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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2006 Klaus Spanderen
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program 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 license for more details.
*/
#include "linearleastsquaresregression.hpp"
#include "utilities.hpp"
#include <ql/math/functional.hpp>
#include <ql/math/randomnumbers/rngtraits.hpp>
#include <ql/math/linearleastsquaresregression.hpp>
using namespace QuantLib;
using namespace boost::unit_test_framework;
void LinearLeastSquaresRegressionTest::testRegression() {
BOOST_MESSAGE("Testing linear least-squares regression...");
SavedSettings backup;
const Real tolerance = 0.025;
const Size nr=100000;
PseudoRandom::rng_type rng(MersenneTwisterUniformRng(1234u));
std::vector<boost::function1<Real, Real> > v;
v.push_back(constant<Real, Real>(1.0));
v.push_back(identity<Real>());
v.push_back(square<Real>());
v.push_back(std::ptr_fun<Real, Real>(std::sin));
std::vector<boost::function1<Real, Real> > w(v);
w.push_back(square<Real>());
for (Size k=0; k<3; ++k) {
Size i;
const Real a[] = {rng.next().value,
rng.next().value,
rng.next().value,
rng.next().value};
std::vector<Real> x(nr), y(nr);
for (i=0; i<nr; ++i) {
x[i] = rng.next().value;
// regression in y = a_1 + a_2*x + a_3*x^2 + a_4*sin(x) + eps
y[i] = a[0]*v[0](x[i]) + a[1]*v[1](x[i]) + a[2]*v[2](x[i])
+ a[3]*v[3](x[i]) + rng.next().value;
}
LinearLeastSquaresRegression<> m(x, y, v);
for (i=0; i<v.size(); ++i) {
if (m.error()[i] > tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "\n error: " << m.error()[i]
<< "\n tolerance: " << tolerance);
}
if (std::fabs(m.a()[i]-a[i]) > 3*m.error()[i]) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "\n calculated: " << m.a()[i]
<< "\n error: " << m.error()[i]
<< "\n expected: " << a[i]);
}
}
m = LinearLeastSquaresRegression<>(x, y, w);
const Real ma[] = {m.a()[0], m.a()[1], m.a()[2]+m.a()[4],m.a()[3]};
const Real err[] = {m.error()[0], m.error()[1],
std::sqrt( m.error()[2]*m.error()[2]
+m.error()[4]*m.error()[4]),
m.error()[3]};
for (i=0; i<v.size(); ++i) {
if (std::fabs(ma[i] - a[i]) > 3*err[i]) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "\n calculated: " << ma[i]
<< "\n error: " << err[i]
<< "\n expected: " << a[i]);
}
}
}
}
test_suite* LinearLeastSquaresRegressionTest::suite() {
test_suite* suite =
BOOST_TEST_SUITE("linear least squares regression tests");
suite->add(BOOST_TEST_CASE(
&LinearLeastSquaresRegressionTest::testRegression));
return suite;
}
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