Fixed compile errors on windows
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				|  | @ -56,7 +56,7 @@ struct LieMatrix : public Matrix, public DerivedValue<LieMatrix> { | |||
|   /// @{
 | ||||
| 
 | ||||
|   /** print @param s optional string naming the object */ | ||||
|   void print(const std::string& name="") const; | ||||
|   GTSAM_EXPORT void print(const std::string& name="") const; | ||||
| 
 | ||||
|   /** equality up to tolerance */ | ||||
|   inline bool equals(const LieMatrix& expected, double tol=1e-5) const { | ||||
|  |  | |||
|  | @ -38,7 +38,7 @@ class DiscreteBayesTree; | |||
| class DiscreteJunctionTree; | ||||
| 
 | ||||
| /** Main elimination function for DiscreteFactorGraph */ | ||||
| std::pair<boost::shared_ptr<DiscreteConditional>, DecisionTreeFactor::shared_ptr> | ||||
| GTSAM_EXPORT std::pair<boost::shared_ptr<DiscreteConditional>, DecisionTreeFactor::shared_ptr> | ||||
| EliminateDiscrete(const DiscreteFactorGraph& factors, const Ordering& keys); | ||||
| 
 | ||||
| /* ************************************************************************* */ | ||||
|  | @ -62,7 +62,7 @@ template<> struct EliminationTraits<DiscreteFactorGraph> | |||
|  * A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e. | ||||
|  *   Factor == DiscreteFactor | ||||
|  */ | ||||
| class DiscreteFactorGraph: public FactorGraph<DiscreteFactor>, | ||||
| class GTSAM_EXPORT DiscreteFactorGraph: public FactorGraph<DiscreteFactor>, | ||||
| public EliminateableFactorGraph<DiscreteFactorGraph> { | ||||
| public: | ||||
| 
 | ||||
|  | @ -120,16 +120,16 @@ public: | |||
|   } | ||||
| 
 | ||||
|   /** Return the set of variables involved in the factors (set union) */ | ||||
|   GTSAM_EXPORT FastSet<Index> keys() const; | ||||
|   FastSet<Index> keys() const; | ||||
| 
 | ||||
|   /** return product of all factors as a single factor */ | ||||
|   GTSAM_EXPORT DecisionTreeFactor product() const; | ||||
|   DecisionTreeFactor product() const; | ||||
| 
 | ||||
|   /** Evaluates the factor graph given values, returns the joint probability of the factor graph given specific instantiation of values*/ | ||||
|   GTSAM_EXPORT double operator()(const DiscreteFactor::Values & values) const; | ||||
|   double operator()(const DiscreteFactor::Values & values) const; | ||||
| 
 | ||||
|   /// print
 | ||||
|   GTSAM_EXPORT void print(const std::string& s = "DiscreteFactorGraph", | ||||
|   void print(const std::string& s = "DiscreteFactorGraph", | ||||
|       const IndexFormatter& formatter =DefaultIndexFormatter) const; | ||||
| 
 | ||||
|   /** Solve the factor graph by performing variable elimination in COLAMD order using
 | ||||
|  |  | |||
|  | @ -62,7 +62,7 @@ TEST( GaussianBayesNet, optimize ) | |||
| { | ||||
|   VectorValues actual = smallBayesNet.optimize(); | ||||
| 
 | ||||
|   VectorValues expected = map_list_of | ||||
|   VectorValues expected = map_list_of<Key, Vector> | ||||
|     (_x_, (Vec(1) << 4.0)) | ||||
|     (_y_, (Vec(1) << 5.0)); | ||||
| 
 | ||||
|  | @ -77,12 +77,12 @@ TEST( GaussianBayesNet, optimize3 ) | |||
|   // 5     1    5
 | ||||
|   // NOTE: we are supplying a new RHS here
 | ||||
| 
 | ||||
|   VectorValues expected = map_list_of | ||||
|   VectorValues expected = map_list_of<Key, Vector> | ||||
|     (_x_, (Vec(1) << -1.0)) | ||||
|     (_y_, (Vec(1) <<  5.0)); | ||||
| 
 | ||||
|   // Test different RHS version
 | ||||
|   VectorValues gx = map_list_of | ||||
|   VectorValues gx = map_list_of<Key, Vector> | ||||
|     (_x_, (Vec(1) << 4.0)) | ||||
|     (_y_, (Vec(1) << 5.0)); | ||||
|   VectorValues actual = smallBayesNet.backSubstitute(gx); | ||||
|  | @ -96,10 +96,10 @@ TEST( GaussianBayesNet, backSubstituteTranspose ) | |||
|   // 2 = 1    2
