Renamed unit tests already converted to 'Obsolete'

gtsam/linear/tests/testGaussianFactorGraphObsolete.cpp

@@ -37,18 +37,18 @@ static SharedDiagonal
   constraintModel = noiseModel::Constrained::All(2);
 
 static GaussianFactorGraph createSimpleGaussianFactorGraph() {
-  GaussianFactorGraph fg;
-  SharedDiagonal unit2 = noiseModel::Unit::Create(2);
-  // linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
-  fg.add(2, 10*eye(2), -1.0*ones(2), unit2);
-  // odometry between x1 and x2: x2-x1=[0.2;-0.1]
-  fg.add(2, -10*eye(2), 0, 10*eye(2), Vector_(2, 2.0, -1.0), unit2);
-  // measurement between x1 and l1: l1-x1=[0.0;0.2]
-  fg.add(2, -5*eye(2), 1, 5*eye(2), Vector_(2, 0.0, 1.0), unit2);
-  // measurement between x2 and l1: l1-x2=[-0.2;0.3]
-  fg.add(0, -5*eye(2), 1, 5*eye(2), Vector_(2, -1.0, 1.5), unit2);
-  return fg;
-}
+  GaussianFactorGraph fg;
+  SharedDiagonal unit2 = noiseModel::Unit::Create(2);
+  // linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
+  fg.add(2, 10*eye(2), -1.0*ones(2), unit2);
+  // odometry between x1 and x2: x2-x1=[0.2;-0.1]
+  fg.add(2, -10*eye(2), 0, 10*eye(2), Vector_(2, 2.0, -1.0), unit2);
+  // measurement between x1 and l1: l1-x1=[0.0;0.2]
+  fg.add(2, -5*eye(2), 1, 5*eye(2), Vector_(2, 0.0, 1.0), unit2);
+  // measurement between x2 and l1: l1-x2=[-0.2;0.3]
+  fg.add(0, -5*eye(2), 1, 5*eye(2), Vector_(2, -1.0, 1.5), unit2);
+  return fg;
+}
 
