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Reading Scala docs written by the experts one can get the impression that tail recursion is better than a while loop, even when the latter is more concise and clearer. This is one example
object Helpers {
implicit class IntWithTimes(val pip:Int) {
// Recursive
def times(f: => Unit):Unit = {
#tailrec
def loop(counter:Int):Unit = {
if (counter >0) { f; loop(counter-1) }
}
loop(pip)
}
// Explicit loop
def :#(f: => Unit) = {
var lc = pip
while (lc > 0) { f; lc -= 1 }
}
}
}
(To be clear, the expert was not addressing looping at all, but in the example they chose to write a loop in this fashion as if by instinct, which is what the raised the question for me: should I develop a similar instinct..)
The only aspect of the while loop that could be better is the iteration variable should be local to the body of the loop, and the mutation of the variable should be in a fixed place, but Scala chooses not to provide that syntax.
Clarity is subjective, but the question is does the (tail) recursive style offer improved performance?
I'm pretty sure that, due to the limitations of the JVM, not every potentially tail-recursive function will be optimised away by the Scala compiler as so, so the short (and sometimes wrong) answer to your question on performance is no.
The long answer to your more general question (having an advantage) is a little more contrived. Note that, by using while, you are in fact:
creating a new variable that holds a counter.
mutating that variable.
Off-by-one errors and the perils of mutability will ensure that, on the long run, you'll introduce bugs with a while pattern. In fact, your times function could easily be implemented as:
def times(f: => Unit) = (1 to pip) foreach f
Which not only is simpler and smaller, but also avoids any creation of transient variables and mutability. In fact, if the type of the function you are calling would be something to which the results matter, then the while construction would start to be even more difficult to read. Please attempt to implement the following using nothing but whiles:
def replicate(l: List[Int])(times: Int) = l.flatMap(x => List.fill(times)(x))
Then proceed to define a tail-recursive function that does the same.
UPDATE:
I hear you saying: "hey! that's cheating! foreach is neither a while nor a tail-rec call". Oh really? Take a look into Scala's definition of foreach for Lists:
def foreach[B](f: A => B) {
var these = this
while (!these.isEmpty) {
f(these.head)
these = these.tail
}
}
If you want to learn more about recursion in Scala, take a look at this blog post. Once you are into functional programming, go crazy and read Rúnar's blog post. Even more info here and here.
In general, a directly tail recursive function (i.e., one that always calls itself directly and cannot be overridden) will always be optimized into a while loop by the compiler. You can use the #tailrec annotation to verify that the compiler is able to do this for a particular function.
As a general rule, any tail recursive function can be rewritten (usually automatically by the compiler) as a while loop and vice versa.
The purpose of writing functions in a (tail) recursive style is not to maximize performance or even conciseness, but to make the intent of the code as clear as possible, while simultaneously minimizing the chance of introducing bugs (by eliminating mutable variables, which generally make it harder to keep track of what the "inputs" and "outputs" of the function are). A properly written recursive function consists of a series of checks for terminating conditions (using either cascading if-else or a pattern match) with the recursive call(s) (plural only if not tail recursive) made if none of the terminating conditions are met.
The benefit of using recursion is most dramatic when there are several different possible terminating conditions. A series of if conditionals or patterns is generally much easier to comprehend than a single while condition with a whole bunch of (potentially complex and inter-related) boolean expressions &&'d together, especially if the return value needs to be different depending on which terminating condition is met.
Did these experts say that performance was the reason? I'm betting their reasons are more to do with expressive code and functional programming. Could you cite examples of their arguments?
One interesting reason why recursive solutions can be more efficient than more imperative alternatives is that they very often operate on lists and in a way that uses only head and tail operations. These operations are actually faster than random-access operations on more complex collections.
Anther reason that while-based solutions may be less efficient is that they can become very ugly as the complexity of the problem increases...
(I have to say, at this point, that your example is not a good one, since neither of your loops do anything useful. Your recursive loop is particularly atypical since it returns nothing, which implies that you are missing a major point about recursive functions. The functional bit. A recursive function is much more than another way of repeating the same operation n times.)
While loops do not return a value and require side effects to achieve anything. It is a control structure which only works at all for very simple tasks. This is because each iteration of the loop has to examine all of the state to decide what to next. The loops boolean expression may also have to be come very complex if there are multiple potential exit paths (or that complexity has to be distributed throughout the code in the loop, which can be ugly and obfuscatory).
