Update fix compilaltion

include/Normals.h → python/Normals.h

@@ -30,8 +30,8 @@
 #include <math.h>
 #include <string>
 #include <sstream>
-#include "Eigen/Dense"
-#include "nanoflann.hpp"
+#include <Eigen/Dense>
+#include <nanoflann.hpp>
 
 #ifdef _OPENMP
 #include <omp.h>
@@ -91,7 +91,6 @@ class Eigen_Normal_Estimator{
 
 	typedef nanoflann::KDTreeEigenMatrixAdaptor< Eigen::MatrixX3d > kd_tree; //a row is a point
 
-
 	// constructor
 	Eigen_Normal_Estimator(const Eigen::MatrixX3d& points, Eigen::MatrixX3d& normals):
 		pts(points),nls(normals){
@@ -128,7 +127,7 @@ class Eigen_Normal_Estimator{
 	    }
 		istr.close();
 		pts.resize(points.size(),3);
-		for(int i=0; i<points.size(); i++){
+		for(uint i=0; i<points.size(); i++){
 			pts.row(i) = points[i];
 		}
 	}
@@ -159,7 +158,7 @@ class Eigen_Normal_Estimator{
 
 		//creating vector of random int
 		std::vector<int> vecInt(1000000);
-		for(int i=0; i<vecInt.size(); i++){
+		for(uint i=0; i<vecInt.size(); i++){
 			vecInt[i] = rand();
 		}
 
@@ -200,7 +199,7 @@ class Eigen_Normal_Estimator{
 
 		//kd tree creation
 		//build de kd_tree
-		kd_tree tree(pts);
+		kd_tree tree(3, pts, 10);
 		tree.index->buildIndex();
 
 		//create the density estimation for each point
@@ -218,7 +217,7 @@ class Eigen_Normal_Estimator{
 			//knn for k_density+1 because the point is itself include in the search tree
 			tree.index->knnSearch(&pt_query[0], k_density+1, &pointIdxSearch[0], &pointSquaredDistance[0]);
 			double d =0;
-			for(int i=0; i<pointSquaredDistance.size(); i++){
+			for(uint i=0; i<pointSquaredDistance.size(); i++){
 				d+=std::sqrt(pointSquaredDistance[i]);
 			}
 			d /= pointSquaredDistance.size()-1;
@@ -367,7 +366,7 @@ class Eigen_Normal_Estimator{
 	unsigned int dichotomic_search_nearest(const std::vector<double> elems, double d){
 		unsigned int i1 = 0;
 		unsigned int i2 = elems.size()-1;
-		unsigned int i3;
+		unsigned int i3 = (i1+i2)/2;
 		while(i2 > i1){
 			i3 = (i1+i2)/2;
 			if(elems[i3] == d){break;}
@@ -391,13 +390,13 @@ class Eigen_Normal_Estimator{
 	{
 		std::vector<double> dists;
 		double sum=0;
-		for(int i=0; i<pointIdxSearch.size(); i++){
+		for(uint i=0; i<pointIdxSearch.size(); i++){
 			sum+=densities[pointIdxSearch[i]];
 			dists.push_back(sum);
 		}
 
 		unsigned int S = vecRandInt.size();
-		unsigned int number_of_points = pointIdxSearch.size();
+		// unsigned int number_of_points = pointIdxSearch.size();
 		triplets.resize(triplet_number,3);
 		unsigned int pos=vecRandInt[0]%S;;
 		for(unsigned int i=0; i<triplet_number; i++){

python/normEstHough.cxx

@@ -69,7 +69,7 @@ void NormEstHough::get_normals(double* array, int m, int n) {
         }
     return ;
 }
-
+ 
 void NormEstHough::set_points(double* array, int m, int n){
     // resize the point cloud
     pts.resize(m,3);

python/setup.py

@@ -4,11 +4,12 @@
 
 
 ext_modules = [Extension(
-       "NormalEstimatorHough",
-       sources=["NormalEstimatorHough.pyx", "normEstHough.cxx"],
-       include_dirs=["third_party_includes/", "../include"],
-       language="c++",             # generate C++ code
-       extra_compile_args = ["-fopenmp", "-std=c++11"]
+        "NormalEstimatorHough",
+        sources=["NormalEstimatorHough.pyx", "normEstHough.cxx"],
+        include_dirs=["../third_party_includes/"],
+        language="c++",             # generate C++ code
+        extra_compile_args = ["-fopenmp", "-std=c++11"],
+        extra_link_args=['-lgomp']
   )]
 
 setup(

third_party_includes/Eigen/Cholesky

@@ -9,6 +9,7 @@
 #define EIGEN_CHOLESKY_MODULE_H
 
 #include "Core"
+#include "Jacobi"
 
 #include "src/Core/util/DisableStupidWarnings.h"
 
