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a0870fc736
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@ -104,6 +104,9 @@ virtual class DiscreteConditional : gtsam::DecisionTreeFactor {
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DiscreteConditional(const gtsam::DecisionTreeFactor& joint,
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const gtsam::DecisionTreeFactor& marginal,
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const gtsam::Ordering& orderedKeys);
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DiscreteConditional(const gtsam::DiscreteKey& key,
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const gtsam::DiscreteKeys& parents,
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const std::vector<double>& table);
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// Standard interface
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double logNormalizationConstant() const;
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@ -66,7 +66,7 @@ void GaussianMixtureFactor::print(const std::string &s,
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[&](const sharedFactor &gf) -> std::string {
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RedirectCout rd;
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std::cout << ":\n";
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if (gf && !gf->empty()) {
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if (gf) {
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gf->print("", formatter);
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return rd.str();
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} else {
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@ -92,7 +92,10 @@ class GaussianMixture : gtsam::HybridFactor {
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const std::vector<gtsam::GaussianConditional::shared_ptr>&
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conditionalsList);
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gtsam::GaussianMixtureFactor* likelihood(const gtsam::VectorValues &frontals) const;
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gtsam::GaussianMixtureFactor* likelihood(
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const gtsam::VectorValues& frontals) const;
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double logProbability(const gtsam::HybridValues& values) const;
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double evaluate(const gtsam::HybridValues& values) const;
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void print(string s = "GaussianMixture\n",
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const gtsam::KeyFormatter& keyFormatter =
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@ -489,7 +489,7 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
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GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
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size_t name2, Matrix T,
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const gtsam::noiseModel::Diagonal* sigmas);
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GaussianConditional(const vector<std::pair<gtsam::Key, Matrix>> terms,
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GaussianConditional(const std::vector<std::pair<gtsam::Key, Matrix>> terms,
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size_t nrFrontals, Vector d,
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const gtsam::noiseModel::Diagonal* sigmas);
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@ -63,6 +63,6 @@ A RegularJacobianFactor that uses some badly documented reduction on the Jacobia
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A RegularJacobianFactor that eliminates a point using sequential elimination.
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### JacobianFactorQR
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### JacobianFactorSVD
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A RegularJacobianFactor that uses the "Nullspace Trick" by Mourikis et al. See the documentation in the file, which *is* well documented.
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