C++ viennacl::matrix类代码示例

double luT(
	viennacl::matrix<T> &vclX,
	viennacl::vector_base<T> &vclD) {

	double logdet=-99.9;

	viennacl::linalg::lu_factorize(vclX);

	// pointer to the actual diagonal
	viennacl::vector_base<T> diagOfVar(
		vclX.handle(), vclX.size1(), 0, 
		vclX.internal_size2() + 1);

		// compute log determinant
	vclD = viennacl::linalg::element_log(diagOfVar);
	logdet = viennacl::linalg::sum(vclD);
// OPERATION_UNARY_LOG_TYPE 	
		//http://viennacl.sourceforge.net/doc/scheduler_8cpp-example.html#a11

		// put the diagonals in D, and 1's on the diagonal of L
	vclD = diagOfVar;

		//diagOfVar = T(1); // problem here
	
	return(logdet);
}

void init_random(viennacl::matrix<T, F> & M)
{
    std::vector<T> cM(M.internal_size());
    for (std::size_t i = 0; i < M.size1(); ++i)
        for (std::size_t j = 0; j < M.size2(); ++j)
            cM[F::mem_index(i, j, M.internal_size1(), M.internal_size2())] = T(rand())/T(RAND_MAX);
    viennacl::fast_copy(&cM[0],&cM[0] + cM.size(),M);
}

void matrix_print(viennacl::matrix<ScalarType, MatrixLayout>& A)
{
    for (unsigned int i = 0; i < A.size1(); i++) {
        for (unsigned int j = 0; j < A.size2(); j++)
           std::cout << std::fixed << A(i, j) << "\t";
        std::cout << "\n";
    }
}

void fill_matrix(viennacl::matrix<NumericT> & mat)
{
  for (std::size_t i = 0; i < mat.size1(); ++i)
  {
    for (std::size_t j = 0; j < mat.size2(); ++j)
      mat(i, j) = static_cast<NumericT>(-0.5) * random<NumericT>();
    mat(i, i) = NumericT(1.0) + NumericT(2.0) * random<NumericT>(); //some extra weight on diagonal for stability
  }
}

void matrix_print(viennacl::matrix<ScalarType>& A_orig)
{
    ublas::matrix<ScalarType> A(A_orig.size1(), A_orig.size2());
    viennacl::copy(A_orig, A);
    std::cout << "matrix_print!\n";
    for (unsigned int i = 0; i < A.size1(); i++) {
        for (unsigned int j = 0; j < A.size2(); j++)
           std::cout << A(i, j) << "\t";
        std::cout << "\n";
    }
}

 viennacl::vector_range<viennacl::vector_base<T> > sharedCol(){
     // viennacl::vector_base<T> tmp(ptr_matrix->handle(), ptr_matrix->internal_size(), 0, 1);
     
     // std::cout << "returning column" << std::endl;
     viennacl::vector_base<T> tmp(ptr_matrix->handle(), ptr_matrix->size1(), begin, ptr_matrix->internal_size2());
     // std::cout << "got column" << std::endl;
     
     viennacl::vector_range<viennacl::vector_base<T> > v_sub(tmp, r);
     // std::cout << "got range" << std::endl;
     
     return v_sub;
 }

bool test_eigen_val_vec(viennacl::matrix<ScalarType, MatrixLayout> & Q,
                      std::vector<ScalarType> & eigenvalues,
                      std::vector<ScalarType> & d,
                      std::vector<ScalarType> & e)
{
  unsigned int Q_size = Q.size2();
  ScalarType value = 0;

  for(unsigned int j = 0; j < Q_size; j++)
  {
    // calculate first row
    value = (d[0]- eigenvalues[j]) * Q(0, j) + e[1] * Q(1, j);
    if(value > EPS)
      return false;

    // calcuate inner rows
    for(unsigned int i = 1; i < Q_size - 1; i++)
    {
        value = e[i] * Q(i - 1, j) + (d[i]- eigenvalues[j]) * Q(i, j) + e[i + 1] * Q(i + 1, j);
        if(value > EPS)
          return false;
    }

    // calculate last row
    value = e[Q_size - 1] * Q(Q_size - 2, j) + (d[Q_size - 1] - eigenvalues[j]) * Q(Q_size - 1, j);
     if(value > EPS)
       return false;
  }
  return true;
}

bool check_tridiag(viennacl::matrix<ScalarType, MatrixLayout>& A_orig)
{
    ublas::matrix<ScalarType> A(A_orig.size1(), A_orig.size2());
    viennacl::copy(A_orig, A);

    for (unsigned int i = 0; i < A.size1(); i++) {
        for (unsigned int j = 0; j < A.size2(); j++) {
            if ((std::abs(A(i, j)) > EPS) && ((i - 1) != j) && (i != j) && ((i + 1) != j))
            {
                // std::cout << "Failed at " << i << " " << j << " " << A(i, j) << "\n";
                return false;
            }
        }
    }
    return true;
}

bool check_hessenberg(viennacl::matrix<ScalarType, MatrixLayout>& A_orig)
{
    ublas::matrix<ScalarType> A(A_orig.size1(), A_orig.size2());
    viennacl::copy(A_orig, A);

