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deprecate error in noisemodel, use loss instead; revise virtual with override

release/4.3a0
yetongumich 2020-07-12 23:05:24 -04:00
parent eb1a2b8fb3
commit 7db7455c12
3 changed files with 113 additions and 97 deletions
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36
gtsam/linear/NoiseModel.cpp
View File

@ -74,6 +74,13 @@ Vector Base::sigmas() const {
throw("Base::sigmas: sigmas() not implemented for this noise model"); throw("Base::sigmas: sigmas() not implemented for this noise model");
} }
/* ************************************************************************* */
double Base::squaredMahalanobisDistance(const Vector& v) const {
// Note: for Diagonal, which does ediv_, will be correct for constraints
Vector w = whiten(v);
return w.dot(w);
}
/* ************************************************************************* */ /* ************************************************************************* */
Gaussian::shared_ptr Gaussian::SqrtInformation(const Matrix& R, bool smart) { Gaussian::shared_ptr Gaussian::SqrtInformation(const Matrix& R, bool smart) {
size_t m = R.rows(), n = R.cols(); size_t m = R.rows(), n = R.cols();
@ -164,13 +171,6 @@ Vector Gaussian::unwhiten(const Vector& v) const {
return backSubstituteUpper(thisR(), v); return backSubstituteUpper(thisR(), v);
} }
/* ************************************************************************* */
double Gaussian::squaredMahalanobisDistance(const Vector& v) const {
// Note: for Diagonal, which does ediv_, will be correct for constraints
Vector w = whiten(v);
return w.dot(w);
}
/* ************************************************************************* */ /* ************************************************************************* */
Matrix Gaussian::Whiten(const Matrix& H) const { Matrix Gaussian::Whiten(const Matrix& H) const {
return thisR() * H; return thisR() * H;
@ -376,6 +376,7 @@ Vector Constrained::whiten(const Vector& v) const {
return c; return c;
} }
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/* ************************************************************************* */ /* ************************************************************************* */
double Constrained::error(const Vector& v) const { double Constrained::error(const Vector& v) const {
Vector w = Diagonal::whiten(v); // get noisemodel for constrained elements Vector w = Diagonal::whiten(v); // get noisemodel for constrained elements
@ -384,6 +385,16 @@ double Constrained::error(const Vector& v) const {
w[i] = v[i] * sqrt(mu_[i]); // TODO: may want to store sqrt rather than rebuild w[i] = v[i] * sqrt(mu_[i]); // TODO: may want to store sqrt rather than rebuild
return 0.5 * w.dot(w); return 0.5 * w.dot(w);
} }
#endif
/* ************************************************************************* */
double Constrained::squaredMahalanobisDistance(const Vector& v) const {
Vector w = Diagonal::whiten(v); // get noisemodel for constrained elements
for (size_t i=0; i<dim_; ++i) // add mu weights on constrained variables
if (constrained(i)) // whiten makes constrained variables zero
w[i] = v[i] * sqrt(mu_[i]); // TODO: may want to store sqrt rather than rebuild
return w.dot(w);
}
/* ************************************************************************* */ /* ************************************************************************* */
Matrix Constrained::Whiten(const Matrix& H) const { Matrix Constrained::Whiten(const Matrix& H) const {
@ -662,14 +673,9 @@ void Robust::WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const{
robust_->reweight(A1,A2,A3,b); robust_->reweight(A1,A2,A3,b);
} }
Robust::shared_ptr Robust::Create(const RobustModel::shared_ptr& robust, Robust::shared_ptr Robust::Create(
const noiseModel::Base::shared_ptr noise) { const RobustModel::shared_ptr &robust, const NoiseModel::shared_ptr noise){
SharedGaussian gaussian; return shared_ptr(new Robust(robust,noise));
if (!(gaussian = boost::dynamic_pointer_cast<noiseModel::Gaussian>(noise)))
{
throw std::invalid_argument("The noise model inside robust must be Gaussian");
};
return shared_ptr(new Robust(robust, gaussian));
} }
/* ************************************************************************* */ /* ************************************************************************* */

172
gtsam/linear/NoiseModel.h
View File

@ -90,13 +90,26 @@ namespace gtsam {
/// Unwhiten an error vector. /// Unwhiten an error vector.
