C++ MatrixBase::colwise方法代码示例

void forward_propagation(Eigen::MatrixBase<Derived1> const &input,
                         Eigen::MatrixBase<Derived2> const &weight,
                         Eigen::MatrixBase<Derived3> const &bias,
                         Eigen::MatrixBase<Derived4> &output,
                         bool no_overlap = true,
                         UnaryFunc func = UnaryFunc())
{
    static_assert(std::is_same<Derived1::Scalar, Derived2::Scalar>::value &&
                  std::is_same<Derived2::Scalar, Derived3::Scalar>::value &&
                  std::is_same<Derived4::Scalar, Derived4::Scalar>::value,
                  "Data type of matrix input, weight, bias and output should be the same");

    if(input.rows() != 0 && weight.rows() != 0 &&
            bias.rows() != 0){        
        if(no_overlap){
            output.noalias() = weight * input;
        }else{
            output = weight * input;
        }

        using Scalar = typename Derived3::Scalar;
        using MatType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
        using Mapper = Eigen::Map<const MatType, Eigen::Aligned>;
        Mapper Map(&bias(0, 0), bias.size());
        output.colwise() += Map;
        func(output);
    }
}

		Eigen::MatrixBase<Eigen::Matrix<typename matrix::Scalar, matrix::Rows + 1, matrix::Rows + 1>> get_rigid_transform(
			const Eigen::MatrixBase<matrix>& a,
			const Eigen::MatrixBase<matrix>& b)
		{
			Eigen::Matrix<typename matrix::Scalar, matrix::Rows + 1, matrix::Rows + 1> transform = 
				Eigen::Matrix<typename matrix::Scalar, matrix::Rows + 1, matrix::Rows + 1>::Identity();

			auto centroid_a = a.colwise() - a.rowwise().mean();
			auto centroid_b = b.colwise() - b.rowwise().mean();

			auto svd = (centered_a * centered_b.transpose()).eval().jacobiSVD(Eigen::ComputeFullU | Eigen::ComputeFullV);
			auto U = svd.matrixU();
			auto V = svd.matrixV();
			auto R = (V * U.transpose()).eval();
			auto C = Eigen::Matrix<typename matrix::Scalar, matrix::Rows, matrix::Rows>::Identity().eval();
			C(matrix::Rows - 1, matrix::Rows - 1) = R.determinant();
			R = V * C * U.transpose();
			auto T = -R * centroid_a + centroid_b;

			transform.block<matrix::Rows, matrix::Rows>(0, 0) = R;
			transform.block<matrix::Rows, 1>(0, matrix::Rows) = T;

			return transform;
		}

void igl::copyleft::offset_surface(
  const Eigen::MatrixBase<DerivedV> & V,
  const Eigen::MatrixBase<DerivedF> & F,
  const isolevelType isolevel,
  const typename Derivedside::Scalar s,
  const SignedDistanceType & signed_distance_type,
  Eigen::PlainObjectBase<DerivedSV> & SV,
  Eigen::PlainObjectBase<DerivedSF> & SF,
  Eigen::PlainObjectBase<DerivedGV> &   GV,
  Eigen::PlainObjectBase<Derivedside> & side,
  Eigen::PlainObjectBase<DerivedS> & S)
{
  typedef typename DerivedV::Scalar Scalar;
  typedef typename DerivedF::Scalar Index;
  {
    Eigen::AlignedBox<Scalar,3> box;
    typedef Eigen::Matrix<Scalar,1,3> RowVector3S;
    assert(V.cols() == 3 && "V must contain positions in 3D");
    RowVector3S min_ext = V.colwise().minCoeff().array() - isolevel;
    RowVector3S max_ext = V.colwise().maxCoeff().array() + isolevel;
    box.extend(min_ext.transpose());
    box.extend(max_ext.transpose());
    igl::voxel_grid(box,s,1,GV,side);
  }

  const Scalar h = 
    (GV.col(0).maxCoeff()-GV.col(0).minCoeff())/((Scalar)(side(0)-1));
  const Scalar lower_bound = isolevel-sqrt(3.0)*h;
  const Scalar upper_bound = isolevel+sqrt(3.0)*h;
  {
    Eigen::Matrix<Index,Eigen::Dynamic,1> I;
    Eigen::Matrix<typename DerivedV::Scalar,Eigen::Dynamic,3> C,N;
    igl::signed_distance(
      GV,V,F,signed_distance_type,lower_bound,upper_bound,S,I,C,N);
  }
  igl::flood_fill(side,S);
  
  DerivedS SS = S.array()-isolevel;
  igl::copyleft::marching_cubes(SS,GV,side(0),side(1),side(2),SV,SF);
}

IGL_INLINE void igl::bounding_box(
  const Eigen::MatrixBase<DerivedV>& V,
  const typename DerivedV::Scalar pad,
  Eigen::PlainObjectBase<DerivedBV>& BV,
  Eigen::PlainObjectBase<DerivedBF>& BF)
{
  using namespace std;

  const int dim = V.cols();
  const auto & minV = V.colwise().minCoeff().array()-pad;
  const auto & maxV = V.colwise().maxCoeff().array()+pad;
  // 2^n vertices
  BV.resize((1<<dim),dim);

  // Recursive lambda to generate all 2^n combinations
  const std::function<void(const int,const int,int*,int)> combos =
  [&BV,&minV,&maxV,&combos](
    const int dim,
    const int i,
    int * X,
    const int pre_index)
  {
    for(X[i] = 0;X[i]<2;X[i]++)
    {
      int index = pre_index*2+X[i];
      if((i+1)<dim)
      {
        combos(dim,i+1,X,index);
      }else
      {
        for(int d = 0;d<dim;d++)
        {
          BV(index,d) = (X[d]?minV[d]:maxV[d]);
        }
      }
    }
  };

  Eigen::VectorXi X(dim);
  combos(dim,0,X.data(),0);
  switch(dim)
  {
    case 2:
      BF.resize(4,2);
      BF<<
        3,1,
        1,0,
        0,2,
        2,3;
      break;
    case 3:
      BF.resize(12,3);
      BF<<
        2,0,6,
        0,4,6,
        5,4,0,
        5,0,1,
        6,4,5,
        5,7,6,
        3,0,2,
        1,0,3,
        3,2,6,
        6,7,3,
        5,1,3,
        3,7,5;
      break;
    default:
      assert(false && "Unsupported dimension.");
      break;
  }
}