Recursive functions offer the possibility of a much cleaner implementation. A good recursive solution breaks a complex problem down in to simpler parts, then delegates each part on to another function which can deal with it - the trick being that that other function is itself (or possibly a mutually recursive function, though that is rarely seen in Scala - unlike the various Lisp dialects, where it is common - because of the poor tail recursion support). The recursively called function receives in its parameters only the simpler subset of data and only the relevant state; it returns only the solution to the simpler problem. So, in contrast to the while loop,
Each iteration of the function only has to deal with a simple subset of the problem
Each iteration only cares about its inputs, not the overall state
Sucess in each subtask is clearly defined by the return value of the call that handled it.
State from different subtasks cannot become entangled (since it is hidden within each recursive function call).
Multiple exit points, if they exist, are much easier to represent clearly.
Given these advantages, recursion can make it easier to achieve an efficient solution. Especially if you count maintainability as an important factor in long-term efficiency.
I'm going to go find some good examples of code to add. Meanwhile, at this point I always recommend The Little Schemer. I would go on about why but this is the second Scala recursion question on this site in two days, so look at my previous answer instead.
In the coursera scala tutorial, most examples are using top-down iterations. Partially, as I can see, iterations are used to avoid for/while loops. I'm from C++ and feel a little confused about this.
Is iteration chosen over for/while loops? Is it practical in production? Any risk of stackoverflow? How about efficiency? How about bottom up Dynamic Programming (especially when they are not tail-recusions)?
Also, should I use less "if" conditions, instead use more "case" and subclasses?
Truly high-quality Scala will use very little iteration and only slightly more recursion. What would be done with looping in lower-level imperative languages is usually best done with higher-order combinators, map and flatmap most especially, but also filter, zip, fold, foreach, reduce, collect, partition, scan, groupBy, and a good few others. Iteration is best done only in performance critical sections, and recursion done only in a some deep edge cases where the higher-order combinators don't quite fit (which usually aren't tail recursive, fwiw). In three years of coding Scala in production systems, I used iteration once, recursion twice, and map about five times per day.
Hmm, several questions in one.
Necessity of Recursion
Recursion is not necessary, but it can sometimes provide a very elegant solution.
If the solution is tail recursive and the compiler supports tail call optimisation, then the solution can even be efficient.
As has been well said already, Scala has many combinator functions which can be used to perform the same tasks more expressively and efficiently.
One classic example is writing a function to return the nth Fibonacci number. Here's a naive recursive implementation:
def fib (n: Long): Long = n match {
case 0 | 1 => n
case _ => fib( n - 2) + fib( n - 1 )
}
Now, this is inefficient (definitely not tail recursive) but it is very obvious how its structure relates to the Fibonacci sequence. We can make it properly tail recursive, though:
def fib (n: Long): Long = {
def fibloop(current: Long, next: => Long, iteration: Long): Long = {
if (n == iteration)
current
else
fibloop(next, current + next, iteration + 1)
}
fibloop(0, 1, 0)
}
That could have been written more tersely, but it is an efficient recursive implementation. That said, it is not as pretty as the first and it's structure is less clearly related to the original problem.
Finally, stolen shamelessly from elsewhere on this site is Luigi Plinge's streams-based implementation:
val fibs: Stream[Int] = 0 #:: fibs.scanLeft(1)(_ + _)
Very terse, efficient, elegant and (if you understand streams and lazy evaluation) very expressive. It is also, in fact, recursive; #:: is a recursive function, but one that operates in a lazily-evaluated context. You certainly have to be able to think recursively to come up with this kind of solution.
Iteration compared to For/While loops
I'm assuming you mean the traditional C-Style for, here.
Recursive solutions can often be preferable to while loops because C/C++/Java-style while loops do not return a value and require side effects to achieve anything (this is also true for C-Style for and Java-style foreach). Frankly, I often wish Scala had never implemented while (or had implemented it as syntactic sugar for something like Scheme's named let), because it allows classically-trained Java developers to keep doing things the way they always did. There are situations where a loop with side effects, which is what while gives you, is a more expressive way of achieving something but I had rather Java-fixated devs were forced to reach a little harder for it (e.g. by abusing a for comprehension).