virtual Vector unwhiten(const Vector& v) const = 0; virtual Vector unwhiten(const Vector& v) const = 0;
/// Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
virtual double squaredMahalanobisDistance(const Vector& v) const;
/// Mahalanobis distance
virtual double mahalanobisDistance(const Vector& v) const {
return std::sqrt(squaredMahalanobisDistance(v));
}
/// loss function, input is Mahalanobis distance
virtual double loss(const double squared_distance) const {
return 0.5 * squared_distance;
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/// calculate the error value given measurement error vector /// calculate the error value given measurement error vector
virtual double error(const Vector& v) const = 0; virtual double error(const Vector& v) const = 0;
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
virtual double distance(const Vector& v) { virtual double distance(const Vector& v) {
return error(v) * 2; return error(v) * 2;
} }
#endif #endif
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const = 0; virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const = 0;
@ -207,42 +220,30 @@ namespace gtsam {
*/ */
static shared_ptr Covariance(const Matrix& covariance, bool smart = true); static shared_ptr Covariance(const Matrix& covariance, bool smart = true);
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
virtual bool equals(const Base& expected, double tol=1e-9) const; bool equals(const Base& expected, double tol=1e-9) const override;
virtual Vector sigmas() const; Vector sigmas() const override;
virtual Vector whiten(const Vector& v) const; Vector whiten(const Vector& v) const override;
virtual Vector unwhiten(const Vector& v) const; Vector unwhiten(const Vector& v) const override;
/**
* Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
*/
virtual double squaredMahalanobisDistance(const Vector& v) const;
/**
* Mahalanobis distance
*/
virtual double mahalanobisDistance(const Vector& v) const {
return std::sqrt(squaredMahalanobisDistance(v));
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
virtual double Mahalanobis(const Vector& v) const { virtual double Mahalanobis(const Vector& v) const {
return squaredMahalanobisDistance(v); return squaredMahalanobisDistance(v);
} }
#endif
/** /**
* error value 0.5 * v'*R'*R*v * error value 0.5 * v'*R'*R*v
*/ */
inline virtual double error(const Vector& v) const { inline double error(const Vector& v) const override {
return 0.5 * squaredMahalanobisDistance(v); return 0.5 * squaredMahalanobisDistance(v);
} }
#endif
/** /**
* Multiply a derivative with R (derivative of whiten) * Multiply a derivative with R (derivative of whiten)
* Equivalent to whitening each column of the input matrix. * Equivalent to whitening each column of the input matrix.
*/ */
virtual Matrix Whiten(const Matrix& H) const; Matrix Whiten(const Matrix& H) const override;
/** /**
* In-place version * In-place version
@ -257,10 +258,10 @@ namespace gtsam {
/** /**
* Whiten a system, in place as well * Whiten a system, in place as well
*/ */
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const; void WhitenSystem(std::vector<Matrix>& A, Vector& b) const override;
virtual void WhitenSystem(Matrix& A, Vector& b) const; void WhitenSystem(Matrix& A, Vector& b) const override;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const; void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const override;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const; void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const override;
/** /**
* Apply appropriately weighted QR factorization to the system [A b] * Apply appropriately weighted QR factorization to the system [A b]
@ -345,13 +346,13 @@ namespace gtsam {
return Variances(precisions.array().inverse(), smart); return Variances(precisions.array().inverse(), smart);
} }
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
virtual Vector sigmas() const { return sigmas_; } Vector sigmas() const override { return sigmas_; }
