrix(T matrix[6][5] = NULL , size_t Row = 0, size_t Col = 0, const T& invalue = T())        :_Row(Row)        , _Col(Col)    {         for (int i = 0; i < Row; i++)        {            for (int j = 0; j < Col; j++)            {                if (matrix[i][j] != invalue)                {                     Triple<T> t;                    t._row = i;                    t._col = j;                    t._value = matrix[i][j];                    v.push_back(t);                }            }        }    }    void Display()    {        int index = 0;        for (int i = 0; i < _Row; i++)        {            for (int j = 0; j < _Col; j++)            {                if (index < v.size() && v[index]._row == i && v[index]._col == j)                {                    cout << v[index]._value << " ";                    index++;                }                else                {                    cout << "0" << " ";                }            }            cout << endl;        }        cout << endl;    }    SparseMatrix<T> Trasport()    {        SparseMatrix<T> tmp;        tmp._Row = _Col;        tmp._Col = _Row;        for (int i = 0; i < _Col; i++)        {            int index = 0;            while (index < v.size())            {                if (v[index]._col == i)                {                    Triple<T> t;                    t._row = v[index]._col;                    t._col = v[index]._row;                    t._value = v[index]._value;                    tmp.v.push_back(t);                }                index++;            }        }        return tmp;    }    //快速转置    SparseMatrix<T> FastTranspost()    {        SparseMatrix<T> tmp;        tmp._Row = _Col;        tmp._Col = _Row;        int* RowCount = new T[_Col];// 统计转置后矩阵每一行的数据个数        int* RowStart = new T[_Col];// 统计转置后的矩阵每行在压缩矩阵中存储的开始位置        memset(RowCount, 0, sizeof(int)*_Col);//初始化RowCount数组        memset(RowStart, 0, sizeof(int)*_Col);//初始化RowStart数组        // 统计转置后矩阵每一行的数据个数 RowCounts = {2, 0, 2, 0, 2};        int index = 0;        while (index < v.size())        {            RowCount[v[index]._col]++;            index++;        }        //统计转置后的矩阵每行在压缩矩阵中存储的开始位置 RowStart ={0, 2, 2, 4, 4};        RowStart[0] = 0;        for (int i = 1; i < _Col;i++)        {            RowStart[i] = RowCount[i - 1] + RowStart[i - 1];        }        // 建立转置以后的压缩三元组数组        index = 0;        while (index < v.size())        {            tmp.v.push_back(Triple<T>());            index++;        }        // 根据计算好每一行的起始位置进行转置        index = 0;        while (index < v.size())        {            size_t FtpIndex = RowStart[v[index]._col]++;            tmp.v[FtpIndex]._row = v[index]._col;            tmp.v[FtpIndex]._col = v[index]._row;            tmp.v[FtpIndex]._value = v[index]._value;            index++;        }        delete[] RowCount;        delete[] RowStart;        return tmp;    }private:    vector<Triple<T>> v;    size_t _Row;    size_t _Col;};

main.cpp

#include"Matrix.h"const int ROW = 6;const int COL = 5;void TestMatrix(){    int matrix[ROW][COL] =    { { 1, 0, 3, 0, 5 },      { 0, 0, 0, 0, 0 },      { 0, 0, 0, 0, 0 },      { 1, 0, 3, 0, 5 },      { 0, 0, 0, 0, 0 },      { 0, 0, 0, 0, 0 } };    SparseMatrix<int> sm(matrix,ROW,COL,0);    sm.Display();    SparseMatrix<int> smtp = sm.Trasport();    smtp.Display();    SparseMatrix<int> smftp = sm.FastTranspost();    smftp.Display();}int main(){    TestMatrix();    getchar();    return 0;}

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