remove test case

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -907,102 +907,6 @@ TEST(HybridGaussianFactorGraph, EliminateSwitchingNetwork) {
   EXPECT(ratioTest(bn, measurements, *posterior));
 }
 
-/* ****************************************************************************/
-// Test printErrors with multivariate example.
-TEST(HybridGaussianFactorGraph, PrintErrors) {
-  HybridGaussianFactorGraph hfg;
-  HybridBayesNet bayesNet;
-
-  size_t num_measurements = 1;
-  // Create Gaussian mixture z_i = x0 + noise for each measurement.
-  for (size_t i = 0; i < num_measurements; i++) {
-    const DiscreteKey mode_i{M(i), 2};
-    bayesNet.emplace_back(new GaussianMixture(
-        {Z(i)}, {X(0)}, {mode_i},
-        {GaussianConditional::sharedMeanAndStddev(Z(i), I_3x3, X(0), Z_3x1, 10),
-         GaussianConditional::sharedMeanAndStddev(Z(i), I_3x3, X(0), Z_3x1,
-                                                  0.1)}));
-  }
-
-  // Create prior on X(0).
-  bayesNet.push_back(GaussianConditional::sharedMeanAndStddev(
-      X(0), Vector3(1.0, 2.0, 5.0), 0.5));
-
-  // Add prior on mode.
-  const size_t nrModes = 1;
-  for (size_t i = 0; i < nrModes; i++) {
-    bayesNet.emplace_back(new DiscreteConditional({M(i), 2}, "4/6"));
-  }
-
-  VectorValues measurements{{Z(0), Vector3(1.0, 2.0, 5.0)}};
-  HybridGaussianFactorGraph measurement_fg =
-      bayesNet.toFactorGraph(measurements);
-  HybridValues values = bayesNet.optimize();
-
-  std::stringstream buffer;
-  // Save the original output stream so we can reset later
-  std::streambuf *old = std::cout.rdbuf(buffer.rdbuf());
-
-  // We test against actual std::cout for faithful reproduction
-  measurement_fg.printErrors(values);
-
-  // Get output string and reset stdout
-  std::string actual = buffer.str();
-  std::cout.rdbuf(old);
-
-  std::string expected = R"(HybridGaussianFactorGraph: size: 3
-
-Factor 0: Hybrid [x0; m0]{
- Choice(m0) 
- 0 Leaf :
-  A[x0] = [
-	-1, -0, -0;
-	-0, -1, -0;
-	-0, -0, -1;
-	0, 0, 0
-]
-  b = [      -1      -2      -5 5.25652 ]
-  Noise model: diagonal sigmas [10; 10; 10; 1];
-
- 1 Leaf :
-  A[x0] = [
-	-1, -0, -0;
-	-0, -1, -0;
-	-0, -0, -1
-]
-  b = [ -1 -2 -5 ]
-isotropic dim=3 sigma=0.1
-
-}
-error =  Choice(m0) 
- 0 Leaf 13.815511
- 1 Leaf    0
-
-
-Factor 1: p(x0)
-  R = [ 1 0 0 ]
-      [ 0 1 0 ]
-      [ 0 0 1 ]
-  d = [ 1 2 5 ]
-  mean: 1 elements
-  x0: 1 2 5
-isotropic dim=3 sigma=0.5
-error = 0
-
-Factor 2:  P( m0 ):
- Choice(m0) 
- 0 Leaf  0.4
- 1 Leaf  0.6
-
-error =  Choice(m0) 
- 0 Leaf 0.91629073
- 1 Leaf 0.51082562
-
-
-)";
-  EXPECT(expected == actual);
-}
-
 /* ************************************************************************* */
 int main() {
   TestResult tr;