theSamyak

Let's compare the fibonacci function with and without memoization to understand the performance difference.

Fibonacci without Memoization

Here's the fibonacci function without memoization:

def fibonacci(n):
    if n < 2:
        return n
 return fibonacci(n-1) + fibonacci(n-2)

theSamyak

def create_multiplier(factor):
    return lambda x: x * factor

# Create specific multiplier functions
double = create_multiplier(2)
triple = create_multiplier(3)

# Use the created functions
print(double(5))  # Output: 10
print(triple(5))  # Output: 15

theSamyak

First-Class Functions

Assigning Functions to Variables

theSamyak

def create_multiplier(n):
    def multiplier(x):
        return x * n
    return multiplier

# Creating specific multiplier functions
multiply_by_2 = create_multiplier(2)
multiply_by_3 = create_multiplier(3)

# Using the returned functions

theSamyak

def apply_operation(operation, x, y):
    return operation(x, y)

# Functions to pass as arguments
def add(x, y):
    return x + y

def multiply(x, y):
    return x * y

theSamyak

def create_multiplier(factor):
    """Returns a function that multiplies its input by the given factor."""
    def multiplier(x):
        return x * factor
    return multiplier

# Create specific multiplier functions
double = create_multiplier(2)
triple = create_multiplier(3)

theSamyak

def double(n):
    return n * 2

def map_function(func, values):
    result = []
    for value in values:
        result.append(func(value))
    return result

# Use the custom map function

theSamyak

def add(a, b):
    return a + b

result = add(3, 4)

print(add)  # Prints the function object
print(result)  # Prints 7