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UNIT 4 APP - ...................

Advanced Programming Practice (SRM Institute of Science and Technology)

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SRM Institute of Science and Technology

School of Computing

Advanced Programming Practice-18CSC207J

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Paradigm types
Unit IV
– Functional Programming Paradigm [Text Book :1]
– Logic Programming Paradigm [Text Book : 1& 3]
– Dependent Type Programming Paradigm
– Network Programming Paradigm [ Text Book :4]

Text Book:

1. Elad Shalom, A Review of Programming Paradigms throughout the History: With

a suggestion Toward a Future Approach, Kindle Edition, 2018
2. John Goerzen, Brandon Rhodes, Foundations of Python Network Programming:
The comprehensive guide to building network applications with Python, 2nd ed.,
Kindle Edition, 2010
3. Amit Saha, Doing Math with Python: Use Programming to Explore Algebra,
Statistics,Calculus and More, Kindle Edition, 2015
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Functional Programming Paradigm

Unit-III (15 Session)
Session 11-15 cover the following topics:-
– Definition - S11-SLO1
– Sequence of Commands – S11-SLO2
– map(), reduce(), filter(), lambda – S12-SLO1
– partial, functools – S12-SLO2
– Other languages:F#, Clojure, Haskell – S13-SLO1
– Demo: Functional Programming in Python - S13-SLO2

Lab 9: Functional Programming ( Case Study) (S14-15)

Assignment : Comparative study of Functional programming in

F#, Clojure, Haskell

– TextBook: Shalom, Elad. A Review of Programming Paradigms

Throughout the History: With a Suggestion Toward a Future
Approach, Kindle Edition
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Functional Programming Paradigm ( TF1)

Definition
• Mainly treat computation to evaluate mathematical Functions
• Avoids changing-state and mutable data
• Called as declarative programming paradigm
• Output depends on argument passing
• Calling same function several times with same values produces
same result
• Uses expressions or declarations rather than statements as in
imperative languages

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Characteristics of Functional Programming

• Functional programming method focuses on results, not the

process
• Emphasis is on what is to be computed
• Data is immutable
• Functional programming Decompose the problem into
'functions
• It is built on the concept of mathematical functions which
uses conditional expressions and recursion to do perform the
calculation
• It does not support iteration like loop statements and
conditional statements like If-Else

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History
• The foundation for Functional Programming is Lambda Calculus.
It was developed in the 1930s for the functional application,
definition, and recursion
• LISP was the first functional programming language. McCarthy
designed it in 1960
• In the late 70's researchers at the University of Edinburgh defined
the ML(Meta Language)
• In the early 80's Hope language adds algebraic data types for
recursion and equational reasoning
• In the year 2004 Innovation of Functional language 'Scala.'

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Real Time applications

• Database processing
• Financial modeling
• Statistical analysis and
• Bio-informatics

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Functional Programming Languages

• Haskell
• SML
• Clojure
• Scala
• Erlang
• Clean
• F#
• ML/OCaml Lisp / Scheme
• XSLT
• SQL
• Mathematica

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Basic Functional Programming Terminology and Concepts

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Functional Vs Procedural
S.No Functional Paradigms Procedural Paradigm

1 Treats computation as the evaluation Derived from structured programming,

of mathematical based on the concept of modular
functions avoiding state and mutable data programming or the procedure call

2 Main traits are Lambda Main traits are Local variables, sequence,
calculus, compositionality, formula, recursio selection, iteration, and modularization
n, referential transparency
3 Functional programming focuses Procedural programming focuses
on expressions on statements
4 Often recursive.Always returns the same The output of a routine does not always
output for a given input. have a direct correlation with the input.

5 Order of evaluation is usually undefined. Everything is done in a specific order.

6 Must be stateless. i.e. No operation can have Execution of a routine may have side
side effects. effects.
7 Good fit for parallel execution, Tends to Tends to emphasize implementing
emphasize a divide and conquer approach. solutions in a linear fashion.
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Functional Vs Object-oriented Programming

S.No Functional Paradigms Object Oriented Paradigm

1 FP uses Immutable data. OOP uses Mutable data.

2 Follows Declarative Programming based Follows Imperative Programming

Model. Model.
3 What it focuses is on: "What you are doing. in What it focuses is on "How you are
the programme." doing your programming."
4 Supports Parallel Programming. No supports for Parallel Programming.
5 Its functions have no-side effects. Method can produce many side effects.
6 Flow Control is performed using function calls Flow control process is conducted using
& function calls with recursion. loops and conditional statements.
7 Execution order of statements is not very Execution order of statements is
important. important.

