Merge.

Eigen/src/Array/ArrayBase.h

@@ -54,6 +54,8 @@ template<typename Derived> class ArrayBase
 #ifndef EIGEN_PARSED_BY_DOXYGEN
     /** The base class for a given storage type. */
     typedef ArrayBase StorageBaseType;
+    
+    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
 
     using ei_special_scalar_op_base<Derived,typename ei_traits<Derived>::Scalar,
                 typename NumTraits<typename ei_traits<Derived>::Scalar>::Real>::operator*;

Eigen/src/Array/GlobalFunctions.h

@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2010 Gael Guennebaud <g.gael@free.fr>
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -25,31 +26,55 @@
 #ifndef EIGEN_GLOBAL_FUNCTIONS_H
 #define EIGEN_GLOBAL_FUNCTIONS_H
 
-#define EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(NAME,FUNCTOR) \
+#define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \
   template<typename Derived> \
   inline const Eigen::CwiseUnaryOp<Eigen::FUNCTOR<typename Derived::Scalar>, Derived> \
   NAME(const Eigen::ArrayBase<Derived>& x) { \
     return x.derived(); \
   }
-  
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
+  \
+  template<typename Derived> \
+  struct NAME##_retval<ArrayBase<Derived> > \
+  { \
+    typedef const Eigen::CwiseUnaryOp<Eigen::FUNCTOR<typename Derived::Scalar>, Derived> type; \
+  }; \
+  template<typename Derived> \
+  struct NAME##_impl<ArrayBase<Derived> > \
+  { \
+    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
+    { \
+      return x.derived(); \
+    } \
+  };
+
+
 namespace std
 {
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(sin,ei_scalar_sin_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(cos,ei_scalar_cos_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(exp,ei_scalar_exp_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(log,ei_scalar_log_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(abs,ei_scalar_abs_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(sqrt,ei_scalar_sqrt_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,ei_scalar_real_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,ei_scalar_imag_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,ei_scalar_sin_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,ei_scalar_cos_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,ei_scalar_exp_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,ei_scalar_log_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,ei_scalar_abs_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,ei_scalar_sqrt_op)
 }
 
 namespace Eigen
 {
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_sin,ei_scalar_sin_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_cos,ei_scalar_cos_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_exp,ei_scalar_exp_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_log,ei_scalar_log_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_abs,ei_scalar_abs_op)
-  EIGEN_ARRAY_DECLARARE_GLOBAL_UNARY(ei_sqrt,ei_scalar_sqrt_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_real,ei_scalar_real_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_imag,ei_scalar_imag_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_sin,ei_scalar_sin_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_cos,ei_scalar_cos_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_exp,ei_scalar_exp_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_log,ei_scalar_log_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_abs,ei_scalar_abs_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_abs2,ei_scalar_abs2_op)
+  EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(ei_sqrt,ei_scalar_sqrt_op)
 }
 
+// TODO: cleanly disable those functions that are not suppored on Array (ei_real_ref, ei_random, ei_isApprox...)
+
 #endif // EIGEN_GLOBAL_FUNCTIONS_H

Eigen/src/CMakeLists.txt

@@ -5,7 +5,6 @@ ADD_SUBDIRECTORY(SVD)
 ADD_SUBDIRECTORY(Cholesky)
 ADD_SUBDIRECTORY(Array)
 ADD_SUBDIRECTORY(Geometry)
-ADD_SUBDIRECTORY(LeastSquares)
 ADD_SUBDIRECTORY(Sparse)
 ADD_SUBDIRECTORY(Jacobi)
 ADD_SUBDIRECTORY(Householder)

Eigen/src/Core/Dot.h

@@ -161,7 +161,7 @@ bool MatrixBase<Derived>::isUnitary(RealScalar prec) const
   typename Derived::Nested nested(derived());
   for(int i = 0; i < cols(); ++i)
   {
-    if(!ei_isApprox(nested.col(i).squaredNorm(), static_cast<Scalar>(1), prec))
+    if(!ei_isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
       return false;
     for(int j = 0; j < i; ++j)
       if(!ei_isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec))

