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Fix 32 bit build of GTSAM 4.2 Python wrapper #2165

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8c99d71
Fix misuses of `size_t` for `gtsam::Key`
ProfFan Jun 5, 2025
e400b23
Two more fixes of 32-bit gtsam::Key vs size_t
ProfFan Jun 7, 2025
c9bf422
Fix usage of GCC extension before C++23
ProfFan Jun 7, 2025
845096f
Fix compiler warning of operator being shadowed
ProfFan Jun 2, 2025
19ef6ff
More fixes to the nonlinear.i
ProfFan Jun 7, 2025
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5 changes: 1 addition & 4 deletions gtsam/discrete/DiscreteBayesNet.cpp
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Expand Up @@ -60,11 +60,8 @@ DiscreteValues DiscreteBayesNet::optimize() const {

DiscreteValues DiscreteBayesNet::optimize(DiscreteValues result) const {
// solve each node in turn in topological sort order (parents first)
#ifdef _MSC_VER
#pragma message("DiscreteBayesNet::optimize (deprecated) does not compute MPE!")
#else
#warning "DiscreteBayesNet::optimize (deprecated) does not compute MPE!"
#endif

for (auto conditional : boost::adaptors::reverse(*this))
conditional->solveInPlace(&result);
return result;
Expand Down
2 changes: 2 additions & 0 deletions gtsam/discrete/DiscreteConditional.h
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Expand Up @@ -111,6 +111,8 @@ class GTSAM_EXPORT DiscreteConditional
const DecisionTreeFactor& marginal,
const Ordering& orderedKeys);

using DecisionTreeFactor::operator*;

/**
* @brief Combine two conditionals, yielding a new conditional with the union
* of the frontal keys, ordered by gtsam::Key.
Expand Down
20 changes: 10 additions & 10 deletions gtsam/gtsam.i
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Expand Up @@ -31,12 +31,12 @@ class KeyList {
// structure specific methods
size_t front() const;
size_t back() const;
void push_back(size_t key);
void push_front(size_t key);
void push_back(gtsam::Key key);
void push_front(gtsam::Key key);
void pop_back();
void pop_front();
void sort();
void remove(size_t key);
void remove(gtsam::Key key);

void serialize() const;
};
Expand All @@ -58,10 +58,10 @@ class KeySet {
void clear();

// structure specific methods
void insert(size_t key);
void insert(gtsam::Key key);
void merge(const gtsam::KeySet& other);
bool erase(size_t key); // returns true if value was removed
bool count(size_t key) const; // returns true if value exists
bool erase(gtsam::Key key); // returns true if value was removed
bool count(gtsam::Key key) const; // returns true if value exists

void serialize() const;
};
Expand All @@ -82,7 +82,7 @@ class KeyVector {
size_t at(size_t i) const;
size_t front() const;
size_t back() const;
void push_back(size_t key) const;
void push_back(gtsam::Key key) const;

void serialize() const;
};
Expand All @@ -99,9 +99,9 @@ class KeyGroupMap {
void clear();

// structure specific methods
size_t at(size_t key) const;
int erase(size_t key);
bool insert2(size_t key, int val);
size_t at(gtsam::Key key) const;
int erase(gtsam::Key key);
bool insert2(gtsam::Key key, int val);
};

// Actually a FastSet<FactorIndex>
Expand Down
76 changes: 38 additions & 38 deletions gtsam/inference/inference.i
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Expand Up @@ -28,9 +28,9 @@ void PrintKeySet(
class Symbol {
Symbol();
Symbol(char c, uint64_t j);
Symbol(size_t key);
Symbol(gtsam::Key key);

size_t key() const;
gtsam::Key key() const;
void print(const string& s = "") const;
bool equals(const gtsam::Symbol& expected, double tol) const;

