Polyfit.h

//
//  PolyfitEigen.hpp
//  Polyfit
//
//  Created by Patrick Löber on 23.11.18.
//  Copyright © 2018 Patrick Loeber. All rights reserved.
//
//  Modified by Beverly Xu on 2.21.24 FIXME: Proper MIT Licensing
//
//  Use the Eigen library for fitting: https://libeigen.gitlab.io
 
// TODO: Add function to get derivative coefficients
 
#include "Eigen/Dense"
#include <iostream>
#include <vector>
 
Eigen::VectorXd polyfit_Eigen(std::vector<double> x, const Eigen::VectorXd &y, int degree)
{
    // Construct the Vandermonde matrix using Eigen's Map for vector initialization
    Eigen::MatrixXd A = Eigen::MatrixXd::NullaryExpr(x.size(), degree + 1, [&x](Eigen::Index i, Eigen::Index j)
                                                     { return std::pow(x[i], j); });
    // Solve the least-squares problem
    return A.bdcSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(y);
}

template <typename T>
std::vector<T> polyDerivatives(std::vector<T> &coefficients)
{
    std::vector<T> derivativeCoefficients;
    for (size_t degree = 0; degree < coefficients.size(); degree++)
    {
        derivativeCoefficients.push_back((degree)*coefficients.at(degree));  // for ax^(n), derivative coefficient is
                                                                             // a*n
    }
    return derivativeCoefficients;
}

template <typename T>
std::vector<T> polyVal(std::vector<T> &coefficients, std::vector<T> &xValues)
{
    std::vector<T> yValues;
    for (T x : xValues)
    {
        T y = (T)(0);
        for (size_t degree = 0; degree < coefficients.size(); degree++)
        {
            y += pow(x, degree) * coefficients.at(degree);  // y = ax^n + bx^(n-1) + .... + c
        }
        yValues.push_back(y);
    }
    return yValues;
}