Eigen/src/Core/Functors.h

@@ -180,7 +180,7 @@ struct ei_functor_traits<ei_scalar_quotient_op<Scalar> > {
     Cost = 2 * NumTraits<Scalar>::MulCost,
     PacketAccess = ei_packet_traits<Scalar>::size>1
                   #if (defined EIGEN_VECTORIZE)
-                  && NumTraits<Scalar>::HasFloatingPoint
+                  && !NumTraits<Scalar>::IsInteger
                   #endif
   };
 };
@@ -384,7 +384,7 @@ template<typename Scalar1,typename Scalar2>
 struct ei_functor_traits<ei_scalar_multiple2_op<Scalar1,Scalar2> >
 { enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
 
-template<typename Scalar, bool HasFloatingPoint>
+template<typename Scalar, bool IsInteger>
 struct ei_scalar_quotient1_impl {
   typedef typename ei_packet_traits<Scalar>::type PacketScalar;
   // FIXME default copy constructors seems bugged with std::complex<>
@@ -396,11 +396,11 @@ struct ei_scalar_quotient1_impl {
   const Scalar m_other;
 };
 template<typename Scalar>
-struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,true> >
+struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,false> >
 { enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = ei_packet_traits<Scalar>::size>1 }; };
 
 template<typename Scalar>
-struct ei_scalar_quotient1_impl<Scalar,false> {
+struct ei_scalar_quotient1_impl<Scalar,true> {
   // FIXME default copy constructors seems bugged with std::complex<>
   EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const ei_scalar_quotient1_impl& other) : m_other(other.m_other) { }
   EIGEN_STRONG_INLINE ei_scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
@@ -408,7 +408,7 @@ struct ei_scalar_quotient1_impl<Scalar,false> {
   typename ei_makeconst<typename NumTraits<Scalar>::Nested>::type m_other;
 };
 template<typename Scalar>
-struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,false> >
+struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,true> >
 { enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
 
 /** \internal
@@ -420,13 +420,13 @@ struct ei_functor_traits<ei_scalar_quotient1_impl<Scalar,false> >
   * \sa class CwiseUnaryOp, MatrixBase::operator/
   */
 template<typename Scalar>
-struct ei_scalar_quotient1_op : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::HasFloatingPoint > {
+struct ei_scalar_quotient1_op : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger > {
   EIGEN_STRONG_INLINE ei_scalar_quotient1_op(const Scalar& other)
-    : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::HasFloatingPoint >(other) {}
+    : ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger >(other) {}
 };
 template<typename Scalar>
 struct ei_functor_traits<ei_scalar_quotient1_op<Scalar> >
-: ei_functor_traits<ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::HasFloatingPoint> >
+: ei_functor_traits<ei_scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger> >
 {};
 
 // nullary functors

Eigen/src/Core/Fuzzy.h

@@ -26,6 +26,8 @@
 #ifndef EIGEN_FUZZY_H
 #define EIGEN_FUZZY_H
 
+// TODO support small integer types properly i.e. do exact compare on coeffs --- taking a HS norm is guaranteed to cause integer overflow.
+
 #ifndef EIGEN_LEGACY_COMPARES
 
 /** \returns \c true if \c *this is approximately equal to \a other, within the precision

Eigen/src/Core/IO.h

@@ -126,8 +126,8 @@ DenseBase<Derived>::format(const IOFormat& fmt) const
   return WithFormat<Derived>(derived(), fmt);
 }
 
-template<typename Scalar>
-struct ei_significant_decimals_impl
+template<typename Scalar, bool IsInteger>
+struct ei_significant_decimals_default_impl
 {
   typedef typename NumTraits<Scalar>::Real RealScalar;
   static inline int run()
@@ -136,6 +136,20 @@ struct ei_significant_decimals_impl
   }
 };
 