Simply, traditional while and for make clunky imperative coding much too easy. If you don't care about that, why are you using Scala?
Efficiency and risk of Stackoverflow
Tail optimisation eliminates the risk of stackoverflow. Rewriting recursive solutions to be properly tail recursive can make them very ugly (particularly in any language running on the JVM).
Recursive solutions can be more efficient than more imperative solutions, sometimes suprisingly so. One reason is that they often operate on lists, in a way that only involves head and tail access. Head and tail operations on lists are actually faster than random access operations on more structured collections.
Dynamic Programming
A good recursive algorithm typically reduces a complex problem to a small set of simpler problems, picks one to solve and delegates the rest to another function (usually a recursive call to itself). Now, to me this sounds like a great fit for dynamic programming. Certainly, if I am trying a recursive approach to a problem, I often start with a naive solution which I know can't solve every case, see where it fails, add that pattern to the solution and iterate towards success.
The Little Schemer has many examples of this iterative approach to recursive programming, particularly because it re-uses earlier solutions as sub-components for later, more complex ones. I would say it is the epitome of the Dynamic Programming approach. (It is also one of the best-written educational books about software ever produced). I can recommend it, not least because it teaches you Scheme at the same time. If you really don't want to learn Scheme (why? why would you not?), it has been adapted for a few other languages
If versus Match
if expressions, in Scala, return values (which is very useful and why Scala has no need for a ternary operator). There is no reason to avoid simple
if (something)
// do something
else
// do something else
expressions. The principle reason to match instead of a simple if...else is to use the power of case statements to extract information from complex objects. Here is one example.
On the other hand, if...else if...else if...else is a terrible pattern
There's no easy way to see if you covered all the possibilities properly, even with a final else in place.
Unintentionally nested if expressions are hard to spot
It is too easy to link unrelated conditions together (accidentally or through bone-headed design)
Wherever you find you have written else if, look for an alternative. match is a good place to start.
I'm assuming that, since you say "recursion" in your title, you also mean "recursion" in your question, and not "iteration" (which cannot be chosen "over for/while loops", because those are iterative :D).
You might be interested in reading Effective Scala, especially the section on control structures, which should mostly answer your question. In short:
Recursion isn't "better" than iteration. Often it is easier to write a recursive algorithm for a given problem, then it is to write an iterative algorithm (of course there are cases where the opposite applies). When "tail call optimization" can be applied to a problem, the compiler actually converts it to an iterative algorithm, thus making it impossible for a StackOverflow to happen, and without performance impact. You can read about tail call optimization in Effective Scala, too.
The main problem with your question is that it is very broad. There are many many resources available on functional programming, idiomatic scala, dynamic programming and so on, and no answer here on Stack Overflow would be able to cover all those topics. It'd be probably a good idea to just roam the interwebs for a while, and then come back with more concrete questions :)
One of the main benefits of recursion is that it lets you create solutions without mutation. for following example, you have to calculate the sum of all the elements of a List.
One of the many ways to solve this problem is as below. The imperative solution to this problem uses for loop as shown:
scala> var total = 0
scala> for(f <- List(1,2,3)) { total += f }
And recursion solution would look like following:
def total(xs: List[Int]): Int = xs match {
case Nil => 0
case x :: ys => x + total(ys)
}
The difference is that a recursive solution doesn’t use any mutable temporary variables by letting you break the problem into smaller pieces. Because Functional programming is all about writing side effect free programs it's always encourage to use recursion vs loops (that use mutating variables).
Head recursion is a traditional way of doing recursion, where you perform the recursive call first and then take the return value from the recursive function and calculate the result.
Generally when you call a function an entry is added to the call stack of a currently running thread. The downside is that the call stack has a defined size so quickly you may get StackOverflowError exception. This is why Java prefers to iterate rather than recurse. Because Scala runs on the JVM, Scala also suffers from this problem. But starting with Scala 2.8.1, Scala gets away this limitation by doing tail call optimization. you can do tail recursion in Scala.
To recap recursion is preferred way in functional programming to avoid using mutation and secondly tail recursion is supported in Scala so you don't get into StackOverFlow exceptions which you get in Java.
Hope this helps.
As for stack overflow, a lot of the time you can get away with it because of tail call elimination.
