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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 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/>.
#include "main.h"
#include <Eigen/Regression>
template<typename VectorType,
typename BigVecType>
void makeNoisyCohyperplanarPoints(int numPoints,
VectorType **points,
BigVecType *coeffs,
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
coeffs->resize(size + 1);
for(int j = 0; j < size + 1; j++)
{
do {
coeffs->coeffRef(j) = ei_random<Scalar>();
} while(ei_abs(coeffs->coeffRef(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 = - (coeffs->start(size).cwise()*cur_point).sum();
cur_point *= coeffs->coeff(size) / x;
} while( ei_abs(cur_point.norm()) < 0.5
|| ei_abs(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,
typename BigVecType>
void check_fitHyperplane(int numPoints,
VectorType **points,
BigVecType *coeffs,
typename VectorType::Scalar tolerance)
{
int size = points[0]->size();
BigVecType result(size + 1);
fitHyperplane(numPoints, points, &result);
result /= result.coeff(size);
result *= coeffs->coeff(size);
typename VectorType::Scalar error = (result - *coeffs).norm() / coeffs->norm();
VERIFY(ei_abs(error) < ei_abs(tolerance));
}
void test_regression()
{
for(int i = 0; i < g_repeat; i++)
{
{
Vector2f points2f [1000];
Vector2f *points2f_ptrs [1000];
for(int i = 0; i < 1000; i++) points2f_ptrs[i] = &(points2f[i]);
Vector3f 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));
}
{
Vector4d points4d [1000];
Vector4d *points4d_ptrs [1000];
for(int i = 0; i < 1000; i++) points4d_ptrs[i] = &(points4d[i]);
Matrix<double,5,1> 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));
}
{
VectorXcd *points11cd_ptrs[1000];
for(int i = 0; i < 1000; i++) points11cd_ptrs[i] = new VectorXcd(11);
VectorXcd *coeffs12cd = new VectorXcd(12);
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));
}
}
}
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