@@ -31,7 +32,11 @@
 #include "src/Cholesky/LLT.h"
 #include "src/Cholesky/LDLT.h"
 #ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
 #include "src/misc/lapacke.h"
+#endif
 #include "src/Cholesky/LLT_LAPACKE.h"
 #endif
 

third_party_includes/Eigen/Core

@@ -14,6 +14,22 @@
 // first thing Eigen does: stop the compiler from committing suicide
 #include "src/Core/util/DisableStupidWarnings.h"
 
+#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA)
+  #define EIGEN_CUDACC __CUDACC__
+#endif
+
+#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA)
+  #define EIGEN_CUDA_ARCH __CUDA_ARCH__
+#endif
+
+#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
+#define EIGEN_CUDACC_VER  ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
+#elif defined(__CUDACC_VER__)
+#define EIGEN_CUDACC_VER __CUDACC_VER__
+#else
+#define EIGEN_CUDACC_VER 0
+#endif
+
 // Handle NVCC/CUDA/SYCL
 #if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
   // Do not try asserts on CUDA and SYCL!
@@ -37,9 +53,9 @@
     #endif
 
     #define EIGEN_DEVICE_FUNC __host__ __device__
-    // We need math_functions.hpp to ensure that that EIGEN_USING_STD_MATH macro
+    // We need cuda_runtime.h to ensure that that EIGEN_USING_STD_MATH macro
     // works properly on the device side
-    #include <math_functions.hpp>
+    #include <cuda_runtime.h>
   #else
     #define EIGEN_DEVICE_FUNC
   #endif
@@ -155,6 +171,9 @@
       #ifdef __AVX512DQ__
         #define EIGEN_VECTORIZE_AVX512DQ
       #endif
+      #ifdef __AVX512ER__
+        #define EIGEN_VECTORIZE_AVX512ER
+      #endif
     #endif
 
     // include files
@@ -229,7 +248,7 @@
 #if defined __CUDACC__
   #define EIGEN_VECTORIZE_CUDA
   #include <vector_types.h>
-  #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500
+  #if EIGEN_CUDACC_VER >= 70500
     #define EIGEN_HAS_CUDA_FP16
   #endif
 #endif
@@ -352,6 +371,7 @@ using std::ptrdiff_t;
 #include "src/Core/MathFunctions.h"
 #include "src/Core/GenericPacketMath.h"
 #include "src/Core/MathFunctionsImpl.h"
+#include "src/Core/arch/Default/ConjHelper.h"
 
 #if defined EIGEN_VECTORIZE_AVX512
   #include "src/Core/arch/SSE/PacketMath.h"
@@ -367,6 +387,7 @@ using std::ptrdiff_t;
   #include "src/Core/arch/AVX/MathFunctions.h"
   #include "src/Core/arch/AVX/Complex.h"
   #include "src/Core/arch/AVX/TypeCasting.h"
+  #include "src/Core/arch/SSE/TypeCasting.h"
 #elif defined EIGEN_VECTORIZE_SSE
   #include "src/Core/arch/SSE/PacketMath.h"
   #include "src/Core/arch/SSE/MathFunctions.h"

third_party_includes/Eigen/Eigenvalues

@@ -45,7 +45,11 @@
 #include "src/Eigenvalues/GeneralizedEigenSolver.h"
 #include "src/Eigenvalues/MatrixBaseEigenvalues.h"
 #ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
 #include "src/misc/lapacke.h"
+#endif
 #include "src/Eigenvalues/RealSchur_LAPACKE.h"
 #include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
 #include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"

third_party_includes/Eigen/LU

@@ -28,7 +28,11 @@
 #include "src/LU/FullPivLU.h"
 #include "src/LU/PartialPivLU.h"
 #ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
 #include "src/misc/lapacke.h"
+#endif
 #include "src/LU/PartialPivLU_LAPACKE.h"
 #endif
 #include "src/LU/Determinant.h"

third_party_includes/Eigen/QR

@@ -36,7 +36,11 @@
 #include "src/QR/ColPivHouseholderQR.h"
 #include "src/QR/CompleteOrthogonalDecomposition.h"
 #ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
 #include "src/misc/lapacke.h"
+#endif
 #include "src/QR/HouseholderQR_LAPACKE.h"
 #include "src/QR/ColPivHouseholderQR_LAPACKE.h"
 #endif

third_party_includes/Eigen/QtAlignedMalloc

@@ -27,7 +27,7 @@ void qFree(void *ptr)
 void *qRealloc(void *ptr, std::size_t size)
 {
   void* newPtr = Eigen::internal::aligned_malloc(size);
-  memcpy(newPtr, ptr, size);
+  std::memcpy(newPtr, ptr, size);
   Eigen::internal::aligned_free(ptr);
   return newPtr;
 }

third_party_includes/Eigen/SVD

@@ -37,7 +37,11 @@
 #include "src/SVD/JacobiSVD.h"
 #include "src/SVD/BDCSVD.h"
 #if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
 #include "src/misc/lapacke.h"
+#endif
 #include "src/SVD/JacobiSVD_LAPACKE.h"
 #endif
 

