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update gtsam:: namespace in linear.i

release/4.3a0
Varun Agrawal 2024-06-28 16:16:36 -04:00
parent ead977df76
commit b6d697291e
1 changed files with 105 additions and 105 deletions
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gtsam/linear/linear.i
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@ -12,52 +12,52 @@ virtual class Base {
// bool isConstrained() const;
// bool isUnit() const;
// size_t dim() const;
// Vector sigmas() const;
// gtsam::Vector sigmas() const;
};
virtual class Gaussian : gtsam::noiseModel::Base {
static gtsam::noiseModel::Gaussian* Information(Matrix R, bool smart = true);
static gtsam::noiseModel::Gaussian* SqrtInformation(Matrix R, bool smart = true);
static gtsam::noiseModel::Gaussian* Covariance(Matrix R, bool smart = true);
static gtsam::noiseModel::Gaussian* Information(gtsam::Matrix R, bool smart = true);
static gtsam::noiseModel::Gaussian* SqrtInformation(gtsam::Matrix R, bool smart = true);
static gtsam::noiseModel::Gaussian* Covariance(gtsam::Matrix R, bool smart = true);
bool equals(gtsam::noiseModel::Base& expected, double tol);
// access to noise model
Matrix R() const;
Matrix information() const;
Matrix covariance() const;
gtsam::Matrix R() const;
gtsam::Matrix information() const;
gtsam::Matrix covariance() const;
// Whitening operations
Vector whiten(Vector v) const;
Vector unwhiten(Vector v) const;
Matrix Whiten(Matrix H) const;
gtsam::Vector whiten(gtsam::Vector v) const;
gtsam::Vector unwhiten(gtsam::Vector v) const;
gtsam::Matrix Whiten(gtsam::Matrix H) const;
// enabling serialization functionality
void serializable() const;
};
virtual class Diagonal : gtsam::noiseModel::Gaussian {
static gtsam::noiseModel::Diagonal* Sigmas(Vector sigmas, bool smart = true);
static gtsam::noiseModel::Diagonal* Variances(Vector variances, bool smart = true);
static gtsam::noiseModel::Diagonal* Precisions(Vector precisions, bool smart = true);
Matrix R() const;
static gtsam::noiseModel::Diagonal* Sigmas(gtsam::Vector sigmas, bool smart = true);
static gtsam::noiseModel::Diagonal* Variances(gtsam::Vector variances, bool smart = true);
static gtsam::noiseModel::Diagonal* Precisions(gtsam::Vector precisions, bool smart = true);
gtsam::Matrix R() const;
// access to noise model
Vector sigmas() const;
Vector invsigmas() const;
Vector precisions() const;
gtsam::Vector sigmas() const;
gtsam::Vector invsigmas() const;
gtsam::Vector precisions() const;
// enabling serialization functionality
void serializable() const;
};
virtual class Constrained : gtsam::noiseModel::Diagonal {
static gtsam::noiseModel::Constrained* MixedSigmas(Vector mu, Vector sigmas);
static gtsam::noiseModel::Constrained* MixedSigmas(double m, Vector sigmas);
static gtsam::noiseModel::Constrained* MixedVariances(Vector mu, Vector variances);
static gtsam::noiseModel::Constrained* MixedVariances(Vector variances);
static gtsam::noiseModel::Constrained* MixedPrecisions(Vector mu, Vector precisions);
static gtsam::noiseModel::Constrained* MixedPrecisions(Vector precisions);
static gtsam::noiseModel::Constrained* MixedSigmas(gtsam::Vector mu, gtsam::Vector sigmas);
static gtsam::noiseModel::Constrained* MixedSigmas(double m, gtsam::Vector sigmas);
static gtsam::noiseModel::Constrained* MixedVariances(gtsam::Vector mu, gtsam::Vector variances);
static gtsam::noiseModel::Constrained* MixedVariances(gtsam::Vector variances);
