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// David Eberly, Geometric Tools, Redmond WA 98052
// Copyright (c) 1998-2021
// Distributed under the Boost Software License, Version 1.0.
// https://www.boost.org/LICENSE_1_0.txt
// https://www.geometrictools.com/License/Boost/LICENSE_1_0.txt
// Version: 4.0.2019.08.13
#pragma once
#include <Mathematics/ApprQuery.h>
#include <Mathematics/Array2.h>
#include <Mathematics/GMatrix.h>
#include <array>
// The samples are (x[i],w[i]) for 0 <= i < S. Think of w as a function of
// x, say w = f(x). The function fits the samples with a polynomial of
// degree d, say w = sum_{i=0}^d c[i]*x^i. The method is a least-squares
// fitting algorithm. The mParameters stores the coefficients c[i] for
// 0 <= i <= d. The observation type is std::array<Real,2>, which represents
// a pair (x,w).
//
// WARNING. The fitting algorithm for polynomial terms
// (1,x,x^2,...,x^d)
// is known to be nonrobust for large degrees and for large magnitude data.
// One alternative is to use orthogonal polynomials
// (f[0](x),...,f[d](x))
// and apply the least-squares algorithm to these. Another alternative is to
// transform
// (x',w') = ((x-xcen)/rng, w/rng)
// where xmin = min(x[i]), xmax = max(x[i]), xcen = (xmin+xmax)/2, and
// rng = xmax-xmin. Fit the (x',w') points,
// w' = sum_{i=0}^d c'[i]*(x')^i.
// The original polynomial is evaluated as
// w = rng*sum_{i=0}^d c'[i]*((x-xcen)/rng)^i
namespace gte
{
template <typename Real>
class ApprPolynomial2 : public ApprQuery<Real, std::array<Real, 2>>
{
public:
// Initialize the model parameters to zero.
ApprPolynomial2(int degree)
:
mDegree(degree),
mSize(degree + 1),
mParameters(mSize, (Real)0)
{
mXDomain[0] = std::numeric_limits<Real>::max();
mXDomain[1] = -mXDomain[0];
}
// Basic fitting algorithm. See ApprQuery.h for the various Fit(...)
// functions that you can call.
virtual bool FitIndexed(
size_t numObservations, std::array<Real, 2> const* observations,
size_t numIndices, int const* indices) override
{
if (this->ValidIndices(numObservations, observations, numIndices, indices))
{
int s, i0, i1;
// Compute the powers of x.
int numSamples = static_cast<int>(numIndices);
int twoDegree = 2 * mDegree;
Array2<Real> xPower(twoDegree + 1, numSamples);
for (s = 0; s < numSamples; ++s)
{
Real x = observations[indices[s]][0];
mXDomain[0] = std::min(x, mXDomain[0]);
mXDomain[1] = std::max(x, mXDomain[1]);
xPower[s][0] = (Real)1;
for (i0 = 1; i0 <= twoDegree; ++i0)
{
xPower[s][i0] = x * xPower[s][i0 - 1];
}
}
// Matrix A is the Vandermonde matrix and vector B is the
// right-hand side of the linear system A*X = B.
GMatrix<Real> A(mSize, mSize);
GVector<Real> B(mSize);
for (i0 = 0; i0 <= mDegree; ++i0)
{
Real sum = (Real)0;
for (s = 0; s < numSamples; ++s)
{
Real w = observations[indices[s]][1];
sum += w * xPower[s][i0];
}
B[i0] = sum;
for (i1 = 0; i1 <= mDegree; ++i1)
{
sum = (Real)0;
for (s = 0; s < numSamples; ++s)
{
sum += xPower[s][i0 + i1];
}
A(i0, i1) = sum;
}
}
// Solve for the polynomial coefficients.
GVector<Real> coefficients = Inverse(A) * B;
bool hasNonzero = false;
for (int i = 0; i < mSize; ++i)
{
mParameters[i] = coefficients[i];
if (coefficients[i] != (Real)0)
{
hasNonzero = true;
}
}
return hasNonzero;
}
std::fill(mParameters.begin(), mParameters.end(), (Real)0);
return false;
}
// Get the parameters for the best fit.
std::vector<Real> const& GetParameters() const
{
return mParameters;
}
virtual size_t GetMinimumRequired() const override
{
return static_cast<size_t>(mSize);
}
// Compute the model error for the specified observation for the
// current model parameters. The returned value for observation
// (x0,w0) is |w(x0) - w0|, where w(x) is the fitted polynomial.
virtual Real Error(std::array<Real, 2> const& observation) const override
{
Real w = Evaluate(observation[0]);
Real error = std::fabs(w - observation[1]);
return error;
}
virtual void CopyParameters(ApprQuery<Real, std::array<Real, 2>> const* input) override
{
auto source = dynamic_cast<ApprPolynomial2 const*>(input);
if (source)
{
*this = *source;
}
}
// Evaluate the polynomial. The domain interval is provided so you can
// interpolate (x in domain) or extrapolate (x not in domain).
std::array<Real, 2> const& GetXDomain() const
{
return mXDomain;
}
Real Evaluate(Real x) const
{
int i = mDegree;
Real w = mParameters[i];
while (--i >= 0)
{
w = mParameters[i] + w * x;
}
return w;
}
private:
int mDegree, mSize;
std::array<Real, 2> mXDomain;
std::vector<Real> mParameters;
};
}