Expand All @@ -39,46 +39,46 @@ class Symbol {
string string() const;
};

size_t symbol(char chr, size_t index);
char symbolChr(size_t key);
size_t symbolIndex(size_t key);
gtsam::Key symbol(char chr, size_t index);
char symbolChr(gtsam::Key key);
size_t symbolIndex(gtsam::Key key);

namespace symbol_shorthand {
size_t A(size_t j);
size_t B(size_t j);
size_t C(size_t j);
size_t D(size_t j);
size_t E(size_t j);
size_t F(size_t j);
size_t G(size_t j);
size_t H(size_t j);
size_t I(size_t j);
size_t J(size_t j);
size_t K(size_t j);
size_t L(size_t j);
size_t M(size_t j);
size_t N(size_t j);
size_t O(size_t j);
size_t P(size_t j);
size_t Q(size_t j);
size_t R(size_t j);
size_t S(size_t j);
size_t T(size_t j);
size_t U(size_t j);
size_t V(size_t j);
size_t W(size_t j);
size_t X(size_t j);
size_t Y(size_t j);
size_t Z(size_t j);
gtsam::Key A(size_t j);
gtsam::Key B(size_t j);
gtsam::Key C(size_t j);
gtsam::Key D(size_t j);
gtsam::Key E(size_t j);
gtsam::Key F(size_t j);
gtsam::Key G(size_t j);
gtsam::Key H(size_t j);
gtsam::Key I(size_t j);
gtsam::Key J(size_t j);
gtsam::Key K(size_t j);
gtsam::Key L(size_t j);
gtsam::Key M(size_t j);
gtsam::Key N(size_t j);
gtsam::Key O(size_t j);
gtsam::Key P(size_t j);
gtsam::Key Q(size_t j);
gtsam::Key R(size_t j);
gtsam::Key S(size_t j);
gtsam::Key T(size_t j);
gtsam::Key U(size_t j);
gtsam::Key V(size_t j);
gtsam::Key W(size_t j);
gtsam::Key X(size_t j);
gtsam::Key Y(size_t j);
gtsam::Key Z(size_t j);
} // namespace symbol_shorthand

#include <gtsam/inference/LabeledSymbol.h>
class LabeledSymbol {
LabeledSymbol(size_t full_key);
LabeledSymbol(gtsam::Key full_key);
LabeledSymbol(const gtsam::LabeledSymbol& key);
LabeledSymbol(unsigned char valType, unsigned char label, size_t j);

size_t key() const;
gtsam::Key key() const;
unsigned char label() const;
unsigned char chr() const;
size_t index() const;
Expand All @@ -91,10 +91,10 @@ class LabeledSymbol {
void print(string s = "") const;
};

size_t mrsymbol(unsigned char c, unsigned char label, size_t j);
unsigned char mrsymbolChr(size_t key);
unsigned char mrsymbolLabel(size_t key);
size_t mrsymbolIndex(size_t key);
gtsam::Key mrsymbol(unsigned char c, unsigned char label, size_t j);
unsigned char mrsymbolChr(gtsam::Key key);
unsigned char mrsymbolLabel(gtsam::Key key);
size_t mrsymbolIndex(gtsam::Key key);

#include <gtsam/inference/Ordering.h>
class Ordering {
Expand Down Expand Up @@ -149,7 +149,7 @@ class Ordering {
// Standard interface
size_t size() const;
size_t at(size_t i) const;
void push_back(size_t key);
void push_back(gtsam::Key key);

// enabling serialization functionality
void serialize() const;
Expand Down
81 changes: 46 additions & 35 deletions gtsam/linear/linear.i
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Expand Up @@ -228,16 +228,16 @@ class VectorValues {

//Standard Interface
size_t size() const;
size_t dim(size_t j) const;
bool exists(size_t j) const;
size_t dim(gtsam::Key j) const;
bool exists(gtsam::Key j) const;
void print(string s = "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;
bool equals(const gtsam::VectorValues& x, double tol) const;
void insert(gtsam::Key j, gtsam::Vector value);
gtsam::Vector vector() const;
gtsam::Vector vector(const gtsam::KeyVector& keys) const;
gtsam::Vector at(gtsam::Key j) const;
void insert(const gtsam::VectorValues& values);
void update(const gtsam::VectorValues& values);

