* add Regression module, from eigen1, improved, with doc and unit-test.

* fix .normalized() so that Random().normalized() works; since the return
type became complicated to write down i just let it return an actual
vector, perhaps not optimal.
* add Sparse/CMakeLists.txt. I suppose that it was intentional that it
didn't have CMakeLists, but in <=2.0 releases I'll just manually remove
Sparse.

Eigen/src/CMakeLists.txt

@@ -4,4 +4,5 @@ ADD_SUBDIRECTORY(QR)
 ADD_SUBDIRECTORY(Cholesky)
 ADD_SUBDIRECTORY(Array)
 ADD_SUBDIRECTORY(Geometry)
+ADD_SUBDIRECTORY(Regression)
 ADD_SUBDIRECTORY(Sparse)

Eigen/src/Core/Dot.h

@@ -292,10 +292,13 @@ inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<
   * \sa norm(), normalize()
   */
 template<typename Derived>
-inline const typename MatrixBase<Derived>::ScalarQuotient1ReturnType
+inline const typename MatrixBase<Derived>::EvalType
 MatrixBase<Derived>::normalized() const
 {
-  return *this / norm();
+  typedef typename ei_nested<Derived>::type Nested;
+  typedef typename ei_unref<Nested>::type _Nested;
+  _Nested n(derived());
+  return n / n.norm();
 }
 
 /** Normalizes the vector, i.e. divides it by its own norm.

Eigen/src/Core/Matrix.h

@@ -198,6 +198,15 @@ class Matrix : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _MaxRows, _MaxCol
       m_storage.resize(rows * cols, rows, cols);
     }
 
+    inline void resize(int size)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix)
+      if(RowsAtCompileTime == 1)
+        m_storage.resize(size, 1, size);
+      else
+        m_storage.resize(size, size, 1);
+    }
+
     /** Copies the value of the expression \a other into *this.
       *
       * *this is resized (if possible) to match the dimensions of \a other.

Eigen/src/Core/MatrixBase.h

@@ -330,7 +330,7 @@ template<typename Derived> class MatrixBase
     Scalar dot(const MatrixBase<OtherDerived>& other) const;
     RealScalar norm2() const;
     RealScalar norm()  const;
-    const ScalarQuotient1ReturnType normalized() const;
+    const EvalType normalized() const;
     void normalize();
 
     Transpose<Derived> transpose();

Eigen/src/Core/util/Macros.h

@@ -37,13 +37,7 @@
 #define EIGEN_UNROLLING_LIMIT 100
 #endif
 
-#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER RowMajorBit
-#else
-#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER 0
-#endif
-
-#define EIGEN_DEFAULT_MATRIX_FLAGS EIGEN_DEFAULT_MATRIX_STORAGE_ORDER
+#define EIGEN_DEFAULT_MATRIX_FLAGS 0
 
 /** Define a hint size when dealing with large matrices and L2 cache friendlyness
   * More precisely, its square value represents the amount of bytes which can be assumed to stay in L2 cache.

Eigen/src/LU/LU.h

@@ -335,7 +335,9 @@ bool LU<MatrixType>::solve(
   return true;
 }
 
-/** \return the LU decomposition of \c *this.
+/** \lu_module
+  *
+  * \return the LU decomposition of \c *this.
   *
   * \sa class LU
   */

Eigen/src/Regression/CMakeLists.txt

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

Eigen/src/Regression/Regression.h

@@ -0,0 +1,199 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
+//
+// 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_REGRESSION_H
+#define EIGEN_REGRESSION_H
+
+/** \ingroup Regression_Module
+  *
+  * \regression_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 retCoefficients 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$r_i=retCoefficients[i]\f$,
+                           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 result 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;
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType)
+  ei_assert(numPoints >= 1);
+  int size = points[0]->size();
+  ei_assert(funcOfOthers >= 0 && funcOfOthers < size);
+  result->resize(size);
+
+  Matrix<Scalar, Dynamic, VectorType::SizeAtCompileTime,
+         Dynamic, VectorType::MaxSizeAtCompileTime, RowMajorBit>
+    m(numPoints, size);
+  if(funcOfOthers>0)
+    for(int i = 0; i < numPoints; i++)
+      m.row(i).start(funcOfOthers) = points[i]->start(funcOfOthers);
+  if(funcOfOthers<size-1)
+    for(int i = 0; i < numPoints; i++)
+      m.row(i).block(funcOfOthers, size-funcOfOthers-1)
+        = points[i]->end(size-funcOfOthers-1);
+  for(int i = 0; i < numPoints; i++)
+    m.row(i).coeffRef(size-1) = Scalar(1);
+
+  VectorType v(size);
+  v.setZero();
+  for(int i = 0; i < numPoints; i++)
+    v += m.row(i).adjoint() * points[i]->coeff(funcOfOthers);
+
+  ei_assert((m.adjoint()*m).lu().solve(v, result));
+}
+
+/** \ingroup Regression_Module
+  *
+  * \regression_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().
+  *
+  * This functions proceeds by first determining which coord has the smallest variance,
+  * and then calls linearRegression() to express that coord as a function of the other ones.
+  *
+  * \sa linearRegression()
+  */
+template<typename VectorType, typename BigVectorType>
+void fitHyperplane(int numPoints,
+                   VectorType **points,
+                   BigVectorType *result)
+{
+  typedef typename VectorType::Scalar Scalar;
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(BigVectorType)
+  ei_assert(numPoints >= 1);
+  int size = points[0]->size();
+  ei_assert(size+1 == result->size());
+
+  // now let's find out which coord varies the least. This is
+  // approximative. All that matters is that we don't pick a coordinate
+  // that varies orders of magnitude more than another one.
+  VectorType mean(size);
+  Matrix<typename NumTraits<Scalar>::Real,
+         VectorType::RowsAtCompileTime, VectorType::ColsAtCompileTime,
+         VectorType::MaxRowsAtCompileTime, VectorType::MaxColsAtCompileTime
+        > variance(size);
+  mean.setZero();
+  variance.setZero();
+  for(int i = 0; i < numPoints; i++)
+    mean += *(points[i]);
+  mean /= numPoints;
+  for(int j = 0; j < size; j++)
+  {
+    for(int i = 0; i < numPoints; i++)
+      variance.coeffRef(j) += ei_abs2(points[i]->coeff(j) - mean.coeff(j));
+  }
+
+  int coord_min_variance;
+  variance.minCoeff(&coord_min_variance);
+
+  // let's now perform a linear regression with respect to that
+  // not-too-much-varying coord
+  VectorType affine(size);
+  linearRegression(numPoints, points, &affine, coord_min_variance);
+
+  if(coord_min_variance>0)
+    result->start(coord_min_variance) = affine.start(coord_min_variance);
+  result->coeffRef(coord_min_variance) = static_cast<Scalar>(-1);
+  result->end(size-coord_min_variance) = affine.end(size-coord_min_variance);
+}
+
+
+#endif // EIGEN_REGRESSION_H

Eigen/src/Sparse/CMakeLists.txt

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