correct documentation and test for ComputeLogNormalizer
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64d9fc67bd
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094db1eb79
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@ -714,6 +714,9 @@ double ComputeLogNormalizer(
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const noiseModel::Gaussian::shared_ptr& noise_model) {
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// Since noise models are Gaussian, we can get the logDeterminant using
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// the same trick as in GaussianConditional
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// Sigma = (R'R)^{-1}, det(Sigma) = det((R'R)^{-1}) = det(R'R)^{-1}
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// log det(Sigma) = -log(det(R'R)) = -2*log(det(R))
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// Hence, log det(Sigma)) = -2.0 * logDetR()
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double logDetR = noise_model->R()
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.diagonal()
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.unaryExpr([](double x) { return log(x); })
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@ -752,7 +752,7 @@ namespace gtsam {
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template<> struct traits<noiseModel::Unit> : public Testable<noiseModel::Unit> {};
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/**
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* @brief Helper function to compute the sqrt(|2πΣ|) normalizer values
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* @brief Helper function to compute the log(|2πΣ|) normalizer values
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* for a Gaussian noise model.
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* We compute this in the log-space for numerical accuracy.
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*
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@ -807,6 +807,26 @@ TEST(NoiseModel, NonDiagonalGaussian)
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}
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}
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TEST(NoiseModel, ComputeLogNormalizer) {
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// Very simple 1D noise model, which we can compute by hand.
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double sigma = 0.1;
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auto noise_model = Isotropic::Sigma(1, sigma);
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double actual_value = ComputeLogNormalizer(noise_model);
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// Compute log(|2πΣ|) by hand.
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// = log(2π) + log(Σ) (since it is 1D)
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constexpr double log2pi = 1.8378770664093454835606594728112;
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double expected_value = log2pi + log(sigma * sigma);
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EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
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// Similar situation in the 3D case
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size_t n = 3;
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auto noise_model2 = Isotropic::Sigma(n, sigma);
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double actual_value2 = ComputeLogNormalizer(noise_model2);
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// We multiply by 3 due to the determinant
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double expected_value2 = n * (log2pi + log(sigma * sigma));
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EXPECT_DOUBLES_EQUAL(expected_value2, actual_value2, 1e-9);
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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