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Learning Functional Data Structures and Algorithms

Learning Functional Data Structures and Algorithms

By : S. Khot, Mishra
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Learning Functional Data Structures and Algorithms

Learning Functional Data Structures and Algorithms

5 (2)
By: S. Khot, Mishra

Overview of this book

Functional data structures have the power to improve the codebase of an application and improve efficiency. With the advent of functional programming and with powerful functional languages such as Scala, Clojure and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit. Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread safe by definition and hence very appealing for writing robust concurrent programs. How do we express traditional algorithms in functional setting? Won’t we end up copying too much? Do we trade performance for versioned data structures? This book attempts to answer these questions by looking at functional implementations of traditional algorithms. It begins with a refresher and consolidation of what functional programming is all about. Next, you’ll get to know about Lists, the work horse data type for most functional languages. We show what structural sharing means and how it helps to make immutable data structures efficient and practical. Scala is the primary implementation languages for most of the examples. At times, we also present Clojure snippets to illustrate the underlying fundamental theme. While writing code, we use ADTs (abstract data types). Stacks, Queues, Trees and Graphs are all familiar ADTs. You will see how these ADTs are implemented in a functional setting. We look at implementation techniques like amortization and lazy evaluation to ensure efficiency. By the end of the book, you will be able to write efficient functional data structures and algorithms for your applications.
Table of Contents (14 chapters)
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Composing functions

We programmers, love reusing code. We use libraries and frameworks, so we use something that already exists out there. No one wants to reinvent the same wheel again. For example, here is how we could weed out zeroes from a Clojure vector:

user=> (filter (complement zero?) [0 1 2 0 3 4]) 
(1 2 3 4) 

The following diagram shows the functional composition:

Composing functions

We composed behavior by composing predicate functions; we did this using complement to negate the zero? predicate function. The zero? predicate returns true if its input is 0; if not, it returns false.

Given a list of numbers, the following Scala snippet checks whether the sequence is strictly increasing:

scala> val x = List(1, 2, 3, 4, 5, 6, 7) 
x: List[Int] = List(1, 2, 3, 4, 5, 6, 7) 
 
scala> val y = x zip x.drop(1) 
y: List[(Int, Int)] = List((1,2), (2,3), (3,4), (4,5), (5,6), (6,7)) 
 
scala> y forall (x => x._1 < x._2) 
res2: Boolean = true 

Just imagine how we would do this in an imperative language.

Using zip, we get each number and its successor as a pair. We pass in a function to know whether the first element of the pair is less than the second.

Here goes the Clojure implementation. First we define a function that takes a list of two elements, de-structures it into its elements, and checks whether these two elements are strictly increasing:

user=> (defn check? [list] 
  #_=>   (let [[x y] list] 
  #_=>     (> y x))) 

Here is a quick test:

user=> (check? '(21 2)) 
false 
user=> (check? '(1 2)) 
true 

Tip

Note that the check? function is a pure function. It works just on its input and nothing else.

Next comes the pair generation; here, each element is paired with its successor:

user=> (defn gen-pairs [list] 
  #_=>   (let [x list 
  #_=>         y (rest list)] 
  #_=>    (partition 2 (interleave x y)))) 

Testing it gives the following:

user=> (gen-pairs '(1 2 3 4)) 
((1 2) (2 3) (3 4)) 

Now comes the magic! We compose these two functions to check whether the input is strictly increasing:

user=> (defn strictly-increasing? [list] 
  #_=>   (every? check? (gen-pairs list))) 
#'user/strictly-increasing? 

Testing it gives the following:

user=> (strictly-increasing? '(1 2 3 4)) 
true 
user=> (strictly-increasing? '(1 2 3 4 2)) 
false 

See how succinct it becomes when we start composing functions:

Composing functions

Note that the functions check? and every? are pure functions. The check? function predicate works on the input only. The gen_pairs is also a pure function. It is, in turn, composed of the interleave and partition functions.

The every? function is also a higher order function. We never wrote any loops (not even any recursion). We composed behavior out of existing pieces. The fun thing is the pieces don't need to know about the composition. We just combine independent processing pieces together.

For a great treatment of the advantages FP brings to the table, we wholeheartedly recommend Functional Thinking-- Paradigm Over Syntax (http://shop.oreilly.com/product/0636920029687.do ).

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