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@ -137,7 +137,7 @@ TEST( GaussianBayesNet, optimize3 )
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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TEST(GaussianBayesNet, ordering)
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TEST(GaussianBayesNet, ordering)
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{
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{
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Ordering expected;
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Ordering expected;
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expected += _x_, _y_;
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expected += _x_, _y_;
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@ -155,7 +155,7 @@ TEST( GaussianBayesNet, MatrixStress )
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bn.emplace_shared<GC>(_z_, Vector2(5, 6), 6 * I_2x2);
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bn.emplace_shared<GC>(_z_, Vector2(5, 6), 6 * I_2x2);
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const VectorValues expected = bn.optimize();
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const VectorValues expected = bn.optimize();
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for (const auto keys :
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for (const auto& keys :
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{KeyVector({_x_, _y_, _z_}), KeyVector({_x_, _z_, _y_}),
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{KeyVector({_x_, _y_, _z_}), KeyVector({_x_, _z_, _y_}),
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KeyVector({_y_, _x_, _z_}), KeyVector({_y_, _z_, _x_}),
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KeyVector({_y_, _x_, _z_}), KeyVector({_y_, _z_, _x_}),
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KeyVector({_z_, _x_, _y_}), KeyVector({_z_, _y_, _x_})}) {
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KeyVector({_z_, _x_, _y_}), KeyVector({_z_, _y_, _x_})}) {
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@ -183,7 +183,7 @@ TEST( GaussianBayesNet, backSubstituteTranspose )
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VectorValues actual = smallBayesNet.backSubstituteTranspose(x);
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VectorValues actual = smallBayesNet.backSubstituteTranspose(x);
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EXPECT(assert_equal(expected, actual));
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EXPECT(assert_equal(expected, actual));
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const auto ordering = noisyBayesNet.ordering();
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const auto ordering = noisyBayesNet.ordering();
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const Matrix R = smallBayesNet.matrix(ordering).first;
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const Matrix R = smallBayesNet.matrix(ordering).first;
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const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
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const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
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@ -206,7 +206,7 @@ TEST( GaussianBayesNet, backSubstituteTransposeNoisy )
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VectorValues actual = noisyBayesNet.backSubstituteTranspose(x);
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VectorValues actual = noisyBayesNet.backSubstituteTranspose(x);
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EXPECT(assert_equal(expected, actual));
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EXPECT(assert_equal(expected, actual));
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const auto ordering = noisyBayesNet.ordering();
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const auto ordering = noisyBayesNet.ordering();
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const Matrix R = noisyBayesNet.matrix(ordering).first;
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const Matrix R = noisyBayesNet.matrix(ordering).first;
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const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
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const Vector expected_vector = R.transpose().inverse() * x.vector(ordering);
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@ -1,37 +0,0 @@
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/*
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* testPlanning.cpp
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* @brief develop code for planning example
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* @date Feb 13, 2012
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* @author Frank Dellaert
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*/
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//#include <gtsam/discrete/Planner.h>
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#include <gtsam/discrete/DiscreteKey.h>
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#include <CppUnitLite/TestHarness.h>
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#include <iostream>
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#include <fstream>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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TEST_UNSAFE(Planner, allInOne)
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{
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// A small planning problem
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// First variable is location
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DiscreteKey location(0,3),
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haveRock(1,2), haveSoil(2,2), haveImage(3,2),
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commRock(4,2), commSoil(5,2), commImage(6,2);
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// There are 3 actions:
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// Drive, communicate, sample
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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@ -75,7 +75,7 @@ DiscreteFactorGraph createExpected() {
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// Mutual exclusion for students
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// Mutual exclusion for students
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expected.addAllDiff(A, J);
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expected.addAllDiff(A, J);
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return expected;
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return std::move(expected);
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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@ -244,7 +244,7 @@ TEST(Similarity3, GroupAction) {
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&Similarity3::transformFrom, _1, _2, boost::none, boost::none);
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&Similarity3::transformFrom, _1, _2, boost::none, boost::none);
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Point3 q(1, 2, 3);
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Point3 q(1, 2, 3);
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for (const auto T : { T1, T2, T3, T4, T5, T6 }) {
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for (const auto& T : { T1, T2, T3, T4, T5, T6 }) {
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Point3 q(1, 0, 0);
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Point3 q(1, 0, 0);
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Matrix H1 = numericalDerivative21<Point3, Similarity3, Point3>(f, T, q);
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Matrix H1 = numericalDerivative21<Point3, Similarity3, Point3>(f, T, q);
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Matrix H2 = numericalDerivative22<Point3, Similarity3, Point3>(f, T, q);
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Matrix H2 = numericalDerivative22<Point3, Similarity3, Point3>(f, T, q);
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