    for (std::size_t i = 0; i < A.size1(); i++) {
        for (std::size_t j = 0; j < A.size2(); j++) {
            if ((std::abs(A(i, j)) > EPS) && (i > (j + 1)))
            {
                // std::cout << "Failed at " << i << " " << j << " " << A(i, j) << "\n";
                return false;
            }
        }
    }
    return true;
}

float matrix_compare(viennacl::matrix<ScalarType>& res,
                     viennacl::matrix<ScalarType>& ref)
{
    std::vector<ScalarType> res_std(res.internal_size());
    std::vector<ScalarType> ref_std(ref.internal_size());

    viennacl::fast_copy(res, &res_std[0]);
    viennacl::fast_copy(ref, &ref_std[0]);

    float diff = 0.0;
    float mx = 0.0;

    for(std::size_t i = 0; i < res_std.size(); i++) {
        diff = std::max(diff, std::abs(res_std[i] - ref_std[i]));
        mx = std::max(mx, res_std[i]);
    }

    return diff / mx;
}

ScalarType diff(ublas::matrix<ScalarType> & mat1, viennacl::matrix<ScalarType, F, ALIGNMENT> & mat2)
{
   ublas::matrix<ScalarType> mat2_cpu(mat2.size1(), mat2.size2());
   copy(mat2, mat2_cpu);
   ScalarType ret = 0;
   ScalarType act = 0;

    for (unsigned int i = 0; i < mat2_cpu.size1(); ++i)
    {
      for (unsigned int j = 0; j < mat2_cpu.size2(); ++j)
      {
         act = fabs(mat2_cpu(i,j) - mat1(i,j)) / std::max( fabs(mat2_cpu(i, j)), fabs(mat1(i,j)) );
         if (act > ret)
           ret = act;
      }
    }
   //std::cout << ret << std::endl;
   return ret;
}

void read_matrix_body(std::fstream& f, viennacl::matrix<ScalarType, MatrixLayout>& A)
{
    if(!f.is_open())
    {
        throw std::invalid_argument("File is not opened");
    }

    boost::numeric::ublas::matrix<ScalarType> h_A(A.size1(), A.size2());

    for(std::size_t i = 0; i < h_A.size1(); i++) {
        for(std::size_t j = 0; j < h_A.size2(); j++) {
            ScalarType val = 0.0;
            f >> val;
            h_A(i, j) = val;
        }
    }

    viennacl::copy(h_A, A);
}

FeatureNormalize<Scalar>::FeatureNormalize(const viennacl::matrix<Scalar> &X):
        blas_matrix_(X.size1(), X.size2()),
        normalized_matrix_(X.size1(), X.size2()),
        mu_(X.size2()), sigma_(X.size2()) {
    viennacl::copy(X, blas_matrix_);
    runNormalization();
}

bool check_bidiag(viennacl::matrix<ScalarType>& A)
{
  const ScalarType EPS = 0.0001f;

  std::vector<ScalarType> aA(A.size1() * A.size2());
  viennacl::fast_copy(A, &aA[0]);

  for(std::size_t i = 0; i < A.size1(); i++)
  {
    for(std::size_t j = 0; j < A.size2(); j++)
    {
      ScalarType val = aA[i * A.size2() + j];
      if((fabs(val) > EPS) && (i != j) && ((i + 1) != j))
      {
        std::cout << "Failed at " << i << " " << j << " " << val << std::endl;
        return false;
      }
    }
  }

  return true;
}

    void svd(viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & A,
              viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & QL,
              viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & QR)
    {
      viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(A).context());
      viennacl::linalg::opencl::kernels::svd<SCALARTYPE>::init(ctx);

      vcl_size_t row_num = A.size1();
      vcl_size_t col_num = A.size2();

      vcl_size_t to = std::min(row_num, col_num);


      //viennacl::vector<SCALARTYPE, ALIGNMENT> d(to);
      //viennacl::vector<SCALARTYPE, ALIGNMENT> s(to + 1);

      // first stage
      detail::bidiag(A, QL, QR);

      // second stage
      //std::vector<SCALARTYPE> dh(to, 0);
      //std::vector<SCALARTYPE> sh(to + 1, 0);
      boost::numeric::ublas::vector<SCALARTYPE> dh = boost::numeric::ublas::scalar_vector<SCALARTYPE>(to, 0);
      boost::numeric::ublas::vector<SCALARTYPE> sh = boost::numeric::ublas::scalar_vector<SCALARTYPE>(to + 1, 0);


      viennacl::linalg::opencl::bidiag_pack_svd(A, dh, sh);

      detail::svd_qr_shift( QL, QR, dh, sh);

      // Write resulting diagonal matrix with singular values to A:
      boost::numeric::ublas::matrix<SCALARTYPE> h_Sigma(row_num, col_num);
      h_Sigma.clear();

      for (vcl_size_t i = 0; i < to; i++)
        h_Sigma(i, i) = dh[i];

      copy(h_Sigma, A);
    }