virtual Vector whiten(const Vector& v) const; Vector whiten(const Vector& v) const override;
virtual Vector unwhiten(const Vector& v) const; Vector unwhiten(const Vector& v) const override;
virtual Matrix Whiten(const Matrix& H) const; Matrix Whiten(const Matrix& H) const override;
virtual void WhitenInPlace(Matrix& H) const; void WhitenInPlace(Matrix& H) const override;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const; void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/** /**
* Return standard deviations (sqrt of diagonal) * Return standard deviations (sqrt of diagonal)
@ -373,7 +374,7 @@ namespace gtsam {
/** /**
* Return R itself, but note that Whiten(H) is cheaper than R*H * Return R itself, but note that Whiten(H) is cheaper than R*H
*/ */
virtual Matrix R() const { Matrix R() const override {
return invsigmas().asDiagonal(); return invsigmas().asDiagonal();
} }
@ -427,10 +428,10 @@ namespace gtsam {
typedef boost::shared_ptr<Constrained> shared_ptr; typedef boost::shared_ptr<Constrained> shared_ptr;
virtual ~Constrained() {} ~Constrained() {}
/// true if a constrained noise mode, saves slow/clumsy dynamic casting /// true if a constrained noise mode, saves slow/clumsy dynamic casting
virtual bool isConstrained() const { return true; } bool isConstrained() const override { return true; }
/// Return true if a particular dimension is free or constrained /// Return true if a particular dimension is free or constrained
bool constrained(size_t i) const; bool constrained(size_t i) const;
@ -482,12 +483,16 @@ namespace gtsam {
return MixedVariances(precisions.array().inverse()); return MixedVariances(precisions.array().inverse());
} }
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
/** /**
* The error function for a constrained noisemodel, * The error function for a constrained noisemodel,
* for non-constrained versions, uses sigmas, otherwise * for non-constrained versions, uses sigmas, otherwise
* uses the penalty function with mu * uses the penalty function with mu
*/ */
virtual double error(const Vector& v) const; double error(const Vector& v) const override;
#endif
double squaredMahalanobisDistance(const Vector& v) const override;
/** Fully constrained variations */ /** Fully constrained variations */
static shared_ptr All(size_t dim) { static shared_ptr All(size_t dim) {
@ -504,16 +509,16 @@ namespace gtsam {
return shared_ptr(new Constrained(Vector::Constant(dim, mu), Vector::Constant(dim,0))); return shared_ptr(new Constrained(Vector::Constant(dim, mu), Vector::Constant(dim,0)));
} }
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
/// Calculates error vector with weights applied /// Calculates error vector with weights applied
virtual Vector whiten(const Vector& v) const; Vector whiten(const Vector& v) const override;
/// Whitening functions will perform partial whitening on rows /// Whitening functions will perform partial whitening on rows
/// with a non-zero sigma. Other rows remain untouched. /// with a non-zero sigma. Other rows remain untouched.
virtual Matrix Whiten(const Matrix& H) const; Matrix Whiten(const Matrix& H) const override;
virtual void WhitenInPlace(Matrix& H) const; void WhitenInPlace(Matrix& H) const override;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const; void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/** /**
* Apply QR factorization to the system [A b], taking into account constraints * Apply QR factorization to the system [A b], taking into account constraints
@ -524,7 +529,7 @@ namespace gtsam {
* @param Ab is the m*(n+1) augmented system matrix [A b] * @param Ab is the m*(n+1) augmented system matrix [A b]
* @return diagonal noise model can be all zeros, mixed, or not-constrained * @return diagonal noise model can be all zeros, mixed, or not-constrained
*/ */
virtual Diagonal::shared_ptr QR(Matrix& Ab) const; Diagonal::shared_ptr QR(Matrix& Ab) const override;
/** /**
