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Merge branch 'develop' into autodiff

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pulver
2019-02-03 20:59:39 -05:00
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parent 7748f2ea19 93ccc669d9
commit 04aeb5fdd9
14 changed files with 3995 additions and 5 deletions
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2
test/Jamfile.v2
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@@ -906,6 +906,8 @@ test-suite misc :
[ run norms_test.cpp ../../test/build//boost_unit_test_framework : : : [ requires cxx17_if_constexpr cxx17_std_apply ] ]
[ run signal_statistics_test.cpp : : : [ requires cxx17_if_constexpr cxx17_std_apply ] ]
[ run bivariate_statistics_test.cpp : : : [ requires cxx17_if_constexpr cxx17_std_apply ] ]
[ run lanczos_smoothing_test.cpp ../../test/build//boost_unit_test_framework : : : [ requires cxx17_if_constexpr cxx17_std_apply ] ]
[ run condition_number_test.cpp ../../test/build//boost_unit_test_framework : : : [ requires cxx17_if_constexpr cxx17_std_apply ] ]
[ run test_real_concept.cpp ../../test/build//boost_unit_test_framework ]
[ run test_remez.cpp pch ../../test/build//boost_unit_test_framework ]
[ run test_roots.cpp pch ../../test/build//boost_unit_test_framework ]

2
test/catmull_rom_test.cpp
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@@ -175,7 +175,7 @@ void test_affine_invariance()
std::vector<std::array<Real, dimension>> v(100);
std::vector<std::array<Real, dimension>> u(100);
std::mt19937_64 gen(438232);
Real inv_denom = (Real) 100/( (Real) gen.max() + (Real) 2);
Real inv_denom = (Real) 100/( (Real) (gen.max)() + (Real) 2);
for(size_t j = 0; j < dimension; ++j)
{
v[0][j] = gen()*inv_denom;

122
test/condition_number_test.cpp Normal file
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@@ -0,0 +1,122 @@
// (C) Copyright Nick Thompson, 2019
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#define BOOST_TEST_MODULE condition_number_test
#include <cmath>
#include <limits>
#include <iostream>
#include <boost/math/constants/constants.hpp>
#include <boost/math/special_functions/lambert_w.hpp>
#include <boost/test/included/unit_test.hpp>
#include <boost/multiprecision/cpp_bin_float.hpp>
#include <boost/math/tools/condition_numbers.hpp>
using std::abs;
using boost::math::constants::half;
using boost::math::constants::ln_two;
using boost::multiprecision::cpp_bin_float_50;
using boost::math::tools::summation_condition_number;
using boost::math::tools::evaluation_condition_number;
template<class Real>
void test_summation_condition_number()
{
Real tol = 1000*std::numeric_limits<float>::epsilon();
auto cond = summation_condition_number<Real>();
// I've checked that the condition number increases with max_n,
// and that the computed sum gets more accurate with increasing max_n.
// But the CI system would die with more terms.
Real max_n = 10000;
for (Real n = 1; n < max_n; n += 2)
{
cond += 1/n;
cond -= 1/(n+1);
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), ln_two<Real>(), tol);
BOOST_TEST(cond() > 14);
}
template<class Real>
void test_exponential_sum()
{
using std::exp;
using std::abs;
Real eps = std::numeric_limits<float>::epsilon();
for (Real x = -20; x <= -1; x += 0.5)
{
auto cond = summation_condition_number<Real>(1);
size_t n = 1;
Real term = x;
while(n++ < 1000)
{
cond += term;
term *= (x/n);
}
BOOST_CHECK_CLOSE_FRACTION(exp(x), cond.sum(), eps*cond());
