linalg.h

Go to the documentation of this file.

/*!
  \file   linalg.h
  \date   Jan 2005
  
  \brief  Linear algebra class.
    
*/

#ifndef _LINALG_H_
#define _LINALG_H_

#include "debug.h"

class matrix {
 protected:
  int rows;
  int columns;
  double **A;
 public:
  matrix(int r = 1, int c = 1) 
    {
      int i,j;
      rows=r; columns=c;
      A = new double*[rows];
      for (i=0;i<=rows-1;i++)
        A[i] = new double[columns];
      for (i=0;i<=rows-1;i++)
        {
          for(j=0;j<=columns-1;j++)
            A[i][j]=0;
        }
    }
  matrix(const matrix &B)
    {
      int i,j;
      rows = B.rows;
      columns = B.columns;
      A = new double*[rows];
      for (i=0;i<=rows-1;i++)
        A[i] = new double[columns];
      for (i=0;i<=rows-1;i++)
        {
          for(j=0;j<=columns-1;j++)
            A[i][j]=B.A[i][j];
        }
    }
  ~matrix() 
    {
      long i;
      for (i=0; i<=rows-1; i++)
        delete [] A[i];
      delete [] A;
    }
  matrix &operator=(const matrix &B)
    {
      long i,j;
      for (i=0; i<=rows-1; i++)
        delete [] A[i];
      delete [] A;
      rows = B.rows;
      columns = B.columns;
      A = new double*[rows];
      for (i=0;i<=rows-1;i++)
        A[i] = new double[columns];
      for (i=0;i<=rows-1;i++)
        {
          for(j=0;j<=columns-1;j++)
            A[i][j] = B.A[i][j];
        }
      return *this;
    }
  long *size() {long *dim = new long[2]; dim[0] = rows; dim[1]=columns; return(dim);}
  long row() const {return(rows);} 
  long column() const {return(columns);} 
  void newsize(long r, long c = 1)                        //warning: This deletes the original matrix
    { 
      long i,j;
      for (i=0; i<=rows-1; i++)
        delete [] A[i];
      delete [] A;
      rows = r; columns = c;
      A = new double*[rows];
      for (i=0;i<=rows-1;i++)
        A[i] = new double[columns];
      for (i=0;i<=rows-1;i++)
        {
          for(j=0;j<=columns-1;j++)
            A[i][j]=0;
        }
    }
  double get(long i, long j = 0) const {return(A[i][j]);}
  void set(double x, long i, long j=0) {A[i][j]=x;}
  void add(double x, long i, long j=0) {A[i][j] += x;};
  void sub(double x, long i, long j=0) {A[i][j] -= x;};
  void mul(double x, long i, long j=0) {A[i][j] *= x;};
  //! Maps function f to every element in matrix
  /*! 
    
  \param f real-function
  
  \return matrix
  */
  matrix map(double(*f)(double));
  matrix getrow(long i) const;
  matrix getcolumn(long i) const;
  matrix addcolumns();
  void setrow(const matrix &B, long i);
  void setcolumn(const matrix &B, long i);
  long operator==(const matrix &B);
  matrix operator*(const double &x);
  matrix operator*(const matrix &B);
  matrix trans();
  matrix operator+(const matrix &B);
  matrix operator-(const matrix &B);
  matrix inv();
  double twonorm();
  double onenorm();
  //! calculates empirical mean of each column in matrix
  /*! 
    
  
  \return vector where entry i contains empirical mean of column i.
  */
  safevector<double> mean() const; 
  //!  calculates empirical variance of each column in matrix
  /*! 
    
  
  \return vector where entry i contains empirical variance of column i.
  */
  safevector<double> var() const;
  //!  calculates empirical covariance matrix of matrix
  /*! Here we interpret the matrix as follows:
    Let n be the number of columns in the matrix, then we interpret the columns
    of the matrix as realizations of the stochastic variables X1,...,Xn.
    This function calculates the covariance matrix of (X1,...,Xn).
  
  \return Covariance-matrix of X1,...,Xn (columns in the incoming matrix).
  */
  matrix covar() const;

  double maxvalue();
  const char* row2string(int r);
  void print() const;
  void stdprint() const;
  void swap(long i, long j)
    { double *tmp = A[i];
    A[i]=A[j];
    A[j] = tmp;
    }
  void swapc(long i, long j)
    { 
      double tmp;
      long k;
      for(k=0;k<=rows-1;k++)
        { tmp=A[k][i];
        A[k][i]= A[k][j];
        A[k][j]=tmp;
        }
    }     
};



//! calculates empirical covariance (columnwise) of two matrices.
/*! 
  If matrix 2 is a (column)-vector. Then the covariance between matrix 2 and 
  each of the columns in matrix 1 is returned.

\param X matrix 1
\param Y matrix 2

\return vector where entry i is the covariance between
*/
matrix covar2(matrix const& X, matrix const& Y);



#endif // _LINALG_H_

---