* Returns a Unit version of a constrained noisemodel in which * Returns a Unit version of a constrained noisemodel in which
@ -586,14 +591,14 @@ namespace gtsam {
return Variance(dim, 1.0/precision, smart); return Variance(dim, 1.0/precision, smart);
} }
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
virtual double squaredMahalanobisDistance(const Vector& v) const; double squaredMahalanobisDistance(const Vector& v) const override;
virtual Vector whiten(const Vector& v) const; Vector whiten(const Vector& v) const override;
virtual Vector unwhiten(const Vector& v) const; Vector unwhiten(const Vector& v) const override;
virtual Matrix Whiten(const Matrix& H) const; Matrix Whiten(const Matrix& H) const override;
virtual void WhitenInPlace(Matrix& H) const; void WhitenInPlace(Matrix& H) const override;
virtual void whitenInPlace(Vector& v) const; void whitenInPlace(Vector& v) const override;
virtual void WhitenInPlace(Eigen::Block<Matrix> H) const; void WhitenInPlace(Eigen::Block<Matrix> H) const override;
/** /**
* Return standard deviation * Return standard deviation
@ -626,7 +631,7 @@ namespace gtsam {
typedef boost::shared_ptr<Unit> shared_ptr; typedef boost::shared_ptr<Unit> shared_ptr;
virtual ~Unit() {} ~Unit() {}
/** /**
* Create a unit covariance noise model * Create a unit covariance noise model
@ -636,19 +641,19 @@ namespace gtsam {
} }
/// true if a unit noise model, saves slow/clumsy dynamic casting /// true if a unit noise model, saves slow/clumsy dynamic casting
virtual bool isUnit() const { return true; } bool isUnit() const override { return true; }
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
virtual double squaredMahalanobisDistance(const Vector& v) const {return v.dot(v); } double squaredMahalanobisDistance(const Vector& v) const override {return v.dot(v); }
virtual Vector whiten(const Vector& v) const { return v; } Vector whiten(const Vector& v) const override { return v; }
virtual Vector unwhiten(const Vector& v) const { return v; } Vector unwhiten(const Vector& v) const override { return v; }
virtual Matrix Whiten(const Matrix& H) const { return H; } Matrix Whiten(const Matrix& H) const override { return H; }
virtual void WhitenInPlace(Matrix& /*H*/) const {} void WhitenInPlace(Matrix& /*H*/) const override {}
virtual void WhitenInPlace(Eigen::Block<Matrix> /*H*/) const {} void WhitenInPlace(Eigen::Block<Matrix> /*H*/) const override {}
virtual void whitenInPlace(Vector& /*v*/) const {} void whitenInPlace(Vector& /*v*/) const override {}
virtual void unwhitenInPlace(Vector& /*v*/) const {} void unwhitenInPlace(Vector& /*v*/) const override {}
virtual void whitenInPlace(Eigen::Block<Vector>& /*v*/) const {} void whitenInPlace(Eigen::Block<Vector>& /*v*/) const override {}
virtual void unwhitenInPlace(Eigen::Block<Vector>& /*v*/) const {} void unwhitenInPlace(Eigen::Block<Vector>& /*v*/) const override {}
private: private:
/** Serialization function */ /** Serialization function */
@ -682,7 +687,7 @@ namespace gtsam {
protected: protected:
typedef mEstimator::Base RobustModel; typedef mEstimator::Base RobustModel;
typedef noiseModel::Gaussian NoiseModel; typedef noiseModel::Base NoiseModel;
const RobustModel::shared_ptr robust_; ///< robust error function used const RobustModel::shared_ptr robust_; ///< robust error function used
const NoiseModel::shared_ptr noise_; ///< noise model used const NoiseModel::shared_ptr noise_; ///< noise model used
@ -697,10 +702,10 @@ namespace gtsam {
: Base(noise->dim()), robust_(robust), noise_(noise) {} : Base(noise->dim()), robust_(robust), noise_(noise) {}
/// Destructor /// Destructor
virtual ~Robust() {} ~Robust() {}
virtual void print(const std::string& name) const; void print(const std::string& name) const override;
virtual bool equals(const Base& expected, double tol=1e-9) const; bool equals(const Base& expected, double tol=1e-9) const override;
/// Return the contained robust error function /// Return the contained robust error function
