Merge branch 'borglab:develop' into develop
commit
074f8896a2
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@ -34,6 +34,7 @@
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#include <CppUnitLite/TestHarness.h>
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#include <memory>
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#include <numeric>
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using namespace std;
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using namespace gtsam;
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@ -552,19 +553,23 @@ TEST(HybridBayesNet, Sampling) {
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EXPECT_LONGS_EQUAL(2, average_continuous.size());
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EXPECT_LONGS_EQUAL(num_samples, discrete_samples.size());
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// Regressions don't work across platforms :-(
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// // regression for specific RNG seed
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// double discrete_sum =
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// std::accumulate(discrete_samples.begin(), discrete_samples.end(),
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// decltype(discrete_samples)::value_type(0));
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// EXPECT_DOUBLES_EQUAL(0.477, discrete_sum / num_samples, 1e-9);
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// regression for specific RNG seed
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double discrete_sum =
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std::accumulate(discrete_samples.begin(), discrete_samples.end(),
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decltype(discrete_samples)::value_type(0));
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EXPECT_DOUBLES_EQUAL(0.477, discrete_sum / num_samples, 1e-9);
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// VectorValues expected;
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// expected.insert({X(0), Vector1(-0.0131207162712)});
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// expected.insert({X(1), Vector1(-0.499026377568)});
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// // regression for specific RNG seed
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// EXPECT(assert_equal(expected, average_continuous.scale(1.0 /
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// num_samples)));
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VectorValues expected;
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// regression for specific RNG seed
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#if __APPLE__ || _WIN32
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expected.insert({X(0), Vector1(-0.0131207162712)});
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expected.insert({X(1), Vector1(-0.499026377568)});
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#elif __linux__
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expected.insert({X(0), Vector1(-0.00799425182219)});
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expected.insert({X(1), Vector1(-0.526463854268)});
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#endif
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EXPECT(assert_equal(expected, average_continuous.scale(1.0 / num_samples)));
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}
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/* ****************************************************************************/
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@ -101,7 +101,7 @@ std::pair<double, double> approximateDiscreteMarginal(
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// Do importance sampling
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double w0 = 0.0, w1 = 0.0;
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std::mt19937_64 rng(42);
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for (int i = 0; i < N; i++) {
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for (size_t i = 0; i < N; i++) {
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HybridValues sample = q.sample(&rng);
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sample.insert(given);
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double weight = hbn.evaluate(sample) / q.evaluate(sample);
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@ -186,9 +186,15 @@ TEST(GaussianBayesNet, sample) {
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std::mt19937_64 rng(4242);
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auto actual3 = gbn.sample(&rng);
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EXPECT_LONGS_EQUAL(2, actual.size());
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// regression is not repeatable across platforms/versions :-(
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// EXPECT(assert_equal(Vector2(20.0129382, 40.0039798), actual[X(1)], 1e-5));
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// EXPECT(assert_equal(Vector2(110.032083, 230.039811), actual[X(0)], 1e-5));
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// regressions
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#if __APPLE__ || _WIN32
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EXPECT(assert_equal(Vector2(20.0129382, 40.0039798), actual[X(1)], 1e-5));
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EXPECT(assert_equal(Vector2(110.032083, 230.039811), actual[X(0)], 1e-5));
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#elif __linux__
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EXPECT(assert_equal(Vector2(20.0070499, 39.9942591), actual[X(1)], 1e-5));
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EXPECT(assert_equal(Vector2(109.976501, 229.990945), actual[X(0)], 1e-5));
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#endif
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}
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/* ************************************************************************* */
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@ -464,8 +464,12 @@ TEST(GaussianConditional, sample) {
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std::mt19937_64 rng(4242);
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auto actual3 = conditional.sample(given, &rng);
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EXPECT_LONGS_EQUAL(1, actual2.size());
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// regression is not repeatable across platforms/versions :-(
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// EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
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// regressions
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#if __APPLE__ || _WIN32
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EXPECT(assert_equal(Vector2(31.0111856, 64.9850775), actual2[X(0)], 1e-5));
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#elif __linux__
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EXPECT(assert_equal(Vector2(30.9809331, 64.9927588), actual2[X(0)], 1e-5));
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#endif
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}
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/* ************************************************************************* */
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@ -84,7 +84,7 @@ class GTSAM_EXPORT PreintegratedCombinedMeasurements
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/// @{
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/// Default constructor only for serialization and wrappers
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PreintegratedCombinedMeasurements() { preintMeasCov_.setZero(); }
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PreintegratedCombinedMeasurements() { resetIntegration(); }
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/**
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* Default constructor, initializes the class with no measurements
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@ -97,7 +97,9 @@ class GTSAM_EXPORT PreintegratedCombinedMeasurements
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const imuBias::ConstantBias& biasHat = imuBias::ConstantBias(),
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const Eigen::Matrix<double, 15, 15>& preintMeasCov =
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Eigen::Matrix<double, 15, 15>::Zero())
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: PreintegrationType(p, biasHat), preintMeasCov_(preintMeasCov) {}
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: PreintegrationType(p, biasHat), preintMeasCov_(preintMeasCov) {
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PreintegrationType::resetIntegration();
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}
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/**
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* Construct preintegrated directly from members: base class and