BOOST_CHECK_CLOSE_FRACTION(exp(2*abs(x)), cond(), eps*cond());
}
}
template<class Real>
void test_evaluation_condition_number()
{
using std::abs;
using std::log;
using std::sqrt;
using std::exp;
using std::sin;
using std::tan;
Real tol = sqrt(std::numeric_limits<Real>::epsilon());
auto f1 = [](auto x) { return log(x); };
for (Real x = 1.125; x < 8; x += 0.125)
{
Real cond = evaluation_condition_number(f1, x);
BOOST_CHECK_CLOSE_FRACTION(cond, 1/log(x), tol);
}
auto f2 = [](auto x) { return exp(x); };
for (Real x = 1.125; x < 8; x += 0.125)
{
Real cond = evaluation_condition_number(f2, x);
BOOST_CHECK_CLOSE_FRACTION(cond, x, tol);
}
auto f3 = [](auto x) { return sin(x); };
for (Real x = 1.125; x < 8; x += 0.125)
{
Real cond = evaluation_condition_number(f3, x);
BOOST_CHECK_CLOSE_FRACTION(cond, abs(x/tan(x)), tol);
}
// Test a function which right differentiable:
using boost::math::constants::e;
auto f4 = [](Real x) { return boost::math::lambert_w0(x); };
Real cond = evaluation_condition_number(f4, -1/e<Real>());
if (std::is_same_v<Real, float>)
{
BOOST_CHECK_GE(cond, 30);
}
else
{
BOOST_CHECK_GE(cond, 4900);
}
}
BOOST_AUTO_TEST_CASE(numerical_differentiation_test)
{
test_summation_condition_number<float>();
test_summation_condition_number<cpp_bin_float_50>();
test_evaluation_condition_number<float>();
test_evaluation_condition_number<double>();
test_evaluation_condition_number<long double>();
test_evaluation_condition_number<cpp_bin_float_50>();
test_exponential_sum<double>();
}

708
test/lanczos_smoothing_test.cpp Normal file
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@@ -0,0 +1,708 @@
/*
* Copyright Nick Thompson, 2019
* Use, modification and distribution are subject to the
* Boost Software License, Version 1.0. (See accompanying file
* LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
*/
#define BOOST_TEST_MODULE lanczos_smoothing_test
#include <random>
#include <array>
#include <boost/range.hpp>
#include <boost/numeric/ublas/vector.hpp>
#include <boost/math/constants/constants.hpp>
#include <boost/test/included/unit_test.hpp>
#include <boost/test/floating_point_comparison.hpp>
#include <boost/math/differentiation/lanczos_smoothing.hpp>
#include <boost/multiprecision/cpp_bin_float.hpp>
#include <boost/math/special_functions/next.hpp> // for float_distance
#include <boost/math/tools/condition_numbers.hpp>
using std::abs;
using std::pow;
using std::sqrt;
using std::sin;
using boost::math::constants::two_pi;
using boost::multiprecision::cpp_bin_float_50;
using boost::multiprecision::cpp_bin_float_100;
using boost::math::differentiation::discrete_lanczos_derivative;
using boost::math::differentiation::detail::discrete_legendre;
using boost::math::differentiation::detail::interior_velocity_filter;
using boost::math::differentiation::detail::boundary_velocity_filter;
using boost::math::tools::summation_condition_number;
template<class Real>
void test_dlp_norms()
{
std::cout << "Testing Discrete Legendre Polynomial norms on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
auto dlp = discrete_legendre<Real>(1, Real(0));
BOOST_CHECK_CLOSE_FRACTION(dlp.norm_sq(0), 3, tol);
BOOST_CHECK_CLOSE_FRACTION(dlp.norm_sq(1), 2, tol);
dlp = discrete_legendre<Real>(2, Real(0));
BOOST_CHECK_CLOSE_FRACTION(dlp.norm_sq(0), Real(5)/Real(2), tol);
BOOST_CHECK_CLOSE_FRACTION(dlp.norm_sq(1), Real(5)/Real(4), tol);
BOOST_CHECK_CLOSE_FRACTION(dlp.norm_sq(2), Real(3*3*7)/Real(pow(2,6)), 2*tol);