The reason scala and other function paradigms avoid for/while loops they are highly dependent on state and time. That makes it much harder to reason about complex "loops" in a formal and precise manor.
I'm trying to learn scala and I'm unable to grasp this concept. Why does making an object immutable help prevent side-effects in functions. Can anyone explain like I'm five?
Interesting question, a bit difficult to answer.
Functional programming is very much about using mathematics to reason about programs. To do so, one needs a formalism that describe the programs and how one can make proofs about properties they might have.
There are many models of computation that provide such formalisms, such as lambda calculus and turing machines. And there's a certain degree of equivalency between them (see this question, for a discussion).
In a very real sense, programs with mutability and some other side effects have a direct mapping to functional program. Consider this example:
a = 0
b = 1
a = a + b
Here are two ways of mapping it to functional program. First one, a and b are part of a "state", and each line is a function from a state into a new state:
state1 = (a = 0, b = ?)
state2 = (a = state1.a, b = 1)
state3 = (a = state2.a + state2.b, b = state2.b)
Here's another, where each variable is associated with a time:
(a, t0) = 0
(b, t1) = 1
(a, t2) = (a, t0) + (b, t1)
So, given the above, why not use mutability?
Well, here's the interesting thing about math: the less powerful the formalism is, the easier it is to make proofs with it. Or, to put it in other words, it's too hard to reason about programs that have mutability.
As a consequence, there's very little advance regarding concepts in programming with mutability. The famous Design Patterns were not arrived at through study, nor do they have any mathematical backing. Instead, they are the result of years and years of trial and error, and some of them have since proved to be misguided. Who knows about the other dozens "design patterns" seen everywhere?
Meanwhile, Haskell programmers came up with Functors, Monads, Co-monads, Zippers, Applicatives, Lenses... dozens of concepts with mathematical backing and, most importantly, actual patterns of how code is composed to make up programs. Things you can use to reason about your program, increase reusability and improve correctness. Take a look at the Typeclassopedia for examples.
It's no wonder people not familiar with functional programming get a bit scared with this stuff... by comparison, the rest of the programming world is still working with a few decades-old concepts. The very idea of new concepts is alien.
Unfortunately, all these patterns, all these concepts, only apply with the code they are working with does not contain mutability (or other side effects). If it does, then their properties cease to be valid, and you can't rely on them. You are back to guessing, testing and debugging.
In short, if a function mutates an object then it has side effects. Mutation is a side effect. This is just true by definition.
In truth, in a purely functional language it should not matter if an object is technically mutable or immutable, because the language will never "try" to mutate an object anyway. A pure functional language doesn't give you any way to perform side effects.
Scala is not a pure functional language, though, and it runs in the Java environment in which side effects are very popular. In this environment, using objects that are incapable of mutation encourages you to use a pure functional style because it makes a side-effect oriented style impossible. You are using data types to enforce purity because the language does not do it for you.
Now I will say a bunch of other stuff in the hope that it helps this make sense to you.
Fundamental to the concept of a variable in functional languages is referential transparency.
Referential transparency means that there is no difference between a value, and a reference to that value. In a language where this is true, it makes it much simpler to think about a program works, since you never have to stop and ask, is this a value, or a reference to a value? Anyone who's ever programmed in C recognizes that a great part of the challenge of learning that paradigm is knowing which is which at all times.
In order to have referential transparency, the value that a reference refers to can never change.
(Warning, I'm about to make an analogy.)
Think of it this way: in your cell phone, you have saved some phone numbers of other people's cell phones. You assume that whenever you call that phone number, you will reach the person you intend to talk to. If someone else wants to talk to your friend, you give them the phone number and they reach that same person.
If someone changes their cell phone number, this system breaks down. Suddenly, you need to get their new phone number if you want to reach them. Maybe you call the same number six months later and reach a different person. Calling the same number and reaching a different person is what happens when functions perform side effects: you have what seems to be the same thing, but you try to use it, it turns out it's different now. Even if you expected this, what about all the people you gave that number to, are you going to call them all up and tell them that the old number doesn't reach the same person anymore?
You counted on the phone number corresponding to that person, but it didn't really. The phone number system lacks referential transparency: the number isn't really ALWAYS the same as the person.
Functional languages avoid this problem. You can give out your phone number and people will always be able to reach you, for the rest of your life, and will never reach anybody else at that number.
