Multi Variable Linear Regression_ipynb_2_ • 3 min read

%jars /home/vishnuaa77/vscode/vishnu/lib/commons-math3-3.6.1.jar
%jars /home/vishnuaa77/vscode/vishnu/lib/jfreechart-1.5.4.jar

import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.LUDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;

public class BasketballPerformanceRegression {

    public static void main(String[] args) {
        // Mock data for 5 basketball players
        double[][] xData = {
            {22, 7, 10, 35}, // Player 1: points, assists, rebounds, minutes
            {15, 12, 8, 30}, // Player 2
            {30, 4, 12, 40}, // Player 3
            {8,  10, 5, 25}, // Player 4
            {27, 3, 7,  38}  // Player 5
        };
        double[] yData = {90, 75, 85, 65, 88}; // Performance scores for each player

        double[] coefficients = calculateCoefficients(xData, yData);
        System.out.println("Coefficients: " + java.util.Arrays.toString(coefficients));
    }

    public static double[] calculateCoefficients(double[][] xData, double[] yData) {
        int n = xData.length;
        int m = xData[0].length;
        RealMatrix X = new Array2DRowRealMatrix(n, m + 1); // +1 for bias term
        RealVector Y = new ArrayRealVector(yData, false);

        for (int i = 0; i < n; i++) {
            X.setEntry(i, 0, 1);  // Bias term
            for (int j = 0; j < m; j++) {
                X.setEntry(i, j + 1, xData[i][j]);
            }
        }

        RealMatrix Xt = X.transpose();
        RealMatrix XtX = Xt.multiply(X);
        RealMatrix XtXInverse = new LUDecomposition(XtX).getSolver().getInverse();
        RealVector XtY = Xt.operate(Y);
        RealVector B = XtXInverse.operate(XtY);

        return B.toArray();
    }
}

BasketballPerformanceRegression.main(null);

Coefficients: [-500.9701492539607, -18.805970149260247, -0.5970149253726049, -0.8208955223876728, 29.059701492544264]