kill the LeastSquares module.

I didn't even put it in Eigen2Support because it requires several other modules. But if you want we can always create a new module, Eigen2Support_LeastSquares...

9 files changed, 1 insertions, 373 deletions

diff --git a/Eigen/Dense b/Eigen/Dense
index 6177b67ac..9ab39b653 100644
--- a/Eigen/Dense
+++ b/Eigen/Dense
@@ -4,4 +4,4 @@
 #include "QR"
 #include "SVD"
 #include "Geometry"
-#include "LeastSquares"
+#include "Eigenvalues"
\ No newline at end of file
diff --git a/Eigen/LeastSquares b/Eigen/LeastSquares
deleted file mode 100644
index 61b83bbc8..000000000
--- a/Eigen/LeastSquares
+++ /dev/null
@@ -1,28 +0,0 @@
-#ifndef EIGEN_REGRESSION_MODULE_H
-#define EIGEN_REGRESSION_MODULE_H
-
-#include "Core"
-
-#include "src/Core/util/DisableMSVCWarnings.h"
-
-#include "Eigenvalues"
-#include "Geometry"
-
-namespace Eigen {
-
-/** \defgroup LeastSquares_Module LeastSquares module
-  * This module provides linear regression and related features.
-  *
-  * \code
-  * #include <Eigen/LeastSquares>
-  * \endcode
-  */
-
-#include "src/LeastSquares/LeastSquares.h"
-
-} // namespace Eigen
-
-#include "src/Core/util/EnableMSVCWarnings.h"
-
-#endif // EIGEN_REGRESSION_MODULE_H
-/* vim: set filetype=cpp et sw=2 ts=2 ai: */
diff --git a/Eigen/src/CMakeLists.txt b/Eigen/src/CMakeLists.txt
index 0d3604f06..9009291d9 100644
--- a/Eigen/src/CMakeLists.txt
+++ b/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)
diff --git a/Eigen/src/LeastSquares/CMakeLists.txt b/Eigen/src/LeastSquares/CMakeLists.txt
deleted file mode 100644
index 89ccca542..000000000
--- a/Eigen/src/LeastSquares/CMakeLists.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-FILE(GLOB Eigen_LeastSquares_SRCS "*.h")
-
-INSTALL(FILES 
-  ${Eigen_LeastSquares_SRCS}
-  DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LeastSquares COMPONENT Devel
-  )
diff --git a/Eigen/src/LeastSquares/LeastSquares.h b/Eigen/src/LeastSquares/LeastSquares.h
deleted file mode 100644
index e0e9af1bc..000000000
--- a/Eigen/src/LeastSquares/LeastSquares.h
+++ /dev/null
@@ -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
diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in
index 4ac47cd05..c44cfabe9 100644
--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@@ -202,7 +202,6 @@ ALIASES                = "only_for_vectors=This is only for vectors (either row-
                          "geometry_module=This is defined in the %Geometry module. \code #include <Eigen/Geometry> \endcode" \
                          "householder_module=This is defined in the %Householder module. \code #include <Eigen/Householder> \endcode" \
                          "jacobi_module=This is defined in the %Jacobi module. \code #include <Eigen/Jacobi> \endcode" \
-                         "leastsquares_module=This is defined in the %LeastSquares module. \code #include <Eigen/LeastSquares> \endcode" \
                          "lu_module=This is defined in the %LU module. \code #include <Eigen/LU> \endcode" \
                          "qr_module=This is defined in the %QR module. \code #include <Eigen/QR> \endcode" \
                          "svd_module=This is defined in the %SVD module. \code #include <Eigen/SVD> \endcode" \
diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index b43a5c56d..90e6d6f2f 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -156,7 +156,6 @@ ei_add_test(geo_eulerangles)
 ei_add_test(geo_hyperplane)
 ei_add_test(geo_parametrizedline)
 ei_add_test(geo_alignedbox)
-ei_add_test(regression)
 ei_add_test(stdvector)
 ei_add_test(stdvector_overload)
 ei_add_test(stdlist)
diff --git a/test/regression.cpp b/test/regression.cpp
deleted file mode 100644
index a0f2ae102..000000000
--- a/test/regression.cpp
+++ /dev/null
@@ -1,153 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 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/>.
-
-#include "main.h"
-#include <Eigen/LeastSquares>
-
-template<typename VectorType,
-         typename HyperplaneType>
-void makeNoisyCohyperplanarPoints(int numPoints,
-                                  VectorType **points,
-                                  HyperplaneType *hyperplane,
-                                  typename VectorType::Scalar noiseAmplitude)
-{
-  typedef typename VectorType::Scalar Scalar;
-  const int size = points[0]->size();
-  // pick a random hyperplane, store the coefficients of its equation
-  hyperplane->coeffs().resize(size + 1);
-  for(int j = 0; j < size + 1; j++)
-  {
-    do {
-      hyperplane->coeffs().coeffRef(j) = ei_random<Scalar>();
-    } while(ei_abs(hyperplane->coeffs().coeff(j)) < 0.5);
-  }
-
-  // now pick numPoints random points on this hyperplane
-  for(int i = 0; i < numPoints; i++)
-  {
-    VectorType& cur_point = *(points[i]);
-    do
-    {
-      cur_point = VectorType::Random(size)/*.normalized()*/;
-      // project cur_point onto the hyperplane
-      Scalar x = - (hyperplane->coeffs().head(size).cwiseProduct(cur_point)).sum();
