└── LinearRegression


/LinearRegression:
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 1 | import java.util.Arrays;
 2 | 
 3 | public class LinearRegression {
 4 |     private double[] x;
 5 |     private double[] y;
 6 |     private double slope;
 7 |     private double intercept;
 8 | 
 9 |     public LinearRegression(double[] x, double[] y) {
10 |         if (x.length != y.length) {
11 |             throw new IllegalArgumentException("Input arrays must have the same length");
12 |         }
13 |         this.x = x;
14 |         this.y = y;
15 |         fit();
16 |     }
17 | 
18 |     private void fit() {
19 |         double xMean = Arrays.stream(x).average().orElseThrow();
20 |         double yMean = Arrays.stream(y).average().orElseThrow();
21 | 
22 |         double numerator = 0;
23 |         double denominator = 0;
24 | 
25 |         for (int i = 0; i < x.length; i++) {
26 |             numerator += (x[i] - xMean) * (y[i] - yMean);
27 |             denominator += Math.pow(x[i] - xMean, 2);
28 |         }
29 | 
30 |         slope = numerator / denominator;
31 |         intercept = yMean - slope * xMean;
32 |     }
33 | 
34 |     public double predict(double xInput) {
35 |         return slope * xInput + intercept;
36 |     }
37 | 
38 |     public static void main(String[] args) {
39 |         double[] x = {1, 2, 3, 4, 5};
40 |         double[] y = {2, 4, 5, 4, 5};
41 | 
42 |         LinearRegression model = new LinearRegression(x, y);
43 | 
44 |         System.out.println("Slope: " + model.slope);
45 |         System.out.println("Intercept: " + model.intercept);
46 | 
47 |         double xInput = 6;
48 |         System.out.println("Predicted value for x=" + xInput + ": " + model.predict(xInput));
49 |     }
50 | }
51 | 


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