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126 lines
No EOL
3.8 KiB
C++
126 lines
No EOL
3.8 KiB
C++
#include "ScatterDraw.h"
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#include <plugin/Eigen/Eigen.h>
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NAMESPACE_UPP
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using namespace Eigen;
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struct Equation_functor : NonLinearOptimizationFunctor<double> {
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DataSource *series;
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ExplicitEquation *fSource;
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Equation_functor() {}
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int operator()(const VectorXd &b, VectorXd &fvec) const {
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ASSERT(b.size() == unknowns);
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ASSERT(fvec.size() == datasetLen);
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for (int i = 0; i < unknowns; ++i)
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(*fSource).SetCoeffVal(i, b(i));
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for(int64 i = 0; i < datasetLen; i++)
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fvec(ptrdiff_t(i)) = (*fSource).f((*series).x(i)) - (*series).y(i);
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return 0;
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}
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};
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void ExplicitEquation::SetNumCoeff(int num) {
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coeff.SetCount(num);
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for (int i = 0; i < num; ++i)
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coeff[i] = 0;
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}
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ExplicitEquation::FitError ExplicitEquation::Fit(DataSource &series, double &r2) {
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if (series.IsExplicit() || series.IsParam())
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return InadequateDataSource;
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if (series.GetCount() < coeff.GetCount())
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return SmallDataSource;
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ptrdiff_t numUnknowns = coeff.GetCount();
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VectorXd x(numUnknowns);
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for (int i = 0; i < numUnknowns; ++i)
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x(i) = coeff[i];
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Equation_functor functor;
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functor.series = &series;
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functor.fSource = this;
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functor.unknowns = numUnknowns;
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functor.datasetLen = series.GetCount();
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NumericalDiff<Equation_functor> numDiff(functor);
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LevenbergMarquardt<NumericalDiff<Equation_functor> > lm(numDiff);
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// ftol is a nonnegative input variable that measures the relative error desired in the sum of squares
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lm.parameters.ftol = 1.E4*NumTraits<double>::epsilon();
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// xtol is a nonnegative input variable that measures the relative error desired in the approximate solution
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lm.parameters.xtol = 1.E4*NumTraits<double>::epsilon();
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lm.parameters.maxfev = maxFitFunctionEvaluations;
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int ret = lm.minimize(x);
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if (ret == LevenbergMarquardtSpace::ImproperInputParameters)
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return ExplicitEquation::ImproperInputParameters;
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if (ret == LevenbergMarquardtSpace::TooManyFunctionEvaluation)
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return TooManyFunctionEvaluation;
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double mean = series.AvgY();
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double sse = 0, sst = 0;
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for (int64 i = 0; i < series.GetCount(); ++i) {
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double res = series.y(i) - f(series.x(i));
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sse += res*res;
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double d = series.y(i) - mean;
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sst += d*d;
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}
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r2 = 1 - sse/sst;
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return NoError;
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}
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int ExplicitEquation::numDigits = 3;
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int ExplicitEquation::maxFitFunctionEvaluations = 1000;
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double PolynomialEquation::f(double x) {
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double y = 0;
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for (int i = 0; i < coeff.GetCount(); ++i)
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y += coeff[i]*pow(x, i);
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return y;
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}
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String PolynomialEquation::GetEquation() {
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if (coeff.IsEmpty())
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return String();
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String y;
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y = FormatDoubleFix(coeff[0], numDigits);
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if (coeff.GetCount() == 1)
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return y;
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y += " " + String(coeff[1] >= 0 ? "+" : "-") + " " + FormatDoubleFix(fabs(coeff[1]), numDigits) + "*x";
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for (int i = 2; i < coeff.GetCount(); ++i)
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y += " " + String(coeff[i] >= 0 ? "+" : "-") + " " + FormatDoubleFix(fabs(coeff[i]), numDigits) + "*x^" + FormatInt(i);
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return y;
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}
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double FourierEquation::f(double x) {
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double y = coeff[0];
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double w = coeff[1];
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for (int i = 2; i < coeff.GetCount(); i += 2) {
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int n = 1 + (i - 2)/2;
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y += coeff[i]*cos(n*w*x) + coeff[i+1]*sin(n*w*x);
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}
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return y;
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}
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String FourierEquation::GetEquation() {
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if (coeff.GetCount() < 4)
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return String();
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String y = FormatDoubleFix(coeff[0], numDigits);
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for (int i = 2; i < coeff.GetCount(); i += 2) {
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int n = 1 + (i - 2)/2;
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String nwx = Format("%d*%s*x", n, FormatDoubleFix(fabs(coeff[1]), numDigits));
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String signA = coeff[i] >= 0 ? "+" : "-";
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String signB = coeff[i+1] >= 0 ? "+" : "-";
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y += " " + signA + " " + FormatDoubleFix(fabs(coeff[i]), numDigits) + "*cos(" + nwx + ")";
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y += " " + signB + " " + FormatDoubleFix(fabs(coeff[i+1]), numDigits) + "*sin(" + nwx + ")";
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}
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return y;
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}
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END_UPP_NAMESPACE |