+template<typename Scalar>
+struct ei_significant_decimals_default_impl<Scalar, true>
+{
+  static inline int run()
+  {
+    return 0;
+  }
+};
+
+template<typename Scalar>
+struct ei_significant_decimals_impl
+  : ei_significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger>
+{};
+
 /** \internal
   * print the matrix \a _m to the output stream \a s using the output format \a fmt */
 template<typename Derived>
@@ -153,13 +167,13 @@ std::ostream & ei_print_matrix(std::ostream & s, const Derived& _m, const IOForm
   }
   else if(fmt.precision == FullPrecision)
   {
-    if (NumTraits<Scalar>::HasFloatingPoint)
+    if (NumTraits<Scalar>::IsInteger)
     {
-      explicit_precision = ei_significant_decimals_impl<Scalar>::run();
+      explicit_precision = 0;
     }
     else
     {
-      explicit_precision = 0;
+      explicit_precision = ei_significant_decimals_impl<Scalar>::run();
     }
   }
   else

Eigen/src/Core/NumTraits.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -27,157 +27,121 @@
 
 /** \class NumTraits
   *
-  * \brief Holds some data about the various numeric (i.e. scalar) types allowed by Eigen.
+  * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
   *
-  * \param T the numeric type about which this class provides data. Recall that Eigen allows
-  *          only the following types for \a T: \c int, \c float, \c double,
-  *          \c std::complex<float>, \c std::complex<double>, and \c long \c double (especially
-  *          useful to enforce x87 arithmetics when SSE is the default).
+  * \param T the numeric type at hand
   *
-  * The provided data consists of everything that is supported by std::numeric_limits, plus:
+  * This class stores enums, typedefs and static methods giving information about a numeric type.
+  *
+  * The provided data consists of:
   * \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real,
   *     then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real
   *     is a typedef to \a U.
-  * \li A typedef \a FloatingPoint, giving the "floating-point type" of \a T. If \a T is
-  *     \c int, then \a FloatingPoint is a typedef to \c double. Otherwise, \a FloatingPoint
-  *     is a typedef to \a T.
+  * \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values,
+  *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
+  *     \a T again. Note however that many Eigen functions such as ei_sqrt simply refuse to
+  *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
+  *     only intended as a helper for code that needs to explicitly promote types.
+  * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
+  *     this means, just use \a T here.
   * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
   *     type, and to 0 otherwise.
-  * \li An enum \a HasFloatingPoint. It is equal to \c 0 if \a T is \c int,
-  *     and to \c 1 otherwise.
+  * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
+  *     and to \c 0 otherwise.
+  * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
+  *     to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
+  *     Stay vague here. No need to do architecture-specific stuff.
+  * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
   * \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T.
-  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used by the fuzzy comparison operators.
-  * \li Two highest() and lowest() functions returning the highest and lowest possible values respectively.
+  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
+  *     value by the fuzzy comparison operators.
+  * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
   */
-template<typename T> struct NumTraits;
 
-template<typename T> struct ei_default_float_numtraits
-  : std::numeric_limits<T>
+template<typename T> struct GenericNumTraits
 {
-  inline static T highest() { return  std::numeric_limits<T>::max(); }
-  inline static T lowest()  { return -std::numeric_limits<T>::max(); }
-};
+  enum {
+    IsInteger = std::numeric_limits<T>::is_integer,
+    IsSigned = std::numeric_limits<T>::is_signed,
+    IsComplex = 0,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
 
-template<typename T> struct ei_default_integral_numtraits
-  : std::numeric_limits<T>
-{
-  inline static T dummy_precision() { return T(0); }
+  typedef T Real;
+  typedef typename ei_meta_if<
+                     IsInteger,
+                     typename ei_meta_if<sizeof(T)<=2, float, double>::ret,
+                     T
+                   >::ret NonInteger;
+  typedef T Nested;
+
+  inline static Real epsilon() { return std::numeric_limits<T>::epsilon(); }
+  inline static Real dummy_precision()
+  {
+    // make sure to override this for floating-point types
+    return Real(0);
+  }
   inline static T highest() { return std::numeric_limits<T>::max(); }
   inline static T lowest()  { return std::numeric_limits<T>::min(); }
 };
 
