Transpose of sparse matrices

Sparse matrix: M*n Matrix, the number of valid values in the matrix is much smaller than the number of invalid values, and the distribution of these data is not regular

As shown in the following:

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In general, we will think as long as the exchange of the corresponding row and column, but this practice is a waste of time and space, so we can use ternary group storage, compressed storage of very few valid data, using {Row,col,value} ternary group to store each valid data, ternary group according to the original matrix position, Store in order of priority.

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#define  _CRT_SECURE_NO_WARNINGS 1#pragma once#include<vector> #include <iostream> using namespace std;template<class t>struct triple  //Define ternary Group {int _row; int _col; t  _value; Triple (Int row, int col, t& value): _row (Row),  _col (col),  _value (value) { }triple (): _row (0),  _col (0),  _value (0) {}};template<class t>class sparsematrix{public: Sparsematrix (T* a, int m, int n, const t& invalid)//invalid is an illegal value: _ Rowsize (M),  _colsize (n),  _invaild (invalid) {for  (int i = 0; i <  m; ++i) {for  (int j = 0; j < n; j++) {if  (a[i*n  + j] != invalid) {triple<t> tmp (i, j, a[i*n + j]); _a.push_back ( TMP);}}} Sparsematrix (size_t rowsize, size_t colsize, t invaild): _rowsize(rowsize), _colsize (colsize), _invaild (invaild) {}void display (t* a, int m, int n,  const t& invalid)   //Print sparse matrix {int p = 0;for  (int i =  0; i < m; ++i) {for  (int j = 0; j < n;  J + +) {if  (p < _a.size ()  && _a[p]._row == i&&_a[p]._col &NBSP;==&NBSP;J) {cout << _a[p]._value <<  " ";p + +;} else{cout << invalid <<  " ";}} Cout << endl;}} Sparsematrix<t> transport ()   //reversal matrix { //Be sure to keep line precedence sparsematrix<t> sm (_colsize ,  _rowsize, _invaild);for  (size_t i = 0; i < _colsize; i+ +) {size_t index = 0;while  (Index < _a.size ()) {if  (_a[index]._col ==  i) {TRIPLE&LT;T&GT;&NBSP;MM;MM._COL&NBSP;=&NBsp;_a[index]._row;mm._row = _a[index]._col;mm._value = _a[index]._value;sm._a.push_back ( mm);} ++index;}} RETURN&NBSP;SM;} Sparsematrix<t> fasttransport ()    //quick Transpose {sparsematrix<t> temp;temp._ A.resize (_a.size ()); int* rowcounts = new int[_col];int* rowstarts = new  int[_col];memset (rowcounts, 0, sizeof ((int) *_col)) memset (rowstarts, 0, sizeof (int) *_col));size_t index = 0;while  (Index < _a.size ()) {rowcounts[_a[index]._col]+ +;++index;} rowstarts[0] = 0;for  (size_t i = 0; i < _col; ++i) { ROWSTARTS[I]&NBSP;=&NBSP;ROWSTARTS[I&NBSP;-&NBSP;1]&NBSP;+&NBSP;ROWCOUNTS[I&NBSP;-&NBSP;1];} while  (Index < _a.size ()) {size_t& begin = rowstarts[_a[index]._col]; triple<t> tp;tp._row = _a[index]._col;tp._col = _a[index]._row;tp._value =  _A[index]._value;tmp._a[rowstarts++] = tp;++index;} Delete[] _a;return tmp;}  protected:size_t _rowsize;size_t _colsize; t _invaild;vector<triple<t>> _a;}; The test code is as follows: Void test () {int a[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> d ((int*) a, 6, 5, 0); Sparsematrix<int> tmp = d.transport ();cout <<  "before transpose:"  <<  Endl;d.display ((int*) a, 6, 5, 0);cout << endl;cout <<  "after transpose:"  << endl;tmp.display ((int*) a, 5, 6, 0);} Int main () {test (); System ("pause"); return 0;}

The results of the operation are as follows:

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Transpose of sparse matrices