Expand Down Expand Up @@ -278,13 +278,13 @@ virtual class GaussianFactor : gtsam::Factor {
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(gtsam::Key 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(gtsam::Key i1, gtsam::Matrix A1, gtsam::Key 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(gtsam::Key i1, gtsam::Matrix A1, gtsam::Key i2, gtsam::Matrix A2, gtsam::Key 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);
Expand Down Expand Up @@ -330,12 +330,12 @@ 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(gtsam::Key j, gtsam::Matrix G, gtsam::Vector g, double f);
HessianFactor(gtsam::Key j, gtsam::Vector mu, gtsam::Matrix Sigma);
HessianFactor(gtsam::Key j1, gtsam::Key j2, gtsam::Matrix G11, gtsam::Matrix G12, gtsam::Vector g1, gtsam::Matrix G22,
gtsam::Vector g2, double f);
HessianFactor(gtsam::Key j1, gtsam::Key j2, gtsam::Key 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);

Expand Down Expand Up @@ -378,12 +378,12 @@ 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(gtsam::Key key1, gtsam::Matrix A1, gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);
void add(gtsam::Key key1, gtsam::Matrix A1, gtsam::Key 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(gtsam::Key key1, gtsam::Matrix A1, gtsam::Key key2, gtsam::Matrix A2, gtsam::Key key3, gtsam::Matrix A3,
gtsam::Vector b, const gtsam::noiseModel::Diagonal* model);

// error and probability
double error(const gtsam::VectorValues& c) const;
Expand Down Expand Up @@ -451,19 +451,24 @@ class GaussianFactorGraph {
#include <gtsam/hybrid/HybridValues.h>
virtual class GaussianConditional : gtsam::JacobianFactor {
// Constructors
GaussianConditional(size_t key, Vector d, Matrix R,
GaussianConditional(gtsam::Key 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(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R, gtsam::Key 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(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R, gtsam::Key name1, gtsam::Matrix S,
gtsam::Key name2, gtsam::Matrix T,
const gtsam::noiseModel::Diagonal* sigmas);
GaussianConditional(const std::vector<std::pair<gtsam::Key, gtsam::Matrix>> terms,
size_t nrFrontals, gtsam::Vector d,
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(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R);
GaussianConditional(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R, gtsam::Key name1, gtsam::Matrix S);
GaussianConditional(gtsam::Key key, gtsam::Vector d, gtsam::Matrix R, gtsam::Key name1, gtsam::Matrix S,
gtsam::Key name2, gtsam::Matrix T);
GaussianConditional(const gtsam::KeyVector& keys, size_t nrFrontals,
const gtsam::VerticalBlockMatrix& augmentedMatrix);

// Named constructors
static gtsam::GaussianConditional FromMeanAndStddev(gtsam::Key key,
Expand Down Expand Up @@ -621,16 +626,22 @@ virtual class GaussianBayesTree {
double error(const gtsam::VectorValues& x) const;
double determinant() const;
double logDeterminant() const;
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;
gtsam::Matrix marginalCovariance(gtsam::Key key) const;
gtsam::GaussianConditional* marginalFactor(gtsam::Key key) const;
gtsam::GaussianFactorGraph* joint(gtsam::Key key1, gtsam::Key key2) const;
gtsam::GaussianBayesNet* jointBayesNet(gtsam::Key key1, gtsam::Key key2) const;
};

#include <gtsam/linear/GaussianISAM.h>
class GaussianISAM {
//Constructor
GaussianISAM();
GaussianISAM(const gtsam::GaussianBayesTree& bayesTree);

gtsam::VectorValues optimize() const;
gtsam::VectorValues optimizeGradientSearch() const;

gtsam::GaussianConditional* marginalFactor(gtsam::Key key) const;