static gtsam::noiseModel::Constrained* MixedPrecisions(gtsam::Vector mu, gtsam::Vector precisions);
static gtsam::noiseModel::Constrained* MixedPrecisions(gtsam::Vector precisions);
static gtsam::noiseModel::Constrained* All(size_t dim);
static gtsam::noiseModel::Constrained* All(size_t dim, double mu);
@ -257,13 +257,13 @@ virtual class Robust : gtsam::noiseModel::Base {
class Sampler {
// Constructors
Sampler(gtsam::noiseModel::Diagonal* model, int seed);
Sampler(Vector sigmas, int seed);
Sampler(gtsam::Vector sigmas, int seed);
// Standard Interface
size_t dim() const;
Vector sigmas() const;
gtsam::Vector sigmas() const;
gtsam::noiseModel::Diagonal* model() const;
Vector sample();
gtsam::Vector sample();
};
#include <gtsam/linear/VectorValues.h>
@ -284,10 +284,10 @@ class VectorValues {
const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter) const;
bool equals(const gtsam::VectorValues& expected, double tol) const;
void insert(size_t j, Vector value);
Vector vector() const;
Vector vector(const gtsam::KeyVector& keys) const;
Vector at(size_t j) const;
void insert(size_t j, gtsam::Vector value);
gtsam::Vector vector() const;
gtsam::Vector vector(const gtsam::KeyVector& keys) const;
gtsam::Vector at(size_t j) const;
void insert(const gtsam::VectorValues& values);
void update(const gtsam::VectorValues& values);
@ -318,23 +318,23 @@ virtual class GaussianFactor : gtsam::Factor {
double error(const gtsam::VectorValues& c) const;
gtsam::GaussianFactor* clone() const;
gtsam::GaussianFactor* negate() const;
Matrix augmentedInformation() const;
Matrix information() const;
Matrix augmentedJacobian() const;
pair<Matrix, Vector> jacobian() const;
gtsam::Matrix augmentedInformation() const;
gtsam::Matrix information() const;
gtsam::Matrix augmentedJacobian() const;
pair<gtsam::Matrix, gtsam::Vector> jacobian() const;
};
#include <gtsam/linear/JacobianFactor.h>
virtual class JacobianFactor : gtsam::GaussianFactor {
//Constructors
JacobianFactor();
JacobianFactor(Vector b_in);
JacobianFactor(size_t i1, Matrix A1, Vector b,
JacobianFactor(gtsam::Vector b_in);
JacobianFactor(size_t i1, gtsam::Matrix A1, gtsam::Vector b,
const gtsam::noiseModel::Diagonal* model);
JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, Vector b,
JacobianFactor(size_t i1, gtsam::Matrix A1, size_t i2, gtsam::Matrix A2, gtsam::Vector b,
const gtsam::noiseModel::Diagonal* model);
JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, size_t i3, Matrix A3,
Vector b, const gtsam::noiseModel::Diagonal* model);
JacobianFactor(size_t i1, gtsam::Matrix A1, size_t i2, gtsam::Matrix A2, size_t i3, gtsam::Matrix A3,
gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);
JacobianFactor(const gtsam::GaussianFactorGraph& graph);
JacobianFactor(const gtsam::GaussianFactorGraph& graph,
const gtsam::VariableSlots& p_variableSlots);
@ -349,25 +349,25 @@ virtual class JacobianFactor : gtsam::GaussianFactor {
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
gtsam::DefaultKeyFormatter) const;
bool equals(const gtsam::GaussianFactor& lf, double tol) const;
Vector unweighted_error(const gtsam::VectorValues& c) const;
Vector error_vector(const gtsam::VectorValues& c) const;
gtsam::Vector unweighted_error(const gtsam::VectorValues& c) const;
gtsam::Vector error_vector(const gtsam::VectorValues& c) const;
double error(const gtsam::VectorValues& c) const;
//Standard Interface
Matrix getA() const;
Vector getb() const;
gtsam::Matrix getA() const;
gtsam::Vector getb() const;
size_t rows() const;
