Trying to understand monads and wondering if they'd be useful for data transformation programming in Python, I looked at many introductory explanations. But I don't understand why monads are important.
Is the following Python code a good representation of the Maybe monad?
class Some:
def __init__(self, val):
self.val=val
def apply(self, func):
return func(self.val)
class Error:
def apply(self, func):
return Error()
a = Some(1)
b = a.apply(lambda x:Some(x+5))
Can you give an example of a monad solution, which cannot be transformed into such OOP code?
(Do you think monads for data transformation in OOP languages can be useful?)
Here is a post that discusses monads using Swift as the language that might help you make more sense of them: http://www.javiersoto.me/post/106875422394
The basic point is that a monad is an object that has three components.
A constructor that can take some other object, or objects, and create a monad wrapping that object.
A method that can apply a function that knows nothing about the monad to its contents and return the result wrapped in a monad.
A method that can apply a function that takes the raw object and returns a result wrapped in the monad, and return that monad.
Note that this means even an Array class can be a monad if the language treats methods as first class objects, and the necessary methods exist.
If the language doesn't treat methods as first class objects, even if it's an OO language, it will not able able to implement Monads. I think your confusion may be coming from the fact that you are using a multi-paradigm language (Python) and assuming it is a pure OO language.
The apply you've written corresponds to the monad function called bind.
Given that and the constructor Some (which for a generalized monad is called unit or return) you can define the functions fmap and apply which promote other kinds of functions: where bind promotes and uses a function that takes plain data and returns a monad, fmap does so for for functions from plain data to plain data, and apply does so for functions that are themselves wrapped inside the monad.
You would have issues, I think, defining the last monadic operation: join. join takes a nested monad and flattens it to a single layer. So this is a method that can't be used on all objects of the class, only those with a particular structure. And while join can be avoided if you define bind instead (you can write join in terms of bind and the identity function), it's inconvenient to define bind for array-like monads. Functions on arrays are more naturally described in terms of fmap (loop over the array and apply the function) and join (take a nested array and flatten it).
I think if you can attack these challenges, you can implement a monad in whatever language you choose. And I do think that monads are useful in many languages, as many sub-computations can be described using them. They represent a known solution to many common problems, and if they're already available to you, that means code you don't have to test or debug; they're tools that already work.
Implementing them yourself lets you use the functional literature and way of thinking to attack problems. Whether that convenience is worth the effort of implementing them is up to you.
Related
Halfway through Architecture Patterns with Python, I have two questions about how should the Domain Model Classes be structured and instantiated. Assume on my Domain Model I have the class DepthMap:
class DepthMap:
def __init__(self, map: np.ndarray):
self.map = map
According to what I understood from the book, this class is not correct since it depends on Numpy, and it should depend only on Python primitives, hence the question: Should Domain Model classes rely only on Python primitives, or is there an exception?
Assuming the answer to the previous question is that classes should solely depend on primitives, what would the correct way create a DepthMap from a Numpy array be? Assume now I have more formats from where I can make a DepthMap object.
class DepthMap:
def __init__(self, map: List):
self.map = map
#classmethod
def from_numpy(cls, map: np.ndarray):
return cls(map.tolist())
#classmethod
def from_str(cls, map: str):
return cls([float(i) for i in s.split(',')])
or a factory:
class DepthMapFactory:
#staticmethod
def from_numpy(map: np.ndarray):
return DepthMap(map.tolist())
#staticmethod
def from_str(map: str):
return DepthMap([float(i) for i in s.split(',')])
I think even the Repository Pattern, which they go through in the book, could fit in here:
class StrRepository:
def get(map: str):
return DepthMap([float(i) for i in s.split(',')])
class NumpyRepository:
def get(map: np.ndarray):
return DepthMap(map.tolist())
The second question: When creating a Domain Model Object from different sources, what is the correct approach?
Note: My background is not software; hence some OOP concepts may be incorrect. Instead of downvoting, please comment and let me know how to improve the question.
