154 lines
3.7 KiB
C++
154 lines
3.7 KiB
C++
class VectorConfig {
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VectorConfig();
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Vector get(string name) const;
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bool contains(string name) const;
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size_t size() const;
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void insert(string name, Vector val);
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void print() const;
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bool equals(const VectorConfig& expected, double tol) const;
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void clear();
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};
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class LinearFactorSet {
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LinearFactorSet();
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void push_back(LinearFactor* factor);
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};
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class LinearFactor {
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LinearFactor(string key1,
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Matrix A1,
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Vector b_in,
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double sigma);
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LinearFactor(string key1,
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Matrix A1,
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string key2,
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Matrix A2,
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Vector b_in,
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double sigma);
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LinearFactor(string key1,
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Matrix A1,
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string key2,
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Matrix A2,
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string key3,
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Matrix A3,
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Vector b_in,
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double sigma);
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bool empty() const;
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Vector get_b() const;
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Matrix get_A(string key) const;
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double error(const VectorConfig& c) const;
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bool involves(string key) const;
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void print() const;
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bool equals(const LinearFactor& lf, double tol) const;
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pair<Matrix,Vector> matrix(const Ordering& ordering) const;
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};
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class ConditionalGaussian {
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ConditionalGaussian();
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ConditionalGaussian(string key,
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Vector d,
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Matrix R,
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Vector precisions);
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ConditionalGaussian(string key,
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Vector d,
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Matrix R,
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string name1,
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Matrix S,
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Vector precisions);
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ConditionalGaussian(string key,
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Vector d,
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Matrix R,
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string name1,
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Matrix S,
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string name2,
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Matrix T,
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Vector precisions);
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void print() const;
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Vector solve(const VectorConfig& x);
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void add(string key, Matrix S);
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bool equals(const ConditionalGaussian &cg) const;
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};
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class Ordering {
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Ordering();
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void push_back(string s);
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void print() const;
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};
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class GaussianBayesNet {
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GaussianBayesNet();
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VectorConfig* optimize();
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void print() const;
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bool equals(const GaussianBayesNet& cbn) const;
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pair<Matrix,Vector> matrix() const;
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};
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class LinearFactorGraph {
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LinearFactorGraph();
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size_t size() const;
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void push_back(LinearFactor* ptr_f);
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double error(const VectorConfig& c) const;
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double probPrime(const VectorConfig& c) const;
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void print() const;
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bool equals(const LinearFactorGraph& lfgraph) const;
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VectorConfig optimize(const Ordering& ordering);
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GaussianBayesNet* eliminate(const Ordering& ordering);
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pair<Matrix,Vector> matrix(const Ordering& ordering) const;
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Matrix sparse(const Ordering& ordering) const;
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};
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class Point2 {
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Point2();
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Point2(double x, double y);
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double x();
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double y();
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size_t dim() const;
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void print() const;
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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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size_t dim() const;
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Point3 exmap(Vector d) 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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void print() const;
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};
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class Point2Prior {
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Point2Prior(Vector mu, double sigma, string key);
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Vector error_vector(const VectorConfig& c) const;
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LinearFactor* linearize(const VectorConfig& c) const;
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double sigma();
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Vector measurement();
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double error(const VectorConfig& c) const;
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void print() const;
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};
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class Simulated2DOdometry {
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Simulated2DOdometry(Vector odo, double sigma, string key, string key2);
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Vector error_vector(const VectorConfig& c) const;
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LinearFactor* linearize(const VectorConfig& c) const;
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double sigma();
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Vector measurement();
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double error(const VectorConfig& c) const;
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void print() const;
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};
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class Simulated2DMeasurement {
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Simulated2DMeasurement(Vector odo, double sigma, string key, string key2);
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Vector error_vector(const VectorConfig& c) const;
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LinearFactor* linearize(const VectorConfig& c) const;
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double sigma();
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Vector measurement();
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double error(const VectorConfig& c) const;
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void print() const;
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};
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