Implementing Linear Regression in Java and creating Machine Learning algorithms

src/main/java/com/thealgorithms/machinelearning/LinearRegression.java

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+
+package com.thealgorithms.machinelearning;
+
+import java.util.ArrayList;
+
+/**
+ * Author : Gowtham Kamalasekar
+ * LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
+ *
+ * Wiki : https://en.wikipedia.org/wiki/Linear_regression
+ * Linear Regression Machine Learning Algorithm is a regression algorithm.
+ * This programs used for computing y = mx + c
+ * Where m is slope and c is intercept
+ * We can use this too predict for a given x.
+ */
+
+class LinearRegression {
+  private ArrayList<Double> dependentX = new ArrayList<Double>();
+  private ArrayList<Double> independentY = new ArrayList<Double>();
+  private double m;
+  private double c;
+
+  /**
+   * @param : X (dependent variable), Y (independent variable) as ArrayList
+   */
+  public LinearRegression(ArrayList<Double> dependentX,
+                          ArrayList<Double> independentY) {
+    this.dependentX = dependentX;
+    this.independentY = independentY;
+    this.Equate();
+  }
+
+  private double Sumation(ArrayList<Double> arr) {
+    double sum = 0.0;
+
+    for (int i = 0; i < arr.size(); i++) {
+      sum += arr.get(i);
+    }
+
+    return sum;
+  }
+
+  private ArrayList<Double> MultiplyNumber(ArrayList<Double> arr1,
+                                           ArrayList<Double> arr2) {
+    ArrayList<Double> temp = new ArrayList<Double>();
+    for (int i = 0; i < arr1.size(); i++) {
+      temp.add((arr1.get(i) * arr2.get(i)));
+    }
+    return temp;
+  }
+
+  private void Equate() {
+    int n = dependentX.size();
+    this.m = (n * Sumation(MultiplyNumber(independentY, dependentX)) -
+              (Sumation(dependentX) * Sumation(independentY)));
+    this.m = this.m / (n * (Sumation(MultiplyNumber(dependentX, dependentX))) -
+                       (Sumation(dependentX) * Sumation(dependentX)));
+
+    this.c = (Sumation(independentY) *
+                  Sumation(MultiplyNumber(dependentX, dependentX)) -
+              (Sumation(dependentX) *
+               Sumation(MultiplyNumber(independentY, dependentX))));
+    this.c = this.c / (n * (Sumation(MultiplyNumber(dependentX, dependentX))) -
+                       (Sumation(dependentX) * Sumation(dependentX)));
+  }
+
+  public double getM() { return this.m; }
+
+  public double getC() { return this.c; }
+
+  public double PredictForX(double x) { return (this.m * x) + this.c; }
+}

src/test/java/com/thealgorithms/machinelearning/LinearRegressionTest.java

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+package com.thealgorithms.machinelearning;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+
+import java.util.ArrayList;
+import org.junit.jupiter.api.Test;
+
+class LinearRegressionTest {
+
+  @Test
+  void testLinearRegression() {
+    ArrayList<Double> dependentX = new ArrayList<>();
+    ArrayList<Double> independentY = new ArrayList<>();
+
+    dependentX.add(1.0);
+    independentY.add(2.0);
+    dependentX.add(2.0);
+    independentY.add(3.0);
+    dependentX.add(3.0);
+    independentY.add(4.0);
+    dependentX.add(4.0);
+    independentY.add(5.0);
+    dependentX.add(5.0);
+    independentY.add(6.0);
+
+    // Create LinearRegression object
+    LinearRegression lr = new LinearRegression(dependentX, independentY);
+
+    // Check the slope (m) and intercept (c)
+    assertEquals(1.0, lr.getM(), 0.001);
+    assertEquals(1.0, lr.getC(), 0.001);
+
+    // Check prediction for X = 6
+    double predictedY = lr.PredictForX(6.0);
+    assertEquals(7.0, predictedY, 0.001);
+  }
+}