Ejemplo n.º 1
0
/**
 * Description not yet available.
 * \param
 */
dmatrix_position::dmatrix_position(const dmatrix& m)
  : lb(m.rowmin(),m.rowmax()), ub(m.rowmin(),m.rowmax()),
  ptr(m.rowmin(),m.rowmax())
{
  row_min=m.rowmin();
  row_max=m.rowmax();
  for (int i=row_min;i<=row_max;i++)
  {
    lb(i)=m(i).indexmin();
    ub(i)=m(i).indexmax();
    ptr(i)=m(i).get_v();
  }
}
Ejemplo n.º 2
0
/**
 * @brief Return molt increment matrix based on empirical data
 * @details Fit's a cubic spline to the empirical data.
 * Note that the spline function strictly requires increasing
 * values for each of the knots.
 * 
 * @param data [description]
 * @return dmatrix of molt increments by sex for each size bin
 */
dmatrix get_empirical_molt_increment(const dvector& bin, const dmatrix& data)
{
	cout<<"In get_empirical_molt_increment"<<endl;
	int n = bin.size();
	ivector sex = ivector(column(data,2));
	int nsex = count_factor(sex);

	dmatrix mi(1,nsex,1,n);
	ivector nh(1,nsex); nh.initialize();
	
	// Count number of observations in each sex.
	for (int i = 1; i <= data.rowmax(); ++i)
	{
		int h = sex(i);
		nh(h) ++;
	}
	
	// get male and famale arrays
	dmatrix x(1,nsex,1,nh);
	dmatrix y(1,nsex,1,nh);
	int bb=1; 
	int gg=1;
	for (int i = 1; i <= data.rowmax(); ++i)
	{
		int h = sex(i);
		int j = h==1 ? bb++ : gg++ ;
		
		x(h,j) = data(i,1);
		y(h,j) = data(i,3);	
	}



	// rescale size to 0-1 over bin width
	for (int h = 1; h <= nsex; ++h)
	{
		dvector knts = (x(h) - min(x(h))) / (max(x(h)) - min(x(h)));
		dvector pnts = (bin  - min(bin)) / (max(bin) - min(bin));
		COUT(knts);
		COUT(y(h));
		cubic_spline_function cSmooth(knts,y(h));
		dvector test = cSmooth(pnts);
		COUT(cSmooth(0.5));
		COUT(test);
	}
	
	cout<<"leaving get_empirical_molt_increment"<<endl;
	return mi;

}
Ejemplo n.º 3
0
/*
Returns dmatrix with pow of each element in m raised to the exponent e.
\param m dmatrix
\param e floating point exponent
*/
dmatrix pow(const dmatrix& m, const double e)
{
  ivector cmin(m.rowmin(), m.rowmax());
  ivector cmax(m.rowmin(), m.rowmax());
  for (int i = m.rowmin(); i <= m.rowmax(); ++i)
  {
    cmin(i) = m(i).indexmin();
    cmax(i) = m(i).indexmax();
  }
  dmatrix tmp(m.rowmin(), m.rowmax(), cmin, cmax);
  for (int i = m.rowmin(); i <= m.rowmax(); ++i)
  {
    tmp(i) = pow(m(i), e);
  }
  return tmp;
}
Ejemplo n.º 4
0
/**
Returns dmatrix with each of element in m is squared.
\param m dmatrix
*/
dmatrix sqr(const dmatrix& m)
{
  ivector cmin(m.rowmin(), m.rowmax());
  ivector cmax(m.rowmin(), m.rowmax());
  for (int i = m.rowmin(); i <= m.rowmax(); ++i)
  {
    cmin(i) = m(i).indexmin();
    cmax(i) = m(i).indexmax();
  }
  dmatrix tmp(m.rowmin(), m.rowmax(), cmin, cmax);
  for (int i = m.rowmin(); i <= m.rowmax(); ++i)
  {
    tmp(i) = sqr(m(i));
  }
  return tmp;
}
Ejemplo n.º 5
0
/**
 * Description not yet available.
 * \param
 */
dmatrix log(const dmatrix& m)
{
  ivector cmin(m.rowmin(),m.rowmax());
  ivector cmax(m.rowmin(),m.rowmax());
  int i;
  for (i=m.rowmin();i<=m.rowmax();i++)
  {
    cmin(i)=m(i).indexmin();
    cmax(i)=m(i).indexmax();
  }
  dmatrix tmp(m.rowmin(),m.rowmax(),cmin,cmax);
  for (i=m.rowmin();i<=m.rowmax();i++)
  {
    tmp(i)=log(m(i));
  }
  return tmp;
}
Ejemplo n.º 6
0
/**
 * Description not yet available.
 * \param
 */
dmatrix elem_div(const dmatrix& m, const dmatrix& m2)
{
  ivector cmin(m.rowmin(),m.rowmax());
  ivector cmax(m.rowmin(),m.rowmax());
  int i;
  for (i=m.rowmin();i<=m.rowmax();i++)
  {
    cmin(i)=m(i).indexmin();
    cmax(i)=m(i).indexmax();
  }
  dmatrix tmp(m.rowmin(),m.rowmax(),cmin,cmax);
  for (i=m.rowmin();i<=m.rowmax();i++)
  {
    tmp(i)=elem_div(m(i),m2(i));
  }
  return tmp;
}
Ejemplo n.º 7
0
/**
Return total sum of all elements in matrix.

