C++ matrix::size1方法代码示例

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();
}

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
  }
}

      bool householder_c(viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT>& A,
                          viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT>& Q,
                          viennacl::vector<SCALARTYPE, ALIGNMENT>& D,
                          vcl_size_t row_start, vcl_size_t col_start)
      {
        viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(A).context());

        if (row_start + 1 >= A.size1())
          return false;

        prepare_householder_vector(A, D, A.size1(), row_start, col_start, row_start, true);

        {
          viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::svd<SCALARTYPE>::program_name(), SVD_HOUSEHOLDER_UPDATE_A_LEFT_KERNEL);

          viennacl::ocl::enqueue(kernel(
                                        A,
                                        D,
                                        static_cast<cl_uint>(row_start),
                                        static_cast<cl_uint>(col_start),
                                        static_cast<cl_uint>(A.size1()),
                                        static_cast<cl_uint>(A.size2()),
                                        static_cast<cl_uint>(A.internal_size2()),
                                        viennacl::ocl::local_mem(static_cast<cl_uint>(128 * sizeof(SCALARTYPE)))
                                ));

        }

        {
          viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::svd<SCALARTYPE>::program_name(), SVD_HOUSEHOLDER_UPDATE_QL_KERNEL);

          viennacl::ocl::enqueue(kernel(
                                        Q,
                                        D,
                                        static_cast<cl_uint>(A.size1()),
                                      //  static_cast<cl_uint>(A.size2()),
                                        static_cast<cl_uint>(Q.internal_size2()),
                                        viennacl::ocl::local_mem(static_cast<cl_uint>(128 * sizeof(SCALARTYPE)))
                                ));

        }

        return true;
      }

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 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 bidiag(viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & Ai,
                  viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & QL,
                  viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT> & QR)
      {
        vcl_size_t row_num = Ai.size1();
        vcl_size_t col_num = Ai.size2();

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

        //for storing householder vector
        viennacl::vector<SCALARTYPE, ALIGNMENT> hh_vector(big_to, viennacl::traits::context(Ai));

        QL = viennacl::identity_matrix<SCALARTYPE>(QL.size1(), viennacl::traits::context(QL));
        QR = viennacl::identity_matrix<SCALARTYPE>(QR.size1(), viennacl::traits::context(QR));

        for (vcl_size_t i = 0; i < to; i++)
        {
          householder_c(Ai, QL, hh_vector, i, i);
          householder_r(Ai, QR, hh_vector, i, i+1);
        }
      }

NumericT diff(std::vector<std::vector<NumericT> > const & A1, viennacl::matrix<NumericT> const & A2)
{
  std::vector<NumericT> host_values(A2.internal_size());
  for (std::size_t i=0; i<A2.size1(); ++i)
    for (std::size_t j=0; j<A2.size2(); ++j)
      host_values[i*A2.internal_size2() + j] = A1[i][j];

  std::vector<NumericT> device_values(A2.internal_size());
  viennacl::fast_copy(A2, &device_values[0]);
  viennacl::vector<NumericT> vcl_device_values(A2.internal_size());  // workaround to avoid code duplication
  viennacl::copy(device_values, vcl_device_values);

  return diff(host_values, vcl_device_values);
}

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;
}

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);
}