 | ||||
|   // 5   1 1  3
 | ||||
|   VectorValues | ||||
|     x = map_list_of | ||||
|     x = map_list_of<Key, Vector> | ||||
|       (_x_, (Vec(1) << 2.0)) | ||||
|       (_y_, (Vec(1) << 5.0)), | ||||
|     expected = map_list_of | ||||
|     expected = map_list_of<Key, Vector> | ||||
|       (_x_, (Vec(1) << 2.0)) | ||||
|       (_y_, (Vec(1) << 3.0)); | ||||
| 
 | ||||
|  |  | |||
|  | @ -98,7 +98,7 @@ TEST( GaussianBayesTree, eliminate ) | |||
| /* ************************************************************************* */ | ||||
| TEST( GaussianBayesTree, optimizeMultiFrontal ) | ||||
| { | ||||
|   VectorValues expected = pair_list_of | ||||
|   VectorValues expected = pair_list_of<Key, Vector> | ||||
|     (x1, (Vec(1) << 0.)) | ||||
|     (x2, (Vec(1) << 1.)) | ||||
|     (x3, (Vec(1) << 0.)) | ||||
|  | @ -263,7 +263,7 @@ TEST(GaussianBayesTree, ComputeSteepestDescentPointBT) { | |||
|   Vector expected = gradient * step; | ||||
| 
 | ||||
|   // Known steepest descent point from Bayes' net version
 | ||||
|   VectorValues expectedFromBN = pair_list_of | ||||
|   VectorValues expectedFromBN = pair_list_of<Key, Vector> | ||||
|     (0, (Vec(2) << 0.000129034, 0.000688183)) | ||||
|     (1, (Vec(2) << 0.0109679, 0.0253767)) | ||||
|     (2, (Vec(2) << 0.0680441, 0.114496)) | ||||
|  |  | |||
|  | @ -183,7 +183,7 @@ TEST( GaussianConditional, solve_simple ) | |||
|   VectorValues actual = map_list_of | ||||
|     (2, sx1); // parent
 | ||||
| 
 | ||||
|   VectorValues expected = map_list_of | ||||
|   VectorValues expected = map_list_of<Key, Vector> | ||||
|     (2, sx1) | ||||
|     (1, (Vec(4) << -3.1,-3.4,-11.9,-13.2)); | ||||
| 
 | ||||
|  | @ -219,7 +219,7 @@ TEST( GaussianConditional, solve_multifrontal ) | |||
|   VectorValues actual = map_list_of | ||||
|     (10, sl1); // parent
 | ||||
| 
 | ||||
|   VectorValues expected = map_list_of | ||||
|   VectorValues expected = map_list_of<Key, Vector> | ||||
|     (1, (Vector)(Vec(2) << -3.1,-3.4)) | ||||
|     (2, (Vector)(Vec(2) << -11.9,-13.2)) | ||||
|     (10, sl1); | ||||
|  | @ -257,10 +257,10 @@ TEST( GaussianConditional, solveTranspose ) { | |||
|   // 5   1 1  3
 | ||||
| 
 | ||||
|   VectorValues | ||||
|     x = map_list_of | ||||
|     x = map_list_of<Key, Vector> | ||||
|       (1, (Vec(1) << 2.)) | ||||
|       (2, (Vec(1) << 5.)), | ||||
|     y = map_list_of | ||||
|     y = map_list_of<Key, Vector> | ||||
|       (1, (Vec(1) << 2.)) | ||||
|       (2, (Vec(1) << 3.)); | ||||
| 
 | ||||
|  |  | |||
|  | @ -74,7 +74,7 @@ TEST(HessianFactor, ConversionConstructor) | |||
| 
 | ||||
|   HessianFactor actual(jacobian); | ||||
| 
 | ||||
|   VectorValues values = pair_list_of | ||||
|   VectorValues values = pair_list_of<Key, Vector> | ||||
|     (0, (Vec(2) << 1.0, 2.0)) | ||||
|     (1, (Vec(4) << 3.0, 4.0, 5.0, 6.0)); | ||||
| 
 | ||||
|  | @ -89,7 +89,6 @@ TEST(HessianFactor, Constructor1) | |||
|   Matrix G = Matrix_(2,2, 3.0, 5.0, 0.0, 6.0); | ||||
|   Vector g = (Vec(2) << -8.0, -9.0); | ||||
|   double f = 10.0; | ||||
| 
 | ||||
|   HessianFactor factor(0, G, g, f); | ||||
| 
 | ||||
|   // extract underlying parts
 | ||||
|  | @ -98,7 +97,7 @@ TEST(HessianFactor, Constructor1) | |||
|   EXPECT(assert_equal(g, Vector(factor.linearTerm()))); | ||||
|   EXPECT_LONGS_EQUAL(1, (long)factor.size()); | ||||
| 
 | ||||
|   VectorValues dx = pair_list_of(0, (Vec(2) << 1.5, 2.5)); | ||||
|   VectorValues dx = pair_list_of<Key, Vector>(0, (Vec(2) << 1.5, 2.5)); | ||||
| 
 | ||||
|   // error 0.5*(f - 2*x'*g + x'*G*x)
 | ||||
|   double expected = 80.375; | ||||
|  | @ -162,7 +161,7 @@ TEST(HessianFactor, Constructor2) | |||
|   EXPECT(assert_equal(G22, factor.info(factor.begin()+1, factor.begin()+1))); | ||||