-template<> struct NumTraits<int>
-  : ei_default_integral_numtraits<int>
-{
-  typedef int Real;
-  typedef double FloatingPoint;
-  typedef int Nested;
-  enum {
-    IsComplex = 0,
-    HasFloatingPoint = 0,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-};
+template<typename T> struct NumTraits : GenericNumTraits<T>
+{};
 
 template<> struct NumTraits<float>
-  : ei_default_float_numtraits<float>
+  : GenericNumTraits<float>
 {
-  typedef float Real;
-  typedef float FloatingPoint;
-  typedef float Nested;
-  enum {
-    IsComplex = 0,
-    HasFloatingPoint = 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-
   inline static float dummy_precision() { return 1e-5f; }
 };
 
-template<> struct NumTraits<double>
-  : ei_default_float_numtraits<double>
+template<> struct NumTraits<double> : GenericNumTraits<double>
 {
-  typedef double Real;
-  typedef double FloatingPoint;
-  typedef double Nested;
-  enum {
-    IsComplex = 0,
-    HasFloatingPoint = 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-
   inline static double dummy_precision() { return 1e-12; }
 };
 
+template<> struct NumTraits<long double>
+  : GenericNumTraits<long double>
+{
+  static inline long double dummy_precision() { return 1e-15l; }
+};
+
 template<typename _Real> struct NumTraits<std::complex<_Real> >
-  : ei_default_float_numtraits<std::complex<_Real> >
+  : GenericNumTraits<std::complex<_Real> >
 {
   typedef _Real Real;
-  typedef std::complex<_Real> FloatingPoint;
-  typedef std::complex<_Real> Nested;
   enum {
     IsComplex = 1,
-    HasFloatingPoint = NumTraits<Real>::HasFloatingPoint,
     ReadCost = 2,
     AddCost = 2 * NumTraits<Real>::AddCost,
     MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
   };
 
-  inline static Real epsilon() { return std::numeric_limits<Real>::epsilon(); }
+  inline static Real epsilon() { return NumTraits<Real>::epsilon(); }
   inline static Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
 };
 
-template<> struct NumTraits<long long int>
-  : ei_default_integral_numtraits<long long int>
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
 {
-  typedef long long int Real;
-  typedef long double FloatingPoint;
-  typedef long long int Nested;
+  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
+  typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
+  typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
+  typedef ArrayType & Nested;
+  
   enum {
-    IsComplex = 0,
-    HasFloatingPoint = 0,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    IsInteger = NumTraits<Scalar>::IsInteger,
+    IsSigned  = NumTraits<Scalar>::IsSigned,
+    ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost,
+    AddCost  = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost,
+    MulCost  = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost
   };
 };
 
-template<> struct NumTraits<long double>
-  : ei_default_float_numtraits<long double>
-{
-  typedef long double Real;
-  typedef long double FloatingPoint;
-  typedef long double Nested;
-  enum {
-    IsComplex = 0,
-    HasFloatingPoint = 1,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
 
-  static inline long double dummy_precision() { return NumTraits<double>::dummy_precision(); }
-};
-
-template<> struct NumTraits<bool>
-  : ei_default_integral_numtraits<bool>
-{
-  typedef bool Real;
-  typedef float FloatingPoint;
-  typedef bool Nested;
-  enum {
-    IsComplex = 0,
-    HasFloatingPoint = 0,
-    ReadCost = 1,
-    AddCost = 1,
-    MulCost = 1
-  };
-};
 
 #endif // EIGEN_NUMTRAITS_H

Eigen/src/Core/SelfCwiseBinaryOp.h

@@ -133,9 +133,11 @@ inline Derived& DenseBase<Derived>::operator*=(const Scalar& other)
 template<typename Derived>
 inline Derived& DenseBase<Derived>::operator/=(const Scalar& other)
 {
-  SelfCwiseBinaryOp<typename ei_meta_if<NumTraits<Scalar>::HasFloatingPoint,ei_scalar_product_op<Scalar>,ei_scalar_quotient_op<Scalar> >::ret, Derived> tmp(derived());
+  SelfCwiseBinaryOp<typename ei_meta_if<NumTraits<Scalar>::IsInteger,
+                                        ei_scalar_quotient_op<Scalar>,
+                                        ei_scalar_product_op<Scalar> >::ret, Derived> tmp(derived());
   typedef typename Derived::PlainObject PlainObject;
-  tmp = PlainObject::Constant(rows(),cols(), NumTraits<Scalar>::HasFloatingPoint ? Scalar(1)/other : other);
+  tmp = PlainObject::Constant(rows(),cols(), NumTraits<Scalar>::IsInteger ? other : Scalar(1)/other);
   return derived();
 }
 

