Skip to main content

Static and Dynamic Polymorphism in OOP


 
There are two types of Polymorphism.
1. Static Polymorphism.
2. Dynamic Polymorphism.

Static polymorphism is achieved through method overloading. Method overloading means there are several methods present in a class having the same name but different types/order/number of parameters.
At compile time, compiler knows which method to invoke by checking the method signatures.  So, this is called compile time polymorphism or static binding. The concept will be clear from the following example:

class Adder{
    static int add(int a, int b){
        return a + b;
    }
    static int add(int a, int b, int c){
        return a + b + c;
    }
}


class Main{
    public static void main(String[] args){
        System.out.println(Adder.add(11, 11);
        System.out.println(Adder.add(11, 11, 11);
    }
}


Dynamic Polymorphism:

Suppose a sub class overrides a particular method of the super class. Let’s say, in the program, we create an object of the subclass and assign it to the super class reference. Now, if we call the overridden method on the super class reference then the sub class version of the method will be called.
Have a look at the following example.
 
class Vehicle
{
  public void move ()
  {
    System.out.println ("Vehicle can move!");
  }
}

class MotorBike extends Vehicle
{
  public void move ()
  {
    System.out.println ("Motorbike can move and accelarate too!");
  }
}



public class Main
{
  public static void main (String[]args)
  {
    Vehicle rickshaw = new Vehicle ();
    Vehicle bike = new MotorBike ();
    rickshaw.move ();		//prints "Vehicle can move!"
    bike.move ();		// prints "Motorbike can move and accelarate too!"
  }
}

Comments

Popular posts from this blog

DFS Performance Measurement

Completeness DFS is not complete, to convince yourself consider that our search start expanding the left subtree of the root for so long path (maybe infinite) when different choice near the root could lead to a solution, now suppose that the left subtree of the root has no solution, and it is unbounded, then the search will continue going deep infinitely, in this case , we say that DFS is not complete. Optimality  Consider the scenario that there is more than one goal node, and our search decided to first expand the left subtree of the root where there is a solution at a very deep level of this left subtree , in the same time the right subtree of the root has a solution near the root, here comes the non-optimality of DFS that it is not guaranteed that the first goal to find is the optimal one, so we conclude that DFS is not optimal. Time Complexity Consider a state space that is identical to that of BFS, with branching factor b, and we start the search fro...

Regularization in Deep Learning / Machine Learning - Prevent Overfitting

image source: mlexplained Overfittng happens in every machine learning (ML) problem.  Learning how to deal with overfitting is essential to mastering machine learning.  The fundamental issue in machine learning is the tension between optimization  and generalization. Optimization refers to the process of adjusting a model to get the  best performance possible on the training data (the learning in machine learning ),  whereas generalization refers to how well the trained model performs on data it has  never seen before . The goal of the game is to get good generalization, of course, but you don’t control generalization; you can only adjust the model based on its training  data.  The processing of fighting overfitting is a way  called regularization . [1].  How do you know whether a model is overfitting? The best initial method is to measure error on a training and test set. If you see a low error on the training set and...

Difference between a Singly LinkedList and Doubly LinkedList