dlp = discrete_legendre<Real>(200, Real(0));
for(size_t r = 0; r < 10; ++r)
{
Real calc = dlp.norm_sq(r);
Real expected = Real(2)/Real(2*r+1);
// As long as r << n, ||q_r||^2 -> 2/(2r+1) as n->infty
BOOST_CHECK_CLOSE_FRACTION(calc, expected, 0.05);
}
}
template<class Real>
void test_dlp_evaluation()
{
std::cout << "Testing evaluation of Discrete Legendre polynomials on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
size_t n = 25;
Real x = 0.72;
auto dlp = discrete_legendre<Real>(n, x);
Real q0 = dlp(x, 0);
BOOST_TEST(q0 == 1);
Real q1 = dlp(x, 1);
BOOST_TEST(q1 == x);
Real q2 = dlp(x, 2);
int N = 2*n+1;
Real expected = 0.5*(3*x*x - Real(N*N - 1)/Real(4*n*n));
BOOST_CHECK_CLOSE_FRACTION(q2, expected, tol);
Real q3 = dlp(x, 3);
expected = (x/3)*(5*expected - (Real(N*N - 4))/(2*n*n));
BOOST_CHECK_CLOSE_FRACTION(q3, expected, 2*tol);
// q_r(x) is even for even r, and odd for odd r:
for (size_t n = 8; n < 22; ++n)
{
dlp = discrete_legendre<Real>(n, x);
for(size_t r = 2; r <= n; ++r)
{
if (r & 1)
{
Real q1 = dlp(x, r);
Real q2 = -dlp(-x, r);
BOOST_CHECK_CLOSE_FRACTION(q1, q2, tol);
}
else
{
Real q1 = dlp(x, r);
Real q2 = dlp(-x, r);
BOOST_CHECK_CLOSE_FRACTION(q1, q2, tol);
}
Real l2_sq = 0;
for (int j = -(int)n; j <= (int) n; ++j)
{
Real y = Real(j)/Real(n);
Real term = dlp(y, r);
l2_sq += term*term;
}
l2_sq /= n;
Real l2_sq_expected = dlp.norm_sq(r);
BOOST_CHECK_CLOSE_FRACTION(l2_sq, l2_sq_expected, 20*tol);
}
}
}
template<class Real>
void test_dlp_next()
{
std::cout << "Testing Discrete Legendre polynomial 'next' function on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
for(size_t n = 2; n < 20; ++n)
{
for(Real x = -1; x <= 1; x += 0.1)
{
auto dlp = discrete_legendre<Real>(n, x);
for (size_t k = 2; k < n; ++k)
{
BOOST_CHECK_CLOSE(dlp.next(), dlp(x, k), tol);
}
dlp = discrete_legendre<Real>(n, x);
for (size_t k = 2; k < n; ++k)
{
BOOST_CHECK_CLOSE(dlp.next_prime(), dlp.prime(x, k), tol);
}
}
}
}
template<class Real>
void test_dlp_derivatives()
{
std::cout << "Testing Discrete Legendre polynomial derivatives on type " << typeid(Real).name() << "\n";
Real tol = 10*std::numeric_limits<Real>::epsilon();
int n = 25;
Real x = 0.72;
auto dlp = discrete_legendre<Real>(n, x);
Real q0p = dlp.prime(x, 0);
BOOST_TEST(q0p == 0);
Real q1p = dlp.prime(x, 1);
BOOST_TEST(q1p == 1);
Real q2p = dlp.prime(x, 2);
Real expected = 3*x;
BOOST_CHECK_CLOSE_FRACTION(q2p, expected, tol);
}
template<class Real>
void test_dlp_second_derivative()
{
std::cout << "Testing Discrete Legendre polynomial derivatives on type " << typeid(Real).name() << "\n";
int n = 25;
Real x = Real(1)/Real(3);
auto dlp = discrete_legendre<Real>(n, x);
Real q2pp = dlp.next_dbl_prime();
BOOST_TEST(q2pp == 3);
}
template<class Real>
void test_interior_velocity_filter()
{
using boost::math::constants::half;
std::cout << "Testing interior filter on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
for(int n = 1; n < 10; ++n)
{
for (int p = 1; p < n; p += 2)
{
auto f = interior_velocity_filter<Real>(n,p);
// Since we only store half the filter coefficients,
// we need to reindex the moment sums:
auto cond = summation_condition_number<Real>(0);
for (size_t j = 0; j < f.size(); ++j)
{
cond += j*f[j];
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), half<Real>(), 2*cond()*tol);
for (int l = 3; l <= p; l += 2)
{
cond = summation_condition_number<Real>(0);
for (size_t j = 0; j < f.size() - 1; ++j)
