unbroke a number of classes in linear, testing them with gtsam_experimental/matlab code
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@ -1,15 +1,6 @@
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// These are considered to be broken and will be added back as they start working
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// It's assumed that there have been interface changes that might break this
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class SharedGaussian {
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SharedGaussian(Matrix covariance);
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SharedGaussian(Vector sigmas);
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};
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class SharedDiagonal {
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SharedDiagonal(Vector sigmas);
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};
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class Ordering {
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Ordering();
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void print(string s) const;
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@ -17,55 +8,11 @@ class Ordering {
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void push_back(string s);
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};
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class VectorValues {
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VectorValues();
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VectorValues(size_t nVars, size_t varDim);
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void print(string s) const;
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bool equals(const VectorValues& expected, double tol) const;
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size_t size() const;
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};
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class GaussianFactor {
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void print(string s) const;
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bool equals(const GaussianFactor& lf, double tol) const;
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bool empty() const;
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Vector getb() const;
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double error(const VectorValues& c) const;
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};
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class GaussianFactorSet {
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GaussianFactorSet();
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void push_back(GaussianFactor* factor);
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};
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class GaussianConditional {
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GaussianConditional();
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void print(string s) const;
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bool equals(const GaussianConditional &cg, double tol) const;
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Vector solve(const VectorValues& x);
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};
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class GaussianBayesNet {
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GaussianBayesNet();
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void print(string s) const;
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bool equals(const GaussianBayesNet& cbn, double tol) const;
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void push_back(GaussianConditional* conditional);
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void push_front(GaussianConditional* conditional);
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};
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class GaussianFactorGraph {
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GaussianFactorGraph();
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void print(string s) const;
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bool equals(const GaussianFactorGraph& lfgraph, double tol) const;
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size_t size() const;
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void push_back(GaussianFactor* ptr_f);
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double error(const VectorValues& c) const;
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double probPrime(const VectorValues& c) const;
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void combine(const GaussianFactorGraph& lfg);
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};
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class Simulated2DValues {
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Simulated2DValues();
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void print(string s) const;
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123
gtsam.h
123
gtsam.h
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@ -1,20 +1,20 @@
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class Point2 {
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Point2();
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Point2(double x, double y);
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void print(string s) const;
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double x();
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double y();
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Point2();
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Point2(double x, double y);
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void print(string s) const;
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double x();
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double y();
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};
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class Point3 {
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Point3();
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Point3(double x, double y, double z);
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Point3(Vector v);
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void print(string s) const;
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Vector vector() const;
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double x();
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double y();
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double z();
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Point3();
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Point3(double x, double y, double z);
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Point3(Vector v);
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void print(string s) const;
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Vector vector() const;
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double x();
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double y();
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double z();
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};
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class Rot2 {
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@ -22,20 +22,87 @@ class Rot2 {
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};
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class Pose2 {
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Pose2();
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Pose2(const Pose2& pose);
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Pose2(double x, double y, double theta);
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Pose2(double theta, const Point2& t);
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Pose2(const Rot2& r, const Point2& t);
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void print(string s) const;
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bool equals(const Pose2& pose, double tol) const;
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double x() const;
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double y() const;
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double theta() const;
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size_t dim() const;
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Pose2 expmap(Vector v) const;
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Vector logmap(const Pose2& pose) const;
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Point2 t() const;
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Rot2 r() const;
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Pose2();
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Pose2(const Pose2& pose);
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Pose2(double x, double y, double theta);
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Pose2(double theta, const Point2& t);
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Pose2(const Rot2& r, const Point2& t);
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void print(string s) const;
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bool equals(const Pose2& pose, double tol) const;
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double x() const;
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double y() const;
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double theta() const;
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size_t dim() const;
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};
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class SharedGaussian {
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SharedGaussian(Matrix covariance);
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SharedGaussian(Vector sigmas);
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};
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class SharedDiagonal {
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SharedDiagonal(Vector sigmas);
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};
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class VectorValues {
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VectorValues();
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VectorValues(size_t nVars, size_t varDim);
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void print(string s) const;
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bool equals(const VectorValues& expected, double tol) const;
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size_t size() const;
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void reserve(size_t nVars, size_t totalDims);
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size_t push_back_preallocated(Vector vector);
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};
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class GaussianConditional {
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GaussianConditional(size_t key, Vector d, Matrix R, Vector sigmas);
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GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
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Vector sigmas);
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GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
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size_t name2, Matrix T, Vector sigmas);
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void print(string s) const;
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bool equals(const GaussianConditional &cg, double tol) const;
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};
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class GaussianBayesNet {
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GaussianBayesNet();
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void print(string s) const;
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bool equals(const GaussianBayesNet& cbn, double tol) const;
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void push_back(GaussianConditional* conditional);
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void push_front(GaussianConditional* conditional);
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};
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class GaussianFactor {
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void print(string s) const;
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bool equals(const GaussianFactor& lf, double tol) const;
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double error(const VectorValues& c) const;
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};
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class JacobianFactor {
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JacobianFactor();
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JacobianFactor(Vector b_in);
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JacobianFactor(size_t i1, Matrix A1, Vector b, const SharedDiagonal& model);
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JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, Vector b,
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const SharedDiagonal& model);
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JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, size_t i3,
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Matrix A3, Vector b, const SharedDiagonal& model);
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void print(string s) const;
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bool equals(const GaussianFactor& lf, double tol) const;
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bool empty() const;
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Vector getb() const;
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double error(const VectorValues& c) const;
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GaussianConditional* eliminateFirst();
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};
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class GaussianFactorGraph {
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GaussianFactorGraph();
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void print(string s) const;
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bool equals(const GaussianFactorGraph& lfgraph, double tol) const;
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size_t size() const;
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void push_back(GaussianFactor* ptr_f);
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double error(const VectorValues& c) const;
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double probPrime(const VectorValues& c) const;
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void combine(const GaussianFactorGraph& lfg);
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};
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