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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2009 Dirk Eddelbuettel
Copyright (C) 2006, 2009, 2010 Klaus Spanderen
Copyright (C) 2010 Kakhkhor Abdijalilov
Copyright (C) 2010 Slava Mazur
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
<https://www.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.
*/
/*! \file linearleastsquaresregression.hpp
\brief general linear least square regression
*/
#ifndef quantlib_linear_least_squares_regression_hpp
#define quantlib_linear_least_squares_regression_hpp
#include <ql/math/generallinearleastsquares.hpp>
#include <functional>
#include <type_traits>
namespace QuantLib {
namespace details {
template <class Container>
class LinearFct {
public:
explicit LinearFct(Size i) : i_(i) {}
Real operator()(const Container& x) const {
return x[i_];
}
private:
const Size i_;
};
template <class xContainer>
class LinearFcts {
public:
typedef typename xContainer::value_type ArgumentType;
LinearFcts(const xContainer &x, Real intercept) {
if (intercept != 0.0)
v.push_back([=](const ArgumentType&){ return intercept; });
if constexpr (std::is_arithmetic_v<ArgumentType>) {
v.push_back([](ArgumentType x){ return x; });
} else {
Size m = x.begin()->size();
for (Size i = 0; i < m; ++i)
v.push_back(LinearFct<ArgumentType>(i));
}
}
const std::vector< std::function<Real(ArgumentType)> > & fcts() {
return v;
}
private:
std::vector< std::function<Real(ArgumentType)> > v;
};
}
class LinearRegression : public GeneralLinearLeastSquares {
public:
//! linear regression y_i = a_0 + a_1*x_0 +..+a_n*x_{n-1} + eps
template <class xContainer, class yContainer>
LinearRegression(const xContainer& x,
const yContainer& y, Real intercept = 1.0);
template <class xContainer, class yContainer, class vContainer>
LinearRegression(const xContainer& x,
const yContainer& y, const vContainer &v);
};
template <class xContainer, class yContainer> inline
LinearRegression::LinearRegression(const xContainer& x,
const yContainer& y, Real intercept)
: GeneralLinearLeastSquares(x, y,
details::LinearFcts<xContainer>(x, intercept).fcts()) {
}
template <class xContainer, class yContainer, class vContainer> inline
LinearRegression::LinearRegression(const xContainer& x,
const yContainer& y,
const vContainer &v)
: GeneralLinearLeastSquares(x, y, v) {
}
// general linear least squares regression
// this interface is support for backward compatibility only
// please use GeneralLinearLeastSquares directly
template <class ArgumentType = Real>
class LinearLeastSquaresRegression : public GeneralLinearLeastSquares {
public:
LinearLeastSquaresRegression(
const std::vector<ArgumentType> & x,
const std::vector<Real> & y,
const std::vector<std::function<Real(ArgumentType)> > & v)
: GeneralLinearLeastSquares(x, y, v) {
}
};
}
#endif
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