showing difference in computed probabilities
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dcdcf30f52
commit
1789bb74fe
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@ -130,6 +130,7 @@ struct Switching {
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* @param K The total number of timesteps.
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* @param K The total number of timesteps.
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* @param between_sigma The stddev between poses.
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* @param between_sigma The stddev between poses.
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* @param prior_sigma The stddev on priors (also used for measurements).
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* @param prior_sigma The stddev on priors (also used for measurements).
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* @param measurements Vector of measurements for each timestep.
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*/
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*/
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Switching(size_t K, double between_sigma = 1.0, double prior_sigma = 0.1,
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Switching(size_t K, double between_sigma = 1.0, double prior_sigma = 0.1,
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std::vector<double> measurements = {})
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std::vector<double> measurements = {})
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@ -201,10 +201,14 @@ TEST(HybridEstimation, Probability) {
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VectorValues values = bayes_net->optimize();
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VectorValues values = bayes_net->optimize();
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std::cout << i << " : " << linear_graph->probPrime(values) << std::endl;
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std::cout << i << " : " << linear_graph->probPrime(values) << std::endl;
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}
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}
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// std::cout << linear_graph->error(values) << std::endl;
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// // values.at();
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// // linearizationPoint.retract(values).print();
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Switching switching(K, between_sigma, measurement_sigma, measurements);
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auto graph = switching.linearizedFactorGraph;
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Ordering ordering = getOrdering(graph, HybridGaussianFactorGraph());
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HybridBayesNet::shared_ptr bayesNet = graph.eliminateSequential(ordering);
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const DecisionTreeFactor::shared_ptr decisionTree =
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bayesNet->discreteConditionals();
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decisionTree->print();
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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