8 Supports both "Abstraction over Data" and Supports only "Abstraction over Data".
"Abstraction over Behavior."

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Example
Functional Programming Procedural Programming

num = 1 num = 1
def function_to_add_one(num): def procedure_to_add_one():
num += 1 global num
return num num += 1
function_to_add_one(num) return num
function_to_add_one(num) procedure_to_add_one()
function_to_add_one(num) procedure_to_add_one()
function_to_add_one(num) procedure_to_add_one()
function_to_add_one(num) procedure_to_add_one()
procedure_to_add_one()
#Final Output: 2
#Final Output: 6
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Features of Functional paradigms

• First-class functions – accept another function as an argument

or return a function
• Pure functions - they are functions without side effects
• Recursion - allows writing smaller algorithms and operating by
looking only at the inputs to a function
• Immutable variables - variables that cannot be changed
• Non-strict evaluation - allows having variables that have not
yet been computed
• Statements - evaluable pieces of code that have a return value
• Pattern matching - allows better type-checking and extracting
elements from an object

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Pure Functions:

• Pure Functions:
• These functions have two main properties.
First, they always produce the same output
for the same arguments irrespective of
anything else.
• Secondly, they have no side-effects i.e. they
do modify any argument or global variables
or output something.

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Example
def multiply_2_pure(numbers):
new_numbers = []
for n in numbers:
new_numbers.append(n * 2)
return new_numbers

original_numbers = [1, 3, 5, 10]

changed_numbers =
multiply_2_pure(original_numbers)
print(original_numbers) # [1, 3, 5, 10]
print(changed_numbers) # [2, 6, 10, 20]

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RECURSION
• During functional programming, there is no
concept of for loop or while loop, instead
recursion is used. Recursion is a process in
which a function calls itself directly or
indirectly

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 Functions are First-Class and can be
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Higher-Order
• they may be stored in data structures, passed
as arguments, or used in control structures.
• A programming language is said to support
first-class functions if it treats functions as
first-class objects.

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PROPERTIES OF FIRST CLASS

FUNCTIONS
• A function is an instance of the Object type.
• You can store the function in a variable.
• You can pass the function as a parameter to
another function.
• You can return the function from a function.
• You can store them in data structures such as
hash tables, lists, …

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Anonymous Functions

• Anonymous (lambda) functions can be very convenient

for functional programming constructs.
• They don’t have names and usually are created ad-hoc,
with a single purpose.
• Exp1: lambda x, y: x + y
• Exp 2: >>> f = lambda x, y: x + y
>>> def g(x, y):
return x + y

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Examples
Anonymous function assigned to a variable. Easy to pass around and
invoke when needed.
const myVar = function(){console.log(‘Anonymous function here!’)}
myVar()
Anonymous function as argument
• setInterval(function(){console.log(new Date().getTime())}, 1000);
Anonymous functions within a higher order function
function mealCall(meal){
return function(message){
return console.log(message + “ “ + meal + ‘!!’) }
}
const announceDinner = mealCall(‘dinner’)
const announceLunch = mealCall(‘breakfast’)
announceDinner(‘hey!, come and get your’)
announceLunch(‘Rise and shine! time for’)
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Mathematical Background

• For example, if f(x)=x2and x is 3, the ordered pair is (-3,

9).
• A function is defined by its set of inputs, called the
domain.
• A set containing the set of outputs, and possibly
additional elements as members, is called its codomain.
• The set of all input-output pairs is called its graph.

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Mathematical Background

General Concepts Functional Rules

• Notation
F(x) = y
x , y -> Arguments or 1. (f+g)(x)=f(x)+g(x)
parameters 2. (f-g)(x)=f(x)-g(x)
x -> domain and
3. (f*g)(x)=f(x)*g(x)
y->codomain
Types: 4. (f/g)(x)=f(x)/g(x)
– Injective if f(a) ≠ f(b) 5. (gof)(x)=g(f(x))
– Surjective if f(X)= Y 6. f f-1=idy
– Bijective ( support both)

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Example using Python

• Anonymous functions are split into two types: lambda functions and closures.
• Functions are made of four parts: name, parameter list, body, and return

Example :
A=5
def impure_sum(b):
return b + A
def pure_sum(a, b):
return a + b
print(impure_sum(6))
>> 11
print(pure_sum(4, 6))
>> 10

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Example Code

Functional style of getting a sum of a list:

Function for computing the
average of two numbers:
new_lst = [1, 2, 3, 4]
def sum_list(lst):
(defun avg(X Y) (/ (+ X Y) if len(lst) == 1:
return lst[0]
2.0)) else:
return lst[0] + sum_list(lst[1:])
print(sum_list(new_lst))
Function is called by:
# or the pure functional way in python using
> (avg 10.0 20.0) higher order function
Function returns: import functools
15.0 print(functools.reduce(lambda x, y: x + y,
new_lst))

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Immutable variables

• Immutable variable (object) is a variable whose state

cannot be modified once it is created.
• In contrast, a mutable variable can be modified after
it is created
• Exp
• String str = “A Simple String.”;
• str.toLowerCase();