I wrote the book, so I can at least have a go at answering your question.
You can use things other than primitives (str, int, boolean etc) in your domain model. Generally, although we couldn't show it in the book, your model classes will contain whole hierarchies of objects.
What you want to avoid is your technical implementation leaking into your code in a way that makes it hard to express your intent. It would probably be inappropriate to pass instances of Numpy arrays around your codebase, unless your domain is Numpy. We're trying to make code easier to read and test by separating the interesting stuff from the glue.
To that end, it's fine for you to have a DepthMap class that exposes some behaviour, and happens to have a Numpy array as its internal storage. That's not any different to you using any other data structure from a library.
If you've got data as a flat file or something, and there is complex logic involved in creating the Numpy array, then I think a Factory is appropriate. That way you can keep the boring, ugly code for producing a DepthMap at the edge of your system, and out of your model.
If creating a DepthMap from a string is really a one-liner, then a classmethod is probably better because it's easier to find and understand.
I think it's perfectly fine to depend on librairies that are pure language extensions or else you will just end up with having to define tons of "interface contracts" (Python doesn't have interfaces as a language construct -- but those can be conceptual) to abstract away these data structures and in the end those newly introduced contracts will probably be poor abstractions anyway and just result in additional complexity.
That means your domain objects can generally depend on these pure types. On the other hand I also think these types should be considered as language "primitives" (native may be more accurate) just like datetime and that you'd want to avoid primitive obsession.
In other words, DepthMap which is a domain concept is allowed to depend on Numpy for it's construction (no abstraction necessary here), but Numpy shouldn't necessarily be allowed to flow deep into the domain (unless it's the appropriate abstraction).
Or in pseudo-code, this could be bad:
someOperation(Numpy: depthMap);
Where this may be better:
class DepthMap(Numpy: data);
someOperation(DepthMap depthMap);
And regarding the second question, from a DDD perspective if the
DepthMap class has a Numpy array as it's internal structure but has to
be constructed from other sources (string or list for example) would
the best approach be a repository pattern? Or is this just for
handling databases and a Factory is a better approach?
The Repository pattern is exclusively for storage/retrieval so it wouldn't be appropriate. Now, you may have a factory method directly on DepthMap that accepts a Numpy or you may have a dedicated factory. If you want to decouple DepthMap from Numpy then it could make sense to introduce a dedicated factory, but it seems unnecessary here at first glance.
Should Domain Model classes rely only on Python primitives
Speaking purely from a domain-driven-design perspective, there's absolutely no reason that this should be true
Your domain dynamics are normally going to be described using the language of your domain, ie the manipulation of ENTITIES and VALUE OBJECTS (Evans, 2003) that are facades that place domain semantics on top of your data structures.
The underlying data structures, behind the facades, are whatever you need to get the job done.
There is nothing in domain driven design requiring that you forsake a well-tested off the shelf implementation of a highly optimized Bazzlefraz and instead write your own from scratch.
Part of the point of domain driven design is that we want to be making our investment into the code that helps the business, not the plumbing.
I would like to understand the data model in python (3.8.2), so maybe someone can explain in simple words, why every basic data type is a class-object and has so many attributes and functions.
Consider the following code:
num = 1337
type(num) # <class 'int'>
dir(num)
>>>
['__abs__',
'__add__',
'__and__',
...
'real',
'to_bytes']
So I can do:
num.__add__(1)
>>> 1338
So my question is: why are such functions part of the user defined variables and isn't there quite some overhead to this?
"everything is object in Python", so "everything" (maybe not exactly everything, I don't know) is a class instance and has potentially attributes and methods.
That said you have to know that, by convention, every method whom name begins with __ is considered as private (and is almost really protected) and you should not use it directly.
Every method whom name starts and ends by __ is called a magic method and has a pretty well defined role. They could be considered as a sort of hooks bound to particular operators or moments in the object lifecycle.
