我是靠谱客的博主 温暖火龙果，最近开发中收集的这篇文章主要介绍C++：实现量化linear least squares regression 线性最小二乘回归测试实例，觉得挺不错的，现在分享给大家，希望可以做个参考。
概述
C++：实现量化linear least squares regression 线性最小二乘回归测试实例
#include "linearleastsquaresregression.hpp"
#include "utilities.hpp"
#include <ql/math/randomnumbers/rngtraits.hpp>
#include <ql/math/linearleastsquaresregression.hpp>
#include <ql/functional.hpp>
#include <boost/circular_buffer.hpp>
using namespace QuantLib;
using namespace boost::unit_test_framework;
void LinearLeastSquaresRegressionTest::testRegression() {
BOOST_TEST_MESSAGE("Testing linear least-squares regression...");
SavedSettings backup;
const Real tolerance = 0.05;
const Size nr=100000;
PseudoRandom::rng_type rng(PseudoRandom::urng_type(1234U));
std::vector<ext::function<Real(Real)>> v = {
[](Real x) -> Real { return 1.0; },
[](Real x) -> Real { return x; },
[](Real x) -> Real { return x*x; },
[](Real x) -> Real { return std::sin(x); }
};
std::vector<ext::function<Real(Real)>> w(v);
w.emplace_back([](Real x){ return x*x; });
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;
}
LinearRegression m(x, y, v);
for (i=0; i<v.size(); ++i) {
if (m.standardErrors()[i] > tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n error: " << m.standardErrors()[i]
<< "n tolerance: " << tolerance);
}
if (std::fabs(m.coefficients()[i]-a[i]) > 3*m.standardErrors()[i]) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n calculated: " << m.coefficients()[i]
<< "n error: " << m.standardErrors()[i]
<< "n expected: " << a[i]);
}
}
m = LinearRegression(x, y, w);
const Real ma[] = {m.coefficients()[0], m.coefficients()[1],
m.coefficients()[2]+m.coefficients()[4],
m.coefficients()[3]};
const Real err[] = {m.standardErrors()[0], m.standardErrors()[1],
std::sqrt( m.standardErrors()[2]*m.standardErrors()[2]
+m.standardErrors()[4]*m.standardErrors()[4]),
m.standardErrors()[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]);
}
}
}
}
namespace linear_least_square_regression_test {
struct get_item {
Size i;
explicit get_item(Size i) : i(i) {}
Real operator()(const Array& a) const {
return a[i];
}
};
}
void LinearLeastSquaresRegressionTest::testMultiDimRegression() {
BOOST_TEST_MESSAGE(
"Testing multi-dimensional linear least-squares regression...");
using namespace linear_least_square_regression_test;
SavedSettings backup;
const Size nr=100000;
const Size dims = 4;
const Real tolerance = 0.01;
PseudoRandom::rng_type rng(PseudoRandom::urng_type(1234U));
std::vector<ext::function<Real(Array)> > v;
v.emplace_back([](const Array& x) { return 1.0; });
for (Size i=0; i < dims; ++i) {
v.emplace_back(get_item(i));
}
Array coeff(v.size());
for (Size i=0; i < v.size(); ++i) {
coeff[i] = rng.next().value;
}
std::vector<Real> y(nr, 0.0);
std::vector<Array> x(nr, Array(dims));
for (Size i=0; i < nr; ++i) {
for (Size j=0; j < dims; ++j) {
x[i][j] = rng.next().value;
}
for (Size j=0; j < v.size(); ++j) {
y[i] += coeff[j]*v[j](x[i]);
}
y[i] += rng.next().value;
}
LinearRegression m(x, y, v);
for (Size i=0; i < v.size(); ++i) {
if (m.standardErrors()[i] > tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n error: " << m.standardErrors()[i]
<< "n tolerance: " << tolerance);
}
