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Test for GBN::sample

release/4.3a0
Frank Dellaert 2022-12-28 16:51:40 -05:00
parent 356b89a165
commit 0495f81104
2 changed files with 21 additions and 9 deletions
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11
gtsam/linear/linear.i
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@ -490,6 +490,8 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
bool equals(const gtsam::GaussianConditional& cg, double tol) const; bool equals(const gtsam::GaussianConditional& cg, double tol) const;
// Standard Interface // Standard Interface
double evaluate(const gtsam::VectorValues& x) const;
double logDensity(const gtsam::VectorValues& x) const;
gtsam::Key firstFrontalKey() const; gtsam::Key firstFrontalKey() const;
gtsam::VectorValues solve(const gtsam::VectorValues& parents) const; gtsam::VectorValues solve(const gtsam::VectorValues& parents) const;
gtsam::JacobianFactor* likelihood( gtsam::JacobianFactor* likelihood(
@ -543,17 +545,20 @@ virtual class GaussianBayesNet {
bool equals(const gtsam::GaussianBayesNet& other, double tol) const; bool equals(const gtsam::GaussianBayesNet& other, double tol) const;
size_t size() const; size_t size() const;
// Standard interface
void push_back(gtsam::GaussianConditional* conditional); void push_back(gtsam::GaussianConditional* conditional);
void push_back(const gtsam::GaussianBayesNet& bayesNet); void push_back(const gtsam::GaussianBayesNet& bayesNet);
gtsam::GaussianConditional* front() const; gtsam::GaussianConditional* front() const;
gtsam::GaussianConditional* back() const; gtsam::GaussianConditional* back() const;
// Standard interface
double evaluate(const gtsam::VectorValues& x) const;
double logDensity(const gtsam::VectorValues& x) const;
gtsam::VectorValues optimize() const; gtsam::VectorValues optimize() const;
gtsam::VectorValues optimize(gtsam::VectorValues given) const; gtsam::VectorValues optimize(const gtsam::VectorValues& given) const;
gtsam::VectorValues optimizeGradientSearch() const; gtsam::VectorValues optimizeGradientSearch() const;
gtsam::VectorValues sample(gtsam::VectorValues given) const; gtsam::VectorValues sample(const gtsam::VectorValues& given) const;
gtsam::VectorValues sample() const; gtsam::VectorValues sample() const;
gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const; gtsam::VectorValues backSubstitute(const gtsam::VectorValues& gx) const;
gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const; gtsam::VectorValues backSubstituteTranspose(const gtsam::VectorValues& gx) const;

19
python/gtsam/tests/test_GaussianBayesNet.py
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@ -29,8 +29,7 @@ def smallBayesNet():
"""Create a small Bayes Net for testing""" """Create a small Bayes Net for testing"""
bayesNet = GaussianBayesNet() bayesNet = GaussianBayesNet()
I_1x1 = np.eye(1, dtype=float) I_1x1 = np.eye(1, dtype=float)
bayesNet.push_back(GaussianConditional( bayesNet.push_back(GaussianConditional(_x_, [9.0], I_1x1, _y_, I_1x1))
_x_, [9.0], I_1x1, _y_, I_1x1))
bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1)) bayesNet.push_back(GaussianConditional(_y_, [5.0], I_1x1))
return bayesNet return bayesNet
@ -41,13 +40,21 @@ class TestGaussianBayesNet(GtsamTestCase):
def test_matrix(self): def test_matrix(self):
"""Test matrix method""" """Test matrix method"""
R, d = smallBayesNet().matrix() # get matrix and RHS R, d = smallBayesNet().matrix() # get matrix and RHS
R1 = np.array([ R1 = np.array([[1.0, 1.0], [0.0, 1.0]])
[1.0, 1.0],
[0.0, 1.0]])
d1 = np.array([9.0, 5.0]) d1 = np.array([9.0, 5.0])
np.testing.assert_equal(R, R1) np.testing.assert_equal(R, R1)
np.testing.assert_equal(d, d1) np.testing.assert_equal(d, d1)
def test_sample(self):
"""Test sample method"""
bayesNet = smallBayesNet()
sample = bayesNet.sample()
self.assertIsInstance(sample, gtsam.VectorValues)
if __name__ == '__main__': # standard deviation is 1.0 for both, so we set tolerance to 3*sigma
mean = bayesNet.optimize()
self.gtsamAssertEquals(sample, mean, tol=3.0)
if __name__ == "__main__":
unittest.main() unittest.main()