Update LinearRegression.java with clang format

Clang formatted

Author: tkgowtham

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

@@ -3,66 +3,68 @@
 import java.util.ArrayList;
 
 /**
-* Author : Gowtham Kamalasekar
-* LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
-*
-* 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.
-*/
+ * Author : Gowtham Kamalasekar
+ * LinkedIn : https://www.linkedin.com/in/gowtham-kamalasekar/
+ *
+ * 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;
+  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;
+  /**
+   * @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();
+  }
 
-        for(int i = 0; i < arr.size(); i++){
-            sum += arr.get(i);
-        }
+  private double Sumation(ArrayList<Double> arr) {
+    double sum = 0.0;
 
-        return sum;
+    for (int i = 0; i < arr.size(); i++) {
+      sum += arr.get(i);
     }
 
-    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;
+    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)));
+  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)));
-    }
+    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 getM() { return this.m; }
 
-    public double getC(){
-        return this.c;
-    }
+  public double getC() { return this.c; }
 
-    public double PredictForX(double x) {
-        return (this.m * x) + this.c;
-    }
+  public double PredictForX(double x) { return (this.m * x) + this.c; }
 }