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Merge pull request #267 from borglab/feature/robust_theory 

Feature/robust theory
release/4.3a0
yetongumich 2020-04-03 13:47:12 -04:00 committed by GitHub
parent e2ed25328b ae85b640a8
commit 84a33e959f
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 24 additions and 11 deletions
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2
gtsam/linear/GaussianBayesNet.h
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@ -143,7 +143,7 @@ namespace gtsam {
* allocateVectorValues */
VectorValues gradientAtZero() const;
/** Mahalanobis norm error. */
/** 0.5 * sum of squared Mahalanobis distances. */
double error(const VectorValues& x) const;
/**

2
gtsam/linear/GaussianBayesTree.h
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@ -106,7 +106,7 @@ namespace gtsam {
* @return A VectorValues storing the gradient. */
VectorValues gradientAtZero() const;
/** Mahalanobis norm error. */
/** 0.5 * sum of squared Mahalanobis distances. */
double error(const VectorValues& x) const;
/** Computes the determinant of a GassianBayesTree, as if the Bayes tree is reorganized into a

4
gtsam/linear/NoiseModel.cpp
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@ -157,7 +157,7 @@ Vector Gaussian::unwhiten(const Vector& v) const {
}
/* ************************************************************************* */
double Gaussian::Mahalanobis(const Vector& v) const {
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);
@ -573,7 +573,7 @@ void Isotropic::print(const string& name) const {
}
/* ************************************************************************* */
double Isotropic::Mahalanobis(const Vector& v) const {
double Isotropic::squaredMahalanobisDistance(const Vector& v) const {
return v.dot(v) * invsigma_ * invsigma_;
}

23
gtsam/linear/NoiseModel.h
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@ -207,12 +207,25 @@ namespace gtsam {
virtual Vector unwhiten(const Vector& v) const;
/**
* Mahalanobis distance v'*R'*R*v = <R*v,R*v>
* Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>
*/
virtual double Mahalanobis(const Vector& v) const;
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
virtual double Mahalanobis(const Vector& v) const {
return squaredMahalanobisDistance(v);
}
#endif
inline virtual double distance(const Vector& v) const {
return Mahalanobis(v);
return squaredMahalanobisDistance(v);
}
/**
@ -564,7 +577,7 @@ namespace gtsam {
}
virtual void print(const std::string& name) const;
virtual double Mahalanobis(const Vector& v) const;
virtual double squaredMahalanobisDistance(const Vector& v) const;
virtual Vector whiten(const Vector& v) const;
virtual Vector unwhiten(const Vector& v) const;
virtual Matrix Whiten(const Matrix& H) const;
@ -616,7 +629,7 @@ namespace gtsam {
virtual bool isUnit() const { return true; }
virtual void print(const std::string& name) const;
virtual double Mahalanobis(const Vector& v) const {return v.dot(v); }
virtual double squaredMahalanobisDistance(const Vector& v) const {return v.dot(v); }
virtual Vector whiten(const Vector& v) const { return v; }
virtual Vector unwhiten(const Vector& v) const { return v; }
virtual Matrix Whiten(const Matrix& H) const { return H; }

4
gtsam/linear/tests/testNoiseModel.cpp
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@ -68,10 +68,10 @@ TEST(NoiseModel, constructors)
for(Gaussian::shared_ptr mi: m)
EXPECT(assert_equal(unwhitened,mi->unwhiten(whitened)));
// test Mahalanobis distance
// test squared Mahalanobis distance
double distance = 5*5+10*10+15*15;
for(Gaussian::shared_ptr mi: m)
DOUBLES_EQUAL(distance,mi->Mahalanobis(unwhitened),1e-9);
DOUBLES_EQUAL(distance,mi->squaredMahalanobisDistance(unwhitened),1e-9);
// test R matrix
for(Gaussian::shared_ptr mi: m)