if (std::fabs(m.coefficients()[i]-coeff[i]) > 3*tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n calculated: " << m.coefficients()[i]
<< "n error: " << m.standardErrors()[i]
<< "n expected: " << coeff[i]);
}
}
// much simpler
LinearRegression m1(x, y, Real(1.0));
for (Size i=0; i < m1.dim(); ++i) {
if (m1.standardErrors()[i] > tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n error: " << m1.standardErrors()[i]
<< "n tolerance: " << tolerance);
}
if (std::fabs(m1.coefficients()[i]-coeff[i]) > 3*tolerance) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n calculated: " << m1.coefficients()[i]
<< "n error: " << m1.standardErrors()[i]
<< "n expected: " << coeff[i]);
}
}
}
void LinearLeastSquaresRegressionTest::test1dLinearRegression() {
BOOST_TEST_MESSAGE("Testing 1D simple linear least-squares regression...");
/* Example taken from the QuantLib-User list, see posting
* Multiple linear regression/weighted regression, Boris Skorodumov */
SavedSettings backup;
std::vector<Real> x = {2.4, 1.8, 2.5, 3.0, 2.1, 1.2, 2.0, 2.7, 3.6};
std::vector<Real> y = {7.8, 5.5, 8.0, 9.0, 6.5, 4.0, 6.3, 8.4, 10.2};
std::vector<ext::function<Real(Real)>> v = {
[](Real x) { return 1.0; },
[](Real x) { return x; }
};
LinearRegression m(x, y);
const Real tol = 0.0002;
const Real coeffExpected[] = { 0.9448, 2.6853 };
const Real errorsExpected[] = { 0.3654, 0.1487 };
for (Size i=0; i < 2; ++i) {
if (std::fabs(m.standardErrors()[i]-errorsExpected[i]) > tol) {
BOOST_ERROR("Failed to reproduce linear regression standard errors"
<< "n calculated: " << m.standardErrors()[i]
<< "n expected: " << errorsExpected[i]
<< "n tolerance: " << tol);
}
if (std::fabs(m.coefficients()[i]-coeffExpected[i]) > tol) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n calculated: " << m.coefficients()[i]
<< "n expected: " << coeffExpected[i]
<< "n tolerance: " << tol);
}
}
// an alternative container type
boost::circular_buffer<Real> cx(x.begin(), x.end()), cy(y.begin(), y.end());
LinearRegression m1(cx, cy);
for (Size i=0; i < 2; ++i) {
if (std::fabs(m1.standardErrors()[i]-errorsExpected[i]) > tol) {
BOOST_ERROR("Failed to reproduce linear regression standard errors"
<< "n calculated: " << m1.standardErrors()[i]
<< "n expected: " << errorsExpected[i]
<< "n tolerance: " << tol);
}
if (std::fabs(m1.coefficients()[i]-coeffExpected[i]) > tol) {
BOOST_ERROR("Failed to reproduce linear regression coef."
<< "n calculated: " << m1.coefficients()[i]
<< "n expected: " << coeffExpected[i]
<< "n tolerance: " << tol);
}
}
}
test_suite* LinearLeastSquaresRegressionTest::suite() {
auto* suite = BOOST_TEST_SUITE("linear least squares regression tests");
suite->add(QUANTLIB_TEST_CASE(
&LinearLeastSquaresRegressionTest::testRegression));
suite->add(QUANTLIB_TEST_CASE(
&LinearLeastSquaresRegressionTest::testMultiDimRegression));
suite->add(QUANTLIB_TEST_CASE(
&LinearLeastSquaresRegressionTest::test1dLinearRegression));
return suite;
}
该博文为原创文章，未经博主同意不得转。
本文章博客地址：https://cplusplus.blog.csdn.net/article/details/128364332
最后
以上就是温暖火龙果为你收集整理的C++：实现量化linear least squares regression 线性最小二乘回归测试实例的全部内容，希望文章能够帮你解决C++：实现量化linear least squares regression 线性最小二乘回归测试实例所遇到的程序开发问题。
如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。
本图文内容来源于网友提供，作为学习参考使用，或来自网络收集整理，版权属于原作者所有。
发表评论 取消回复