For example __add__ is the method bound to the + operator. When you do an addition, you are actually calling the __add__ method of a number, giving it another number as parameter.
As you noticed, you also can call directly the __add__ method if you want, but it is not encouraged.
Python is a high-level interpreted language. It provides many facilities and a clear syntax that allows to concentrate on real problems rather than implementation details.
Those facilities do not come from nowhere and are a benefit provided by (among others) an internal data model.
This data model is certainly not as lightweight as a custom handcrafted C data structure specialy designed for a single problem but it is pratical and usable in many situations (and still reasonably lightweight and well performing for many usages).
If you are concerned by memory optimisation and extreme-performance, You probably should look some lower level languages like C or Go.
But keep in mind that solving any problem could then probably take much more time :)
Why so many methods:
Python is an object-oriented language. It's built on objects interacting with each other. Maybe there's no specific reason why Python has to be object-oriented, but the guy who wrote it gets to decide what kind of language it is and he chose this type.
In Python, the objects get to decide how an operation is implemented. That's why you can have natural syntax like 'str1' + 'str2' or 23 + 40. Note, these are both written in the same way, but will produce different results because the + operation is carried out on different objects.
What about memory management:
If the objects didn't decide how to implement an operation, the logic would still have to be stored somewhere. It would probably take up at least some memory.
The question is how much memory does an object use? To do this, you can use the sys.getsizeof function:
import sys
print(sys.getsizeof(int)) # Prints size in bytes
>>> 416
print(sys.getsizeof(1))
>>> 28
So, the memory of the class (int) is much larger than the object (1). This means that the instances of integers are much smaller than their classes, so memory doesn't inflate as drastically.
I have a class hierarchy representing linear algebra expression (as expression trees), something like this (it’s actually more complicated than that, but that should be enough to give you an idea).
Expression
Operator
Times
Plus
Symbol
Matrix
Scalar
I’m manipulating those expression in many different ways, so I have to copy those expression a lot because I want to explore different ways of manipulating them (I really don’t see any way around copying them). Not surprisingly, copy.deepcopy() is slow.
Not all class attributes need do be deepcopied, and those expression trees will always be trees (if not, something is fundamentally wrong), so I can probably save some time by simplifying the memoization (or don’t use it at all).
I want to make copying faster by implementing a custom copy function. This could either mean implementing __deepcopy__(), writing an entirely new function or using something like get_state and set_state, I really don’t care. At the moment, I don’t care about pickling.
I’m using Python 3.
Since I have this class hierarchy where new attributes are introduced at different levels, I would prefer not to start from scratch on each level. Instead, it would be desirable to reuse some functionality of the superclass similar to how in __init__, one usually calls __init__ of the superclass. However, I would prefer not to call __init__ because that sometimes does some extra stuff that is not necessary when copying.
How do I do that in the fastest and most pythonic way? I was not able to find any reasonable guidelines regarding how to implement this copy functionality under those circumstances. I looked at the implementation of deepcopy both in Python 2 and 3. In Python 2, a bunch of different methods are used (get_state/set_state, using __dict__,…), in Python 3, I’m not able to find the corresponding function at all.
I found one possible solution myself:
class Expression(object):
...
def __deepcopy__(self, memo):
cpy = object.__new__(type(self))
# manually copy all attributes of Expression here
return cpy
class Operator(Expression):
...
def __deepcopy__(self, memo):
cpy = Expression.__deepcopy__(self, memo)
# manually copy all attributes exclusive to Operator here
return cpy
The new object is always created in __deepcopy__ of Expression. By using object.__new__(type(self)), the new object has the type of the object on which __deepcopy__ was originally called (for example Operator).
Unfortunately, this solution is not much faster than the default deepcopy. I'm wondering if this is because of all those calls to __deepcopy__ all the way to the top of the class hierarchy. I will investigate this.
