Polynomial Fitting – JAVA Code/Program (works on Android as well)

Okay, so the following is a code for fitting a polynomial to a given set of data using the Least Squares Approximation Method(Wikipedia).

Formula Used:
\begin{bmatrix}   N&  \Sigma x_i&  \Sigma x_i^2&  \dots&  \Sigma x_i^n& \\    \Sigma x_i&  \Sigma x_i^2&  \Sigma x_i^3&  \dots&  \Sigma x_i^{n+1}& \\    \Sigma x_i^2&  \Sigma x_i^3&  \Sigma x_i^4&  \dots&  \Sigma x_i^{n+2}& \\    &  \vdots&  &  &  \vdots& \\    \Sigma x_i^n&  \Sigma x_i^{n+1}&  \Sigma x_i^{n+2}&  \dots&  \Sigma x_i^{2n}&   \end{bmatrix}\begin{bmatrix}  a  \end{bmatrix}=\begin{bmatrix}  \Sigma y_i\\  \Sigma x_iy_i\\   \Sigma x_i^2y_i\\   \vdots\\  \Sigma x_i^ny_i     \end{bmatrix}
where x_i and y_i are the data-points entered by the user.

I had written a C++ code for this a long time ago, and coincidentally it got very popular for some reason. But then I felt the need to make an Android app that does the same.

So I ported my code to JAVA so that it works in my Android App.

Let’s say you have x-axis values and y-axis values in two double arrays called x[] and y[] of size N(no. of data points). You can either have the user enter them or have them imported from a CSV. It’s your call.
Then you need to have the user enter the degree(order) of the polynomial to use to fit the curve.
Note: If you have n points then an n-degree polynomial will interpolate(fit your data perfectly) your data.

The following information is required:

int n;                       //degree of polynomial to fit the data
int N;                       //no. of data points
double[] x=double[];         //array to store x-axis data points  
double[] y=double[];         //array to store y-axis data points

Once you have all the values for x[], y[], n, and N, the following code will fit a polynomial of nth degree to the given set of data-points and return the coefficients of the polynomial in an array a[], such that a[0] is the coefficient of x^0 , a[1] is the coefficient of x^1,… and so on.

            double X[] = new double[2 * n + 1];
            for (int i = 0; i < 2 * n + 1; i++) {
                X[i] = 0;
                for (int j = 0; j < N; j++)
                    X[i] = X[i] + Math.pow(x[j], i);        //consecutive positions of the array will store N,sigma(xi),sigma(xi^2),sigma(xi^3)....sigma(xi^2n)
            double B[][] = new double[n + 1][n + 2], a[] = new double[n + 1];            //B is the Normal matrix(augmented) that will store the equations, 'a' is for value of the final coefficients
            for (int i = 0; i <= n; i++)
                for (int j = 0; j <= n; j++)
                    B[i][j] = X[i + j];            //Build the Normal matrix by storing the corresponding coefficients at the right positions except the last column of the matrix
            double Y[] = new double[n + 1];                    //Array to store the values of sigma(yi),sigma(xi*yi),sigma(xi^2*yi)...sigma(xi^n*yi)
            for (int i = 0; i < n + 1; i++) {
                Y[i] = 0;
                for (int j = 0; j < N; j++)
                    Y[i] = Y[i] + Math.pow(x[j], i) * y[j];        //consecutive positions will store sigma(yi),sigma(xi*yi),sigma(xi^2*yi)...sigma(xi^n*yi)
            for (int i = 0; i <= n; i++)
                B[i][n + 1] = Y[i];                //load the values of Y as the last column of B(Normal Matrix but augmented)
            n = n + 1;
            for (int i = 0; i < n; i++)                    //From now Gaussian Elimination starts(can be ignored) to solve the set of linear equations (Pivotisation)
                for (int k = i + 1; k < n; k++)
                    if (B[i][i] < B[k][i])
                        for (int j = 0; j <= n; j++) {
                            double temp = B[i][j];
                            B[i][j] = B[k][j];
                            B[k][j] = temp;

            for (int i = 0; i < n - 1; i++)            //loop to perform the gauss elimination
                for (int k = i + 1; k < n; k++) {
                    double t = B[k][i] / B[i][i];
                    for (int j = 0; j <= n; j++)
                        B[k][j] = B[k][j] - t * B[i][j];    //make the elements below the pivot elements equal to zero or elimnate the variables
            for (int i = n - 1; i >= 0; i--)                //back-substitution
            {                        //x is an array whose values correspond to the values of x,y,z..
                a[i] = B[i][n];                //make the variable to be calculated equal to the rhs of the last equation
                for (int j = 0; j < n; j++)
                    if (j != i)            //then subtract all the lhs values except the coefficient of the variable whose value                                   is being calculated
                        a[i] = a[i] - B[i][j] * a[j];
                a[i] = a[i] / B[i][i];            //now finally divide the rhs by the coefficient of the variable to be calculated

Well, that’s it. The array a[] contains the coefficients such that a[i]=coefficient of x^i.

To understand the theory behind this, refer to this link.

Hope you guys find it useful!
If you have any questions/doubts, hit me up in the comments section below.

You can refer to the following links for more info:
Linear Fitting – Lab Write-Up
Linear Fitting – C++ Program
Linear Fitting – Scilab Code
Curve Fit Tools – Android App (using the above code)
Curve Fit Tools – Documentation
Curve Fit Tools – Play Store
Curve Fit Tools – GitHub Repository
Curve Fitters – Scilab Toolbox


PhD researcher at Friedrich-Schiller University Jena, Germany. I'm a physicist specializing in computational material science. I write efficient codes for simulating light-matter interactions at atomic scales. I like to develop Physics, DFT and Machine Learning related apps and softwares from time to time. Can code in most of the popular languages. Like to share my knowledge in Physics and applications using this Blog and a YouTube channel.
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