However, in the Java platform, things can change. What you thought was one thing, might turn into another thing a minute later. If this is the case, how can you stop it?
Scala uses the power of types to prevent this, by making classes that have referential transparency. So, even though the language as a whole isn't referentially transparent, your code will be referentially transparent as long as you use immutable types.
Practically speaking, the advantages of coding with immutable types are:
Your code is simpler to read when the reader doesn't have to look out for surprising side effects.
If you use multiple threads, you don't have to worry about locking because shared objects can never change. When you have side effects, you have to really think through the code and figure out all the places where two threads might try to change the same object at the same time, and protect against the problems that this might cause.
Theoretically, at least, the compiler can optimize some code better if it uses only immutable types. I don't know if Java can do this effectively, though, since it allows side effects. This is a toss-up at best, anyway, because there are some problems that can be solved much more efficiently by using side effects.
I'm running with this 5 year old explanation:
class Account(var myMoney:List[Int] = List(10, 10, 1, 1, 1, 5)) {
def getBalance = println(myMoney.sum + " dollars available")
def myMoneyWithInterest = {
myMoney = myMoney.map(_ * 2)
println(myMoney.sum + " dollars will accru in 1 year")
}
}
Assume we are at an ATM and it is using this code to give us account information.
You do the following:
scala> val myAccount = new Account()
myAccount: Account = Account#7f4a6c40
scala> myAccount.getBalance
28 dollars available
scala> myAccount.myMoneyWithInterest
56 dollars will accru in 1 year
scala> myAccount.getBalance
56 dollars available
We mutated the account balance when we wanted to check our current balance plus a years worth of interest. Now we have an incorrect account balance. Bad news for the bank!
If we were using val instead of var to keep track of myMoney in the class definition, we would not have been able to mutate the dollars and raise our balance.
When defining the class (in the REPL) with val:
error: reassignment to val
myMoney = myMoney.map(_ * 2
Scala is telling us that we wanted an immutable value but are trying to change it!
Thanks to Scala, we can switch to val, re-write our myMoneyWithInterest method and rest assured that our Account class will never alter the balance.
One important property of functional programming is: If I call the same function twice with the same arguments I'll get the same result. This makes reasoning about code much easier in many cases.
Now imagine a function returning the attribute content of some object. If that content can change the function might return different results on different calls with the same argument. => no more functional programming.
First a few definitions:
A side effect is a change in state -- also called a mutation.
An immutable object is an object which does not support mutation, (side effects).
A function which is passed mutable objects (either as parameters or in the global environment) may or may not produce side effects. This is up to the implementation.
However, it is impossible for a function which is passed only immutable objects (either as parameters or in the global environment) to produce side effects. Therefore, exclusive use of immutable objects will preclude the possibility of side effects.
Nate's answer is great, and here is some example.
In functional programming, there is an important feature that when you call a function with same argument, you always get same return value.
This is always true for immutable objects, because you can't modify them after create it:
class MyValue(val value: Int)
def plus(x: MyValue) = x.value + 10
val x = new MyValue(10)
val y = plus(x) // y is 20
val z = plus(x) // z is still 20, plus(x) will always yield 20
But if you have mutable objects, you can't guarantee that plus(x) will always return same value for same instance of MyValue.
class MyValue(var value: Int)
def plus(x: MyValue) = x.value + 10
val x = new MyValue(10)
val y = plus(x) // y is 20
x.value = 30
val z = plus(x) // z is 40, you can't for sure what value will plus(x) return because MyValue.value may be changed at any point.
Why do immutable objects enable functional programming?
They don't.
Take one definition of "function," or "prodecure," "routine" or "method," which I believe applies to many programming languages: "A section of code, typically named, accepting arguments and/or returning a value."
Take one definition of "functional programming:" "Programming using functions." The ability to program with functions is indepedent of whether state is modified.
For instance, Scheme is considered a functional programming language. It features tail calls, higher-order functions and aggregate operations using functions. It also has mutable objects. While mutability destroys some nice mathematical qualities, it does not necessarily prevent "functional programming."
I've read all the answers and they don't satisfy me, because they mostly talk about "immutability", and not about its relation to FP.
The main question is:
Why do immutable objects enable functional programming?