const RobustModel::shared_ptr& robust() const { return robust_; } const RobustModel::shared_ptr& robust() const { return robust_; }
@ -709,37 +714,42 @@ namespace gtsam {
const NoiseModel::shared_ptr& noise() const { return noise_; } const NoiseModel::shared_ptr& noise() const { return noise_; }
// TODO: functions below are dummy but necessary for the noiseModel::Base // TODO: functions below are dummy but necessary for the noiseModel::Base
inline virtual Vector whiten(const Vector& v) const inline Vector whiten(const Vector& v) const override
{ Vector r = v; this->WhitenSystem(r); return r; } { Vector r = v; this->WhitenSystem(r); return r; }
inline virtual Matrix Whiten(const Matrix& A) const inline Matrix Whiten(const Matrix& A) const override
{ Vector b; Matrix B=A; this->WhitenSystem(B,b); return B; } { Vector b; Matrix B=A; this->WhitenSystem(B,b); return B; }
inline virtual Vector unwhiten(const Vector& /*v*/) const inline Vector unwhiten(const Vector& /*v*/) const override
{ throw std::invalid_argument("unwhiten is not currently supported for robust noise models."); } { throw std::invalid_argument("unwhiten is not currently supported for robust noise models."); }
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
inline virtual double distance(const Vector& v) { inline double distance(const Vector& v) override {
return robust_->loss(this->unweightedWhiten(v).norm()); return robust_->loss(this->unweightedWhiten(v).norm());
} }
#endif
// Fold the use of the m-estimator loss(...) function into error(...) // Fold the use of the m-estimator loss(...) function into error(...)
inline virtual double error(const Vector& v) const inline double error(const Vector& v) const override
{ return robust_->loss(noise_->mahalanobisDistance(v)); } { return robust_->loss(noise_->mahalanobisDistance(v)); }
#endif
double loss(const double squared_distance) const override {
return robust_->loss(std::sqrt(squared_distance));
}
// TODO: these are really robust iterated re-weighting support functions // TODO: these are really robust iterated re-weighting support functions
virtual void WhitenSystem(Vector& b) const; virtual void WhitenSystem(Vector& b) const;
virtual void WhitenSystem(std::vector<Matrix>& A, Vector& b) const; void WhitenSystem(std::vector<Matrix>& A, Vector& b) const override;
virtual void WhitenSystem(Matrix& A, Vector& b) const; void WhitenSystem(Matrix& A, Vector& b) const override;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const; void WhitenSystem(Matrix& A1, Matrix& A2, Vector& b) const override;
virtual void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const; void WhitenSystem(Matrix& A1, Matrix& A2, Matrix& A3, Vector& b) const override;
virtual Vector unweightedWhiten(const Vector& v) const { Vector unweightedWhiten(const Vector& v) const override {
return noise_->unweightedWhiten(v); return noise_->unweightedWhiten(v);
} }
virtual double weight(const Vector& v) const { double weight(const Vector& v) const override {
// Todo(mikebosse): make the robust weight function input a vector. // Todo(mikebosse): make the robust weight function input a vector.
return robust_->weight(v.norm()); return robust_->weight(v.norm());
} }
static shared_ptr Create( static shared_ptr Create(
const RobustModel::shared_ptr &robust, const noiseModel::Base::shared_ptr noise); const RobustModel::shared_ptr &robust, const NoiseModel::shared_ptr noise);
private: private:
/** Serialization function */ /** Serialization function */

2
gtsam/nonlinear/NonlinearFactor.cpp
View File

@ -121,7 +121,7 @@ double NoiseModelFactor::error(const Values& c) const {
const Vector b = unwhitenedError(c); const Vector b = unwhitenedError(c);
check(noiseModel_, b.size()); check(noiseModel_, b.size());
if (noiseModel_) if (noiseModel_)
return noiseModel_->error(b); return noiseModel_->loss(noiseModel_->squaredMahalanobisDistance(b));
else else
return 0.5 * b.squaredNorm(); return 0.5 * b.squaredNorm();
} else { } else {