From ec5149265a778554325cf4dfa5bda3285de4d76d Mon Sep 17 00:00:00 2001
From: Varun Agrawal <varagrawal@gmail.com>
Date: Sat, 7 Jan 2023 06:33:30 -0500
Subject: [PATCH] remove extraneous file

---
 gtsam/hybrid/tests/testHybridPruning.cpp | 158 -----------------------
 1 file changed, 158 deletions(-)
 delete mode 100644 gtsam/hybrid/tests/testHybridPruning.cpp

diff --git a/gtsam/hybrid/tests/testHybridPruning.cpp b/gtsam/hybrid/tests/testHybridPruning.cpp
deleted file mode 100644
index 2e564013d..000000000
--- a/gtsam/hybrid/tests/testHybridPruning.cpp
+++ /dev/null
@@ -1,158 +0,0 @@
-/* ----------------------------------------------------------------------------
-
- * GTSAM Copyright 2010, Georgia Tech Research Corporation,
- * Atlanta, Georgia 30332-0415
- * All Rights Reserved
- * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
-
- * See LICENSE for the license information
-
- * -------------------------------------------------------------------------- */
-
-/**
- * @file    testHybridPruning.cpp
- * @brief   Unit tests for end-to-end Hybrid Estimation
- * @author  Varun Agrawal
- */
-
-#include <gtsam/discrete/DiscreteBayesNet.h>
-#include <gtsam/geometry/Pose2.h>
-#include <gtsam/hybrid/HybridBayesNet.h>
-#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
-#include <gtsam/hybrid/HybridNonlinearISAM.h>
-#include <gtsam/hybrid/HybridSmoother.h>
-#include <gtsam/hybrid/MixtureFactor.h>
-#include <gtsam/inference/Symbol.h>
-#include <gtsam/linear/GaussianBayesNet.h>
-#include <gtsam/linear/GaussianBayesTree.h>
-#include <gtsam/linear/GaussianFactorGraph.h>
-#include <gtsam/linear/JacobianFactor.h>
-#include <gtsam/linear/NoiseModel.h>
-#include <gtsam/nonlinear/NonlinearFactorGraph.h>
-#include <gtsam/nonlinear/PriorFactor.h>
-#include <gtsam/slam/BetweenFactor.h>
-
-// Include for test suite
-#include <CppUnitLite/TestHarness.h>
-
-#include "Switching.h"
-
-using namespace std;
-using namespace gtsam;
-
-using symbol_shorthand::X;
-
-/****************************************************************************/
-// Test approximate inference with an additional pruning step.
-TEST_DISABLED(HybridPruning, ISAM) {
-  size_t K = 16;
-  std::vector<double> measurements = {0, 1, 2, 2, 2, 2,  3,  4,  5,  6, 6,
-                                      7, 8, 9, 9, 9, 10, 11, 11, 11, 11};
-  // Ground truth discrete seq
-  std::vector<size_t> discrete_seq = {1, 1, 0, 0, 0, 1, 1, 1, 1, 0,
-                                      1, 1, 1, 0, 0, 1, 1, 0, 0, 0};
-  // Switching example of robot moving in 1D
-  // with given measurements and equal mode priors.
-  Switching switching(K, 1.0, 0.1, measurements, "1/1 1/1");
-  HybridNonlinearISAM isam;
-  HybridNonlinearFactorGraph graph;
-  Values initial;
-
-  // Add the X(0) prior
-  graph.push_back(switching.nonlinearFactorGraph.at(0));
-  initial.insert(X(0), switching.linearizationPoint.at<double>(X(0)));
-
-  HybridGaussianFactorGraph linearized;
-  HybridGaussianFactorGraph bayesNet;
-
-  for (size_t k = 1; k < K; k++) {
-    // Motion Model
-    graph.push_back(switching.nonlinearFactorGraph.at(k));
-    // Measurement
-    graph.push_back(switching.nonlinearFactorGraph.at(k + K - 1));
-
-    initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
-
-    isam.update(graph, initial, 3);
-    graph.resize(0);
-    initial.clear();
-  }
-
-  Values result  = isam.estimate();
-  DiscreteValues assignment = isam.assignment();
-
-  DiscreteValues expected_discrete;
-  for (size_t k = 0; k < K - 1; k++) {
-    expected_discrete[M(k)] = discrete_seq[k];
-  }
-
-  std::cout << "\n\n\nNonlinear Version!!\n\n" << std::endl;
-  GTSAM_PRINT(expected_discrete);
-  GTSAM_PRINT(assignment);
-  EXPECT(assert_equal(expected_discrete, assignment));
-
-  Values expected_continuous;
-  for (size_t k = 0; k < K; k++) {
-    expected_continuous.insert(X(k), measurements[k]);
-  }
-  EXPECT(assert_equal(expected_continuous, result));
-}
-
-/****************************************************************************/
-// Test approximate inference with an additional pruning step.
-TEST(HybridPruning, GaussianISAM) {
-  size_t K = 16;
-  std::vector<double> measurements = {0, 1, 2, 2, 2, 2,  3,  4,  5,  6, 6,
-                                      7, 8, 9, 9, 9, 10, 11, 11, 11, 11};
-  // Ground truth discrete seq
-  std::vector<size_t> discrete_seq = {1, 1, 0, 0, 0, 1, 1, 1, 1, 0,
-                                      1, 1, 1, 0, 0, 1, 1, 0, 0, 0};
-  // Switching example of robot moving in 1D
-  // with given measurements and equal mode priors.
-  Switching switching(K, 1.0, 0.1, measurements, "1/1 1/1");
-  HybridGaussianISAM isam;
-  HybridGaussianFactorGraph graph;
-  Values initial;
-
-  // Add the X(0) prior
-  graph.push_back(switching.linearizedFactorGraph.at(0));
-  initial.insert(X(0), switching.linearizationPoint.at<double>(X(0)));
-
-  HybridGaussianFactorGraph linearized;
-  HybridGaussianFactorGraph bayesNet;
-
-  for (size_t k = 1; k < K; k++) {
-    // Motion Model
-    graph.push_back(switching.linearizedFactorGraph.at(k));
-    // Measurement
-    graph.push_back(switching.linearizedFactorGraph.at(k + K - 1));
-
-    // initial.insert(X(k), switching.linearizationPoint.at<double>(X(k)));
-
-    isam.update(graph, 3);
-    graph.resize(0);
-    // initial.clear();
-  }
-
-  HybridValues values = isam.optimize();
-
-  DiscreteValues expected_discrete;
-  for (size_t k = 0; k < K - 1; k++) {
-    expected_discrete[M(k)] = discrete_seq[k];
-  }
-
-  EXPECT(assert_equal(expected_discrete, values.discrete()));
-
-  // Values expected_continuous;
-  // for (size_t k = 0; k < K; k++) {
-  //   expected_continuous.insert(X(k), measurements[k]);
-  // }
-  // EXPECT(assert_equal(expected_continuous, result));
-}
-
-/* ************************************************************************* */
-int main() {
-  TestResult tr;
-  return TestRegistry::runAllTests(tr);
-}
-/* ************************************************************************* */