make logNormalizationConstant return -log(k)

gtsam/inference/Conditional-inst.h

@@ -89,7 +89,7 @@ bool Conditional<FACTOR, DERIVEDCONDITIONAL>::CheckInvariants(
                    // normalization constant
   const double error = conditional.error(values);
   if (error < 0.0) return false;  // prob_or_density is negative.
-  const double expected = conditional.logNormalizationConstant() - error;
+  const double expected = -(conditional.logNormalizationConstant() + error);
   if (std::abs(logProb - expected) > 1e-9)
     return false;  // logProb is not consistent with error
   return true;

gtsam/inference/Conditional.h

@@ -34,9 +34,10 @@ namespace gtsam {
    * `logProbability` is the main methods that need to be implemented in derived
    * classes. These two methods relate to the `error` method in the factor by:
    *   probability(x) = k exp(-error(x))
-   * where k is a normalization constant making \int probability(x) == 1.0, and
-   *   logProbability(x) = K - error(x)
-   * i.e., K = log(K). The normalization constant K is assumed to *not* depend
+   * where k is a normalization constant making
+   * \int probability(x) = \int k exp(-error(x)) == 1.0, and
+   * logProbability(x) = -(K + error(x))
+   * i.e., K = -log(k). The normalization constant k is assumed to *not* depend
    * on any argument, only (possibly) on the conditional parameters.
    * This class provides a default logNormalizationConstant() == 0.0.
    * 
@@ -163,8 +164,9 @@ namespace gtsam {
     }
 
     /**
-     * All conditional types need to implement a log normalization constant to
-     * make it such that error>=0.
+     * All conditional types need to implement a
+     * (negative) log normalization constant
+     * to make it such that error>=0.
      */
     virtual double logNormalizationConstant() const;
 

gtsam/linear/GaussianBayesNet.cpp

@@ -246,14 +246,15 @@ namespace gtsam {
   double GaussianBayesNet::logNormalizationConstant() const {
     /*
     normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
-    logConstant = -0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
+    logConstant = -log(normalizationConstant)
+      = 0.5 * n*log(2*pi) + 0.5 * log(det(Sigma))
     
-    log det(Sigma)) = -2.0 * logDeterminant()
-    thus, logConstant = -0.5*n*log(2*pi) + logDeterminant()
+    log(det(Sigma)) = -2.0 * logDeterminant()
+    thus, logConstant = 0.5*n*log(2*pi) - logDeterminant()
 
-    BayesNet logConstant = sum(-0.5*n_i*log(2*pi) + logDeterminant_i())
-    = sum(-0.5*n_i*log(2*pi)) + sum(logDeterminant_i())
-    = sum(-0.5*n_i*log(2*pi)) + bn->logDeterminant()
+    BayesNet logConstant = sum(0.5*n_i*log(2*pi) - logDeterminant_i())
+    = sum(0.5*n_i*log(2*pi)) - sum(logDeterminant_i())
+    = sum(0.5*n_i*log(2*pi)) - bn->logDeterminant()
     */
     double logNormConst = 0.0;
     for (const sharedConditional& cg : *this) {

gtsam/linear/GaussianConditional.cpp

@@ -181,24 +181,24 @@ namespace gtsam {
 
   /* ************************************************************************* */
   //  normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
-  //  log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
+  //  neg-log = 0.5 * n*log(2*pi) + 0.5 * log det(Sigma)
   double GaussianConditional::logNormalizationConstant() const {
     constexpr double log2pi = 1.8378770664093454835606594728112;
     size_t n = d().size();
     // Sigma = (R'R)^{-1}, det(Sigma) = det((R'R)^{-1}) = det(R'R)^{-1}
     // log det(Sigma) = -log(det(R'R)) = -2*log(det(R))
     // Hence, log det(Sigma)) = -2.0 * logDeterminant()
-    // which gives log = -0.5*n*log(2*pi) - 0.5*(-2.0 * logDeterminant())
-    //     = -0.5*n*log(2*pi) + (0.5*2.0 * logDeterminant())
-    //     = -0.5*n*log(2*pi) + logDeterminant()
-    return -0.5 * n * log2pi + logDeterminant();
+    // which gives neg-log = 0.5*n*log(2*pi) + 0.5*(-2.0 * logDeterminant())
+    //     = 0.5*n*log(2*pi) - (0.5*2.0 * logDeterminant())
+    //     = 0.5*n*log(2*pi) - logDeterminant()
+    return 0.5 * n * log2pi - logDeterminant();
   }
 