size_t cols() const;
bool isConstrained() const;
pair<Matrix, Vector> jacobianUnweighted() const;
Matrix augmentedJacobianUnweighted() const;
pair<gtsam::Matrix, gtsam::Vector> jacobianUnweighted() const;
gtsam::Matrix augmentedJacobianUnweighted() const;
void transposeMultiplyAdd(double alpha, Vector e, gtsam::VectorValues& x) const;
void transposeMultiplyAdd(double alpha, gtsam::Vector e, gtsam::VectorValues& x) const;
gtsam::JacobianFactor whiten() const;
pair<gtsam::GaussianConditional*, gtsam::JacobianFactor*> eliminate(const gtsam::Ordering& keys) const;
void setModel(bool anyConstrained, Vector sigmas);
void setModel(bool anyConstrained, gtsam::Vector sigmas);
gtsam::noiseModel::Diagonal* get_model() const;
@ -382,12 +382,12 @@ virtual class HessianFactor : gtsam::GaussianFactor {
//Constructors
HessianFactor();
HessianFactor(const gtsam::GaussianFactor& factor);
HessianFactor(size_t j, Matrix G, Vector g, double f);
HessianFactor(size_t j, Vector mu, Matrix Sigma);
HessianFactor(size_t j1, size_t j2, Matrix G11, Matrix G12, Vector g1, Matrix G22,
Vector g2, double f);
HessianFactor(size_t j1, size_t j2, size_t j3, Matrix G11, Matrix G12, Matrix G13,
Vector g1, Matrix G22, Matrix G23, Vector g2, Matrix G33, Vector g3,
HessianFactor(size_t j, gtsam::Matrix G, gtsam::Vector g, double f);
HessianFactor(size_t j, gtsam::Vector mu, gtsam::Matrix Sigma);
HessianFactor(size_t j1, size_t j2, gtsam::Matrix G11, gtsam::Matrix G12, gtsam::Vector g1, gtsam::Matrix G22,
gtsam::Vector g2, double f);
HessianFactor(size_t j1, size_t j2, size_t j3, gtsam::Matrix G11, gtsam::Matrix G12, gtsam::Matrix G13,
gtsam::Vector g1, gtsam::Matrix G22, gtsam::Matrix G23, gtsam::Vector g2, gtsam::Matrix G33, gtsam::Vector g3,
double f);
HessianFactor(const gtsam::GaussianFactorGraph& factors);
@ -399,9 +399,9 @@ virtual class HessianFactor : gtsam::GaussianFactor {
//Standard Interface
size_t rows() const;
Matrix information() const;
gtsam::Matrix information() const;
double constantTerm() const;
Vector linearTerm() const;
gtsam::Vector linearTerm() const;
// enabling serialization functionality
void serialize() const;
@ -430,12 +430,12 @@ class GaussianFactorGraph {
void push_back(const gtsam::GaussianBayesNet& bayesNet);
void push_back(const gtsam::GaussianBayesTree& bayesTree);
void add(const gtsam::GaussianFactor& factor);
void add(Vector b);
void add(size_t key1, Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
void add(size_t key1, Matrix A1, size_t key2, Matrix A2, Vector b,
void add(gtsam::Vector b);
void add(size_t key1, gtsam::Matrix A1, gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);
void add(size_t key1, gtsam::Matrix A1, size_t key2, gtsam::Matrix A2, gtsam::Vector b,
const gtsam::noiseModel::Diagonal* model);
void add(size_t key1, Matrix A1, size_t key2, Matrix A2, size_t key3, Matrix A3,
Vector b, const gtsam::noiseModel::Diagonal* model);
void add(size_t key1, gtsam::Matrix A1, size_t key2, gtsam::Matrix A2, size_t key3, gtsam::Matrix A3,
gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);
// error and probability
double error(const gtsam::VectorValues& c) const;
@ -477,15 +477,15 @@ class GaussianFactorGraph {
gtsam::GaussianFactorGraph* marginal(const gtsam::KeyVector& key_vector);
// Conversion to matrices
Matrix sparseJacobian_() const;
Matrix augmentedJacobian() const;
Matrix augmentedJacobian(const gtsam::Ordering& ordering) const;
pair<Matrix,Vector> jacobian() const;