\param matrix dmatrix
*/
double sum(const dmatrix& matrix)
{
  double total = 0.0;
  for (int i = matrix.rowmin(); i <= matrix.rowmax(); ++i)
  {
    total += sum(matrix.elem(i));
  }
  return total;
}
Ejemplo n.º 8
0
/**
 * Description not yet available.
 * \param
 */
double max(const dmatrix& m)
{
  double tmp=max(m(m.rowmin()));
  for (int i=m.rowmin()+1;i<=m.rowmax();i++)
  {
    double tmp1=max(m(i));
    if (tmp<tmp1) tmp=tmp1;
  }
  return tmp;
}
Ejemplo n.º 9
0
/**
Returns dvector where each element contains the sum total of each row
in matrix.

\param matrix dmatrix
*/
dvector rowsum(const dmatrix& matrix)
{
  int min = matrix.rowmin();
  int max = matrix.rowmax();

  dvector sums(min, max);
  for (int i = min; i <= max; ++i)
  {
    sums(i) = sum(matrix(i));
  }
  return sums;
}
Ejemplo n.º 10
0
Archivo: dmat12.cpp Proyecto: pwoo/admb
/**
 * Description not yet available.
 * \param
 */
dmatrix symmetrize(const dmatrix& m)
{
    if (m.rowmin() != m.colmin() || m.rowmax() != m.colmax() )
    {
        cerr << " Non square matrix passed to dmatrix symmetrize\n";
        ad_exit(1);
    }
    int rmin=m.rowmin();
    int rmax=m.rowmax();

    dmatrix s(rmin,rmax,rmin,rmax);
    for (int i=rmin; i<=rmax; i++)
    {
        s(i,i)=m(i,i);

        for (int j=rmin; j<i; j++)
        {
            s(i,j)=(m(i,j)+m(j,i))/2.;
            s(j,i)=s(i,j);
        }
    }
    return s;
}
Ejemplo n.º 11
0
/**
Return the sum of the diagonal of a square matrix mat.

\param mat is a square scalar matrix
*/
double trace(const dmatrix& mat)
{
  if (mat.colmin() != mat.rowmin() || mat.colmax() != mat.rowmax() )
  {
    cerr << "Matrix not square in trace\n";
    ad_exit(1);
  }

  double sum = 0.0;
  for (int i = mat.colmin(); i <= mat.colmax(); ++i)
  {
    sum += mat.elem(i, i);
  }
  return sum;
}
Ejemplo n.º 12
0
/**
Return computed column(col) sum for dmatrix m.
*/
double colsum(const dmatrix& m, int col)
{
  int mmin = m.rowmin();
  int mmax = m.rowmax();
  if (col < mmin || col > mmax)
  {
    cerr << "Row out of bounds in function"
            " colsum(const imatrix& m,int col)" << endl;
    ad_exit(1);
  }
  double sum = 0;
  for (int i = mmin; i <= mmax; ++i)
  {
    sum += m(i, col);
  }
  return sum;
}
Ejemplo n.º 13
0
/**
Returns dvector where each element contains the sum total of each column 
in matrix.

\param matrix dmatrix
*/
dvector colsum(const dmatrix& matrix)
{
  int cmin = matrix.colmin();
  int cmax = matrix.colmax();
  int rmin = matrix.rowmin();
  int rmax = matrix.rowmax();

  dvector sums(cmin, cmax);
  sums.initialize();
  for (int j=cmin; j<=cmax; ++j)
  {
    for (int i=rmin; i<=rmax; ++i)
    {
      sums(j) += matrix(i, j);
    }
  }
  return sums;
}
Ejemplo n.º 14
0
/**
 * \ingroup eigen
 * Eigenvectors
 * \param m \f$m\f$
 * \return a variable matrix with the
 *         eigenvectors of \f$m\f$ stored in its columns.
 */
dmatrix eigenvectors(const dmatrix &m)
{
   if (m.rowsize() != m.colsize())
   {
      cerr <<
	 "error -- non square matrix passed to dmatrix eigenvectors(const dmatrix& m)\n";
      ad_exit(1);
   }

   int rmin = m.rowmin();
   int rmax = m.rowmax();

   dmatrix evecs(rmin, rmax, rmin, rmax);
   dvector evals(rmin, rmax);

   eigens(m, evecs, evals);

   return evecs;
}
Ejemplo n.º 15
0
dvariable mult_likelihood(const dmatrix &o, const dvar_matrix &p, dvar_matrix &nu, 
                          const dvariable &log_vn)
{