third_party_includes/Eigen/src/Cholesky/LDLT.h

@@ -248,7 +248,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
     /** \brief Reports whether previous computation was successful.
       *
       * \returns \c Success if computation was succesful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
+      *          \c NumericalIssue if the factorization failed because of a zero pivot.
       */
     ComputationInfo info() const
     {
@@ -305,7 +305,8 @@ template<> struct ldlt_inplace<Lower>
     if (size <= 1)
     {
       transpositions.setIdentity();
-      if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
+      if(size==0) sign = ZeroSign;
+      else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
       else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
       else sign = ZeroSign;
       return true;
@@ -376,6 +377,8 @@ template<> struct ldlt_inplace<Lower>
 
       if((rs>0) && pivot_is_valid)
         A21 /= realAkk;
+      else if(rs>0)
+        ret = ret && (A21.array()==Scalar(0)).all();
 
       if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
       else if(!pivot_is_valid) found_zero_pivot = true;
@@ -568,13 +571,14 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) cons
   // more precisely, use pseudo-inverse of D (see bug 241)
   using std::abs;
   const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
-  // In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon
-  // as motivated by LAPACK's xGELSS:
+  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
+  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
   // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
   // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
   // diagonal element is not well justified and leads to numerical issues in some cases.
   // Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
-  RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest();
+  // Using numeric_limits::min() gives us more robustness to denormals.
+  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
 
   for (Index i = 0; i < vecD.size(); ++i)
   {

third_party_includes/Eigen/src/Cholesky/LLT.h

@@ -24,7 +24,7 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
   *
   * \tparam _MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition
   * \tparam _UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper.
-  *             The other triangular part won't be read.
+  *               The other triangular part won't be read.
   *
   * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
   * matrix A such that A = LL^* = U^*U, where L is lower triangular.
@@ -41,14 +41,18 @@ template<typename MatrixType, int UpLo> struct LLT_Traits;
   * Example: \include LLT_example.cpp
   * Output: \verbinclude LLT_example.out
   *
+  * \b Performance: for best performance, it is recommended to use a column-major storage format
+  * with the Lower triangular part (the default), or, equivalently, a row-major storage format
+  * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
+  * step, and rank-updates can be up to 3 times slower.
+  *
   * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
   *
+  * Note that during the decomposition, only the lower (or upper, as defined by _UpLo) triangular part of A is considered.
+  * Therefore, the strict lower part does not have to store correct values.
+  *
   * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
   */
- /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH)
-  * Note that during the decomposition, only the upper triangular part of A is considered. Therefore,
-  * the strict lower part does not have to store correct values.
-  */
 template<typename _MatrixType, int _UpLo> class LLT
 {
   public:
@@ -146,7 +150,7 @@ template<typename _MatrixType, int _UpLo> class LLT
     }
 
     template<typename Derived>
-    void solveInPlace(MatrixBase<Derived> &bAndX) const;
+    void solveInPlace(const MatrixBase<Derived> &bAndX) const;
 
     template<typename InputType>
     LLT& compute(const EigenBase<InputType>& matrix);
@@ -177,7 +181,7 @@ template<typename _MatrixType, int _UpLo> class LLT
     /** \brief Reports whether previous computation was successful.
       *
       * \returns \c Success if computation was succesful,
-      *          \c NumericalIssue if the matrix.appears to be negative.
+      *          \c NumericalIssue if the matrix.appears not to be positive definite.
       */
     ComputationInfo info() const
     {
@@ -425,7 +429,8 @@ LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>
   eigen_assert(a.rows()==a.cols());
   const Index size = a.rows();
   m_matrix.resize(size, size);
-  m_matrix = a.derived();
+  if (!internal::is_same_dense(m_matrix, a.derived()))
+    m_matrix = a.derived();
 
   // Compute matrix L1 norm = max abs column sum.
   m_l1_norm = RealScalar(0);
@@ -485,11 +490,14 @@ void LLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) const
   *
   * This version avoids a copy when the right hand side matrix b is not needed anymore.
   *
+  * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+  * This function will const_cast it, so constness isn't honored here.
+  *
   * \sa LLT::solve(), MatrixBase::llt()
   */
 template<typename MatrixType, int _UpLo>
 template<typename Derived>
-void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const
+void LLT<MatrixType,_UpLo>::solveInPlace(const MatrixBase<Derived> &bAndX) const
 {
   eigen_assert(m_isInitialized && "LLT is not initialized.");
   eigen_assert(m_matrix.rows()==bAndX.rows());

third_party_includes/Eigen/src/Core/Array.h

@@ -153,8 +153,6 @@ class Array
       : Base(std::move(other))
     {
       Base::_check_template_params();
-      if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic)
-        Base::_set_noalias(other);
     }
     EIGEN_DEVICE_FUNC
     Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)