The Functional Approach to Programming

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Functional Programming Paradigm

Instead, it returns a copy of the list that has the modifications in place, allowing us to keep the original. The practical advantages of using functional programming include the following:. Note that concepts such as list comprehension in Python are already functionals in their approach, as they are designed to avoid side effects. We'll see in the following that some of the functional functions Python provide can actually be expressed as list comprehension!

You might repeatedly encounter the same set of problems when manipulating data using functional programming.


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To help you deal with this situation efficiently, Python includes a number of functions for functional programming. Here, we'll see with a quick overview some of these built-in functions that allows you to build fully functional programs.

A beginner friendly intro to functional programming

The map function takes the form map function, iterable and applies function to each item in iterable to return an iterable map object:. You could also write an equivalent of map using list comprehension, which would look like this:. The filter function takes the form filter function or None, iterable and filters the items in iterable based on the result returned by function. This will return iterable filter object:. You could also write an equivalent of filter using list comprehension, like so:.

The enumerate function takes the form enumerate iterable[, start] and returns an iterable object that provides a sequence of tuples, each consisting of an integer index starting with start , if provided and the corresponding item in iterable. This function is useful when you need to write code that refers to array indexes. For example, instead of writing this:. The key argument allows you to provide a function that returns the value to sort on:.

JuliaCon 2016 - A functional approach to High Performance Computing - Erik Schnetter

The any iterable and all iterable functions both return a Boolean depending on the values returned by iterable. These simple functions are equivalent to the following full Python code:. These functions are useful for checking whether any or all of the values in an iterable satisfy a given condition. For example, the following checks a list for two conditions:. The key difference here, as you can see, is that any returns True when at least one element meets the condition, while all returns True only if every element meets the condition.

The all function will also return True for an empty iterable, since none of the elements is False. The zip function takes the form zip iter1 [,iter2 [ This is useful when you need to combine a list of keys and a list of values into a dict. Like the other functions described here, zip returns an iterable.

Here we have a list of keys that we map to a list of values to create a dictionary:. You treat functions like other objects that you can assign to variables. Because of this, functions can also accept other functions as parameters or return other functions.

Functional Approach to Programming - AbeBooks - Guy Cousineau:

Functions that accept or return other functions are called higher-order functions. It accepts another function as a parameter. This other function accepts a single value from the array as input, checks whether that value belongs and returns a Bool. The output array contains only the array elements whose parameter function returns true.

Instead of thinking about this imperatively, think of it declaratively , i. Start by creating a function that has a Ride object as an input parameter to the function:. Since Swift functions are also known as closures , you can produce the same result by passing a trailing closure to filter and using closure syntax:. You are being declarative and telling the program what you want it to do.

This can look rather cryptic the first few times you work with it. It outputs an array of the same length after applying that function to each element of the collection. The return type of the mapped function does not have to be the same type as the collection elements. You can also sort the ride names as shown below, when you use the sorted by: method on the Collection type to perform the sorting:. The Collection method sorted by: takes a function that compares two elements and returns a Bool as a parameter.

Swift provides the left- and right-hand sides by default. Your declarative code is easier to read and you can figure out how it works without too much trouble. The input function applies to each element of the calling collection, one by one, until it reaches the end of the collection and produces a final accumulated value. Here you start with an empty string. This code can juice any array so be careful what you put in it :]. To be more practical, add the following method that lets you know the total wait time of all the rides in the park. This function works by passing the starting value of 0.

The code uses Swift shorthand again to omit the return keyword.


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As you can see, the resulting total carries over as the initial value for the following iteration. This continues until reduce iterates through every Ride in parkRides. This allows you to get the total with one line of code! You have learned about some of the common FP methods. Partial functions allow you to encapsulate one function within another.

To see how this works, add the following method to the playground:. The output function takes an array of Ride objects and returns an array of Ride objects filtered by the provided category. A primary concept in FP which lets you reason about program structure, as well as test program results, is the idea of a pure function. Add the following test to your playground:. Remember that functions are first-class citizens and you can pass them around like any other data. This will come in handy for the next section. Pure functions are related to the concept of referential transparency.

An element of a program is referentially transparent if you can replace it with its definition and always produce the same result. It makes for predictable code and allows the compiler to perform optimizations. Pure functions satisfy this condition. Referentially transparent code is not only easy to test, but it also lets you move code around without having to verify implementations. The final concept to discuss is recursion. Recursion occurs whenever a function calls itself as part of its function body. In functional languages, recursion replaces many of the looping constructs that you use in imperative languages.

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To avoid an infinite stack of function calls, recursive functions need a base case to end them. First make Ride conform to Comparable using the following extension:. In this extension, you use operator overloading to create functions that allow you to compare two rides. One ride is less than another ride if the wait time is less, and the rides are equal if the rides have the same name.

The Quick Sort algorithm first picks a pivot element.

You then divide the collection into two parts. One part holds all the elements that are less than or equal to the pivot, the other holds the remaining elements greater than the pivot. Recursion is then used to sort the two parts. Here, you check that your solution matches the expected value from the trusted Swift standard library function. Keep in mind that recursive functions have extra memory usage and runtime overhead.

Functional Programming: Concepts, Advantages & Applications

A family with young kids wants to go on as many rides as possible between frequent bathroom breaks. They need to find which kid-friendly rides have the shortest lines. Help them out by finding all family rides with wait times less than 20 minutes and sort them by the shortest to longest wait time. Think about how you would solve this problem with an imperative algorithm. Have a try at implementing your own solution to the problem. Add this to your playground and execute it.

You should see that Mountain Railroad, Crazy Funhouse and Grand Carousel are the best ride choices and that the list is in order of increasing wait time. You have to pause to look at the algorithm in detail to grasp it. You can make your code a lot more self-explanatory with an FP solution. Add the following code to your playground:. You want to filter your parkRides to. That cleanly solves the problem stated above. This is different from imperative code, which reads like the steps the computer has to take to solve the problem statement.

Swift is not purely a functional language, but it does combine multiple programming paradigms to give you flexibility for app development. Reactive programming is an example of an FP-like approach for UI development. By taking a functional, declarative approach, your code becomes more concise and clear.

When you want to maximize the full potential of your multi-core CPU, minimizing side effects and issues from concurrency is important. FP is a great tool to have in your skill set for those kind of problems. You can download the complete playground with all the code in this tutorial from the Download Materials button at the top or bottom of the tutorial. Once you have some experience with these basic FP concepts, take the plunge into the heart of FP by exploring:. You might be able to do things you never dreamed possible with your new-found knowledge.