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Made CheckInvariants a static method in Conditional.*

release/4.3a0
Frank Dellaert 2023-01-13 14:55:14 -08:00
parent b99d464049
commit cd2d37e724
3 changed files with 31 additions and 23 deletions
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18
gtsam/inference/Conditional-inst.h
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@ -63,4 +63,22 @@ double Conditional<FACTOR, DERIVEDCONDITIONAL>::normalizationConstant() const {
return std::exp(logNormalizationConstant()); return std::exp(logNormalizationConstant());
} }
/* ************************************************************************* */
template <class FACTOR, class DERIVEDCONDITIONAL>
template <class VALUES>
bool Conditional<FACTOR, DERIVEDCONDITIONAL>::CheckInvariants(
const DERIVEDCONDITIONAL& conditional, const VALUES& values) {
const double probability = conditional.evaluate(values);
if (probability < 0.0 || probability > 1.0)
return false; // probability is not in [0,1]
const double logProb = conditional.logProbability(values);
if (std::abs(probability - std::exp(logProb)) > 1e-9)
return false; // logProb is not consistent with probability
const double expected =
conditional.logNormalizationConstant() - conditional.error(values);
if (std::abs(logProb - expected) > 1e-9)
return false; // logProb is not consistent with error
return true;
}
} // namespace gtsam } // namespace gtsam

4
gtsam/inference/Conditional.h
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@ -181,6 +181,10 @@ namespace gtsam {
/** Mutable iterator pointing past the last parent key. */ /** Mutable iterator pointing past the last parent key. */
typename FACTOR::iterator endParents() { return asFactor().end(); } typename FACTOR::iterator endParents() { return asFactor().end(); }
template <class VALUES>
static bool CheckInvariants(const DERIVEDCONDITIONAL& conditional,
const VALUES& values);
/// @} /// @}
private: private:

32
gtsam/linear/tests/testGaussianConditional.cpp
View File

@ -135,23 +135,6 @@ static const auto unitPrior =
noiseModel::Isotropic::Sigma(1, sigma)); noiseModel::Isotropic::Sigma(1, sigma));
} // namespace density } // namespace density
/* ************************************************************************* */
template <class VALUES>
bool checkInvariants(const GaussianConditional& conditional,
const VALUES& values) {
const double probability = conditional.evaluate(values);
if (probability < 0.0 || probability > 1.0)
return false; // probability is not in [0,1]
const double logProb = conditional.logProbability(values);
if (std::abs(probability - std::exp(logProb)) > 1e-9)
return false; // logProb is not consistent with probability
const double expected =
conditional.logNormalizationConstant() - conditional.error(values);
if (std::abs(logProb - expected) > 1e-9)
return false; // logProb is not consistent with error
return true;
}
/* ************************************************************************* */ /* ************************************************************************* */
// Check that the evaluate function matches direct calculation with R. // Check that the evaluate function matches direct calculation with R.
TEST(GaussianConditional, Evaluate1) { TEST(GaussianConditional, Evaluate1) {
@ -174,8 +157,9 @@ TEST(GaussianConditional, Evaluate1) {
// Check Invariants at the mean and a different value // Check Invariants at the mean and a different value
for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) { for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) {
EXPECT(checkInvariants(density::unitPrior, vv)); EXPECT(GaussianConditional::CheckInvariants(density::unitPrior, vv));
EXPECT(checkInvariants(density::unitPrior, HybridValues{vv, {}, {}})); EXPECT(GaussianConditional::CheckInvariants(density::unitPrior,
HybridValues{vv, {}, {}}));
} }
// Let's numerically integrate and see that we integrate to 1.0. // Let's numerically integrate and see that we integrate to 1.0.
@ -206,8 +190,9 @@ TEST(GaussianConditional, Evaluate2) {
// Check Invariants at the mean and a different value // Check Invariants at the mean and a different value
for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) { for (auto vv : {mean, VectorValues{{key, Vector1(4)}}}) {
EXPECT(checkInvariants(density::widerPrior, vv)); EXPECT(GaussianConditional::CheckInvariants(density::widerPrior, vv));
EXPECT(checkInvariants(density::widerPrior, HybridValues{vv, {}, {}})); EXPECT(GaussianConditional::CheckInvariants(density::widerPrior,
HybridValues{vv, {}, {}}));
} }
// Let's numerically integrate and see that we integrate to 1.0. // Let's numerically integrate and see that we integrate to 1.0.
@ -422,8 +407,9 @@ TEST(GaussianConditional, FromMeanAndStddev) {
// Check Invariants for both conditionals // Check Invariants for both conditionals
for (auto conditional : {conditional1, conditional2}) { for (auto conditional : {conditional1, conditional2}) {
EXPECT(checkInvariants(conditional, values)); EXPECT(GaussianConditional::CheckInvariants(conditional, values));
EXPECT(checkInvariants(conditional, HybridValues{values, {}, {}})); EXPECT(GaussianConditional::CheckInvariants(conditional,
HybridValues{values, {}, {}}));
} }
} }