I'm new to Python, and I'm learning about the class function. Does anyone know of any good sources/examples for this? Here's an example I wrote up. I'm trying to read more about self and init
class Example:
def __init__(a, b, c, d):
self.a = a
self.b = b
self.c = c
self.d = d
test = Example(1, 1, 1, 1)
I've been to python.org, as well as this site. I've also been reading beginners Python books, but I'd like more information.
Thanks.
A couple of generic clarifications here:
Python's "class" keyword is not a function, it's a statement which signals to the language that the following code describes a class (a user-defined data type and its associated behavior). "class" takes a name (and a possibly empty list of "parent" classes) ... and introduces a "suite" (an indented block of code).
The "def" keyboard is a similar statement which defines a function. In your example, which should have read: *def _init_(self, a, b, c)*) you're defining a special type of function which is "part of" (associated with, bound to) the Example class. It's also possible (and fairly common) to create unbound functions. Commonly, in Python, unbound functions are simple called "functions" while those which are part of a class are called "methods" ... or "instance functions."
classes are templates for instantiating objects. The terms "instance" and "object" are synonymous in this context. Your example "test" is an instance ... and the Python interpreter "instantiates" and initializes that object according to the class description.
A class is also a "type", that is to say that it's a user definition of a type of data and its associated methods. "class" and "type" are somewhat synonymous in Python though they are conventionally used in different ways. The core "types" of Python data (integers, real numbers, imaginary/complex numbers, strings, lists, tuples, and dictionaries) are all referred to as "types" while the more complex data/operational structures are called classes. Early versions of Python were implemented with constraints that made the distinction between "type" and "class" more than merely a matter of terminological difference. However, the last several versions of Python have eliminated those underlying technical distinctions. Those distinctions related to "subclassing" (inheritance).
classes can be described as a set of additions and modifications to another class. This is called "inheritance" and the class which is derived from another in this manner is referred to as a "subclass." It's common for programmers to create hierarchies of classes ... with specific variations all deriving from more common bases. It's also common to define related functionality within the same files or sets of files. These are "class libraries" and sometimes they are built as "packages."
_init_() is a method; in particular it's the initializer for Python objects (instances of a class).
Python generally uses _..._ (prefixing and suffixing pairs of underscore characters around selected keywords) for "special" method or attribute names ... which is intended to reduce the likelihood that its naming choices will conflict with the meaningful names that you might wish to give to your own methods. While you can name your other methods and attributes with this _XXXX_ --- Python will not inherently treat that as an error --- it's an extremely bad idea to do so. Even if you don't pick any of the currently defined special names there's no guarantee that some future version of Python won't conflict with your usage later.
"methods" are functions ... but they are a type of function which is bound (associated with) a particular instance of a particular class. There are also "class methods" which are associated with the class rather than with a specific instance of the class.
In your example self.b, self.c and so on are "attributes" or "members" of the object (instance). (The terms are synonymous).
In general the purpose of object orient programming is to provide ways of describing types of data and operations on those types of data in a way that's amenable to both human comprehension and computerized interpretation and execution. (All programming languages are intended to strike some balance between human readability/comprehension and machine parsing/execution; but object-oriented languages do so specifically with a focus on the description and definition of "types," and the instantiation of those types into "objects" and finally the various interactions among those objects.
"self" is a Python specific convention for naming the special (and generally required) first argument to any bound method. It's a "self" reference (a way for the code in a method to refer to the object/instance's own attributes and other methods without any ambiguity). While you can call your first argument to your bound methods "a" (as you've unwittingly done in your Example) it's an extremely bad idea to do so. Not only will it likely confuse you later ... it will make no sense to anyone else trying to read your Python code).
The term "object-oriented" is confusing unless one is aware of the comparisons to other forms of programming language. It's an evolution from "procedural" programming. The simplest gist of that comparison is when you consider the sorts of functions one would define in a procedural language were one might have to define and separately name different functions to perform analogous operations on different types of data: print_student_record(this_student) vs. print_teacher_report(some_report) --- a programming model which necessitates a fair amount of overhead on the part of the programmer, to keep track of which functions work on which types. This sort of problem is eliminated in OO (object oriented) programming where one can, conceivably, call on this.print_() ... and, assuming one has created compatible classes, this will "print" regardless of whether "this" is a student (record/object) or a teacher (report/record/object). That's greatly oversimplified but useful for understanding the pressures which led to the development and adoption of OO based programming.