	// kludge to ensure observed and predicted matrixes are the same size
	if(o.colsize()!=p.colsize() || o.rowsize()!=p.rowsize())
	{
		cerr<<"Error in multivariate_t_likelihood, observed and predicted matrixes"
		" are not the same size\n";
		ad_exit(1);
	}
	dvariable vn = mfexp(log_vn);
	dvariable ff = 0.0;
	int r1 = o.rowmin();
	int r2 = o.rowmax();
	int c1 = o.colmin();
	int c2 = o.colmax();

	for(int i = r1; i <= r2; i++ )
	{
		dvar_vector sobs = vn * o(i)/sum(o(i));  //scale observed numbers by effective sample size.
		ff -= gammln(vn);
		for(int j = c1; j <= c2; j++ )
		{
			if( value(sobs(j)) > 0.0 )
				ff += gammln(sobs(j));
		}
		ff -= sobs * log(TINY + p(i));
		dvar_vector o1=o(i)/sum(o(i));
		dvar_vector p1=p(i)/sum(p(i));
		nu(i) = elem_div(o1-p1,sqrt(elem_prod(p1,1.-p1)/vn));


	}
	// exit(1);
	return ff;
}
Ejemplo n.º 16
0
/**
 * Description not yet available.
 * \param
 */
void dmatrix::allocate(const dmatrix& dm)
{
  int nrl=dm.rowmin();
  int nrh=dm.rowmax();
  index_min=nrl;
  index_max=nrh;

  if ( (m = new dvector [rowsize()]) == 0)
  {
    cerr << " Error allocating memory in dmatrix contructor" << endl;
    ad_exit(21);
  }

  if ( (shape = new mat_shapex(m))== 0)
  {
    cerr << " Error allocating memory in dmatrix contructor" << endl;
    ad_exit(21);
  }
  m -= rowmin();
  for (int i=rowmin(); i<=rowmax(); i++)
  {
    m[i].allocate(dm(i));
  }
}
Ejemplo n.º 17
0
/**
 * Description not yet available.
 * \param
 */
dvar_matrix operator*(const dvar_matrix& m1, const dmatrix& cm2)
 {
   if (m1.colmin() != cm2.rowmin() || m1.colmax() != cm2.rowmax())
   {
     cerr << " Incompatible array bounds in "
     "dmatrix operator*(const dvar_matrix& x, const dmatrix& m)\n";
     ad_exit(21);
   }
   dmatrix cm1=value(m1);
   //dmatrix cm2=value(m2);
   dmatrix tmp(m1.rowmin(),m1.rowmax(), cm2.colmin(), cm2.colmax());
#ifdef OPT_LIB
   const size_t rowsize = (size_t)cm2.rowsize();
#else
   const int _rowsize = cm2.rowsize();
   assert(_rowsize > 0);
   const size_t rowsize = (size_t)_rowsize;
#endif
   try
   {
     double* temp_col = new double[rowsize];
     temp_col-=cm2.rowmin();
     for (int j=cm2.colmin(); j<=cm2.colmax(); j++)
     {
       for (int k=cm2.rowmin(); k<=cm2.rowmax(); k++)
       {
         temp_col[k] = cm2.elem(k,j);
       }
       for (int i=cm1.rowmin(); i<=cm1.rowmax(); i++)
       {
         double sum=0.0;
         dvector& temp_row = cm1(i);
         for (int k=cm1.colmin(); k<=cm1.colmax(); k++)
         {
           sum+=temp_row(k) * (temp_col[k]);
           // sum+=temp_row(k) * cm2(k,j);
         }
         tmp(i,j)=sum;
       }
     }
     temp_col+=cm2.rowmin();
     delete [] temp_col;
     temp_col = 0;
   }
   catch (std::bad_alloc& e)
   {
     cerr << "Error[" << __FILE__ << ':' << __LINE__
          << "]: Unable to allocate array.\n";
     //ad_exit(21);
     throw e;
   }
   dvar_matrix vtmp=nograd_assign(tmp);
   save_identifier_string("TEST1");
   //m1.save_dvar_matrix_value();
   m1.save_dvar_matrix_position();
   cm2.save_dmatrix_value();
   cm2.save_dmatrix_position();
   vtmp.save_dvar_matrix_position();
   save_identifier_string("TEST6");
   gradient_structure::GRAD_STACK1->
            set_gradient_stack(dmcm_prod);
   return vtmp;
 }