So I've searched a bit more and I have another answer, I believe the easy answer to this question is: "Because Functional Programming is basically defined on the basis of functions that are easy to reason about". Here's the definition of Functional Programming:
The process of building software by composing pure functions.
If a function is not pure -- which means receiving the same input, it's not guaranteed to always produce the same output (e.g., if the function relies on a global object, or date and time, or a random number to compute the output) -- then that function is unpredictable, that's it! Now exactly the same story goes about "immutability" as well, if objects are not immutable, a function with the same object as its input may have different results (aka side effects) each time used, and this will make it hard to reason about the program.
I first tried to put this in a comment, but it got longer than the limit, I'm by no means a pro so please take this answer with a grain of salt.
This may seem really obvious to the FP cognoscenti here, but what is point free style in Scala good for? What would really sell me on the topic is an illustration that shows how point free style is significantly better in some dimension (e.g. performance, elegance, extensibility, maintainability) than code solving the same problem in non-point free style.
Quite simply, it's about being able to avoid specifying a name where none is needed, consider a trivial example:
List("a","b","c") foreach println
In this case, foreach is looking to accept String => Unit, a function that accepts a String and returns Unit (essentially, that there's no usable return and it works purely through side effect)
There's no need to bind a name here to each String instance that's passed to println. Arguably, it just makes the code more verbose to do so:
List("a","b","c") foreach {println(_)}
Or even
List("a","b","c") foreach {s => println(s)}
Personally, when I see code that isn't written in point-free style, I take it as an indicator that the bound name may be used twice, or that it has some significance in documenting the code. Likewise, I see point-free style as a sign that I can reason about the code more simply.
One appeal of point-free style in general is that without a bunch of "points" (values as opposed to functions) floating around, which must be repeated in several places to thread them through the computation, there are fewer opportunities to make a mistake, e.g. when typing a variable's name.
However, the advantages of point-free are quickly counterbalanced in Scala by its meagre ability to infer types, a fact which is exacerbated by point-free code because "points" serve as clues to the type inferencer. In Haskell, with its almost-complete type inferencing, this is usually not an issue.
I see no other advantage than "elegance": It's a little bit shorter, and may be more readable. It allows to reason about functions as entities, without going mentally a "level deeper" to function application, but of course you need getting used to it first.
I don't know any example where performance improves by using it (maybe it gets worse in cases where you end up with a function when a method would be sufficient).
Scala's point-free syntax is part of the magic Scala operators-which-are-really-functions. Even the most basic operators are functions:
For example:
val x = 1
val y = x + 1
...is the same as...
val x = 1
val y = x.+(1)
...but of course, the point-free style reads more naturally (the plus appears to be an operator).
Sometimes I'm writing ugly if-else statements in C# 3.5; I'm aware of some different approaches to simplifying that with table-driven development, class hierarchy, anonimous methods and some more.
The problem is that alternatives are still less wide-spread than writing traditional ugly if-else statements because there is no convention for that.
What depth of nested if-else is normal for C# 3.5? What methods do you expect to see instead of nested if-else the first? the second?
if i have ten input parameters with 3 states in each, i should map functions to combination of each state of each parameter (really less, because not all the states are valid, but sometimes still a lot). I can express these states as a hashtable key and a handler (lambda) which will be called if key matches.
It is still mix of table-driven, data-driven dev. ideas and pattern matching.
what i'm looking for is extending for C# such approaches as this for scripting (C# 3.5 is rather like scripting)
http://blogs.msdn.com/ericlippert/archive/2004/02/24/79292.aspx
Good question. "Conditional Complexity" is a code smell. Polymorphism is your friend.
Conditional logic is innocent in its infancy, when it’s simple to understand and contained within a
few lines of code. Unfortunately, it rarely ages well. You implement several new features and
suddenly your conditional logic becomes complicated and expansive. [Joshua Kerevsky: Refactoring to Patterns]
One of the simplest things you can do to avoid nested if blocks is to learn to use Guard Clauses.
double getPayAmount() {
if (_isDead) return deadAmount();
if (_isSeparated) return separatedAmount();
if (_isRetired) return retiredAmount();
return normalPayAmount();
};
The other thing I have found simplifies things pretty well, and which makes your code self-documenting, is Consolidating conditionals.
double disabilityAmount() {
if (isNotEligableForDisability()) return 0;
// compute the disability amount
Other valuable refactoring techniques associated with conditional expressions include Decompose Conditional, Replace Conditional with Visitor, Specification Pattern, and Reverse Conditional.
