Implementing Linear Regression in Java and creating Machine Learning algorithms

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

@@ -0,0 +1,72 @@
+
+package com.thealgorithms.machinelearning;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * 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
+     */
+    LinearRegression(ArrayList<Double> dependentX, ArrayList<Double> independentY) {
+        this.dependentX = dependentX;
+        this.independentY = independentY;
+        this.equate();
+    }
+
+    private double sumation(List<Double> arr) {
+        double sum = 0.0;
+
+        for (int i = 0; i < arr.size(); i++) {
+            sum += arr.get(i);
+        }
+
+        return sum;
+    }
+
+    private List<Double> multiplyNumber(List<Double> arr1, List<Double> arr2) {
+        List<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

@@ -0,0 +1,37 @@
+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);
+    }
+}