//Standard Interface
void update(const gtsam::GaussianFactorGraph& newFactors);
Expand Down
26 changes: 13 additions & 13 deletions gtsam/navigation/navigation.i
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Expand Up @@ -139,8 +139,8 @@ class PreintegratedImuMeasurements {
};

virtual class ImuFactor: gtsam::NonlinearFactor {
ImuFactor(size_t pose_i, size_t vel_i, size_t pose_j, size_t vel_j,
size_t bias,
ImuFactor(gtsam::Key pose_i, gtsam::Key vel_i, gtsam::Key pose_j, gtsam::Key vel_j,
gtsam::Key bias,
const gtsam::PreintegratedImuMeasurements& preintegratedMeasurements);

// Standard Interface
Expand Down Expand Up @@ -200,9 +200,9 @@ class PreintegratedCombinedMeasurements {
const gtsam::imuBias::ConstantBias& bias) const;
};

virtual class CombinedImuFactor: gtsam::NonlinearFactor {
CombinedImuFactor(size_t pose_i, size_t vel_i, size_t pose_j, size_t vel_j,
size_t bias_i, size_t bias_j,
virtual class CombinedImuFactor: gtsam::NoiseModelFactor {
CombinedImuFactor(gtsam::Key pose_i, gtsam::Key vel_i, gtsam::Key pose_j, gtsam::Key vel_j,
gtsam::Key bias_i, gtsam::Key bias_j,
const gtsam::PreintegratedCombinedMeasurements& CombinedPreintegratedMeasurements);

// Standard Interface
Expand Down Expand Up @@ -240,9 +240,9 @@ class PreintegratedAhrsMeasurements {
};

virtual class AHRSFactor : gtsam::NonlinearFactor {
AHRSFactor(size_t rot_i, size_t rot_j,size_t bias,
AHRSFactor(gtsam::Key rot_i, gtsam::Key rot_j, gtsam::Key bias,
const gtsam::PreintegratedAhrsMeasurements& preintegratedMeasurements, Vector omegaCoriolis);
AHRSFactor(size_t rot_i, size_t rot_j, size_t bias,
AHRSFactor(gtsam::Key rot_i, gtsam::Key rot_j, gtsam::Key bias,
const gtsam::PreintegratedAhrsMeasurements& preintegratedMeasurements, Vector omegaCoriolis,
const gtsam::Pose3& body_P_sensor);

Expand Down Expand Up @@ -272,11 +272,11 @@ virtual class Rot3AttitudeFactor : gtsam::NonlinearFactor{
gtsam::Unit3 bRef() const;
};

virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor {
Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ,
virtual class Pose3AttitudeFactor : gtsam::NoiseModelFactor {
Pose3AttitudeFactor(gtsam::Key key, const gtsam::Unit3& nRef,
const gtsam::noiseModel::Diagonal* model,
const gtsam::Unit3& bRef);
Pose3AttitudeFactor(size_t key, const gtsam::Unit3& nZ,
const gtsam::Unit3& bMeasured);
Pose3AttitudeFactor(gtsam::Key key, const gtsam::Unit3& nRef,
const gtsam::noiseModel::Diagonal* model);
Pose3AttitudeFactor();
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
Expand All @@ -288,7 +288,7 @@ virtual class Pose3AttitudeFactor : gtsam::NonlinearFactor {

#include <gtsam/navigation/GPSFactor.h>
virtual class GPSFactor : gtsam::NonlinearFactor{
GPSFactor(size_t key, const gtsam::Point3& gpsIn,
GPSFactor(gtsam::Key key, const gtsam::Point3& gpsIn,
const gtsam::noiseModel::Base* model);

// Testable
Expand All @@ -301,7 +301,7 @@ virtual class GPSFactor : gtsam::NonlinearFactor{
};

virtual class GPSFactor2 : gtsam::NonlinearFactor {
GPSFactor2(size_t key, const gtsam::Point3& gpsIn,
GPSFactor2(gtsam::Key key, const gtsam::Point3& gpsIn,
const gtsam::noiseModel::Base* model);

// Testable
Expand Down
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