//create a List of points (here manually inputed, the training data) List<Point> points = Arrays.asList(new Point(1, 1), new Point(2, 3), new Point(4, 3), new Point(3, 2), new Point(5, 5)); //create the linear regression object passing the training data as the argument SimpleLinearRegression regression = new SimpleLinearRegression(points); //get slope coefficient double slopeCoefficient = regression.getSlopeCoefficient(); //get intercept coefficient double intercept coefficient = regression.getInterceptCoefficient(); //get a prediction for the training data supplied double valueToPredict = 3.4; double predictedValue = regression.getPrediction(valueToPredict); //get root mean square error of training data double rmse = regression.getRootMeanSquareError();

//example using the data in testData3.txt File file = new File("src/testData3.txt"); Scanner sc = new Scanner(file); sc.nextLine(); //ignores first line with comment List<MultiplePoint> points = new ArrayList<>(); while (sc.hasNext()) { //assume number of tokens is multiple of 3 String y = sc.next(); String x1 = sc.next(); String x2 = sc.next(); List<Double> list = new ArrayList<>(); list.add(Double.parseDouble(x1)); list.add(Double.parseDouble(x2)); points.add(new MultiplePoint(list, Double.parseDouble(y))); } //creates the multiple linear regression object and computes the coefficients MultipleLinearRegression mlr = new MultipleLinearRegression(points); //get the coefficients double[][] coefficients = mlr.getCoefficients(); //get the List of Multiple Points List<MultiplePoint> list = mlr.getPoints(); //get a prediction for the given independent variables double prediction = mlr.getPrediction(1.3, 2.78); double[] values = new double[] {1.3, 2.78}; double prediction2 = mlr.getPrediction(values);

//as with Simple Linear Regression, supply a List of Points and the polynomial degree desired for the regression int polynomialDegreeDesired = 3; PolynomialRegression plr = new PolynomialRegression(points, polynomialDegreeDesired); //get back the list with points List<Point> list = plr.getPoints(); //get the polynomial degree int pd = plr.getPolynomialDegree(); //get the computed coefficients double[][] coefficients = plr.getCoefficients(); //get a prediction double value = 3.54; double prediction = plr.getPrediction(value); //get RMSE error for training data: double rmse = plr.getTrainingDataRootMeanSquareError(); //get RMSE error for test data (used in computation of optimal polynomial regression) double rmse = plr.getTestDataRootMeanSquareError(testData);

//computes the optimal polynomial regression for the supplied training data and test data //if we want to have standard output boolean terminalOutput = true; PolynomialRegression optimalPLR = PolynomialRegression.getOptimalPolynomialRegression(trainingData, testData, terminalOutput);