pair<Matrix,Vector> jacobian(const gtsam::Ordering& ordering) const;
Matrix augmentedHessian() const;
Matrix augmentedHessian(const gtsam::Ordering& ordering) const;
pair<Matrix,Vector> hessian() const;
pair<Matrix,Vector> hessian(const gtsam::Ordering& ordering) const;
gtsam::Matrix sparseJacobian_() const;
gtsam::Matrix augmentedJacobian() const;
gtsam::Matrix augmentedJacobian(const gtsam::Ordering& ordering) const;
pair<gtsam::Matrix,gtsam::Vector> jacobian() const;
pair<gtsam::Matrix,gtsam::Vector> jacobian(const gtsam::Ordering& ordering) const;
gtsam::Matrix augmentedHessian() const;
gtsam::Matrix augmentedHessian(const gtsam::Ordering& ordering) const;
pair<gtsam::Matrix,gtsam::Vector> hessian() const;
pair<gtsam::Matrix,gtsam::Vector> hessian(const gtsam::Ordering& ordering) const;
string dot(
const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
@ -503,37 +503,37 @@ class GaussianFactorGraph {
#include <gtsam/hybrid/HybridValues.h>
virtual class GaussianConditional : gtsam::JacobianFactor {
// Constructors
GaussianConditional(size_t key, Vector d, Matrix R,
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R,
const gtsam::noiseModel::Diagonal* sigmas);
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R, size_t name1, gtsam::Matrix S,
const gtsam::noiseModel::Diagonal* sigmas);
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
size_t name2, Matrix T,
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R, size_t name1, gtsam::Matrix S,
size_t name2, gtsam::Matrix T,
const gtsam::noiseModel::Diagonal* sigmas);
// Constructors with no noise model
GaussianConditional(size_t key, Vector d, Matrix R);
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S);
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
size_t name2, Matrix T);
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R);
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R, size_t name1, gtsam::Matrix S);
GaussianConditional(size_t key, gtsam::Vector d, gtsam::Matrix R, size_t name1, gtsam::Matrix S,
size_t name2, gtsam::Matrix T);
// Named constructors
static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key,
const Vector& mu,
const gtsam::Vector& mu,
double sigma);
static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key,
const Matrix& A,
const gtsam::Matrix& A,
gtsam::Key parent,
const Vector& b,
const gtsam::Vector& b,
double sigma);
static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key,
const Matrix& A1,
const gtsam::Matrix& A1,
gtsam::Key parent1,
const Matrix& A2,
const gtsam::Matrix& A2,
gtsam::Key parent2,
const Vector& b,
const gtsam::Vector& b,
double sigma);
// Testable
void print(string s = "GaussianConditional",
@ -550,7 +550,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
gtsam::VectorValues solve(const gtsam::VectorValues& parents) const;
gtsam::JacobianFactor* likelihood(
const gtsam::VectorValues& frontalValues) const;
gtsam::JacobianFactor* likelihood(Vector frontal) const;
gtsam::JacobianFactor* likelihood(gtsam::Vector frontal) const;
gtsam::VectorValues sample(const gtsam::VectorValues& parents) const;
gtsam::VectorValues sample() const;
@ -558,9 +558,9 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
gtsam::VectorValues solveOtherRHS(const gtsam::VectorValues& parents,
const gtsam::VectorValues& rhs) const;
void solveTransposeInPlace(gtsam::VectorValues& gy) const;
Matrix R() const;
Matrix S() const;
Vector d() const;
gtsam::Matrix R() const;
gtsam::Matrix S() const;