   /* ************************************************************************* */
-  //  density = k exp(-error(x))
-  //  log = log(k) - error(x)
+  //  density = 1/k exp(-error(x))
+  //  log = -log(k) - error(x)
   double GaussianConditional::logProbability(const VectorValues& x) const {
-    return logNormalizationConstant() - error(x);
+    return -logNormalizationConstant() - error(x);
   }
 
   double GaussianConditional::logProbability(const HybridValues& x) const {

gtsam/linear/GaussianConditional.h

@@ -133,8 +133,10 @@ namespace gtsam {
     /// @{
 
     /**
-     * normalization constant = 1.0 / sqrt((2*pi)^n*det(Sigma))
-     * log = - 0.5 * n*log(2*pi) - 0.5 * log det(Sigma)
+     * Return normalization constant in negative log space.
+     *
+     * normalization constant k = 1.0 / sqrt((2*pi)^n*det(Sigma))
+     * -log(k) = 0.5 * n*log(2*pi) + 0.5 * log det(Sigma)
      */
     double logNormalizationConstant() const override;
 

gtsam/linear/NoiseModel.cpp

@@ -257,13 +257,13 @@ double Gaussian::logDeterminant() const {
 /* *******************************************************************************/
 double Gaussian::logNormalizationConstant() const {
   // log(det(Sigma)) = -2.0 * logDetR
-  // which gives log = -0.5*n*log(2*pi) - 0.5*(-2.0 * logDetR())
-  //     = -0.5*n*log(2*pi) + (0.5*2.0 * logDetR())
-  //     = -0.5*n*log(2*pi) + logDetR()
+  // which gives neg-log = 0.5*n*log(2*pi) + 0.5*(-2.0 * logDetR())
+  //     = 0.5*n*log(2*pi) - (0.5*2.0 * logDetR())
+  //     = 0.5*n*log(2*pi) - logDetR()
   size_t n = dim();
   constexpr double log2pi = 1.8378770664093454835606594728112;
-  // Get 1/log(\sqrt(|2pi Sigma|)) = -0.5*log(|2pi Sigma|)
-  return -0.5 * n * log2pi + logDetR();
+  // Get -log(1/\sqrt(|2pi Sigma|)) = 0.5*log(|2pi Sigma|)
+  return 0.5 * n * log2pi - logDetR();
 }
 
 

gtsam/linear/NoiseModel.h

@@ -274,8 +274,8 @@ namespace gtsam {
       /**
        * @brief Method to compute the normalization constant
        * for a Gaussian noise model k = \sqrt(1/|2πΣ|).
-       * We compute this in the log-space for numerical accuracy,
-       * thus returning log(k).
+       * We compute this in the negative log-space for numerical accuracy,
+       * thus returning -log(k).
        *
        * @return double
        */

gtsam/linear/tests/testGaussianBayesNet.cpp

@@ -76,11 +76,11 @@ TEST(GaussianBayesNet, Evaluate1) {
   // the normalization constant 1.0/sqrt((2*pi*Sigma).det()).
   // The covariance matrix inv(Sigma) = R'*R, so the determinant is
   const double constant = sqrt((invSigma / (2 * M_PI)).determinant());
-  EXPECT_DOUBLES_EQUAL(log(constant),
+  EXPECT_DOUBLES_EQUAL(-log(constant),
                        smallBayesNet.at(0)->logNormalizationConstant() +
                            smallBayesNet.at(1)->logNormalizationConstant(),
                        1e-9);
-  EXPECT_DOUBLES_EQUAL(log(constant), smallBayesNet.logNormalizationConstant(),
+  EXPECT_DOUBLES_EQUAL(-log(constant), smallBayesNet.logNormalizationConstant(),
                        1e-9);
   const double actual = smallBayesNet.evaluate(mean);
   EXPECT_DOUBLES_EQUAL(constant, actual, 1e-9);

gtsam/linear/tests/testGaussianConditional.cpp

@@ -492,9 +492,10 @@ TEST(GaussianConditional, LogNormalizationConstant) {
   VectorValues x;
   x.insert(X(0), Vector3::Zero());
   Matrix3 Sigma = I_3x3 * sigma * sigma;
-  double expectedLogNormalizingConstant = log(1 / sqrt((2 * M_PI * Sigma).determinant()));
+  double expectedLogNormalizationConstant =
+      -log(1 / sqrt((2 * M_PI * Sigma).determinant()));
 