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@ -108,7 +110,9 @@ class GTSAM_EXPORT PreintegratedCombinedMeasurements
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PreintegratedCombinedMeasurements(
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const PreintegrationType& base,
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const Eigen::Matrix<double, 15, 15>& preintMeasCov)
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: PreintegrationType(base), preintMeasCov_(preintMeasCov) {}
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: PreintegrationType(base), preintMeasCov_(preintMeasCov) {
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PreintegrationType::resetIntegration();
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}
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/// Virtual destructor
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~PreintegratedCombinedMeasurements() override {}
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@ -79,7 +79,7 @@ public:
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/// Default constructor for serialization and wrappers
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PreintegratedImuMeasurements() {
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preintMeasCov_.setZero();
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resetIntegration();
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}
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/**
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@ -90,7 +90,7 @@ public:
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PreintegratedImuMeasurements(const std::shared_ptr<PreintegrationParams>& p,
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const imuBias::ConstantBias& biasHat = imuBias::ConstantBias()) :
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PreintegrationType(p, biasHat) {
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preintMeasCov_.setZero();
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resetIntegration();
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}
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/**
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@ -101,6 +101,7 @@ public:
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PreintegratedImuMeasurements(const PreintegrationType& base, const Matrix9& preintMeasCov)
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: PreintegrationType(base),
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preintMeasCov_(preintMeasCov) {
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PreintegrationType::resetIntegration();
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}
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/// Virtual destructor
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@ -113,7 +114,7 @@ public:
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/// equals
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bool equals(const PreintegratedImuMeasurements& expected, double tol = 1e-9) const;
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/// Re-initialize PreintegratedIMUMeasurements
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/// Re-initialize PreintegratedImuMeasurements
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void resetIntegration() override;
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/**
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@ -159,7 +160,7 @@ public:
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* the vehicle at previous time step), current state (pose and velocity at
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* current time step), and the bias estimate. Following the preintegration
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* scheme proposed in [2], the ImuFactor includes many IMU measurements, which
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* are "summarized" using the PreintegratedIMUMeasurements class.
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* are "summarized" using the PreintegratedImuMeasurements class.
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* Note that this factor does not model "temporal consistency" of the biases
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* (which are usually slowly varying quantities), which is up to the caller.
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* See also CombinedImuFactor for a class that does this for you.
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@ -156,6 +156,22 @@ virtual class ImuFactor: gtsam::NonlinearFactor {
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void serialize() const;
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};
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virtual class ImuFactor2: gtsam::NonlinearFactor {
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ImuFactor2();
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ImuFactor2(size_t state_i, size_t state_j,
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size_t bias,
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const gtsam::PreintegratedImuMeasurements& preintegratedMeasurements);
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// Standard Interface
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gtsam::PreintegratedImuMeasurements preintegratedMeasurements() const;
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gtsam::Vector evaluateError(const gtsam::NavState& state_i,
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gtsam::NavState& state_j,
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const gtsam::imuBias::ConstantBias& bias_i);
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// enable serialization functionality
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void serialize() const;
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};
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#include <gtsam/navigation/CombinedImuFactor.h>
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virtual class PreintegrationCombinedParams : gtsam::PreintegrationParams {
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PreintegrationCombinedParams(gtsam::Vector n_gravity);
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@ -205,14 +205,25 @@ TEST(ShonanAveraging3, CheckWithEigen) {
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ShonanAveraging3::LiftwithDescent(4, Qstar3, descentDirection);
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EXPECT_LONGS_EQUAL(5, initialQ4.size());
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// TODO(frank): uncomment this regression test: currently not repeatable
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// across platforms.
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// Matrix expected(4, 4);
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// expected << 0.0459224, -0.688689, -0.216922, 0.690321, //
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// 0.92381, 0.191931, 0.255854, 0.21042, //
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// -0.376669, 0.301589, 0.687953, 0.542111, //
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// -0.0508588, 0.630804, -0.643587, 0.43046;
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// EXPECT(assert_equal(SOn(expected), initialQ4.at<SOn>(0), 1e-5));
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Matrix expected(4, 4);
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#if __APPLE__
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expected << 0.145767, -0.938445, 0.135713, -0.282233, //
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0.780348, -0.0104323, 0.266238, 0.565743, //
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-0.383624, 0.0434887, 0.917211, 0.0983088, //
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-0.471849, -0.342523, -0.263482, 0.768514;
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#elif __linux__
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expected << 0.100724, -0.987231, 0.104092, 0.0662867, //
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0.571527, 0.0292782, 0.226546, -0.788147, //
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-0.349294, 0.064102, 0.93465, 0.0177471, //
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0.735667, 0.142857, 0.253519, 0.611649;
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#elif _WIN32
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expected << 0.0825862, -0.645931, 0.271896, 0.708537, //
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0.927285, -0.0156335, 0.291603, -0.234236, //
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-0.36419, -0.132115, 0.831933, -0.39724, //
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0.0262425, 0.751715, 0.385912, 0.534143;
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#endif
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EXPECT(assert_equal(SOn(expected), initialQ4.at<SOn>(0), 1e-5));
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}
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/* ************************************************************************* */
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