Eigen/src/Core/util/StaticAssert.h

@@ -65,7 +65,7 @@
         YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR,
         YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR,
         UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC,
-        NUMERIC_TYPE_MUST_BE_FLOATING_POINT,
+        THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES,
         NUMERIC_TYPE_MUST_BE_REAL,
         COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED,
         WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED,
@@ -158,6 +158,9 @@
        ) \
      )
 
+#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
+  EIGEN_STATIC_ASSERT(!NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
+
 // static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes
 #define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
   EIGEN_STATIC_ASSERT( \

Eigen/src/Geometry/AlignedBox.h

@@ -46,7 +46,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
   typedef _Scalar                                   Scalar;
   typedef NumTraits<Scalar>                         ScalarTraits;
   typedef typename ScalarTraits::Real               RealScalar;
-  typedef typename ScalarTraits::FloatingPoint      FloatingPoint;
+  typedef typename ScalarTraits::NonInteger      NonInteger;
   typedef Matrix<Scalar,AmbientDimAtCompileTime,1>  VectorType;
 
   /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
@@ -174,11 +174,10 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
     VectorType r;
     for(int d=0; d<dim(); ++d)
     {
-      if(ScalarTraits::HasFloatingPoint)
+      if(!ScalarTraits::IsInteger)
       {
         r[d] = m_min[d] + (m_max[d]-m_min[d])
-             * (ei_random<Scalar>() + ei_random_amplitude<Scalar>())
-             / (Scalar(2)*ei_random_amplitude<Scalar>() );
+             * ei_random<Scalar>(Scalar(0), Scalar(1));
       }
       else
         r[d] = ei_random(m_min[d], m_max[d]);
@@ -260,15 +259,15 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
     * \sa squaredExteriorDistance()
     */
   template<typename Derived>
-  inline FloatingPoint exteriorDistance(const MatrixBase<Derived>& p) const
-  { return ei_sqrt(FloatingPoint(squaredExteriorDistance(p))); }
+  inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const
+  { return ei_sqrt(NonInteger(squaredExteriorDistance(p))); }
 
   /** \returns the distance between the boxes \a b and \c *this,
     * and zero if the boxes intersect.
     * \sa squaredExteriorDistance()
     */
-  inline FloatingPoint exteriorDistance(const AlignedBox& b) const
-  { return ei_sqrt(FloatingPoint(squaredExteriorDistance(b))); }
+  inline NonInteger exteriorDistance(const AlignedBox& b) const
+  { return ei_sqrt(NonInteger(squaredExteriorDistance(b))); }
 
   /** \returns \c *this with scalar type casted to \a NewScalarType
     *

Eigen/src/LU/Inverse.h

@@ -1,7 +1,7 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // Eigen is free software; you can redistribute it and/or
 // modify it under the terms of the GNU Lesser General Public
@@ -325,7 +325,7 @@ struct ei_inverse_impl : public ReturnByValue<ei_inverse_impl<MatrixType> >
 template<typename Derived>
 inline const ei_inverse_impl<Derived> MatrixBase<Derived>::inverse() const
 {
-  EIGEN_STATIC_ASSERT(NumTraits<Scalar>::HasFloatingPoint,NUMERIC_TYPE_MUST_BE_FLOATING_POINT)
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
   ei_assert(rows() == cols());
   return ei_inverse_impl<Derived>(derived());
 }

Eigen/src/LeastSquares/CMakeLists.txt

@@ -1,6 +0,0 @@
-FILE(GLOB Eigen_LeastSquares_SRCS "*.h")
-
-INSTALL(FILES 
-  ${Eigen_LeastSquares_SRCS}
-  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LeastSquares COMPONENT Devel
-  )

