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reimplement linearRegression on top of fitHyperplane which is much better

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This commit is contained in:
Benoit Jacob 2009-05-05 17:15:35 +00:00
parent 2b2f0c0220
commit 46e1162a39
2 changed files with 11 additions and 28 deletions
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1
Eigen/LeastSquares
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@ -5,7 +5,6 @@
#include "src/Core/util/DisableMSVCWarnings.h"
#include "LU"
#include "QR"
#include "Geometry"

38
Eigen/src/LeastSquares/LeastSquares.h
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@ -1,7 +1,7 @@
// 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.benoit.1@gmail.com>
// 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
@ -80,11 +80,11 @@
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$r_i=retCoefficients[i]\f$,
\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 result equation is:
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
@ -101,31 +101,15 @@ void linearRegression(int numPoints,
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);
typedef Hyperplane<Scalar, VectorType::SizeAtCompileTime> HyperplaneType;
const int size = points[0]->size();
result->resize(size);
Matrix<Scalar, Dynamic, VectorType::SizeAtCompileTime,
RowMajor, Dynamic, VectorType::MaxSizeAtCompileTime>
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).segment(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));
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