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numerical expected results rather than regression

release/4.3a0
Frank Dellaert 2019-04-08 16:49:17 -04:00
parent fe0c951e23
commit 50d9e1ef1c
1 changed files with 12 additions and 9 deletions
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21
gtsam/linear/tests/testJacobianFactor.cpp
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@ -322,27 +322,30 @@ TEST(JacobianFactor, matrices)
/* ************************************************************************* */ /* ************************************************************************* */
TEST(JacobianFactor, operators ) TEST(JacobianFactor, operators )
{ {
SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1); const double sigma = 0.1;
SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2, sigma);
Matrix I = I_2x2; Matrix I = I_2x2;
Vector b = Vector2(0.2,-0.1); Vector b = Vector2(0.2,-0.1);
JacobianFactor lf(1, -I, 2, I, b, sigma0_1); JacobianFactor lf(1, -I, 2, I, b, sigma0_1);
VectorValues c; VectorValues x;
c.insert(1, Vector2(10.,20.)); Vector2 x1(10,20), x2(30,60);
c.insert(2, Vector2(30.,60.)); x.insert(1, x1);
x.insert(2, x2);
// test A*x // test A*x
Vector expectedE = Vector2(200.,400.); Vector expectedE = (x2 - x1)/sigma;
Vector actualE = lf * c; Vector actualE = lf * x;
EXPECT(assert_equal(expectedE, actualE)); EXPECT(assert_equal(expectedE, actualE));
// test A^e // test A^e
VectorValues expectedX; VectorValues expectedX;
expectedX.insert(1, Vector2(-2000.,-4000.)); const double alpha = 0.5;
expectedX.insert(2, Vector2(2000., 4000.)); expectedX.insert(1, - alpha * expectedE /sigma);
expectedX.insert(2, alpha * expectedE /sigma);
VectorValues actualX = VectorValues::Zero(expectedX); VectorValues actualX = VectorValues::Zero(expectedX);
lf.transposeMultiplyAdd(1.0, actualE, actualX); lf.transposeMultiplyAdd(alpha, actualE, actualX);
EXPECT(assert_equal(expectedX, actualX)); EXPECT(assert_equal(expectedX, actualX));
// test gradient at zero // test gradient at zero