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Add optional model parameter to sample methods

release/4.3a0
Varun Agrawal 2022-12-23 23:51:20 +05:30
parent 1ab922b253
commit 4fc02a6aa2
4 changed files with 43 additions and 25 deletions
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15
gtsam/linear/GaussianBayesNet.cpp
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@ -59,27 +59,30 @@ namespace gtsam {
}
/* ************************************************************************ */
VectorValues GaussianBayesNet::sample(std::mt19937_64* rng) const {
VectorValues GaussianBayesNet::sample(std::mt19937_64* rng,
const SharedDiagonal& model) const {
VectorValues result; // no missing variables -> create an empty vector
return sample(result, rng);
return sample(result, rng, model);
}
VectorValues GaussianBayesNet::sample(VectorValues result,
std::mt19937_64* rng) const {
std::mt19937_64* rng,
const SharedDiagonal& model) const {
// sample each node in reverse topological sort order (parents first)
for (auto cg : boost::adaptors::reverse(*this)) {
const VectorValues sampled = cg->sample(result, rng);
const VectorValues sampled = cg->sample(result, rng, model);
result.insert(sampled);
}
return result;
}
/* ************************************************************************ */
VectorValues GaussianBayesNet::sample() const {
VectorValues GaussianBayesNet::sample(const SharedDiagonal& model) const {
return sample(&kRandomNumberGenerator);
}
VectorValues GaussianBayesNet::sample(VectorValues given) const {
VectorValues GaussianBayesNet::sample(VectorValues given,
const SharedDiagonal& model) const {
return sample(given, &kRandomNumberGenerator);
}

11
gtsam/linear/GaussianBayesNet.h
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@ -101,7 +101,8 @@ namespace gtsam {
* std::mt19937_64 rng(42);
* auto sample = gbn.sample(&rng);
*/
VectorValues sample(std::mt19937_64* rng) const;
VectorValues sample(std::mt19937_64* rng,
const SharedDiagonal& model = nullptr) const;
/**
* Sample from an incomplete BayesNet, given missing variables
@ -110,13 +111,15 @@ namespace gtsam {
* VectorValues given = ...;
* auto sample = gbn.sample(given, &rng);
*/
VectorValues sample(VectorValues given, std::mt19937_64* rng) const;
VectorValues sample(VectorValues given, std::mt19937_64* rng,
const SharedDiagonal& model = nullptr) const;
/// Sample using ancestral sampling, use default rng
VectorValues sample() const;
VectorValues sample(const SharedDiagonal& model = nullptr) const;
/// Sample from an incomplete BayesNet, use default rng
VectorValues sample(VectorValues given) const;
VectorValues sample(VectorValues given,
const SharedDiagonal& model = nullptr) const;
/**
* Return ordering corresponding to a topological sort.

31
gtsam/linear/GaussianConditional.cpp
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@ -293,39 +293,48 @@ double GaussianConditional::logDeterminant() const {
/* ************************************************************************ */
VectorValues GaussianConditional::sample(const VectorValues& parentsValues,
std::mt19937_64* rng) const {
std::mt19937_64* rng,
const SharedDiagonal& model) const {
if (nrFrontals() != 1) {
throw std::invalid_argument(
"GaussianConditional::sample can only be called on single variable "
"conditionals");
}
if (!model_) {
VectorValues solution = solve(parentsValues);
Key key = firstFrontalKey();
Vector sigmas;
if (model_) {
sigmas = model_->sigmas();
} else if (model) {
sigmas = model->sigmas();
} else {
throw std::invalid_argument(
"GaussianConditional::sample can only be called if a diagonal noise "
"model was specified at construction.");
}
VectorValues solution = solve(parentsValues);
Key key = firstFrontalKey();
const Vector& sigmas = model_->sigmas();
solution[key] += Sampler::sampleDiagonal(sigmas, rng);
return solution;
}
VectorValues GaussianConditional::sample(std::mt19937_64* rng) const {
VectorValues GaussianConditional::sample(std::mt19937_64* rng,
const SharedDiagonal& model) const {
if (nrParents() != 0)
throw std::invalid_argument(
"sample() can only be invoked on no-parent prior");
VectorValues values;
return sample(values);
return sample(values, rng, model);
}
/* ************************************************************************ */
VectorValues GaussianConditional::sample() const {
return sample(&kRandomNumberGenerator);
VectorValues GaussianConditional::sample(const SharedDiagonal& model) const {
return sample(&kRandomNumberGenerator, model);
}
VectorValues GaussianConditional::sample(const VectorValues& given) const {
return sample(given, &kRandomNumberGenerator);
VectorValues GaussianConditional::sample(const VectorValues& given,
const SharedDiagonal& model) const {
return sample(given, &kRandomNumberGenerator, model);
}
/* ************************************************************************ */

11
gtsam/linear/GaussianConditional.h
View File

@ -188,7 +188,8 @@ namespace gtsam {
* std::mt19937_64 rng(42);
* auto sample = gbn.sample(&rng);
*/
VectorValues sample(std::mt19937_64* rng) const;
VectorValues sample(std::mt19937_64* rng,
const SharedDiagonal& model = nullptr) const;
/**
* Sample from conditional, given missing variables
@ -198,13 +199,15 @@ namespace gtsam {
* auto sample = gbn.sample(given, &rng);
*/
VectorValues sample(const VectorValues& parentsValues,
std::mt19937_64* rng) const;
std::mt19937_64* rng,
const SharedDiagonal& model = nullptr) const;
/// Sample, use default rng
VectorValues sample() const;
VectorValues sample(const SharedDiagonal& model = nullptr) const;
/// Sample with given values, use default rng
VectorValues sample(const VectorValues& parentsValues) const;
VectorValues sample(const VectorValues& parentsValues,
const SharedDiagonal& model = nullptr) const;
/// @}