-  EXPECT_DOUBLES_EQUAL(expectedLogNormalizingConstant,
+  EXPECT_DOUBLES_EQUAL(expectedLogNormalizationConstant,
                        conditional.logNormalizationConstant(), 1e-9);
 }
 
@@ -516,7 +517,7 @@ TEST(GaussianConditional, Print) {
     "  d = [ 20 40 ]\n"
     "  mean: 1 elements\n"
     "  x0: 20 40\n"
-    "  logNormalizationConstant: -4.0351\n"
+    "  logNormalizationConstant: 4.0351\n"
     "isotropic dim=2 sigma=3\n";
   EXPECT(assert_print_equal(expected, conditional, "GaussianConditional"));
 
@@ -531,7 +532,7 @@ TEST(GaussianConditional, Print) {
     "  S[x1] = [ -1 -2 ]\n"
     "          [ -3 -4 ]\n"
     "  d = [ 20 40 ]\n"
-    "  logNormalizationConstant: -4.0351\n"
+    "  logNormalizationConstant: 4.0351\n"
     "isotropic dim=2 sigma=3\n";
   EXPECT(assert_print_equal(expected1, conditional1, "GaussianConditional"));
 
@@ -547,7 +548,7 @@ TEST(GaussianConditional, Print) {
     "  S[y1] = [ -5 -6 ]\n"
     "          [ -7 -8 ]\n"
     "  d = [ 20 40 ]\n"
-    "  logNormalizationConstant: -4.0351\n"
+    "  logNormalizationConstant: 4.0351\n"
     "isotropic dim=2 sigma=3\n";
   EXPECT(assert_print_equal(expected2, conditional2, "GaussianConditional"));
 }

gtsam/linear/tests/testGaussianDensity.cpp

@@ -55,7 +55,7 @@ TEST(GaussianDensity, FromMeanAndStddev) {
   double expected1 = 0.5 * e.dot(e);
   EXPECT_DOUBLES_EQUAL(expected1, density.error(values), 1e-9);
 
-  double expected2 = density.logNormalizationConstant()- 0.5 * e.dot(e);
+  double expected2 = -(density.logNormalizationConstant() + 0.5 * e.dot(e));
   EXPECT_DOUBLES_EQUAL(expected2, density.logProbability(values), 1e-9);
 }
 

gtsam/linear/tests/testNoiseModel.cpp

@@ -810,9 +810,9 @@ TEST(NoiseModel, NonDiagonalGaussian)
 TEST(NoiseModel, LogNormalizationConstant1D) {
   // Very simple 1D noise model, which we can compute by hand.
   double sigma = 0.1;
-  // For expected values, we compute 1/log(sqrt(|2πΣ|)) by hand.
-  // = -0.5*(log(2π) + log(Σ)) (since it is 1D)
-  double expected_value = -0.5 * log(2 * M_PI * sigma * sigma);
+  // For expected values, we compute -log(1/sqrt(|2πΣ|)) by hand.
+  // = 0.5*(log(2π) + log(Σ)) (since it is 1D)
+  double expected_value = 0.5 * log(2 * M_PI * sigma * sigma);
 
   // Gaussian
   {
@@ -839,7 +839,7 @@ TEST(NoiseModel, LogNormalizationConstant1D) {
     auto noise_model = Unit::Create(1);
     double actual_value = noise_model->logNormalizationConstant();
     double sigma = 1.0;
-    expected_value = -0.5 * log(2 * M_PI * sigma * sigma);
+    expected_value = 0.5 * log(2 * M_PI * sigma * sigma);
     EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
   }
 }
@@ -850,7 +850,7 @@ TEST(NoiseModel, LogNormalizationConstant3D) {
   size_t n = 3;
   // We compute the expected values just like in the LogNormalizationConstant1D
   // test, but we multiply by 3 due to the determinant.
-  double expected_value = -0.5 * n * log(2 * M_PI * sigma * sigma);
+  double expected_value = 0.5 * n * log(2 * M_PI * sigma * sigma);
 
   // Gaussian
   {
@@ -879,7 +879,7 @@ TEST(NoiseModel, LogNormalizationConstant3D) {
     auto noise_model = Unit::Create(3);
     double actual_value = noise_model->logNormalizationConstant();
     double sigma = 1.0;
-    expected_value = -0.5 * n * log(2 * M_PI * sigma * sigma);
+    expected_value = 0.5 * n * log(2 * M_PI * sigma * sigma);
     EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
   }
 }