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Test sampling using Monte Carlo integration

release/4.3a0
Frank Dellaert 2022-12-28 12:31:13 -05:00
parent 8391c783bf
commit b3b635cd94
1 changed files with 28 additions and 7 deletions
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35
gtsam/linear/tests/testGaussianBayesNet.cpp
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@ -172,14 +172,18 @@ TEST( GaussianBayesNet, optimize3 )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST(GaussianBayesNet, sample) { namespace sampling {
GaussianBayesNet gbn; static Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished();
Matrix A1 = (Matrix(2, 2) << 1., 2., 3., 4.).finished(); static const Vector2 mean(20, 40), b(10, 10);
const Vector2 mean(20, 40), b(10, 10); static const double sigma = 0.01;
const double sigma = 0.01; static const GaussianBayesNet gbn =
list_of(GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma))(
GaussianDensity::FromMeanAndStddev(X(1), mean, sigma));
} // namespace sampling
gbn.add(GaussianConditional::FromMeanAndStddev(X(0), A1, X(1), b, sigma)); /* ************************************************************************* */
gbn.add(GaussianDensity::FromMeanAndStddev(X(1), mean, sigma)); TEST(GaussianBayesNet, sample) {
using namespace sampling;
auto actual = gbn.sample(); auto actual = gbn.sample();
EXPECT_LONGS_EQUAL(2, actual.size()); EXPECT_LONGS_EQUAL(2, actual.size());
@ -195,6 +199,23 @@ TEST(GaussianBayesNet, sample) {
// EXPECT(assert_equal(Vector2(110.032083, 230.039811), actual[X(0)], 1e-5)); // EXPECT(assert_equal(Vector2(110.032083, 230.039811), actual[X(0)], 1e-5));
} }
/* ************************************************************************* */
// Do Monte Carlo integration of square deviation, should be equal to 9.0.
TEST(GaussianBayesNet, MonteCarloIntegration) {
GaussianBayesNet gbn;
gbn.push_back(noisyBayesNet.at(1));
double sum = 0.0;
constexpr size_t N = 500;
// loop for N samples:
for (size_t i = 0; i < N; i++) {
const auto X_i = gbn.sample();
sum += pow(X_i[_y_].x() - 5.0, 2.0);
}
// Expected is variance = 3*3
EXPECT_DOUBLES_EQUAL(9.0, sum / N, 0.1);
}
/* ************************************************************************* */ /* ************************************************************************* */
TEST(GaussianBayesNet, ordering) TEST(GaussianBayesNet, ordering)
{ {