Eigen/src/LeastSquares/LeastSquares.h

@@ -1,181 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
-//
-// Eigen is free software; you can redistribute it and/or
-// modify it under the terms of the GNU Lesser General Public
-// License as published by the Free Software Foundation; either
-// version 3 of the License, or (at your option) any later version.
-//
-// Alternatively, you can redistribute it and/or
-// modify it under the terms of the GNU General Public License as
-// published by the Free Software Foundation; either version 2 of
-// the License, or (at your option) any later version.
-//
-// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
-// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
-// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
-// GNU General Public License for more details.
-//
-// You should have received a copy of the GNU Lesser General Public
-// License and a copy of the GNU General Public License along with
-// Eigen. If not, see <http://www.gnu.org/licenses/>.
-
-#ifndef EIGEN_LEASTSQUARES_H
-#define EIGEN_LEASTSQUARES_H
-
-/** \ingroup LeastSquares_Module
-  *
-  * \leastsquares_module
-  *
-  * For a set of points, this function tries to express
-  * one of the coords as a linear (affine) function of the other coords.
-  *
-  * This is best explained by an example. This function works in full
-  * generality, for points in a space of arbitrary dimension, and also over
-  * the complex numbers, but for this example we will work in dimension 3
-  * over the real numbers (doubles).
-  *
-  * So let us work with the following set of 5 points given by their
-  * \f$(x,y,z)\f$ coordinates:
-  * @code
-    Vector3d points[5];
-    points[0] = Vector3d( 3.02, 6.89, -4.32 );
-    points[1] = Vector3d( 2.01, 5.39, -3.79 );
-    points[2] = Vector3d( 2.41, 6.01, -4.01 );
-    points[3] = Vector3d( 2.09, 5.55, -3.86 );
-    points[4] = Vector3d( 2.58, 6.32, -4.10 );
-  * @endcode
-  * Suppose that we want to express the second coordinate (\f$y\f$) as a linear
-  * expression in \f$x\f$ and \f$z\f$, that is,
-  * \f[ y=ax+bz+c \f]
-  * for some constants \f$a,b,c\f$. Thus, we want to find the best possible
-  * constants \f$a,b,c\f$ so that the plane of equation \f$y=ax+bz+c\f$ fits
-  * best the five above points. To do that, call this function as follows:
-  * @code
-    Vector3d coeffs; // will store the coefficients a, b, c
-    linearRegression(
-      5,
-      &points,
-      &coeffs,
-      1 // the coord to express as a function of
-        // the other ones. 0 means x, 1 means y, 2 means z.
-    );
-  * @endcode
-  * Now the vector \a coeffs is approximately
-  * \f$( 0.495 ,  -1.927 ,  -2.906 )\f$.
-  * Thus, we get \f$a=0.495, b = -1.927, c = -2.906\f$. Let us check for
-  * instance how near points[0] is from the plane of equation \f$y=ax+bz+c\f$.
-  * Looking at the coords of points[0], we see that:
-  * \f[ax+bz+c = 0.495 * 3.02 + (-1.927) * (-4.32) + (-2.906) = 6.91.\f]
-  * On the other hand, we have \f$y=6.89\f$. We see that the values
-  * \f$6.91\f$ and \f$6.89\f$
-  * are near, so points[0] is very near the plane of equation \f$y=ax+bz+c\f$.
-  *
-  * Let's now describe precisely the parameters:
-  * @param numPoints the number of points
-  * @param points the array of pointers to the points on which to perform the linear regression
-  * @param result pointer to the vector in which to store the result.
-                  This vector must be of the same type and size as the
-                  data points. The meaning of its coords is as follows.
-                  For brevity, let \f$n=Size\f$,
-                  \f$r_i=result[i]\f$,
-                  and \f$f=funcOfOthers\f$. Denote by
-                  \f$x_0,\ldots,x_{n-1}\f$
-                  the n coordinates in the n-dimensional space.
-                  Then the resulting equation is:
