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