There are very old "formalisms" for trying to encapsulate extremely complex expressions that evaluate many possibly independent variables, for example, "decision tables" :
http://en.wikipedia.org/wiki/Decision_table
But, I'll join in the choir here to second the ideas mentioned of judicious use of the ternary operator if possible, identifying the most unlikely conditions which if met allow you to terminate the rest of the evaluation by excluding them first, and add ... the reverse of that ... trying to factor out the most probable conditions and states that can allow you to proceed without testing of the "fringe" cases.
The suggestion by Miriam (above) is fascinating, even elegant, as "conceptual art;" and I am actually going to try it out, trying to "bracket" my suspicion that it will lead to code that is harder to maintain.
My pragmatic side says there is no "one size fits all" answer here in the absence of a pretty specific code example, and complete description of the conditions and their interactions.
I'm a fan of "flag setting" : meaning anytime my application goes into some less common "mode" or "state" I set a boolean flag (which might even be static for the class) : for me that simplifies writing complex if/then else evaluations later on.
best, Bill
Simple. Take the body of the if and make a method out of it.
This works because most if statements are of the form:
if (condition):
action()
In other cases, more specifically :
if (condition1):
if (condition2):
action()
simplify to:
if (condition1 && condition2):
action()
I'm a big fan of the ternary operator which get's overlooked by a lot of people. It's great for assigning values to variables based on conditions. like this
foobarString = (foo == bar) ? "foo equals bar" : "foo does not equal bar";
Try this article for more info.
It wont solve all your problems, but it is very economical.
I know that this is not the answer you are looking for, but without context your questions is very hard to answer. The problem is that the way to refactor such a thing really depends on your code, what it is doing, and what you are trying to accomplish. If you had said that you were checking the type of an object in these conditionals we could throw out an answer like 'use polymorphism', but sometimes you actually do just need some if statements, and sometimes those statements can be refactored into something more simple. Without a code sample it is hard to say which category you are in.
I was told years ago by an instructor that 3 is a magic number. And as he applied it it-else statements he suggested that if I needed more that 3 if's then I should probably use a case statement instead.
switch (testValue)
{
case = 1:
// do something
break;
case = 2:
// do something else
break;
case = 3:
// do something more
break;
case = 4
// do what?
break;
default:
throw new Exception("I didn't do anything");
}
If you're nesting if statements more than 3 deep then you should probably take that as a sign that there is a better way. Probably like Avirdlg suggested, separating the nested if statements into 1 or more methods. If you feel you are absolutely stuck with multiple if-else statements then I would wrap all the if-else statements into a single method so it didn't ugly up other code.
If the entire purpose is to assign a different value to some variable based upon the state of various conditionals, I use a ternery operator.
If the If Else clauses are performing separate chunks of functionality. and the conditions are complex, simplify by creating temporary boolean variables to hold the true/false value of the complex boolean expressions. These variables should be suitably named to represent the business sense of what the complex expression is calculating. Then use the boolean variables in the If else synatx instead of the complex boolean expressions.
One thing I find myself doing at times is inverting the condition followed by return; several such tests in a row can help reduce nesting of if and else.
Not a C# answer, but you probably would like pattern matching. With pattern matching, you can take several inputs, and do simultaneous matches on all of them. For example (F#):
let x=
match cond1, cond2, name with
| _, _, "Bob" -> 9000 // Bob gets 9000, regardless of cond1 or 2
| false, false, _ -> 0
| true, false, _ -> 1
| false, true, _ -> 2
| true, true, "" -> 0 // Both conds but no name gets 0
| true, true, _ -> 3 // Cond1&2 give 3
You can express any combination to create a match (this just scratches the surface). However, C# doesn't support this, and I doubt it will any time soon. Meanwhile, there are some attempts to try this in C#, such as here: http://codebetter.com/blogs/matthew.podwysocki/archive/2008/09/16/functional-c-pattern-matching.aspx. Google can turn up many more; perhaps one will suit you.
try to use patterns like strategy or command
In simple cases you should be able to get around with basic functional decomposition. For more complex scenarios I used Specification Pattern with great success.