{
cond += pow(Real(j), l)*f[j];
}
Real expected = -pow(Real(f.size() - 1), l)*f[f.size()-1];
BOOST_CHECK_CLOSE_FRACTION(expected, cond.sum(), 7*cond()*tol);
}
//std::cout << "(n,p) = (" << n << "," << p << ") = {";
//for (auto & x : f)
//{
// std::cout << x << ", ";
//}
//std::cout << "}\n";
}
}
}
template<class Real>
void test_interior_lanczos()
{
std::cout << "Testing interior Lanczos on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
std::vector<Real> v(500);
std::fill(v.begin(), v.end(), 7);
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; p += 2)
{
auto dld = discrete_lanczos_derivative(Real(0.1), n, p);
for (size_t m = n; m < v.size() - n; ++m)
{
Real dvdt = dld(v, m);
BOOST_CHECK_SMALL(dvdt, tol);
}
auto dvdt = dld(v);
for (size_t m = n; m < v.size() - n; ++m)
{
BOOST_CHECK_SMALL(dvdt[m], tol);
}
}
}
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i+8;
}
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; p += 2)
{
auto dld = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = n; m < v.size() - n; ++m)
{
Real dvdt = dld(v, m);
BOOST_CHECK_CLOSE_FRACTION(dvdt, 7, 2000*tol);
}
auto dvdt = dld(v);
for (size_t m = n; m < v.size() - n; ++m)
{
BOOST_CHECK_CLOSE_FRACTION(dvdt[m], 7, 2000*tol);
}
}
}
//std::random_device rd{};
//auto seed = rd();
//std::cout << "Seed = " << seed << "\n";
std::mt19937 gen(4172378669);
std::normal_distribution<> dis{0, 0.01};
for (size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i+8 + dis(gen);
}
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; p += 2)
{
auto dld = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = n; m < v.size() - n; ++m)
{
BOOST_CHECK_CLOSE_FRACTION(dld(v, m), Real(7), Real(0.0042));
}
}
}
for (size_t i = 0; i < v.size(); ++i)
{
v[i] = 15*i*i + 7*i+8 + dis(gen);
}
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; p += 2)
{
auto dld = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = n; m < v.size() - n; ++m)
{
BOOST_CHECK_CLOSE_FRACTION(dld(v,m), Real(30*m + 7), Real(0.00008));
}
}
}
std::normal_distribution<> dis1{0, 0.0001};
Real omega = Real(1)/Real(16);
for (size_t i = 0; i < v.size(); ++i)
{
v[i] = sin(i*omega) + dis1(gen);
}
for (size_t n = 10; n < 20; ++n)
{
for (size_t p = 3; p < 100 && p < n/2; p += 2)
{
auto dld = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = n; m < v.size() - n && m < n + 10; ++m)
{
BOOST_CHECK_CLOSE_FRACTION(dld(v,m), omega*cos(omega*m), Real(0.03));
}
}
}
}
template<class Real>
void test_boundary_velocity_filters()
{
std::cout << "Testing boundary filters on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
for(int n = 1; n < 5; ++n)
{
for (int p = 1; p < 2*n+1; ++p)
{
for (int s = -n; s <= n; ++s)
{
auto f = boundary_velocity_filter<Real>(n, p, s);
// Sum is zero:
auto cond = summation_condition_number<Real>(0);
for (size_t i = 0; i < f.size() - 1; ++i)
{
cond += f[i];
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), -f[f.size()-1], 6*cond()*tol);
cond = summation_condition_number<Real>(0);
for (size_t k = 0; k < f.size(); ++k)
{
Real j = Real(k) - Real(n);
// note the shifted index here:
cond += (j-s)*f[k];
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), 1, 6*cond()*tol);
for (int l = 2; l <= p; ++l)
{
cond = summation_condition_number<Real>(0);
for (size_t k = 0; k < f.size() - 1; ++k)
{
Real j = Real(k) - Real(n);
// The condition number of this sum is infinite!
// No need to get to worked up about the tolerance.