gtsam::Vector d() const;
// enabling serialization functionality
void serialize() const;
@ -574,11 +574,11 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
#include <gtsam/linear/GaussianDensity.h>
virtual class GaussianDensity : gtsam::GaussianConditional {
// Constructors
GaussianDensity(gtsam::Key key, Vector d, Matrix R,
GaussianDensity(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R,
const gtsam::noiseModel::Diagonal* sigmas);
static gtsam::GaussianDensity FromMeanAndStddev(gtsam::Key key,
const Vector& mean,
const gtsam::Vector& mean,
double sigma);
// Testable
@ -588,8 +588,8 @@ virtual class GaussianDensity : gtsam::GaussianConditional {
bool equals(const gtsam::GaussianDensity& cg, double tol) const;
// Standard Interface
Vector mean() const;
Matrix covariance() const;
gtsam::Vector mean() const;
gtsam::Matrix covariance() const;
};
#include <gtsam/linear/GaussianBayesNet.h>
@ -632,7 +632,7 @@ virtual class GaussianBayesNet {
void saveGraph(const string& s) const;
std::pair<Matrix, Vector> matrix() const;
std::pair<gtsam::Matrix, gtsam::Vector> matrix() const;
gtsam::VectorValues gradient(const gtsam::VectorValues& x0) const;
gtsam::VectorValues gradientAtZero() const;
double error(const gtsam::VectorValues& x) const;
@ -673,7 +673,7 @@ virtual class GaussianBayesTree {
double error(const gtsam::VectorValues& x) const;
double determinant() const;
double logDeterminant() const;
Matrix marginalCovariance(size_t key) const;
gtsam::Matrix marginalCovariance(size_t key) const;
gtsam::GaussianConditional* marginalFactor(size_t key) const;
gtsam::GaussianFactorGraph* joint(size_t key1, size_t key2) const;
gtsam::GaussianBayesNet* jointBayesNet(size_t key1, size_t key2) const;
@ -751,20 +751,20 @@ virtual class SubgraphSolver {
#include <gtsam/linear/KalmanFilter.h>
class KalmanFilter {
KalmanFilter(size_t n);
// gtsam::GaussianDensity* init(Vector x0, const gtsam::SharedDiagonal& P0);
gtsam::GaussianDensity* init(Vector x0, Matrix P0);
// gtsam::GaussianDensity* init(gtsam::Vector x0, const gtsam::SharedDiagonal& P0);
gtsam::GaussianDensity* init(gtsam::Vector x0, gtsam::Matrix P0);
void print(string s = "") const;
static size_t step(gtsam::GaussianDensity* p);
gtsam::GaussianDensity* predict(gtsam::GaussianDensity* p, Matrix F,
Matrix B, Vector u, const gtsam::noiseModel::Diagonal* modelQ);
gtsam::GaussianDensity* predictQ(gtsam::GaussianDensity* p, Matrix F,
Matrix B, Vector u, Matrix Q);
gtsam::GaussianDensity* predict2(gtsam::GaussianDensity* p, Matrix A0,
Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
gtsam::GaussianDensity* update(gtsam::GaussianDensity* p, Matrix H,
Vector z, const gtsam::noiseModel::Diagonal* model);
gtsam::GaussianDensity* updateQ(gtsam::GaussianDensity* p, Matrix H,
Vector z, Matrix Q);
gtsam::GaussianDensity* predict(gtsam::GaussianDensity* p, gtsam::Matrix F,
gtsam::Matrix B, gtsam::Vector u, const gtsam::noiseModel::Diagonal* modelQ);
gtsam::GaussianDensity* predictQ(gtsam::GaussianDensity* p, gtsam::Matrix F,
gtsam::Matrix B, gtsam::Vector u, gtsam::Matrix Q);
gtsam::GaussianDensity* predict2(gtsam::GaussianDensity* p, gtsam::Matrix A0,
gtsam::Matrix A1, gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);
gtsam::GaussianDensity* update(gtsam::GaussianDensity* p, gtsam::Matrix H,
gtsam::Vector z, const gtsam::noiseModel::Diagonal* model);
gtsam::GaussianDensity* updateQ(gtsam::GaussianDensity* p, gtsam::Matrix H,
gtsam::Vector z, gtsam::Matrix Q);
};
}