| 
 | ||||
|   // Check case when vector values is larger than factor
 | ||||
|   VectorValues dxLarge = pair_list_of | ||||
|   VectorValues dxLarge = pair_list_of<Key, Vector> | ||||
|     (0, dx0) | ||||
|     (1, dx1) | ||||
|     (2, (Vec(2) << 0.1, 0.2)); | ||||
|  |  | |||
|  | @ -170,7 +170,7 @@ TEST(Values, expmap_a) | |||
|   config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0)); | ||||
|   config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0)); | ||||
| 
 | ||||
|   VectorValues increment = pair_list_of | ||||
|   VectorValues increment = pair_list_of<Key, Vector> | ||||
|     (key1, (Vec(3) << 1.0, 1.1, 1.2)) | ||||
|     (key2, (Vec(3) << 1.3, 1.4, 1.5)); | ||||
| 
 | ||||
|  | @ -188,8 +188,8 @@ TEST(Values, expmap_b) | |||
|   config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0)); | ||||
|   config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0)); | ||||
| 
 | ||||
|   VectorValues increment = pair_list_of | ||||
|     (key2, LieVector(3, 1.3, 1.4, 1.5)); | ||||
|   VectorValues increment = pair_list_of<Key, Vector> | ||||
|     (key2, (Vec(3) << 1.3, 1.4, 1.5)); | ||||
| 
 | ||||
|   Values expected; | ||||
|   expected.insert(key1, LieVector(3, 1.0, 2.0, 3.0)); | ||||
|  | @ -241,7 +241,7 @@ TEST(Values, localCoordinates) | |||
|   valuesA.insert(key1, LieVector(3, 1.0, 2.0, 3.0)); | ||||
|   valuesA.insert(key2, LieVector(3, 5.0, 6.0, 7.0)); | ||||
| 
 | ||||
|   VectorValues expDelta = pair_list_of | ||||
|   VectorValues expDelta = pair_list_of<Key, Vector> | ||||
|     (key1, (Vec(3) << 0.1, 0.2, 0.3)) | ||||
|     (key2, (Vec(3) << 0.4, 0.5, 0.6)); | ||||
| 
 | ||||
|  |  | |||
|  | @ -157,7 +157,7 @@ Point3 triangulateDLT(const std::vector<Pose3>& poses, | |||
|  * @return Returns a Point3 on success, boost::none otherwise. | ||||
|  */ | ||||
| template<class CALIBRATION> | ||||
| GTSAM_UNSTABLE_EXPORT Point3 triangulatePoint3(const std::vector<Pose3>& poses, | ||||
| Point3 triangulatePoint3(const std::vector<Pose3>& poses, | ||||
|     const std::vector<Point2>& measurements, const CALIBRATION& K, | ||||
|     double rank_tol = 1e-9, bool optimize = false) { | ||||
| 
 | ||||
|  |  | |||
|  | @ -222,7 +222,7 @@ Values createValues() { | |||
| /* ************************************************************************* */ | ||||
| VectorValues createVectorValues() { | ||||
|   using namespace impl; | ||||
|   VectorValues c = boost::assign::pair_list_of | ||||
|   VectorValues c = boost::assign::pair_list_of<Key, Vector> | ||||
|     (_l1_, (Vec(2) << 0.0, -1.0)) | ||||
|     (_x1_, (Vec(2) << 0.0, 0.0)) | ||||
|     (_x2_, (Vec(2) << 1.5, 0.0)); | ||||
|  | @ -483,7 +483,7 @@ GaussianFactorGraph createSingleConstraintGraph() { | |||
| /* ************************************************************************* */ | ||||
| VectorValues createSingleConstraintValues() { | ||||
|   using namespace impl; | ||||
|   VectorValues config = boost::assign::pair_list_of | ||||
|   VectorValues config = boost::assign::pair_list_of<Key, Vector> | ||||
|     (_x_, (Vec(2) << 1.0, -1.0)) | ||||
|     (_y_, (Vec(2) << 0.2, 0.1)); | ||||
|   return config; | ||||
|  | @ -547,7 +547,7 @@ GaussianFactorGraph createMultiConstraintGraph() { | |||
| /* ************************************************************************* */ | ||||
| VectorValues createMultiConstraintValues() { | ||||
|   using namespace impl; | ||||
|   VectorValues config = boost::assign::pair_list_of | ||||
|   VectorValues config = boost::assign::pair_list_of<Key, Vector> | ||||
|     (_x_, (Vec(2) << -2.0, 2.0)) | ||||
|     (_y_, (Vec(2) << -0.1, 0.4)) | ||||
|     (_z_, (Vec(2) <<-4.0, 5.0)); | ||||
|  |  | |||
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