cond += pow(j-s, l)*f[k];
}
Real expected = -pow(Real(f.size()-1) - Real(n) - Real(s), l)*f[f.size()-1];
if (expected == 0)
{
BOOST_CHECK_SMALL(cond.sum(), cond()*tol);
}
else
{
BOOST_CHECK_CLOSE_FRACTION(expected, cond.sum(), 200*cond()*tol);
}
}
//std::cout << "(n,p,s) = ("<< n << ", " << p << "," << s << ") = {";
//for (auto & x : f)
//{
// std::cout << x << ", ";
//}
//std::cout << "}\n";*/
}
}
}
}
template<class Real>
void test_boundary_lanczos()
{
std::cout << "Testing Lanczos boundary on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
std::vector<Real> v(500, 7);
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; ++p)
{
auto lsd = discrete_lanczos_derivative(Real(0.0125), n, p);
for (size_t m = 0; m < n; ++m)
{
Real dvdt = lsd(v,m);
BOOST_CHECK_SMALL(dvdt, 4*sqrt(tol));
}
for (size_t m = v.size() - n; m < v.size(); ++m)
{
Real dvdt = lsd(v,m);
BOOST_CHECK_SMALL(dvdt, 4*sqrt(tol));
}
}
}
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i+8;
}
for (size_t n = 3; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; ++p)
{
auto lsd = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = 0; m < n; ++m)
{
Real dvdt = lsd(v,m);
BOOST_CHECK_CLOSE_FRACTION(dvdt, 7, sqrt(tol));
}
for (size_t m = v.size() - n; m < v.size(); ++m)
{
Real dvdt = lsd(v,m);
BOOST_CHECK_CLOSE_FRACTION(dvdt, 7, 4*sqrt(tol));
}
}
}
for (size_t i = 0; i < v.size(); ++i)
{
v[i] = 15*i*i + 7*i+8;
}
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < 2*n; ++p)
{
auto lsd = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = 0; m < v.size(); ++m)
{
BOOST_CHECK_CLOSE_FRACTION(lsd(v,m), 30*m+7, 30*sqrt(tol));
}
}
}
// Demonstrate that the boundary filters are also denoising:
//std::random_device rd{};
//auto seed = rd();
//std::cout << "seed = " << seed << "\n";
std::mt19937 gen(311354333);
std::normal_distribution<> dis{0, 0.01};
for (size_t i = 0; i < v.size(); ++i)
{
v[i] += dis(gen);
}
for (size_t n = 1; n < 10; ++n)
{
for (size_t p = 2; p < n; ++p)
{
auto lsd = discrete_lanczos_derivative(Real(1), n, p);
for (size_t m = 0; m < v.size(); ++m)
{
BOOST_CHECK_CLOSE_FRACTION(lsd(v,m), 30*m+7, 0.005);
}
auto dvdt = lsd(v);
for (size_t m = 0; m < v.size(); ++m)
{
BOOST_CHECK_CLOSE_FRACTION(dvdt[m], 30*m+7, 0.005);
}
}
}
}
template<class Real>
void test_acceleration_filters()
{
Real eps = std::numeric_limits<Real>::epsilon();
for (size_t n = 1; n < 5; ++n)
{
for(size_t p = 3; p <= 2*n; ++p)
{
for(int64_t s = -int64_t(n); s <= 0; ++s)
{
auto g = boost::math::differentiation::detail::acceleration_filter<long double>(n,p,s);
std::vector<Real> f(g.size());
for (size_t i = 0; i < g.size(); ++i)
{
f[i] = static_cast<Real>(g[i]);
}
auto cond = summation_condition_number<Real>(0);
for (size_t i = 0; i < f.size() - 1; ++i)
{
cond += f[i];
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), -f[f.size()-1], cond()*eps);
cond = summation_condition_number<Real>(0);
for (size_t k = 0; k < f.size() -1; ++k)
{
Real j = Real(k) - Real(n);
cond += (j-s)*f[k];
}
Real expected = -(Real(f.size()-1)- Real(n) - s)*f[f.size()-1];
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), expected, cond()*eps);
cond = summation_condition_number<Real>(0);
for (size_t k = 0; k < f.size(); ++k)
{
Real j = Real(k) - Real(n);
cond += (j-s)*(j-s)*f[k];
}
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), 2, cond()*eps);
// See unlabelled equation in McDevitt, 2012, just after equation 26:
// It appears that there is an off-by-one error in that equation, since p + 1 moments don't vanish, only p.
// This test is itself suspect; the condition number of the moment sum is infinite.
// So the *slightest* error in the filter gets amplified by the test; in terms of the
// behavior of the actual filter, it's not a big deal.