-      cur_point *= hyperplane->coeffs().coeff(size) / x;
-    } while( cur_point.norm() < 0.5
-          || cur_point.norm() > 2.0 );
-  }
-
-  // add some noise to these points
-  for(int i = 0; i < numPoints; i++ )
-    *(points[i]) += noiseAmplitude * VectorType::Random(size);
-}
-
-template<typename VectorType>
-void check_linearRegression(int numPoints,
-                            VectorType **points,
-                            const VectorType& original,
-                            typename VectorType::Scalar tolerance)
-{
-  int size = points[0]->size();
-  assert(size==2);
-  VectorType result(size);
-  linearRegression(numPoints, points, &result, 1);
-  typename VectorType::Scalar error = (result - original).norm() / original.norm();
-  VERIFY(ei_abs(error) < ei_abs(tolerance));
-}
-
-template<typename VectorType,
-         typename HyperplaneType>
-void check_fitHyperplane(int numPoints,
-                         VectorType **points,
-                         const HyperplaneType& original,
-                         typename VectorType::Scalar tolerance)
-{
-  int size = points[0]->size();
-  HyperplaneType result(size);
-  fitHyperplane(numPoints, points, &result);
-  result.coeffs() *= original.coeffs().coeff(size)/result.coeffs().coeff(size);
-  typename VectorType::Scalar error = (result.coeffs() - original.coeffs()).norm() / original.coeffs().norm();
-  VERIFY(ei_abs(error) < ei_abs(tolerance));
-}
-
-void test_regression()
-{
-  for(int i = 0; i < g_repeat; i++)
-  {
-#ifdef EIGEN_TEST_PART_1
-    {
-      Vector2f points2f [1000];
-      Vector2f *points2f_ptrs [1000];
-      for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]);
-      Vector2f coeffs2f;
-      Hyperplane<float,2> coeffs3f;
-      makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f);
-      coeffs2f[0] = -coeffs3f.coeffs()[0]/coeffs3f.coeffs()[1];
-      coeffs2f[1] = -coeffs3f.coeffs()[2]/coeffs3f.coeffs()[1];
-      CALL_SUBTEST(check_linearRegression(10, points2f_ptrs, coeffs2f, 0.05f));
-      CALL_SUBTEST(check_linearRegression(100, points2f_ptrs, coeffs2f, 0.01f));
-      CALL_SUBTEST(check_linearRegression(1000, points2f_ptrs, coeffs2f, 0.002f));
-    }
-#endif
-
-#ifdef EIGEN_TEST_PART_2
-    {
-      Vector2f points2f [1000];
-      Vector2f *points2f_ptrs [1000];
-      for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]);
-      Hyperplane<float,2> coeffs3f;
-      makeNoisyCohyperplanarPoints(1000, points2f_ptrs, &coeffs3f, 0.01f);
-      CALL_SUBTEST(check_fitHyperplane(10, points2f_ptrs, coeffs3f, 0.05f));
-      CALL_SUBTEST(check_fitHyperplane(100, points2f_ptrs, coeffs3f, 0.01f));
-      CALL_SUBTEST(check_fitHyperplane(1000, points2f_ptrs, coeffs3f, 0.002f));
-    }
-#endif
-
-#ifdef EIGEN_TEST_PART_3
-    {
-      Vector4d points4d [1000];
-      Vector4d *points4d_ptrs [1000];
-      for(int i = 0; i < 1000; i++) points4d_ptrs[i] = &(points4d[i]);
-      Hyperplane<double,4> coeffs5d;
-      makeNoisyCohyperplanarPoints(1000, points4d_ptrs, &coeffs5d, 0.01);
-      CALL_SUBTEST(check_fitHyperplane(10, points4d_ptrs, coeffs5d, 0.05));
-      CALL_SUBTEST(check_fitHyperplane(100, points4d_ptrs, coeffs5d, 0.01));
-      CALL_SUBTEST(check_fitHyperplane(1000, points4d_ptrs, coeffs5d, 0.002));
-    }
-#endif
-
-#ifdef EIGEN_TEST_PART_4
-    {
-      VectorXcd *points11cd_ptrs[1000];
-      for(int i = 0; i < 1000; i++) points11cd_ptrs[i] = new VectorXcd(11);
-      Hyperplane<std::complex<double>,Dynamic> *coeffs12cd = new Hyperplane<std::complex<double>,Dynamic>(11);
-      makeNoisyCohyperplanarPoints(1000, points11cd_ptrs, coeffs12cd, 0.01);
-      CALL_SUBTEST(check_fitHyperplane(100, points11cd_ptrs, *coeffs12cd, 0.025));
-      CALL_SUBTEST(check_fitHyperplane(1000, points11cd_ptrs, *coeffs12cd, 0.006));
-      delete coeffs12cd;
-      for(int i = 0; i < 1000; i++) delete points11cd_ptrs[i];
-    }
-#endif
-  }
-}
diff --git a/unsupported/doc/Doxyfile.in b/unsupported/doc/Doxyfile.in
index 12f2f39eb..7d5f24b4e 100644
--- a/unsupported/doc/Doxyfile.in
+++ b/unsupported/doc/Doxyfile.in
@@ -202,7 +202,6 @@ ALIASES                = "only_for_vectors=This is only for vectors (either row-
                          "qr_module=This is defined in the %QR module. \code #include <Eigen/QR> \endcode" \
                          "svd_module=This is defined in the %SVD module. \code #include <Eigen/SVD> \endcode" \
                          "geometry_module=This is defined in the %Geometry module. \code #include <Eigen/Geometry> \endcode" \
-                         "leastsquares_module=This is defined in the %LeastSquares module. \code #include <Eigen/LeastSquares> \endcode" \
                          "label=\bug" \
                          "redstar=<a href='#warningarraymodule' style='color:red;text-decoration: none;'>*</a>" \
                          "nonstableyet=\warning This is not considered to be part of the stable public API yet. Changes may happen in future releases. See \ref Experimental \"Experimental parts of Eigen\""