 
 /* ************************************************************************* */
@@ -464,73 +464,73 @@ TEST(GaussianFactorGraph, matrices) {
   EXPECT(assert_equal(expectedL, actualL));
   EXPECT(assert_equal(expectedeta, actualeta));
 }
-
-/* ************************************************************************* */
-TEST( GaussianFactorGraph, gradient )
-{
-  GaussianFactorGraph fg = createSimpleGaussianFactorGraph();
-
-  // Construct expected gradient
-  VectorValues expected;
-
-  // 2*f(x) = 100*(x1+c[X(1)])^2 + 100*(x2-x1-[0.2;-0.1])^2 + 25*(l1-x1-[0.0;0.2])^2 + 25*(l1-x2-[-0.2;0.3])^2
-  // worked out: df/dx1 = 100*[0.1;0.1] + 100*[0.2;-0.1]) + 25*[0.0;0.2] = [10+20;10-10+5] = [30;5]
-  expected.insert(1,Vector_(2,  5.0,-12.5));
-  expected.insert(2,Vector_(2, 30.0,  5.0));
-  expected.insert(0,Vector_(2,-25.0, 17.5));
-
-  // Check the gradient at delta=0
-  VectorValues zero = VectorValues::Zero(expected);
-  VectorValues actual = gradient(fg, zero);
-  EXPECT(assert_equal(expected,actual));
-
-  // Check the gradient at the solution (should be zero)
-  VectorValues solution = *GaussianSequentialSolver(fg).optimize();
-  VectorValues actual2 = gradient(fg, solution);
-  EXPECT(assert_equal(VectorValues::Zero(solution), actual2));
-}
-
-/* ************************************************************************* */
-TEST( GaussianFactorGraph, transposeMultiplication )
-{
-  GaussianFactorGraph A = createSimpleGaussianFactorGraph();
-
-  VectorValues e;
-  e.insert(0, Vector_(2, 0.0, 0.0));
-  e.insert(1, Vector_(2,15.0, 0.0));
-  e.insert(2, Vector_(2, 0.0,-5.0));
-  e.insert(3, Vector_(2,-7.5,-5.0));
-
-  VectorValues expected;
-  expected.insert(1, Vector_(2, -37.5,-50.0));
-  expected.insert(2, Vector_(2,-150.0, 25.0));
-  expected.insert(0, Vector_(2, 187.5, 25.0));
-
-  VectorValues actual = VectorValues::SameStructure(expected);
-  transposeMultiply(A, e, actual);
-  EXPECT(assert_equal(expected,actual));
-}
-
-/* ************************************************************************* */
-TEST(GaussianFactorGraph, eliminate_empty )
-{
-  // eliminate an empty factor
-  GaussianFactorGraph gfg;
-  gfg.push_back(boost::make_shared<JacobianFactor>());
-  GaussianConditional::shared_ptr actualCG;
-  GaussianFactorGraph remainingGFG;
-  boost::tie(actualCG, remainingGFG) = gfg.eliminateOne(0);
-
-  // expected Conditional Gaussian is just a parent-less node with P(x)=1
-  GaussianConditional expectedCG(0, Vector(), Matrix(), Vector());
-
-  // expected remaining graph should be the same as the original, still empty :-)
-  GaussianFactorGraph expectedLF = gfg;
-
-  // check if the result matches
-  EXPECT(actualCG->equals(expectedCG));
-  EXPECT(remainingGFG.equals(expectedLF));
-}
+
+/* ************************************************************************* */
+TEST( GaussianFactorGraph, gradient )
+{
+  GaussianFactorGraph fg = createSimpleGaussianFactorGraph();
+
+  // Construct expected gradient
+  VectorValues expected;
+
+  // 2*f(x) = 100*(x1+c[X(1)])^2 + 100*(x2-x1-[0.2;-0.1])^2 + 25*(l1-x1-[0.0;0.2])^2 + 25*(l1-x2-[-0.2;0.3])^2
+  // worked out: df/dx1 = 100*[0.1;0.1] + 100*[0.2;-0.1]) + 25*[0.0;0.2] = [10+20;10-10+5] = [30;5]
+  expected.insert(1,Vector_(2,  5.0,-12.5));
+  expected.insert(2,Vector_(2, 30.0,  5.0));
+  expected.insert(0,Vector_(2,-25.0, 17.5));
+
+  // Check the gradient at delta=0
+  VectorValues zero = VectorValues::Zero(expected);
+  VectorValues actual = gradient(fg, zero);
+  EXPECT(assert_equal(expected,actual));
+
+  // Check the gradient at the solution (should be zero)
+  VectorValues solution = *GaussianSequentialSolver(fg).optimize();
+  VectorValues actual2 = gradient(fg, solution);
+  EXPECT(assert_equal(VectorValues::Zero(solution), actual2));
+}
+
+/* ************************************************************************* */
+TEST( GaussianFactorGraph, transposeMultiplication )
+{
+  GaussianFactorGraph A = createSimpleGaussianFactorGraph();
+
+  VectorValues e;
+  e.insert(0, Vector_(2, 0.0, 0.0));
+  e.insert(1, Vector_(2,15.0, 0.0));
+  e.insert(2, Vector_(2, 0.0,-5.0));
+  e.insert(3, Vector_(2,-7.5,-5.0));
+
+  VectorValues expected;
+  expected.insert(1, Vector_(2, -37.5,-50.0));
+  expected.insert(2, Vector_(2,-150.0, 25.0));
+  expected.insert(0, Vector_(2, 187.5, 25.0));
+
+  VectorValues actual = VectorValues::SameStructure(expected);
+  transposeMultiply(A, e, actual);
+  EXPECT(assert_equal(expected,actual));
+}
+
+/* ************************************************************************* */
+TEST(GaussianFactorGraph, eliminate_empty )
+{
+  // eliminate an empty factor
+  GaussianFactorGraph gfg;
+  gfg.push_back(boost::make_shared<JacobianFactor>());
+  GaussianConditional::shared_ptr actualCG;
+  GaussianFactorGraph remainingGFG;
+  boost::tie(actualCG, remainingGFG) = gfg.eliminateOne(0);
+
+  // expected Conditional Gaussian is just a parent-less node with P(x)=1
+  GaussianConditional expectedCG(0, Vector(), Matrix(), Vector());
+
+  // expected remaining graph should be the same as the original, still empty :-)
+  GaussianFactorGraph expectedLF = gfg;
+
+  // check if the result matches
+  EXPECT(actualCG->equals(expectedCG));
+  EXPECT(remainingGFG.equals(expectedLF));
+}
 
 /* ************************************************************************* */
 int main() { TestResult tr; return TestRegistry::runAllTests(tr);}