From aa86af2d778953a1bbf4d0123aa208f6b0c3ea00 Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Sat, 24 Dec 2022 20:11:19 +0530
Subject: [PATCH] Revert "add optional model parameter to sample method"

This reverts commit ffd1802ceaafd574517335bd34ddd525c8e1227b.
---
 gtsam/linear/GaussianBayesNet.cpp    | 10 ++++------
 gtsam/linear/GaussianBayesNet.h      |  6 ++----
 gtsam/linear/GaussianConditional.cpp | 22 +++++++---------------
 gtsam/linear/GaussianConditional.h   |  7 +++----
 4 files changed, 16 insertions(+), 29 deletions(-)

diff --git a/gtsam/linear/GaussianBayesNet.cpp b/gtsam/linear/GaussianBayesNet.cpp
index 8db301aa5..6dcf662a9 100644
--- a/gtsam/linear/GaussianBayesNet.cpp
+++ b/gtsam/linear/GaussianBayesNet.cpp
@@ -59,18 +59,16 @@ namespace gtsam {
   }
 
   /* ************************************************************************ */
-  VectorValues GaussianBayesNet::sample(std::mt19937_64* rng,
-                                        const SharedDiagonal& model) const {
+  VectorValues GaussianBayesNet::sample(std::mt19937_64* rng) const {
     VectorValues result;  // no missing variables -> create an empty vector
-    return sample(result, rng, model);
+    return sample(result, rng);
   }
 
   VectorValues GaussianBayesNet::sample(VectorValues result,
-                                        std::mt19937_64* rng,
-                                        const SharedDiagonal& model) const {
+                                        std::mt19937_64* rng) 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, model);
+      const VectorValues sampled = cg->sample(result, rng);
       result.insert(sampled);
     }
     return result;
diff --git a/gtsam/linear/GaussianBayesNet.h b/gtsam/linear/GaussianBayesNet.h
index e6dae6126..83328576f 100644
--- a/gtsam/linear/GaussianBayesNet.h
+++ b/gtsam/linear/GaussianBayesNet.h
@@ -101,8 +101,7 @@ namespace gtsam {
      *   std::mt19937_64 rng(42);
      *   auto sample = gbn.sample(&rng);
      */
-    VectorValues sample(std::mt19937_64* rng,
-                        const SharedDiagonal& model = nullptr) const;
+    VectorValues sample(std::mt19937_64* rng) const;
 
     /**
      * Sample from an incomplete BayesNet, given missing variables
@@ -111,8 +110,7 @@ namespace gtsam {
      *   VectorValues given = ...;
      *   auto sample = gbn.sample(given, &rng);
      */
-    VectorValues sample(VectorValues given, std::mt19937_64* rng,
-                        const SharedDiagonal& model = nullptr) const;
+    VectorValues sample(VectorValues given, std::mt19937_64* rng) const;
 
     /// Sample using ancestral sampling, use default rng
     VectorValues sample() const;
diff --git a/gtsam/linear/GaussianConditional.cpp b/gtsam/linear/GaussianConditional.cpp
index 1a6620d62..951577641 100644
--- a/gtsam/linear/GaussianConditional.cpp
+++ b/gtsam/linear/GaussianConditional.cpp
@@ -279,38 +279,30 @@ namespace gtsam {
 
   /* ************************************************************************ */
   VectorValues GaussianConditional::sample(const VectorValues& parentsValues,
-                                           std::mt19937_64* rng,
-                                           const SharedDiagonal& model) const {
+                                           std::mt19937_64* rng) const {
     if (nrFrontals() != 1) {
       throw std::invalid_argument(
           "GaussianConditional::sample can only be called on single variable "
           "conditionals");
     }
-
-    VectorValues solution = solve(parentsValues);
-    Key key = firstFrontalKey();
-
-    Vector sigmas;
-    if (model_) {
-      sigmas = model_->sigmas();
-    } else if (model) {
-      sigmas = model->sigmas();
-    } else {
+    if (!model_) {
       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 SharedDiagonal& model) const {
+  VectorValues GaussianConditional::sample(std::mt19937_64* rng) const {
     if (nrParents() != 0)
       throw std::invalid_argument(
           "sample() can only be invoked on no-parent prior");
     VectorValues values;
-    return sample(values, rng, model);
+    return sample(values);
   }
 
   /* ************************************************************************ */
diff --git a/gtsam/linear/GaussianConditional.h b/gtsam/linear/GaussianConditional.h
index ccf916cd7..4822e3eaf 100644
--- a/gtsam/linear/GaussianConditional.h
+++ b/gtsam/linear/GaussianConditional.h
@@ -166,8 +166,7 @@ namespace gtsam {
      *   std::mt19937_64 rng(42);
      *   auto sample = gbn.sample(&rng);
      */
-    VectorValues sample(std::mt19937_64* rng,
-                        const SharedDiagonal& model = nullptr) const;
+    VectorValues sample(std::mt19937_64* rng) const;
 
     /**
      * Sample from conditional, given missing variables
@@ -176,8 +175,8 @@ namespace gtsam {
      *   VectorValues given = ...;
      *   auto sample = gbn.sample(given, &rng);
      */
-    VectorValues sample(const VectorValues& parentsValues, std::mt19937_64* rng,
-                        const SharedDiagonal& model = nullptr) const;
+    VectorValues sample(const VectorValues& parentsValues,
+                        std::mt19937_64* rng) const;
 
     /// Sample, use default rng
     VectorValues sample() const;