C++マトリックス演算クラス(Matrix.h)
このクラスのデータ型はdoubleで、よく使われるマトリクス計算が含まれており、多くの方法が実践的に検証されているため、発見があれば指摘してください.
https://github.com/ims0/comTutor/tree/master/matrix
https://github.com/ims0/comTutor/tree/master/matrix
#include
#include
#include
#include
using namespace std;
#ifndef _In_opt_
#define _In_opt_
#endif
#ifndef _Out_
#define _Out_
#endif
typedef unsigned Index_T;
class Matrix
{
private:
Index_T m_row, m_col;
Index_T m_size;
Index_T m_curIndex;
double *m_ptr;//
public:
Matrix(Index_T r, Index_T c) :m_row(r), m_col(c)//
{
m_size = r*c;
if (m_size>0)
{
m_ptr = new double[m_size];
}
else
m_ptr = NULL;
};
Matrix(Index_T r, Index_T c, double val ) :m_row(r), m_col(c)// val
{
m_size = r*c;
if (m_size>0)
{
m_ptr = new double[m_size];
}
else
m_ptr = NULL;
};
Matrix(Index_T n) :m_row(n), m_col(n)//
{
m_size = n*n;
if (m_size>0)
{
m_ptr = new double[m_size];
}
else
m_ptr = NULL;
};
Matrix(const Matrix &rhs)//
{
m_row = rhs.m_row;
m_col = rhs.m_col;
m_size = rhs.m_size;
m_ptr = new double[m_size];
for (Index_T i = 0; i>(istream&, Matrix&);
friend ofstream &operator<max ? 0 : m_ptr[i*m_col + j];
}
}
}
Matrix Matrix::eye()//
{
for (Index_T i = 0; i< m_row; i++)
{
for (Index_T j = 0; j < m_col; j++)
{
if (i == j)
{
m_ptr[i*m_col + j] = 1.0;
}
}
}
return *this;
}
void Matrix::zeromean(_In_opt_ bool flag)
{
if (flag == true) //
{
double *mean = new double[m_col];
for (Index_T j = 0; j < m_col; j++)
{
mean[j] = 0.0;
for (Index_T i = 0; i < m_row; i++)
{
mean[j] += m_ptr[i*m_col + j];
}
mean[j] /= m_row;
}
for (Index_T j = 0; j < m_col; j++)
{
for (Index_T i = 0; i < m_row; i++)
{
m_ptr[i*m_col + j] -= mean[j];
}
}
delete[]mean;
}
else //
{
double *mean = new double[m_row];
for (Index_T i = 0; i< m_row; i++)
{
mean[i] = 0.0;
for (Index_T j = 0; j < m_col; j++)
{
mean[i] += m_ptr[i*m_col + j];
}
mean[i] /= m_col;
}
for (Index_T i = 0; i < m_row; i++)
{
for (Index_T j = 0; j < m_col; j++)
{
m_ptr[i*m_col + j] -= mean[i];
}
}
delete[]mean;
}
}
void Matrix::normalize(_In_opt_ bool flag)
{
if (flag == true) //
{
double *mean = new double[m_col];
for (Index_T j = 0; j < m_col; j++)
{
mean[j] = 0.0;
for (Index_T i = 0; i < m_row; i++)
{
mean[j] += m_ptr[i*m_col + j];
}
mean[j] /= m_row;
}
for (Index_T j = 0; j < m_col; j++)
{
for (Index_T i = 0; i < m_row; i++)
{
m_ptr[i*m_col + j] -= mean[j];
}
}
///
for (Index_T j = 0; j < m_col; j++)
{
mean[j] = 0;
for (Index_T i = 0; i < m_row; i++)
{
mean[j] += pow(m_ptr[i*m_col + j],2);//
}
mean[j] = sqrt(mean[j] / m_row); //
}
for (Index_T j = 0; j < m_col; j++)
{
for (Index_T i = 0; i < m_row; i++)
{
m_ptr[i*m_col + j] /= mean[j];//
}
}
delete[]mean;
}
else //
{
double *mean = new double[m_row];
for (Index_T i = 0; i< m_row; i++)
{
mean[i] = 0.0;
for (Index_T j = 0; j < m_col; j++)
{
mean[i] += m_ptr[i*m_col + j];
}
mean[i] /= m_col;
}
for (Index_T i = 0; i < m_row; i++)
{
for (Index_T j = 0; j < m_col; j++)
{
m_ptr[i*m_col + j] -= mean[i];
}
}
///
for (Index_T i = 0; i< m_row; i++)
{
mean[i] = 0.0;
for (Index_T j = 0; j < m_col; j++)
{
mean[i] += pow(m_ptr[i*m_col + j], 2);//
}
mean[i] = sqrt(mean[i] / m_col); //
}
for (Index_T i = 0; i < m_row; i++)
{
for (Index_T j = 0; j < m_col; j++)
{
m_ptr[i*m_col + j] /= mean[i];
}
}
delete[]mean;
}
}
double Matrix::det()
{
if (m_col == m_row)
return calcDet(m_row, m_ptr);
else
{
cout << (" ") << endl;
return 0;
}
}
/////////////////////////////////////////////////////////////////////
istream& operator>>(istream &is, Matrix &obj)
{
for (Index_T i = 0; i> obj.m_ptr[i];
}
return is;
}
ostream& operator< obj.m_size - 1 )
{
return obj;
}
*(obj.m_ptr + obj.m_curIndex) = val;
++obj.m_curIndex;
return obj;
}
Matrix operator+(const Matrix& lm, const Matrix& rm)
{
if (lm.m_col != rm.m_col || lm.m_row != rm.m_row)
{
Matrix temp(0, 0);
temp.m_ptr = NULL;
cout << "operator+(): shape ,m_col:"
<< lm.m_col << "," << rm.m_col << ". m_row:" << lm.m_row << ", " << rm.m_row << endl;
return temp; // ，
}
Matrix ret(lm.m_row, lm.m_col);
for (Index_T i = 0; im_ptr[j])
{
tem = m_ptr[i];
m_ptr[i] = m_ptr[j];
m_ptr[j] = tem;
}
}
else
{
if (m_ptr[i]= 0; --m)
{
sum = 0;
for (Index_T j = m + 1; j < m_col; ++j)
{
sum += m_ptr[m * m_col + j] * ret.m_ptr[j * ret.m_col + count];
}
sum = -sum / m_ptr[m * m_col + m];
midSum += sum * sum;
ret.m_ptr[m * ret.m_col + count] = sum;
}
midSum = sqrt(midSum);
for (Index_T i = 0; i < ret.m_row; ++i)
{
ret.m_ptr[i * ret.m_col + count] /= midSum; //
}
}
*this = matcopy;// ；
return ret;
}
Matrix Matrix::cov(bool flag)
{
//m_row ，column
if (m_col == 0)
{
Matrix m(0);
return m;
}
double *mean = new double[m_col]; //
for (Index_T j = 0; j