for (size_t l = 3; l <= p; ++l)
{
cond = summation_condition_number<Real>(0);
for (size_t k = 0; k < f.size() - 1; ++k)
{
Real j = Real(k) - Real(n);
cond += pow((j-s), l)*f[k];
}
Real expected = -pow(Real(f.size()- 1 - n -s), l)*f[f.size()-1];
BOOST_CHECK_CLOSE_FRACTION(cond.sum(), expected, cond()*eps);
}
}
}
}
}
template<class Real>
void test_lanczos_acceleration()
{
Real eps = std::numeric_limits<Real>::epsilon();
std::vector<Real> v(100, 7);
auto lanczos = discrete_lanczos_derivative<Real, 2>(Real(1), 4, 3);
for (size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_SMALL(lanczos(v, i), eps);
}
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i + 6;
}
for (size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_SMALL(lanczos(v,i), 200*eps);
}
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i*i + 9*i + 6;
}
for (size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(lanczos(v, i), 14, 1500*eps);
}
// Now add noise, and kick up the smoothing of the Lanzcos derivative (increase n):
//std::random_device rd{};
//auto seed = rd();
//std::cout << "seed = " << seed << "\n";
size_t seed = 2507134629;
std::mt19937 gen(seed);
Real std_dev = 0.1;
std::normal_distribution<Real> dis{0, std_dev};
for (size_t i = 0; i < v.size(); ++i)
{
v[i] += dis(gen);
}
lanczos = discrete_lanczos_derivative<Real, 2>(Real(1), 18, 3);
auto w = lanczos(v);
for (size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(w[i], 14, std_dev/200);
}
}
template<class Real>
void test_rescaling()
{
std::cout << "Test rescaling on type " << typeid(Real).name() << "\n";
Real tol = std::numeric_limits<Real>::epsilon();
std::vector<Real> v(500);
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 7*i*i + 9*i + 6;
}
std::vector<Real> dvdt1(500);
std::vector<Real> dvdt2(500);
auto lanczos1 = discrete_lanczos_derivative(Real(1));
auto lanczos2 = discrete_lanczos_derivative(Real(2));
lanczos1(v, dvdt1);
lanczos2(v, dvdt2);
for(size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(dvdt1[i], 2*dvdt2[i], tol);
}
auto lanczos3 = discrete_lanczos_derivative<Real, 2>(Real(1));
auto lanczos4 = discrete_lanczos_derivative<Real, 2>(Real(2));
std::vector<Real> dv2dt21(500);
std::vector<Real> dv2dt22(500);
for(size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(dv2dt21[i], 4*dv2dt22[i], tol);
}
}
template<class Real>
void test_data_representations()
{
std::cout << "Test rescaling on type " << typeid(Real).name() << "\n";
Real tol = 150*std::numeric_limits<Real>::epsilon();
std::array<Real, 500> v;
for(size_t i = 0; i < v.size(); ++i)
{
v[i] = 9*i + 6;
}
std::array<Real, 500> dvdt;
auto lanczos = discrete_lanczos_derivative(Real(1));
lanczos(v, dvdt);
for(size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(dvdt[i], 9, tol);
}
boost::numeric::ublas::vector<Real> w(500);
boost::numeric::ublas::vector<Real> dwdt(500);
for(size_t i = 0; i < w.size(); ++i)
{
w[i] = 9*i + 6;
}
lanczos(w, dwdt);
for(size_t i = 0; i < v.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(dwdt[i], 9, tol);
}
auto v1 = boost::make_iterator_range(v.begin(), v.end());
auto v2 = boost::make_iterator_range(dvdt.begin(), dvdt.end());
lanczos(v1, v2);
for(size_t i = 0; i < v2.size(); ++i)
{
BOOST_CHECK_CLOSE_FRACTION(v2[i], 9, tol);
}
auto lanczos2 = discrete_lanczos_derivative<Real, 2>(Real(1));
lanczos2(v1, v2);
for(size_t i = 0; i < v2.size(); ++i)
{
BOOST_CHECK_SMALL(v2[i], tol);
}
}
BOOST_AUTO_TEST_CASE(lanczos_smoothing_test)
{
test_dlp_second_derivative<double>();
test_dlp_norms<double>();
test_dlp_evaluation<double>();
test_dlp_derivatives<double>();
test_dlp_next<double>();
// Takes too long!
//test_dlp_norms<cpp_bin_float_50>();
test_boundary_velocity_filters<double>();
test_boundary_velocity_filters<long double>();
// Takes too long!
//test_boundary_velocity_filters<cpp_bin_float_50>();
test_boundary_lanczos<double>();
test_boundary_lanczos<long double>();
// Takes too long!
//test_boundary_lanczos<cpp_bin_float_50>();
test_interior_velocity_filter<double>();
test_interior_velocity_filter<long double>();
test_interior_lanczos<double>();
test_acceleration_filters<double>();
test_lanczos_acceleration<float>();
test_lanczos_acceleration<double>();
test_rescaling<double>();
test_data_representations<double>();
}

1
test/univariate_statistics_test.cpp
View File

@@ -748,7 +748,6 @@ int main()
test_median_absolute_deviation<double>();
test_median_absolute_deviation<long double>();
test_median_absolute_deviation<cpp_bin_float_50>();
test_median_absolute_deviation<int>();
test_gini_coefficient<float>();
test_gini_coefficient<double>();