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GTSAM
4.0.2
C++ library for smoothing and mapping (SAM)
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#include <Basis.h>


Public Member Functions | |
| EIGEN_MAKE_ALIGNED_OPERATOR_NEW | VectorDerivativeFunctor () |
| For serialization. | |
| VectorDerivativeFunctor (size_t N, double x) | |
| Default Constructor. | |
| VectorDerivativeFunctor (size_t N, double x, double a, double b) | |
| Constructor, with optional interval [a,b]. | |
| VectorM | apply (const ParameterMatrix< M > &P, OptionalJacobian< -1, -1 > H={}) const |
| VectorM | operator() (const ParameterMatrix< M > &P, OptionalJacobian< -1, -1 > H={}) const |
| c++ sugar | |
Protected Types | |
| using | VectorM = Eigen::Matrix< double, M, 1 > |
| using | Jacobian = Eigen::Matrix< double, M, -1 > |
Protected Member Functions | |
| void | calculateJacobian () |
| void | print (const std::string &s="") const |
Protected Attributes | |
| Jacobian | H_ |
| Weights | weights_ |
VectorDerivativeFunctor at a given x, applied to ParameterMatrix<M>.
This functor is used to evaluate the derivatives of a parameterized function at a given scalar value x. When given a specific M*N parameters, returns an M-vector the M corresponding function derivatives at x, possibly with Jacobians wrpt the parameters.
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inlineprotected |
Calculate the M*(M*N) Jacobian of this functor with respect to the M*N parameter matrix P. We flatten assuming column-major order, e.g., if N=3 and M=2, we have H =[ w(0) 0 w(1) 0 w(2) 0 0 w(0) 0 w(1) 0 w(2) ] i.e., the Kronecker product of weights_ with the MxM identity matrix.
1.8.13