In Python it's possible to create classes with little or no functionality. Your example does nothing, yet, but transfer a set of arguments into "attributes" (members) during initialization (instantiation). After that you could use these attributes in programming statements like: test.a += 1 and print (test.a). This is possible because Python is a "multi-paradigm" language. It supports procedural as well as object-orient programming styles. Objects used this way are very similar to "structs" from the C programming language (predecessor to C++) and to the "records" in Pascal, etc. That style of programming is largely considered to be obsolete (particularly when using a modern, "very high level" language such as Python).
The gist of what I'm getting at is this ... you'll want to learn how to think of your data as the combination of it's "parts" (attributes) and the functionality that changes, manipulates, validates, and handles input, output, and possibly storage, of those attributes.
For example if you were writing a "code breaker" program to solve simply ciphers you might implement a "Histogram" object which counts the letter frequencies of a given coded message. That would have attributes (one integer for every letter) and behavior (feeding ports of the coded message(s) into the instance, splitting the strings into individual characters, filtering out all the non-letter characters, converting all the letters to upper or lower case, and counting them --- that is incrementing the integer corresponding to each letter). Additionally you'd need to have some way of querying the histogram ... for example getting list of the letters sorted by their frequency in the cipher text.
Once you had such a "histogram" class then you could think of ways to use that for your solver. For example to solve a cryptogram puzzle you might computer the histogram then try substituting "etaon" for the five most common ciphered letters ... then check how many of the "partial" strings (t.e for "the") match words, trying permutations, and so on. Each of these might be it's own class. A key point of programming is that your histogram class might be useful for counting all sorts of other things (even in a simple voting system or popularity context). A particular subclass or instantiation might make it a histogram of letters while others could be re-used for other types of "things" you want counted. Similarly the code that iterates over permutions of some list might be used in any number of simulation, optimization, and related programs. (In fact Python's standard libraries already including "counters" and "permutations" functions in the "collections" and "itertools" modules, respectively).
Of course you're going to hear of all of these concepts repeatedly as you study programming. This has been a rather rambling attempt to kickstart that process. I know I've been a bit repetitious in a few points here --- part of that is because I'm typing this a 4am after having started work at 7am yesterday; but part of it serves a pedagogical purpose as well.
There's an error in your class definition. Always include the variable self in your __init__ method. It represents the instance of the object itself and should be included as the first parameter to all of your methods.
What do you want to accomplish with this class? Up until now, it just stores a few variables. Try adding a few methods to spice things up a little bit! There is a plethora of available resources on classes in Python. To start, you may wanna give this one a try:
Python Programming - Classes
I'm learning python now also, and there is an intro class that's pretty good on codecademy.com
http://www.codecademy.com/tracks/python
It has a section that goes through an exercise on classes. Hope this helps
What is polymorphism? I'm not sure I am understanding it correctly.
In the Python scope, what I am getting out of it is that I can define parameters as followed:
def blah (x, y)
without having to specify the type, as opposed to another language like Java, where it'd look more along the lines of:
public void blah (int x, string y)
Am I getting that right?
Beware that different people use different terminology; in particular there is often a rift between the object oriented community and the (rest of the) programming language theory community.
Generally speaking, polymorphism means that a method or function is able to cope with different types of input. For example the add method (or + operator) in the Integer class might perform integer addition, while the add method in the Float class performs floating-point addition, and the add method in the Bignum class performs the correct operations for an arbitrary-size number. Polymorphism is the ability to call the add method on an object without knowing what kind of a number it is.
