C++ MatrixBase::array方法代码示例

IGL_INLINE void igl::barycentric_coordinates(
  const Eigen::MatrixBase<DerivedP> & P,
  const Eigen::MatrixBase<DerivedA> & A,
  const Eigen::MatrixBase<DerivedB> & B,
  const Eigen::MatrixBase<DerivedC> & C,
  Eigen::PlainObjectBase<DerivedL> & L)
{
  using namespace Eigen;
#ifndef NDEBUG
  const int DIM = P.cols();
  assert(A.cols() == DIM && "corners must be in same dimension as query");
  assert(B.cols() == DIM && "corners must be in same dimension as query");
  assert(C.cols() == DIM && "corners must be in same dimension as query");
  assert(P.rows() == A.rows() && "Must have same number of queries as corners");
  assert(A.rows() == B.rows() && "Corners must be same size");
  assert(A.rows() == C.rows() && "Corners must be same size");
#endif

  // http://gamedev.stackexchange.com/a/23745
  typedef 
    Eigen::Array<
      typename DerivedP::Scalar,
               DerivedP::RowsAtCompileTime,
               DerivedP::ColsAtCompileTime>
    ArrayS;
  typedef 
    Eigen::Array<
      typename DerivedP::Scalar,
               DerivedP::RowsAtCompileTime,
               1>
    VectorS;

  const ArrayS v0 = B.array() - A.array();
  const ArrayS v1 = C.array() - A.array();
  const ArrayS v2 = P.array() - A.array();
  VectorS d00 = (v0*v0).rowwise().sum();
  VectorS d01 = (v0*v1).rowwise().sum();
  VectorS d11 = (v1*v1).rowwise().sum();
  VectorS d20 = (v2*v0).rowwise().sum();
  VectorS d21 = (v2*v1).rowwise().sum();
  VectorS denom = d00 * d11 - d01 * d01;
  L.resize(P.rows(),3);
  L.col(1) = (d11 * d20 - d01 * d21) / denom;
  L.col(2) = (d00 * d21 - d01 * d20) / denom;
  L.col(0) = 1.0f -(L.col(1) + L.col(2)).array();
}

void ba81NormalQuad::layer::detectTwoTier(Eigen::ArrayBase<T1> &param,
					  Eigen::MatrixBase<T2> &mean, Eigen::MatrixBase<T3> &cov)
{
	if (mean.rows() < 3) return;

	std::vector<int> orthogonal;

	Eigen::Matrix<Eigen::DenseIndex, Eigen::Dynamic, 1>
		numCov((cov.array() != 0.0).matrix().colwise().count());
	std::vector<int> candidate;
	for (int fx=0; fx < numCov.rows(); ++fx) {
		if (numCov(fx) == 1) candidate.push_back(fx);
	}
	if (candidate.size() > 1) {
		std::vector<bool> mask(numItems());
		for (int cx=candidate.size() - 1; cx >= 0; --cx) {
			std::vector<bool> loading(numItems());
			for (int ix=0; ix < numItems(); ++ix) {
				loading[ix] = param(candidate[cx], itemsMap[ix]) != 0;
			}
			std::vector<bool> overlap(loading.size());
			std::transform(loading.begin(), loading.end(),
				       mask.begin(), overlap.begin(),
				       std::logical_and<bool>());
			if (std::find(overlap.begin(), overlap.end(), true) == overlap.end()) {
				std::transform(loading.begin(), loading.end(),
					       mask.begin(), mask.begin(),
					       std::logical_or<bool>());
				orthogonal.push_back(candidate[cx]);
			}
		}
	}
	std::reverse(orthogonal.begin(), orthogonal.end());

	if (orthogonal.size() == 1) orthogonal.clear();
	if (orthogonal.size() && orthogonal[0] != mean.rows() - int(orthogonal.size())) {
		mxThrow("Independent specific factors must be given after general dense factors");
	}

	numSpecific = orthogonal.size();

	if (numSpecific) {
		Sgroup.assign(numItems(), 0);
		for (int ix=0; ix < numItems(); ix++) {
			for (int dx=orthogonal[0]; dx < mean.rows(); ++dx) {
				if (param(dx, itemsMap[ix]) != 0) {
					Sgroup[ix] = dx - orthogonal[0];
					continue;
				}
			}
		}
		//Eigen::Map< Eigen::ArrayXi > foo(Sgroup.data(), param.cols());
		//mxPrintMat("sgroup", foo);
	}
}

Eigen::Matrix<typename Derived::Scalar, 3, 3> rpy2rotmat(const Eigen::MatrixBase<Derived>& rpy)
{
  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Eigen::MatrixBase<Derived>, 3);
  auto rpy_array = rpy.array();
  auto s = rpy_array.sin();
  auto c = rpy_array.cos();

  Eigen::Matrix<typename Derived::Scalar, 3, 3> R;
  R.row(0) << c(2) * c(1), c(2) * s(1) * s(0) - s(2) * c(0), c(2) * s(1) * c(0) + s(2) * s(0);
  R.row(1) << s(2) * c(1), s(2) * s(1) * s(0) + c(2) * c(0), s(2) * s(1) * c(0) - c(2) * s(0);
  R.row(2) << -s(1), c(1) * s(0), c(1) * c(0);

  return R;
}

IGL_INLINE void igl::normalize_row_sums(
  const Eigen::MatrixBase<DerivedA>& A,
  Eigen::MatrixBase<DerivedB> & B)
{
#ifndef NDEBUG
  // loop over rows
  for(int i = 0; i < A.rows();i++)
  {
    typename DerivedB::Scalar sum = A.row(i).sum();
    assert(sum != 0);
  }
#endif
  B = (A.array().colwise() / A.rowwise().sum().array()).eval();
}

IGL_INLINE Scalar igl::random_search(
  const std::function< Scalar (DerivedX &) > f,
  const Eigen::MatrixBase<DerivedLB> & LB,
  const Eigen::MatrixBase<DerivedUB> & UB,
  const int iters,
  DerivedX & X)
{
  Scalar min_f = std::numeric_limits<Scalar>::max();
  const int dim = LB.size();
  assert(UB.size() == dim && "UB should match LB size");
  for(int iter = 0;iter<iters;iter++)
  {
    const DerivedX R = DerivedX::Random(dim).array()*0.5+0.5;
    DerivedX Xr = LB.array() + R.array()*(UB-LB).array();
    const Scalar fr = f(Xr);
    if(fr<min_f)
    {
      min_f = fr;
      X = Xr;
    }
  }
  return min_f;
}

inline bool isFucked(const Eigen::MatrixBase<Derived>& x)
{
  return !((x.array() == x.array())).all() && !( (x - x).array() == (x - x).array()).all();
}

inline bool is_nan(const Eigen::MatrixBase<Derived>& x) {
  return ((x.array() == x.array())).all();
}

 void operator()(Eigen::MatrixBase<Derived> &inout) const
 {
     inout = 1.0 / (1.0 + (-1.0 * inout.array()).exp());
 }

 void log_activation(const Eigen::MatrixBase<T>& inputs, Eigen::VectorXd& activation)
 {
     activation = inputs.array().log().matrix();
 }