Update LinearRegressionTest.java

Author: tkgowtham

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

@@ -1,37 +1,37 @@
 package com.thealgorithms.machinelearning;
 
-import org.junit.jupiter.api.Test;
 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);
-    }
+  @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);
+  }
 }