One particular kind of polymorphism, usually called parametric polymorphism in the functional community and generic programming in the OOP community, is the ability to perform certain operations on an object without caring about its precise type. For example, to reverse a list, you don't need to care about the type of the elements of the list, you just need to know that it's a list. So you can write generic (in this sense) list reversal code: it'll work identically on lists of integers, strings, widgets, arbitrary objects, whatever. But you can't write code that adds the elements of a list in a generic way, because the way the elements are interpreted as numbers depends on what type they are.
Another kind of polymorphism, usually called ad-hoc polymorphism or (at least for some forms of it) generic programming in the functional community, and often subtyping polymorphism (though this somewhat restricts the concept) in the OOP community, it the ability to have a single method or function that behaves differently depending on the precise type of its arguments (or, for methods, the type of the object whose method is being invoked). The add example above is ad-hoc polymorphism. In dynamically typed languages this ability goes without saying; statically-typed languages tend to (but don't have to) have restrictions such as requiring that the argument be a subclass of some particular class (Addable).
Polymorphism is not about having to specify types when you define a function. That's more related to static vs. dynamic typing, though it's not an intrinsic part of the issue. Dynamically typed languages have no need for type declarations, while statically typed languages usually need some type declarations (going from quite a lot in Java to almost none in ML).
Hope from this example, you will understand what Polymorphism is. In this picture, all objects have a method Speak() but each has a different implementation. Polymorphism allows you to do this, you can declare an action for a class and its subclasses but for each subclass, you can write exactly what you want later.
The answers you've gotten are good, and explain what polymorphism is. I think it can also help to understand some of the reasons it is useful.
In some languages that lack polymorphism you find yourself in situations where you want to perform what is conceptually the same operation on different types of objects, in cases where that operation has to be implemented differently for each type. For instance, in a python-like syntax:
def dosomething(thing):
if type(thing)==suchandsuch:
#do some stuff
elif type(thing)==somesuch:
#do some other stuff
elif type(thing)==nonesuch:
#yet more stuff
There are some problems with this. The biggest is that it causes very tight coupling and a lot of repetition. You are likely to have this same set of tests in a lot of places in your code. What happens if you add a new type that has to support this operation? You have to go find every place you have this sort of conditional and add a new branch. And of course you have to have access to all the source code involved to be able to make those changes. On top of that conditional logic like this is wordy, and hard to understand in real cases.
It's nicer to be able to just write:
thing.dosomething()
On top of being a lot shorter this leads to much looser coupling. This example/explanation is geared to traditional OO languages like Python. The details are a bit different in, say, functional languages. But a lot of the general utility of polymorphism remains the same.
Polymorphism literally means "many shapes", and that's pretty good at explaining its purpose. The idea behind polymorphism is that one can use the same calls on different types and have them behave appropriately.
It is important to distinguish this from the typing system - strongly typed languages require that objects be related through an inheritance chain to be polymorphic, but with weakly typed languages, this is not necessary.
In Java (et al.), this is why interfaces are useful - they define the set of functions that can be called on objects without specifying the exact object - the objects that implement that interface are polymorphic with respect to that interface.
In Python, since things are dynamically typed, the interface is less formal, but the idea is the same - calling foo() will succeed on two objects that know how to properly implement foo(), but we don't care about what type they really are.
No, that is not polymorphism. Polymorphism is the concept that there can be many different implementations of an executable unit and the difference happen all behind the scene without the caller awareness.
For example, Bird and Plane are FlyingObject. So FlyingObject has a method call fly() and both Bird and Plane implement fly() method. Bird and Plan flies differently so the implementations are different. To the clients point of view, it does not matter how Bird or Plane fly, they can just call fly() method to a FlyingObject object does not matter if that object is Plan or Bird.
What you are describing is dynamic and static type checking which the type compatibility is done at run-time and compile-time respectively.
Hope this out.
NawaMan
Short answer: The ability to treat programmatic type instances of different types as the same for certain purposes.
Long answer:
From Ancient Greek poly (many) + morph (form) + -ism.
