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				|  | @ -770,9 +770,10 @@ static HybridGaussianFactorGraph CreateFactorGraph( | |||
|           ->linearize(values); | ||||
| 
 | ||||
|   // Create HybridGaussianFactor
 | ||||
|   // We multiply by -2 since the we want the underlying scalar to be log(|2πΣ|)
 | ||||
|   std::vector<GaussianFactorValuePair> factors{ | ||||
|       {f0, ComputeLogNormalizerConstant(model0)}, | ||||
|       {f1, ComputeLogNormalizerConstant(model1)}}; | ||||
|       {f0, -2 * model0->logNormalizationConstant()}, | ||||
|       {f1, -2 * model1->logNormalizationConstant()}}; | ||||
|   HybridGaussianFactor motionFactor({X(0), X(1)}, m1, factors); | ||||
| 
 | ||||
|   HybridGaussianFactorGraph hfg; | ||||
|  |  | |||
|  | @ -868,9 +868,10 @@ static HybridNonlinearFactorGraph CreateFactorGraph( | |||
|       std::make_shared<BetweenFactor<double>>(X(0), X(1), means[1], model1); | ||||
| 
 | ||||
|   // Create HybridNonlinearFactor
 | ||||
|   // We multiply by -2 since the we want the underlying scalar to be log(|2πΣ|)
 | ||||
|   std::vector<NonlinearFactorValuePair> factors{ | ||||
|       {f0, ComputeLogNormalizerConstant(model0)}, | ||||
|       {f1, ComputeLogNormalizerConstant(model1)}}; | ||||
|       {f0, -2 * model0->logNormalizationConstant()}, | ||||
|       {f1, -2 * model1->logNormalizationConstant()}}; | ||||
| 
 | ||||
|   HybridNonlinearFactor mixtureFactor({X(0), X(1)}, m1, factors); | ||||
| 
 | ||||
|  |  | |||
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