-                  \f[ x_f = r_0 x_0 + \cdots + r_{f-1}x_{f-1}
-                   + r_{f+1}x_{f+1} + \cdots + r_{n-1}x_{n-1} + r_n. \f]
-  * @param funcOfOthers Determines which coord to express as a function of the
-                        others. Coords are numbered starting from 0, so that a
-                        value of 0 means \f$x\f$, 1 means \f$y\f$,
-                        2 means \f$z\f$, ...
-  *
-  * \sa fitHyperplane()
-  */
-template<typename VectorType>
-void linearRegression(int numPoints,
-                      VectorType **points,
-                      VectorType *result,
-                      int funcOfOthers )
-{
-  typedef typename VectorType::Scalar Scalar;
-  typedef Hyperplane<Scalar, VectorType::SizeAtCompileTime> HyperplaneType;
-  const int size = points[0]->size();
-  result->resize(size);
-  HyperplaneType h(size);
-  fitHyperplane(numPoints, points, &h);
-  for(int i = 0; i < funcOfOthers; i++)
-    result->coeffRef(i) = - h.coeffs()[i] / h.coeffs()[funcOfOthers];
-  for(int i = funcOfOthers; i < size; i++)
-    result->coeffRef(i) = - h.coeffs()[i+1] / h.coeffs()[funcOfOthers];
-}
-
-/** \ingroup LeastSquares_Module
-  *
-  * \leastsquares_module
-  *
-  * This function is quite similar to linearRegression(), so we refer to the
-  * documentation of this function and only list here the differences.
-  *
-  * The main difference from linearRegression() is that this function doesn't
-  * take a \a funcOfOthers argument. Instead, it finds a general equation
-  * of the form
-  * \f[ r_0 x_0 + \cdots + r_{n-1}x_{n-1} + r_n = 0, \f]
-  * where \f$n=Size\f$, \f$r_i=retCoefficients[i]\f$, and we denote by
-  * \f$x_0,\ldots,x_{n-1}\f$ the n coordinates in the n-dimensional space.
-  *
-  * Thus, the vector \a retCoefficients has size \f$n+1\f$, which is another
-  * difference from linearRegression().
-  *
-  * In practice, this function performs an hyper-plane fit in a total least square sense
-  * via the following steps:
-  *  1 - center the data to the mean
-  *  2 - compute the covariance matrix
-  *  3 - pick the eigenvector corresponding to the smallest eigenvalue of the covariance matrix
-  * The ratio of the smallest eigenvalue and the second one gives us a hint about the relevance
-  * of the solution. This value is optionally returned in \a soundness.
-  *
-  * \sa linearRegression()
-  */
-template<typename VectorType, typename HyperplaneType>
-void fitHyperplane(int numPoints,
-                   VectorType **points,
-                   HyperplaneType *result,
-                   typename NumTraits<typename VectorType::Scalar>::Real* soundness = 0)
-{
-  typedef typename VectorType::Scalar Scalar;
-  typedef Matrix<Scalar,VectorType::SizeAtCompileTime,VectorType::SizeAtCompileTime> CovMatrixType;
-  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType)
-  ei_assert(numPoints >= 1);
-  int size = points[0]->size();
-  ei_assert(size+1 == result->coeffs().size());
-
-  // compute the mean of the data
-  VectorType mean = VectorType::Zero(size);
-  for(int i = 0; i < numPoints; ++i)
-    mean += *(points[i]);
-  mean /= numPoints;
-
-  // compute the covariance matrix
-  CovMatrixType covMat = CovMatrixType::Zero(size, size);
-  for(int i = 0; i < numPoints; ++i)
-  {
-    VectorType diff = (*(points[i]) - mean).conjugate();
-    covMat += diff * diff.adjoint();
-  }
-
-  // now we just have to pick the eigen vector with smallest eigen value
-  SelfAdjointEigenSolver<CovMatrixType> eig(covMat);
-  result->normal() = eig.eigenvectors().col(0);
-  if (soundness)
-    *soundness = eig.eigenvalues().coeff(0)/eig.eigenvalues().coeff(1);
-
-  // let's compute the constant coefficient such that the
-  // plane pass trough the mean point:
-  result->offset() = - (result->normal().cwiseProduct(mean)).sum();
-}
-
-
-#endif // EIGEN_LEASTSQUARES_H