Polymorphism is a general technique enabling different types to be treated uniformly in some way. Examples in the programming world include:
parametric polymorphism (seen as
generics in Java)
subtyping
polymorphism, implemented in Java
using dynamic message dispatch
between object instances.
ad-hoc
polymorphism, which relates to the
ability to define functions of the
same name that vary only by the types
they deal with (overloading in Java).
The word polymorphism is also used to describe concepts in other, unrelated, domains such as genetics.
What you are talking about is auto-typing--or maybe type detection. It is something a Dynamic language tends to do--it allows the user to not know or care about the types at build time--the types are figured out at runtime and not restricted to a specific type.
Polymorphism is where you have two classes that inherit from a main class but each implement a method differently.
For instance, if you have Vehicle as a root class and Car and Bicycle as instances, then vehicle.calculateDistance() would operate based on gas available if the vehicle is an instance of Car, and would operate based on the endurance of the cyclist if it was a Bicycle type.
It's generally used like this:
getTotalDistance(Vehicle[] vehicle) {
int dist=0
for each vehicle
dist+=vehicle.calculateDistance();
Note that you are passing in the base type, but the instances will never be Vehicle itself, but always a child class of Vehicle--but you DO NOT cast it to the child type. Polymorphis means that vehicle morphs into whatever child is required.
Yes, that is an example of "type-polymorphism". However, when talking about object-oriented programming "polymorphism" typically relates to "subtype polymorphism." The example you gave is often called "typing".
Java, C, C++ and others, use static typing. In that, you have to specify the types are compile time.
Python, and Ruby use dynamic in that the typing is inferred during interpretation.
Subtype polymorphism or just "polymorphism" is the ability for a base class reference that is a derived type, to properly invoke the derived type's method.
For example (in near pseudo code):
class Base
{
virtual void Draw() { //base draw}
}
class Derived: public Base
{
void Draw() { //derived draw}
}
Base* myBasePtr = new Derived();
myBasePtr->Draw(); //invokes Derived::Draw(), even though it's a base pointer.
This is polymorphism.
Polymorphism:
One method call works on several classes, even if the classes need different implementations;
Ability to provide multiple implementations of an action, and to select the correct implementation based on the surrounding context;
Provides overloading and overriding;
Could occurs both in Runtime and Compile-Time;
Run time Polymorphism :
Run time Polymorphism also known as method overriding
Method overriding means having two or more methods with the same name , same signature but with different implementation
Compile time Polymorphism :
Compile time Polymorphism also known as method overloading
Method overloading means having two or more methods with the same name but with different signatures
In computer science, polymorphism is a programming language feature that allows values of different data types to be handled using a uniform interface. The concept of parametric polymorphism applies to both data types and functions. A function that can evaluate to or be applied to values of different types is known as a polymorphic function. A data type that can appear to be of a generalized type (e.g., a list with elements of arbitrary type) is designated polymorphic data type like the generalized type from which such specializations are made.
Disadvantages of Polymorphism:
Polymorphism reduces readability of the program. One needs to visualize runtime behaviour of program to identify actual execution time class involved. It also becomes difficult to navigate through classes implementing this concept. Even sofasticated IDEs can not provide this navigation feature. This adds to the maintainance cost to some extent.
Polymorphism - The same object acting differently based on the scenario it is in. For example, if a 12-year old kid was in a room with a bunch of kids, the type of music they would listen to would be different than if a 12 year old kid was in a room full of adults. The 12 year old kid is the same, however the kid acting differently based on the scenario it is in (the different music).
The ability to define a function in multiple forms is called Polymorphism. In C#, Java, C++ there are two types of polymorphism: compile time polymorphism (overloading) and runtime polymorphism (overriding).
Overriding: Overriding occurs when a class method has the same name and signature as a method in parent class.
Overloading: Overloading is determined at the compile time. It occurs when several methods have same names with:
Different method signature and different number or type of
parameters.
Same method signature but different number of parameters.
Same method signature and same number of parameters but of different type