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Other Examples

• int i = 12; //int is a primitive type

• i = 30; //this is ok
• final int j = 12;
• j = 30;
• final MyClass c = new MyClass();
• m.field = 100; //this is ok;
• m = new MyClass();

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Scenario1

• What if we wanted to def hof_write_repeat(message, n, action):

write to a file 5 times, for i in range(n):
or log the message 5 action(message)
times? Instead of
writing 3 different hof_write_repeat('Hello', 5, print)
functions that all loop,
we can write 1 Higher
# Import the logging library
Order Function that
import logging
accepts those
# Log the output as an error instead
functions as an
argument: hof_write_repeat('Hello', 5, logging.error)

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Scenario2
def hof_add(increment):
# Create a function that loops and adds
the increment
• Imagine that we're def add_increment(numbers):
tasked with creating new_numbers = []
functions that for n in numbers:
new_numbers.append(n +
increment numbers increment)
in a list by 2, 5, and return new_numbers
10. So instead of # We return the function as we do any
creating many other value
return add_increment
different increment add2=hof_add(2)
functions, we create print(add2([23, 88])) # [25, 90]
1 Higher Order add5 = hof_add(5)
Function: print(add5([23, 88])) # [28, 93]
add10 = hof_add(10)
print(add10([23, 88])) # [33, 98]
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Functional programming tools

• filter(function, sequence)
def f(x): return x%2 != 0 and x%3 ==0
filter(f, range(2,25))
• map(function, sequence)
– call function for each item
– return list of return values
• reduce(function, sequence)
– return a single value
– call binary function on the first two items
– then on the result and next item
– iterate

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Lambda Expression
# Using `def` (old way).
def old_add(a, b):
return a + b

# Using `lambda` (new way).

new_add = lambda a, b: a + b old_add(10, 5) == new_add(10, 5)
>> True

unsorted = [('b', 6), ('a', 10), ('d', 0), ('c', 4)]

print(sorted(unsorted, key=lambda x: x[1]))
>> [('d', 0), ('c', 4), ('b', 6), ('a', 10)]

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Map Function
• Takes in an iterable (ie. list), and creates a new iterable object, a
special map object.
• The new object has the first-class function applied to every element.

# Pseudocode for map. Example:

values = [1, 2, 3, 4, 5]
def map(func, seq):
add_10 = list(map(lambda x: x + 10,
return Map( values))
func(x) add_20 = list(map(lambda x: x + 20,
values))
for x in seq
print(add_10)
) >> [11, 12, 13, 14, 15]

print(add_20)
>> [21, 22, 23, 24, 25]

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Filter Function
• Takes in an iterable (ie. list), and creates a new iterable object
• The new object has the first-class function applied to every element.

# Pseudocode for map. Example:

return Map( even = list(filter(lambda x: x % 2 == 0, values))

odd = list(filter(lambda x: x % 2 == 1, values))
x for x in seq
if evaluate(x) is True print(even)
) >> [2, 4, 6, 8, 10]

print(odd)
>> [1, 3, 5, 7, 9]

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Advantages

• Allows you to avoid confusing problems and errors in the code

• Easier to test and execute Unit testing and debug FP Code.
• Parallel processing and concurrency
• Hot code deployment and fault tolerance
• Offers better modularity with a shorter code
• Increased productivity of the developer
• Supports Nested Functions
• Functional Constructs like Lazy Map & Lists, etc.
• Allows effective use of Lambda Calculus

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Disadvantages

• Perhaps less efficiency

• Problems involving many variables or a lot of sequential
activity are sometimes easier to handle imperatively
• Functional programming paradigm is not easy, so it is
difficult to understand for the beginner
• Hard to maintain as many objects evolve during the
coding
• Needs lots of mocking and extensive environmental setup
• Re-use is very complicated and needs constantly
refactoring
• Objects may not represent the problem correctly

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Transforming code from imperative to functional

1. Introduce higher-order functions.

2. Convert existing methods into pure functions.
3. Convert loops over to recursive/tail-recursive
methods (if possible).
4. Convert mutable variables into immutable
variables.
5. Use pattern matching (if possible).

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Sample Scenario
1) (The MyTriangle module) Create a module named MyTriangle that contains the
following two functions:
# Returns true if the sum of any two sides is greater than the third side.
def isValid(side1, side2, side3):
# Returns the area of the triangle.
def area(side1, side2, side3):
Write a test program that reads three sides for a triangle and computes the area if the
input is valid. Otherwise, it displays that the input is invalid.

2) A prime number is called a Mersenne prime if it can be written in the form for some
positive integer p. Write a program that finds all Mersenne primes with and displays the
output as follows:
p 2^p - 1
2 3
3 7
5 31

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Sample Scenarios

3) Write a function named ack that evaluates the Ackermann

function. Use your function to evaluate ack(3, 4), which should be
125.

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Thank you

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