Go back. Launching Xcode Launching Visual Studio Fetching latest commit…. If you have a C compiler and make, like gcc and gmake, you can build the program with: make If compilation is successful, you can test lispkit - this will attempt some of the examples from table For example: echo You signed in with another tab or window.
Reload to refresh your session. You signed out in another tab or window. Oct 14, Reimplemented parser, compiler bootstraps. Jun 10, Added lispkitc shell script to shortcut compiling. Doing some research, I found functional programming concepts like immutability and pure function.
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Those concepts are big advantages to build side-effect-free functions, so it is easier to maintain systems — with some other benefits. In this post, I will tell you more about functional programming, and some important concepts, with a lot of code examples. This article uses Clojure as a programming language example to explain Functional Programming.
Imagine we want to implement a function that calculates the area of a circle. Why is this an impure function? Simply because it uses a global object that was not passed as a parameter to the function. Now imagine some mathematicians argue that the PI value is actually 42 and change the value of the global object. Let's fix it! So now we are just accessing parameters passed to the function. No e xternal object. Examples of observable side effects include modifying a global object or a parameter passed by reference. Now we want to implement a function to receive an integer value and return the value increased by 1.
We have the counter value. Our impure function receives that value and re-assigns the counter with the value increased by 1. We are modifying the global object. But how would we make it pure?
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Just return the value increased by 1. Simple as that. See that our pure function increase-counter returns 2, but the counter value is still the same. The function returns the incremented value without altering the value of the variable. If we follow these two simple rules, it gets easier to understand our programs. Now every function is isolated and unable to impact other parts of our system. Pure functions are stable, consistent, and predictable. Given the same parameters, pure functions will always return the same result.
So we can unit test pure functions with different contexts:.
A simple example would be a function to receive a collection of numbers and expect it to increment each element of this collection. We receive the numbers collection, use map with the inc function to increment each number, and return a new list of incremented numbers. For the input [1 2 3 4 5] , the expected output would be [2 3 4 5 6].
This next for statement has some mutable variables. For each iteration, we are changing the i and the sumOfValue state.
But how do we handle mutability in iteration? Back to Clojure!
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So here we have the sum function that receives a vector of numerical values. The recur jumps back into the loop until we get the vector empty our recursion base case. For each "iteration" we will add the value to the total accumulator.
Observation : Yes! We can use reduce to implement this function. We will see this in the Higher Order Functions topic. It is also very common to build up the final state of an object. Imagine we have a string, and we want to transform this string into a url slug. And this class will have a slugify! Here we have imperative programming saying exactly what we want to do in each slugify process — first lower case, then remove useless white spaces and, finally, replace remaining white spaces with hyphens.
We can handle this mutation by doing function composition, or function chaining. It is also very common to build up the final state of an object. Imagine we have a string, and we want to transform this string into a url slug. And this class will have a slugify! Here we have imperative programming saying exactly what we want to do in each slugify process — first lower case, then remove useless white spaces and, finally, replace remaining white spaces with hyphens. We can handle this mutation by doing function composition, or function chaining. In other words, the result of a function will be used as an input for the next function, without modifying the original input string.
Speaking of combining functions , we can use the comp function to compose all three functions. Let's take a look:. So now we can replace the square 2 with 4. That's it! Our function is referentially transparent. Basically, if a function consistently yields the same result for the same input, it is referentially transparent. With this concept, a cool thing we can do is to memoize the function.
Imagine we have this function:. This function will always result in So we can do this:. And this expression will always result in We can replace the entire expression with a numerical constant and memoize it. The idea of functions as first-class entities is that functions are also treated as values and used as data. The idea is to treat functions as values and pass functions like data.
This way we can combine different functions to create new functions with new behavior. These functions have similar logic, but the difference is the operators functions. If we can treat functions as values and pass these as arguments, we can build a function that receives the operator function and use it inside our function. Now we have an f argument, and use it to process a and b. The double-operator function we implemented above is a higher-order function because it takes an operator function as an argument and uses it.kick-cocoa.info/components/guvofygow/fil-note-4.php
Functional Programming: Concepts, Advantages & Applications
Let's take a look at these. Given a collection, we want to filter by an attribute. The filter function expects a true or false value to determine if the element should or should not be included in the result collection. Basically, if the callback expression is true , the filter function will include the element in the result collection. Otherwise, it will not. We can use the filter higher order function to receive the even? The problem idea is to filter a given array of integers and output only those values that are less than a specified value X.
We say exactly what our function needs to do — iterate over the collection, compare the collection current item with x , and push this element to the resultArray if it pass the condition. But we want a more declarative way to solve this problem, and using the filter higher order function as well. We can also do this with maps. Imagine we have a map of people with their name and age. And we want to filter only people over a specified value of age, in this example people who are more than 21 years old.
We just want a list of strings, something like TK is 26 years old. So the final string might be :name is :age years old where :name and :age are attributes from each element in the people collection. Another interesting Hacker Rank problem was the update list problem. We just want to update the values of a given collection with their absolute values. For example, the input [1 2 3 -4 5] needs the output to be [1 2 3 4 5]. The absolute value of -4 is 4.
We use the Math. First, we learned about immutability. We know how immutability is important to make our functions more consistent and predictable. The idea is to build a new collection with all absolute values. If it is negative, we want to transform it in a positive value the absolute value. Now that we know how to do absolute for one value, we can use this function to pass as an argument to the map function.
Do you remember that a higher order function can receive a function as an argument and use it? Yes, map can do it! The idea of reduce is to receive a function and a collection, and return a value created by combining the items.